!757 update securec repository link for r0.6

Merge pull request !757 from liangyongxiong/r0.6

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "third_party/securec"]
 	path = third_party/securec
-	url = https://gitee.com/openeuler/bounds_checking_function.git
+	url = https://gitee.com/openeuler/libboundscheck.git

README.md

@@ -105,11 +105,11 @@ See [Install MindInsight](https://www.mindspore.cn/install/en).
 
 # QuickStart
 
-See [guidance](https://www.mindspore.cn/tutorial/en/0.1.0-alpha/advanced_use/visualization_tutorials.html)
+See [guidance](https://www.mindspore.cn/tutorial/en/r0.6/advanced_use/visualization_tutorials.html)
 
 # Docs
 
-See [API Reference](https://www.mindspore.cn/api/en/master/index.html) 
+See [API Reference](https://www.mindspore.cn/api/en/r0.6/index.html) 
 
 # Community
 

RELEASE.md

@@ -1,5 +1,25 @@
 ## MindInsight
 
+# Release 0.6.0-beta
+
+## Major Features and Improvements
+* Provide monitoring capabilities for each of Ascend AI processor and other hardware resources, including CPU and memory.
+* Visualization of weight, gradient and other tensor data in model training.
+    * Provide tabular from presentation of tensor data.
+    * Provide histogram to show the distribution of tensor data and its change over time.
+
+## Bugfixes
+* UI fix for the error message display mode of the tensor during real-time training. ([!465](https://gitee.com/mindspore/mindinsight/pulls/465))
+* The summary file size is larger than max_file_size. ([!3481](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/3481))
+* Fix real-time training error when disk is full. ([!3058](https://gitee.com/mindspore/mindspore/pulls/3058))
+
+## Thanks to our Contributors
+Thanks goes to these wonderful people:
+
+Congli Gao, Weifeng Huang, Zhenzhong Kou, Hongzhang Li, Longfei Li, Yongxiong Liang, Chongming Liu, Pengting Luo, Yanming Miao, Gongchang Ou, Yongxiu Qu, Hui Pan, Luyu Qiu, Junyan Qin, Kai Wen, Weining Wang, Yue Wang, Zhuanke Wu, Yifan Xia, Lihua Ye, Weibiao Yu, Ximiao Yu, Yunshu Zhang, Ting Zhao, Jianfeng Zhu, Ning Ma, Yihui Zhang, Shuide Wang.
+
+Contributions of any kind are welcome!
+
 # Release 0.5.0-beta
 
 ## Major Features and Improvements

mindinsight/_version.py

@@ -14,4 +14,4 @@
 # ============================================================================
 """Mindinsight version module."""
 
-VERSION = '0.5.0'
+VERSION = '0.6.0'

mindinsight/backend/application.py

@@ -111,6 +111,7 @@ def create_app():
     static_folder_path = os.path.realpath(os.path.join(os.path.dirname(__file__), os.pardir, 'ui', 'dist', 'static'))
 
     app = Flask(__name__, static_url_path=static_url_path, static_folder=static_folder_path)
+    app.config['JSON_SORT_KEYS'] = False
 
     if settings.ENABLE_CORS:
         CORS(app, supports_credentials=True)

mindinsight/backend/datavisual/__init__.py

@@ -17,6 +17,7 @@
 from mindinsight.backend.datavisual.static_resource_api import init_module as static_init_module
 from mindinsight.backend.datavisual.task_manager_api import init_module as task_init_module
 from mindinsight.backend.datavisual.train_visual_api import init_module as train_init_module
+from mindinsight.backend.datavisual.sysmetric_api import init_module as sysmetric_init_module
 
 
 def init_module(app):
@@ -30,3 +31,4 @@ def init_module(app):
     static_init_module(app)
     task_init_module(app)
     train_init_module(app)
+    sysmetric_init_module(app)

mindinsight/backend/datavisual/sysmetric_api.py

@@ -0,0 +1,39 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""System metrics API."""
+
+from flask import Blueprint, jsonify
+from mindinsight.conf import settings
+from mindinsight.sysmetric.collector import get_metrics
+
+BLUEPRINT = Blueprint("sysmetric", __name__, url_prefix=settings.URL_PATH_PREFIX + settings.API_PREFIX)
+
+
+@BLUEPRINT.route("/sysmetric/current", methods=["GET"])
+def query_sysmetric():
+    """Query the system metrics."""
+
+    return jsonify(get_metrics())
+
+
+def init_module(app):
+    """
+    Init module entry.
+
+    Args:
+        app: the application obj.
+
+    """
+    app.register_blueprint(BLUEPRINT)

mindinsight/backend/datavisual/task_manager_api.py

@@ -66,12 +66,13 @@ def query_train_jobs():
     """Query train jobs."""
     offset = request.args.get("offset", default=0)
     limit = request.args.get("limit", default=10)
+    train_id = get_train_id(request)
 
     offset = Validation.check_offset(offset=offset)
     limit = Validation.check_limit(limit, min_value=1, max_value=SummaryWatcher.MAX_SUMMARY_DIR_COUNT)
 
     processor = TrainTaskManager(DATA_MANAGER)
-    total, train_jobs = processor.query_train_jobs(offset, limit)
+    total, train_jobs = processor.query_train_jobs(offset, limit, train_id)
 
     return jsonify({
         'name': os.path.basename(os.path.realpath(settings.SUMMARY_BASE_DIR)),

mindinsight/backend/datavisual/train_visual_api.py

@@ -25,6 +25,7 @@ from mindinsight.conf import settings
 from mindinsight.datavisual.utils.tools import get_train_id
 from mindinsight.datavisual.utils.tools import if_nan_inf_to_none
 from mindinsight.datavisual.processors.histogram_processor import HistogramProcessor
+from mindinsight.datavisual.processors.tensor_processor import TensorProcessor
 from mindinsight.datavisual.processors.images_processor import ImageProcessor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 from mindinsight.datavisual.processors.graph_processor import GraphProcessor
@@ -173,6 +174,25 @@ def get_scalars():
     return jsonify({'scalars': scalars})
 
 
+@BLUEPRINT.route("/datavisual/tensors", methods=["GET"])
+def get_tensors():
+    """
+    Interface to obtain tensor data.
+
+    Returns:
+        Response, which contains a JSON object.
+    """
+    train_ids = request.args.getlist('train_id')
+    tags = request.args.getlist('tag')
+    step = request.args.get("step", default=None)
+    dims = request.args.get("dims", default=None)
+    detail = request.args.get("detail", default=None)
+
+    processor = TensorProcessor(DATA_MANAGER)
+    response = processor.get_tensors(train_ids, tags, step, dims, detail)
+    return jsonify(response)
+
+
 def init_module(app):
     """
     Init module entry.

mindinsight/backend/run.py

@@ -236,9 +236,10 @@ def start():
     process = subprocess.Popen(
         shlex.split(cmd),
         shell=False,
-        stdin=subprocess.PIPE,
-        stdout=subprocess.PIPE,
-        stderr=subprocess.PIPE
+        # Change stdout to DEVNULL to prevent broken pipe error when creating new processes.
+        stdin=subprocess.DEVNULL,
+        stdout=subprocess.DEVNULL,
+        stderr=subprocess.STDOUT
     )
 
     # sleep 1 second for gunicorn appplication to load modules
@@ -246,9 +247,7 @@ def start():
 
     # check if gunicorn application is running
     if process.poll() is not None:
-        _, stderr = process.communicate()
-        for line in stderr.decode().split('\n'):
-            console.error(line)
+        console.error("Start MindInsight failed. See log for details.")
     else:
         state_result = _check_server_start_stat(errorlog_abspath, log_size)
         # print gunicorn start state to stdout

mindinsight/conf/constants.py

@@ -14,6 +14,36 @@
 # ============================================================================
 """Constants module for mindinsight settings."""
 import logging
+import math
+import os
+
+
+_DEFAULT_MAX_THREADS_COUNT = 15
+
+
+def _calc_default_max_processes_cnt():
+    """Calc default processes count."""
+
+    # We need to make sure every summary directory has a process to load data.
+    min_cnt = _DEFAULT_MAX_THREADS_COUNT
+    # Do not use too many processes to avoid system problems (eg. out of memory).
+    max_cnt = 45
+    used_cpu_ratio = 0.75
+
+    cpu_count = os.cpu_count()
+    if cpu_count is None:
+        return min_cnt
+
+    processes_cnt = math.floor(cpu_count * used_cpu_ratio)
+
+    if processes_cnt < min_cnt:
+        return min_cnt
+
+    if processes_cnt > max_cnt:
+        return max_cnt
+
+    return processes_cnt
+
 
 ####################################
 # Global default settings.
@@ -47,13 +77,17 @@ API_PREFIX = '/v1/mindinsight'
 ####################################
 # Datavisual default settings.
 ####################################
-MAX_THREADS_COUNT = 15
+MAX_THREADS_COUNT = _DEFAULT_MAX_THREADS_COUNT
+MAX_PROCESSES_COUNT = _calc_default_max_processes_cnt()
 
 MAX_TAG_SIZE_PER_EVENTS_DATA = 300
 DEFAULT_STEP_SIZES_PER_TAG = 500
 
 MAX_GRAPH_TAG_SIZE = 10
+MAX_TENSOR_TAG_SIZE = 6
 MAX_IMAGE_STEP_SIZE_PER_TAG = 10
 MAX_SCALAR_STEP_SIZE_PER_TAG = 1000
 MAX_GRAPH_STEP_SIZE_PER_TAG = 1
 MAX_HISTOGRAM_STEP_SIZE_PER_TAG = 50
+MAX_TENSOR_STEP_SIZE_PER_TAG = 20
+MAX_TENSOR_RESPONSE_DATA_SIZE = 100000

mindinsight/datavisual/common/enums.py

@@ -32,12 +32,20 @@ class DataManagerStatus(BaseEnum):
     INVALID = 'INVALID'
 
 
+class DetailCacheManagerStatus(BaseEnum):
+    """Data manager status."""
+    INIT = 'INIT'
+    LOADING = 'LOADING'
+    DONE = 'DONE'
+
+
 class PluginNameEnum(BaseEnum):
     """Plugin Name Enum."""
     IMAGE = 'image'
     SCALAR = 'scalar'
     GRAPH = 'graph'
     HISTOGRAM = 'histogram'
+    TENSOR = 'tensor'
 
 
 @enum.unique

mindinsight/datavisual/common/exceptions.py

@@ -161,6 +161,33 @@ class HistogramNotExistError(MindInsightException):
                                                      http_code=400)
 
 
+class TensorNotExistError(MindInsightException):
+    """Unable to get tensor values based on a given condition."""
+    def __init__(self, error_detail):
+        error_msg = f'Tensor value is not exist. Detail: {error_detail}'
+        super(TensorNotExistError, self).__init__(DataVisualErrors.TENSOR_NOT_EXIST,
+                                                  error_msg,
+                                                  http_code=400)
+
+
+class StepTensorDataNotInCacheError(MindInsightException):
+    """Tensor data with specific step does not in cache."""
+    def __init__(self, error_detail):
+        error_msg = f'Tensor data not in cache. Detail: {error_detail}'
+        super(StepTensorDataNotInCacheError, self).__init__(DataVisualErrors.STEP_TENSOR_DATA_NOT_IN_CACHE,
+                                                            error_msg,
+                                                            http_code=400)
+
+
+class ResponseDataExceedMaxValueError(MindInsightException):
+    """Response data exceed max value based on a given condition."""
+    def __init__(self, error_detail):
+        error_msg = f'Response data exceed max value. Detail: {error_detail}'
+        super(ResponseDataExceedMaxValueError, self).__init__(DataVisualErrors.MAX_RESPONSE_DATA_EXCEEDED_ERROR,
+                                                              error_msg,
+                                                              http_code=400)
+
+
 class TrainJobDetailNotInCacheError(MindInsightException):
     """Detail info of given train job is not in cache."""
     def __init__(self, error_detail="no detail provided."):

mindinsight/datavisual/data_transform/data_loader.py

@@ -34,8 +34,13 @@ class DataLoader:
         self._summary_dir = summary_dir
         self._loader = None
 
-    def load(self):
-        """Load the data when loader is exist."""
+    def load(self, computing_resource_mgr):
+        """Load the data when loader is exist.
+
+        Args:
+            computing_resource_mgr (ComputingResourceManager): The ComputingResourceManager instance.
+        """
+
         if self._loader is None:
             ms_dataloader = MSDataLoader(self._summary_dir)
             loaders = [ms_dataloader]
@@ -48,7 +53,7 @@ class DataLoader:
                 logger.warning("No valid files can be loaded, summary_dir: %s.", self._summary_dir)
                 raise exceptions.SummaryLogPathInvalid()
 
-        self._loader.load()
+        self._loader.load(computing_resource_mgr)
 
     def get_events_data(self):
         """

mindinsight/datavisual/data_transform/data_manager.py

@@ -35,14 +35,16 @@ from mindinsight.conf import settings
 from mindinsight.datavisual.common import exceptions
 from mindinsight.datavisual.common.enums import CacheStatus
 from mindinsight.datavisual.common.log import logger
-from mindinsight.datavisual.common.enums import DataManagerStatus
+from mindinsight.datavisual.common.enums import DataManagerStatus, DetailCacheManagerStatus
 from mindinsight.datavisual.common.enums import PluginNameEnum
 from mindinsight.datavisual.common.exceptions import TrainJobNotExistError
 from mindinsight.datavisual.data_transform.loader_generators.loader_generator import MAX_DATA_LOADER_SIZE
 from mindinsight.datavisual.data_transform.loader_generators.data_loader_generator import DataLoaderGenerator
+from mindinsight.utils.computing_resource_mgr import ComputingResourceManager
 from mindinsight.utils.exceptions import MindInsightException
 from mindinsight.utils.exceptions import ParamValueError
 from mindinsight.utils.exceptions import UnknownError
+from mindinsight.datavisual.utils.tools import exception_wrapper
 
 
 class _BasicTrainJob:
@@ -414,6 +416,13 @@ class _DetailCacheManager(_BaseCacheManager):
         self._loader_pool_mutex = threading.Lock()
         self._max_threads_count = 30
         self._loader_generators = loader_generators
+        self._status = DetailCacheManagerStatus.INIT.value
+        self._loading_mutex = threading.Lock()
+
+    @property
+    def status(self):
+        """Get loading status, if it is loading, return True."""
+        return self._status
 
     def has_content(self):
         """Whether this cache manager has train jobs."""
@@ -434,6 +443,20 @@ class _DetailCacheManager(_BaseCacheManager):
         """Get loader pool size."""
         return len(self._loader_pool)
 
+    def _load_in_cache(self):
+        """Generate and execute loaders."""
+        def load():
+            self._generate_loaders()
+            self._execute_load_data()
+        try:
+            exception_wrapper(load())
+        except UnknownError as ex:
+            logger.warning("Load event data failed. Detail: %s.", str(ex))
+        finally:
+            self._status = DetailCacheManagerStatus.DONE.value
+        logger.info("Load event data end, status: %r, and loader pool size is %r.",
+                    self._status, self.loader_pool_size())
+
     def update_cache(self, disk_train_jobs: Iterable[_BasicTrainJob]):
         """
         Update cache.
@@ -444,8 +467,13 @@ class _DetailCacheManager(_BaseCacheManager):
             disk_train_jobs (Iterable[_BasicTrainJob]): Basic info about train jobs on disk.
 
         """
-        self._generate_loaders()
-        self._execute_load_data()
+        with self._loading_mutex:
+            if self._status == DetailCacheManagerStatus.LOADING.value:
+                logger.debug("Event data is loading, and loader pool size is %r.", self.loader_pool_size())
+                return
+            self._status = DetailCacheManagerStatus.LOADING.value
+            thread = threading.Thread(target=self._load_in_cache, name="load_detail_in_cache")
+            thread.start()
 
     def cache_train_job(self, train_id):
         """Cache given train job."""
@@ -510,7 +538,7 @@ class _DetailCacheManager(_BaseCacheManager):
             logger.debug("delete loader %s", loader_id)
             self._loader_pool.pop(loader_id)
 
-    def _execute_loader(self, loader_id):
+    def _execute_loader(self, loader_id, computing_resource_mgr):
         """
         Load data form data_loader.
 
@@ -518,7 +546,7 @@ class _DetailCacheManager(_BaseCacheManager):
 
         Args:
             loader_id (str): An ID for `Loader`.
-
+            computing_resource_mgr (ComputingResourceManager): The ComputingResourceManager instance.
         """
         try:
             with self._loader_pool_mutex:
@@ -527,7 +555,7 @@ class _DetailCacheManager(_BaseCacheManager):
                     logger.debug("Loader %r has been deleted, will not load data.", loader_id)
                     return
 
-            loader.data_loader.load()
+            loader.data_loader.load(computing_resource_mgr)
 
             # Update loader cache status to CACHED.
             # Loader with cache status CACHED should remain the same cache status.
@@ -580,13 +608,17 @@ class _DetailCacheManager(_BaseCacheManager):
 
         logger.info("Start to execute load data. threads_count: %s.", threads_count)
 
-        with ThreadPoolExecutor(max_workers=threads_count) as executor:
-            futures = []
-            loader_pool = self._get_snapshot_loader_pool()
-            for loader_id in loader_pool:
-                future = executor.submit(self._execute_loader, loader_id)
-                futures.append(future)
-            wait(futures, return_when=ALL_COMPLETED)
+        with ComputingResourceManager(
+                executors_cnt=threads_count,
+                max_processes_cnt=settings.MAX_PROCESSES_COUNT) as computing_resource_mgr:
+
+            with ThreadPoolExecutor(max_workers=threads_count) as executor:
+                futures = []
+                loader_pool = self._get_snapshot_loader_pool()
+                for loader_id in loader_pool:
+                    future = executor.submit(self._execute_loader, loader_id, computing_resource_mgr)
+                    futures.append(future)
+                wait(futures, return_when=ALL_COMPLETED)
 
     def _get_threads_count(self):
         """
@@ -706,8 +738,7 @@ class _DetailCacheManager(_BaseCacheManager):
 
         loader = self._get_loader(train_id)
         if loader is None:
-            logger.warning("No valid summary log in train job %s, "
-                           "or it is not in the cache.", train_id)
+            logger.info("No valid summary log in train job %s, or it is not in the cache.", train_id)
             return None
 
         train_job = loader.to_dict()
@@ -831,6 +862,11 @@ class DataManager:
         self._detail_cache = _DetailCacheManager(loader_generators)
         self._brief_cache = _BriefCacheManager()
 
+        # This lock is used to make sure that only one self._load_data_in_thread() is running.
+        # Because self._load_data_in_thread() will create process pool when loading files, we can not
+        # afford to run multiple self._load_data_in_thread() simultaneously (will create too many processes).
+        self._load_data_lock = threading.Lock()
+
     @property
     def summary_base_dir(self):
         """Get summary base dir."""
@@ -886,19 +922,12 @@ class DataManager:
     def _load_data_in_thread_wrapper(self):
         """Wrapper for load data in thread."""
         try:
-            self._load_data_in_thread()
-        except MindInsightException as exc:
+            with self._load_data_lock:
+                exception_wrapper(self._load_data())
+        except UnknownError as exc:
             # Not raising the exception here to ensure that data reloading does not crash.
             logger.warning(exc.message)
 
-    def _load_data_in_thread(self):
-        """Log (but not swallow) exceptions in thread to help debugging."""
-        try:
-            self._load_data()
-        except Exception as exc:
-            logger.exception(exc)
-            raise UnknownError('Load data thread error.')
-
     def _load_data(self):
         """This function will load data once and ignore it if the status is loading."""
         logger.info("Start to load data, reload interval: %r.", self._reload_interval)
@@ -928,13 +957,13 @@ class DataManager:
         self._brief_cache.update_cache(basic_train_jobs)
         self._detail_cache.update_cache(basic_train_jobs)
 
-        if not self._brief_cache.has_content() and not self._detail_cache.has_content():
+        if not self._brief_cache.has_content() and not self._detail_cache.has_content() \
+                and self._detail_cache.status == DetailCacheManagerStatus.DONE.value:
             self.status = DataManagerStatus.INVALID.value
         else:
             self.status = DataManagerStatus.DONE.value
 
-        logger.info("Load event data end, status: %r, and loader pool size is %r.",
-                    self.status, self._detail_cache.loader_pool_size())
+        logger.info("Load brief data end, and loader pool size is %r.", self._detail_cache.loader_pool_size())
 
     @staticmethod
     def check_reload_interval(reload_interval):
@@ -1035,14 +1064,6 @@ class DataManager:
 
         return TrainJob(brief_train_job, detail_train_job)
 
-    def list_train_jobs(self):
-        """
-        List train jobs.
-
-        To be implemented.
-        """
-        raise NotImplementedError()
-
     @property
     def status(self):
         """
@@ -1077,5 +1098,9 @@ class DataManager:
         """Get brief train job."""
         return self._brief_cache.get_train_job(train_id)
 
+    def get_detail_cache_status(self):
+        """Get detail status, just for ut/st."""
+        return self._detail_cache.status
+
 
 DATA_MANAGER = DataManager(settings.SUMMARY_BASE_DIR)

mindinsight/datavisual/data_transform/events_data.py

@@ -35,13 +35,15 @@ CONFIG = {
     'max_tag_sizes_per_plugin':
         {
             PluginNameEnum.GRAPH.value: settings.MAX_GRAPH_TAG_SIZE,
+            PluginNameEnum.TENSOR.value: settings.MAX_TENSOR_TAG_SIZE
         },
     'max_step_sizes_per_tag':
         {
             PluginNameEnum.SCALAR.value: settings.MAX_SCALAR_STEP_SIZE_PER_TAG,
             PluginNameEnum.IMAGE.value: settings.MAX_IMAGE_STEP_SIZE_PER_TAG,
             PluginNameEnum.GRAPH.value: settings.MAX_GRAPH_STEP_SIZE_PER_TAG,
-            PluginNameEnum.HISTOGRAM.value: settings.MAX_HISTOGRAM_STEP_SIZE_PER_TAG
+            PluginNameEnum.HISTOGRAM.value: settings.MAX_HISTOGRAM_STEP_SIZE_PER_TAG,
+            PluginNameEnum.TENSOR.value: settings.MAX_TENSOR_STEP_SIZE_PER_TAG
         }
 }
 
@@ -84,6 +86,7 @@ class EventsData:
             deleted_tag = self._check_tag_out_of_spec(plugin_name)
             if deleted_tag is not None:
                 if tag in self._deleted_tags:
+                    logger.debug("Tag is in deleted tags: %s.", tag)
                     return
                 self.delete_tensor_event(deleted_tag)
 

mindinsight/datavisual/data_transform/histogram.py

@@ -0,0 +1,237 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Histogram data."""
+import math
+
+from mindinsight.utils.exceptions import ParamValueError
+from mindinsight.datavisual.utils.utils import calc_histogram_bins
+
+
+def mask_invalid_number(num):
+    """Mask invalid number to 0."""
+    if math.isnan(num) or math.isinf(num):
+        return type(num)(0)
+
+    return num
+
+
+class Bucket:
+    """
+    Bucket data class.
+
+    Args:
+        left (double): Left edge of the histogram bucket.
+        width (double): Width of the histogram bucket.
+        count (int): Count of numbers fallen in the histogram bucket.
+    """
+    def __init__(self, left, width, count):
+        self._left = left
+        self._width = width
+        self._count = count
+
+    @property
+    def left(self):
+        """Gets left edge of the histogram bucket."""
+        return self._left
+
+    @property
+    def count(self):
+        """Gets count of numbers fallen in the histogram bucket."""
+        return self._count
+
+    @property
+    def width(self):
+        """Gets width of the histogram bucket."""
+        return self._width
+
+    @property
+    def right(self):
+        """Gets right edge of the histogram bucket."""
+        return self._left + self._width
+
+    def as_tuple(self):
+        """Gets the bucket as tuple."""
+        return self._left, self._width, self._count
+
+    def __repr__(self):
+        """Returns repr(self)."""
+        return "Bucket(left={}, width={}, count={})".format(self._left, self._width, self._count)
+
+
+class Histogram:
+    """
+    Histogram data class.
+
+    Args:
+        buckets (tuple[Bucket]): The buckets of histogram data.
+        max_val (number): The max value of histogram data.
+        min_val (number): The min value of histogram data.
+        count (int): The count of histogram data.
+    """
+
+    # Max quantity of original buckets.
+    MAX_ORIGINAL_BUCKETS_COUNT = 90
+
+    def __init__(self, buckets, max_val, min_val, count):
+        self._visual_max = max_val
+        self._visual_min = min_val
+        self._count = count
+        self._original_buckets = buckets
+        # default bin number
+        self._visual_bins = calc_histogram_bins(count)
+        # Note that tuple is immutable, so sharing tuple is often safe.
+        self._re_sampled_buckets = ()
+
+    @property
+    def original_buckets_count(self):
+        """Gets original buckets quantity."""
+        return len(self._original_buckets)
+
+    def set_visual_range(self, max_val: float, min_val: float, bins: int) -> None:
+        """
+        Sets visual range for later re-sampling.
+
+        It's caller's duty to ensure input is valid.
+
+        Why we need visual range for histograms? Aligned buckets between steps can help users know about the trend of
+        tensors. Miss aligned buckets between steps might miss-lead users about the trend of a tensor. Because for
+        given tensor, if you have thinner buckets, count of every bucket will get lower, however, if you have
+        thicker buckets, count of every bucket will get higher.  When they are displayed together, user might think
+        the histogram with thicker buckets has more values. This is miss-leading. So we need to unify buckets across
+        steps. Visual range for histogram is a technology for unifying buckets.
+
+        Args:
+            max_val (float): Max value for visual histogram.
+            min_val (float): Min value for visual histogram.
+            bins (int): Bins number for visual histogram.
+        """
+        if max_val < min_val:
+            raise ParamValueError(
+                "Invalid input. max_val({}) is less or equal than min_val({}).".format(max_val, min_val))
+
+        if bins < 1:
+            raise ParamValueError("Invalid input bins({}). Must be greater than 0.".format(bins))
+
+        self._visual_max = max_val
+        self._visual_min = min_val
+        self._visual_bins = bins
+
+        # mark _re_sampled_buckets to empty
+        self._re_sampled_buckets = ()
+
+    def _calc_intersection_len(self, max1, min1, max2, min2):
+        """Calculates intersection length of [min1, max1] and [min2, max2]."""
+        if max1 < min1:
+            raise ParamValueError(
+                "Invalid input. max1({}) is less than min1({}).".format(max1, min1))
+
+        if max2 < min2:
+            raise ParamValueError(
+                "Invalid input. max2({}) is less than min2({}).".format(max2, min2))
+
+        if min1 <= min2:
+            if max1 <= min2:
+                # return value must be calculated by max1.__sub__
+                return max1 - max1
+            if max1 <= max2:
+                return max1 - min2
+            # max1 > max2
+            return max2 - min2
+
+        # min1 > min2
+        if max2 <= min1:
+            return max2 - max2
+        if max2 <= max1:
+            return max2 - min1
+        return max1 - min1
+
+    def _re_sample_buckets(self):
+        """Re-samples buckets according to visual_max, visual_min and visual_bins."""
+        if self._visual_max == self._visual_min:
+            # Adjust visual range if max equals min.
+            self._visual_max += 0.5
+            self._visual_min -= 0.5
+
+        width = (self._visual_max - self._visual_min) / self._visual_bins
+
+        if not self._count:
+            self._re_sampled_buckets = tuple(
+                Bucket(self._visual_min + width * i, width, 0)
+                for i in range(self._visual_bins))
+            return
+
+        re_sampled = []
+        original_pos = 0
+        original_bucket = self._original_buckets[original_pos]
+        for i in range(self._visual_bins):
+            cur_left = self._visual_min + width * i
+            cur_right = cur_left + width
+            cur_estimated_count = 0.0
+
+            # Skip no bucket range.
+            if cur_right <= original_bucket.left:
+                re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
+                continue
+
+            # Skip no intersect range.
+            while cur_left >= original_bucket.right:
+                original_pos += 1
+                if original_pos >= len(self._original_buckets):
+                    break
+                original_bucket = self._original_buckets[original_pos]
+
+            # entering with this condition: cur_right > original_bucket.left and cur_left < original_bucket.right
+            while True:
+                if original_pos >= len(self._original_buckets):
+                    break
+                original_bucket = self._original_buckets[original_pos]
+
+                intersection = self._calc_intersection_len(
+                    min1=cur_left, max1=cur_right,
+                    min2=original_bucket.left, max2=original_bucket.right)
+                if not original_bucket.width:
+                    estimated_count = original_bucket.count
+                else:
+                    estimated_count = (intersection / original_bucket.width) * original_bucket.count
+
+                cur_estimated_count += estimated_count
+                if cur_right > original_bucket.right:
+                    # Need to sample next original bucket to this visual bucket.
+                    original_pos += 1
+                else:
+                    # Current visual bucket has taken all intersect buckets into account.
+                    break
+
+            re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
+
+        self._re_sampled_buckets = tuple(re_sampled)
+
+    def buckets(self, convert_to_tuple=True):
+        """
+        Get visual buckets instead of original buckets.
+
+        Args:
+            convert_to_tuple (bool): Whether convert bucket object to tuple.
+
+        Returns:
+            tuple, contains buckets.
+        """
+        if not self._re_sampled_buckets:
+            self._re_sample_buckets()
+
+        if not convert_to_tuple:
+            return self._re_sampled_buckets
+
+        return tuple(bucket.as_tuple() for bucket in self._re_sampled_buckets)

mindinsight/datavisual/data_transform/histogram_container.py

@@ -13,90 +13,26 @@
 # limitations under the License.
 # ============================================================================
 """Histogram data container."""
-import math
-
+from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket, mask_invalid_number
 from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Summary
-from mindinsight.utils.exceptions import ParamValueError
-from mindinsight.datavisual.utils.utils import calc_histogram_bins
-
-
-def _mask_invalid_number(num):
-    """Mask invalid number to 0."""
-    if math.isnan(num) or math.isinf(num):
-        return type(num)(0)
-
-    return num
-
-
-class Bucket:
-    """
-    Bucket data class.
-
-    Args:
-        left (double): Left edge of the histogram bucket.
-        width (double): Width of the histogram bucket.
-        count (int): Count of numbers fallen in the histogram bucket.
-    """
-    def __init__(self, left, width, count):
-        self._left = left
-        self._width = width
-        self._count = count
-
-    @property
-    def left(self):
-        """Gets left edge of the histogram bucket."""
-        return self._left
-
-    @property
-    def count(self):
-        """Gets count of numbers fallen in the histogram bucket."""
-        return self._count
-
-    @property
-    def width(self):
-        """Gets width of the histogram bucket."""
-        return self._width
-
-    @property
-    def right(self):
-        """Gets right edge of the histogram bucket."""
-        return self._left + self._width
-
-    def as_tuple(self):
-        """Gets the bucket as tuple."""
-        return self._left, self._width, self._count
-
-    def __repr__(self):
-        """Returns repr(self)."""
-        return "Bucket(left={}, width={}, count={})".format(self._left, self._width, self._count)
 
 
 class HistogramContainer:
     """
-    Histogram data container.
+        Histogram data container.
 
-    Args:
-        histogram_message (Summary.Histogram): Histogram message in summary file.
+        Args:
+            histogram_message (Summary.Histogram): Histogram message in summary file.
     """
 
-    # Max quantity of original buckets.
-    MAX_ORIGINAL_BUCKETS_COUNT = 90
-
     def __init__(self, histogram_message: Summary.Histogram):
-        self._msg = histogram_message
-        original_buckets = [Bucket(bucket.left, bucket.width, bucket.count) for bucket in self._msg.buckets]
+        original_buckets = [Bucket(bucket.left, bucket.width, bucket.count) for bucket in histogram_message.buckets]
         # Ensure buckets are sorted from min to max.
         original_buckets.sort(key=lambda bucket: bucket.left)
-        self._original_buckets = tuple(original_buckets)
-        self._count = sum(bucket.count for bucket in self._original_buckets)
-        self._max = _mask_invalid_number(histogram_message.max)
-        self._min = _mask_invalid_number(histogram_message.min)
-        self._visual_max = self._max
-        self._visual_min = self._min
-        # default bin number
-        self._visual_bins = calc_histogram_bins(self._count)
-        # Note that tuple is immutable, so sharing tuple is often safe.
-        self._re_sampled_buckets = ()
+        self._count = sum(bucket.count for bucket in original_buckets)
+        self._max = mask_invalid_number(histogram_message.max)
+        self._min = mask_invalid_number(histogram_message.min)
+        self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
 
     @property
     def max(self):
@@ -114,148 +50,10 @@ class HistogramContainer:
         return self._count
 
     @property
-    def original_msg(self):
-        """Gets original proto message."""
-        return self._msg
-
-    @property
-    def original_buckets_count(self):
-        """Gets original buckets quantity."""
-        return len(self._original_buckets)
-
-    def set_visual_range(self, max_val: float, min_val: float, bins: int) -> None:
-        """
-        Sets visual range for later re-sampling.
-
-        It's caller's duty to ensure input is valid.
-
-        Why we need visual range for histograms? Aligned buckets between steps can help users know about the trend of
-        tensors. Miss aligned buckets between steps might miss-lead users about the trend of a tensor. Because for
-        given tensor, if you have thinner buckets, count of every bucket will get lower, however, if you have
-        thicker buckets, count of every bucket will get higher.  When they are displayed together, user might think
-        the histogram with thicker buckets has more values. This is miss-leading. So we need to unify buckets across
-        steps. Visual range for histogram is a technology for unifying buckets.
-
-        Args:
-            max_val (float): Max value for visual histogram.
-            min_val (float): Min value for visual histogram.
-            bins (int): Bins number for visual histogram.
-        """
-        if max_val < min_val:
-            raise ParamValueError(
-                "Invalid input. max_val({}) is less or equal than min_val({}).".format(max_val, min_val))
-
-        if bins < 1:
-            raise ParamValueError("Invalid input bins({}). Must be greater than 0.".format(bins))
-
-        self._visual_max = max_val
-        self._visual_min = min_val
-        self._visual_bins = bins
-
-        # mark _re_sampled_buckets to empty
-        self._re_sampled_buckets = ()
-
-    def _calc_intersection_len(self, max1, min1, max2, min2):
-        """Calculates intersection length of [min1, max1] and [min2, max2]."""
-        if max1 < min1:
-            raise ParamValueError(
-                "Invalid input. max1({}) is less than min1({}).".format(max1, min1))
-
-        if max2 < min2:
-            raise ParamValueError(
-                "Invalid input. max2({}) is less than min2({}).".format(max2, min2))
-
-        if min1 <= min2:
-            if max1 <= min2:
-                # return value must be calculated by max1.__sub__
-                return max1 - max1
-            if max1 <= max2:
-                return max1 - min2
-            # max1 > max2
-            return max2 - min2
-
-        # min1 > min2
-        if max2 <= min1:
-            return max2 - max2
-        if max2 <= max1:
-            return max2 - min1
-        return max1 - min1
-
-    def _re_sample_buckets(self):
-        """Re-samples buckets according to visual_max, visual_min and visual_bins."""
-        if self._visual_max == self._visual_min:
-            # Adjust visual range if max equals min.
-            self._visual_max += 0.5
-            self._visual_min -= 0.5
-
-        width = (self._visual_max - self._visual_min) / self._visual_bins
-
-        if not self.count:
-            self._re_sampled_buckets = tuple(
-                Bucket(self._visual_min + width * i, width, 0)
-                for i in range(self._visual_bins))
-            return
-
-        re_sampled = []
-        original_pos = 0
-        original_bucket = self._original_buckets[original_pos]
-        for i in range(self._visual_bins):
-            cur_left = self._visual_min + width * i
-            cur_right = cur_left + width
-            cur_estimated_count = 0.0
-
-            # Skip no bucket range.
-            if cur_right <= original_bucket.left:
-                re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
-                continue
-
-            # Skip no intersect range.
-            while cur_left >= original_bucket.right:
-                original_pos += 1
-                if original_pos >= len(self._original_buckets):
-                    break
-                original_bucket = self._original_buckets[original_pos]
-
-            # entering with this condition: cur_right > original_bucket.left and cur_left < original_bucket.right
-            while True:
-                if original_pos >= len(self._original_buckets):
-                    break
-                original_bucket = self._original_buckets[original_pos]
-
-                intersection = self._calc_intersection_len(
-                    min1=cur_left, max1=cur_right,
-                    min2=original_bucket.left, max2=original_bucket.right)
-                if not original_bucket.width:
-                    estimated_count = original_bucket.count
-                else:
-                    estimated_count = (intersection / original_bucket.width) * original_bucket.count
-
-                cur_estimated_count += estimated_count
-                if cur_right > original_bucket.right:
-                    # Need to sample next original bucket to this visual bucket.
-                    original_pos += 1
-                else:
-                    # Current visual bucket has taken all intersect buckets into account.
-                    break
-
-            re_sampled.append(Bucket(cur_left, width, math.ceil(cur_estimated_count)))
-
-        self._re_sampled_buckets = tuple(re_sampled)
-
-    def buckets(self, convert_to_tuple=True):
-        """
-        Get visual buckets instead of original buckets.
-
-        Args:
-            convert_to_tuple (bool): Whether convert bucket object to tuple.
-
-        Returns:
-            tuple, contains buckets.
-        """
-        if not self._re_sampled_buckets:
-            self._re_sample_buckets()
-
-        if not convert_to_tuple:
-            return self._re_sampled_buckets
+    def histogram(self):
+        """Gets histogram data"""
+        return self._histogram
 
-        return tuple(bucket.as_tuple() for bucket in self._re_sampled_buckets)
+    def buckets(self):
+        """Gets histogram buckets"""
+        return self._histogram.buckets()

mindinsight/datavisual/data_transform/image_container.py

@@ -0,0 +1,30 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Image container."""
+from mindinsight.datavisual.proto_files.mindinsight_summary_pb2 import Summary
+
+
+class ImageContainer:
+    """
+    Container for image to allow pickling.
+
+    Args:
+        image_message (Summary.Image): Image proto buffer message.
+    """
+    def __init__(self, image_message: Summary.Image):
+        self.height = image_message.height
+        self.width = image_message.width
+        self.colorspace = image_message.colorspace
+        self.encoded_image = image_message.encoded_image

mindinsight/datavisual/data_transform/ms_data_loader.py

@@ -32,14 +32,18 @@ from mindinsight.datavisual.data_access.file_handler import FileHandler
 from mindinsight.datavisual.data_transform.events_data import EventsData
 from mindinsight.datavisual.data_transform.events_data import TensorEvent
 from mindinsight.datavisual.data_transform.graph import MSGraph
-from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
+from mindinsight.datavisual.data_transform.histogram import Histogram
+from mindinsight.datavisual.data_transform.histogram_container import HistogramContainer
+from mindinsight.datavisual.data_transform.image_container import ImageContainer
+from mindinsight.datavisual.data_transform.tensor_container import TensorContainer, MAX_TENSOR_COUNT
 from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
+from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
 from mindinsight.datavisual.utils import crc32
 from mindinsight.utils.exceptions import UnknownError
-from mindinsight.datavisual.data_transform.histogram_container import HistogramContainer
 
 HEADER_SIZE = 8
 CRC_STR_SIZE = 4
+MAX_EVENT_STRING = 500000000
 
 
 class MSDataLoader:
@@ -77,11 +81,14 @@ class MSDataLoader:
                            "we will reload all files in path %s.", self._summary_dir)
             self.__init__(self._summary_dir)
 
-    def load(self):
+    def load(self, computing_resource_mgr):
         """
         Load all log valid files.
 
         When the file is reloaded, it will continue to load from where it left off.
+
+        Args:
+            computing_resource_mgr (ComputingResourceManager): The ComputingResourceManager instance.
         """
         logger.debug("Start to load data in ms data loader.")
         filenames = self.filter_valid_files()
@@ -92,8 +99,9 @@ class MSDataLoader:
         self._valid_filenames = filenames
         self._check_files_deleted(filenames, old_filenames)
 
-        for parser in self._parser_list:
-            parser.parse_files(filenames, events_data=self._events_data)
+        with computing_resource_mgr.get_executor() as executor:
+            for parser in self._parser_list:
+                parser.parse_files(executor, filenames, events_data=self._events_data)
 
     def filter_valid_files(self):
         """
@@ -123,11 +131,12 @@ class _Parser:
         self._latest_mtime = 0
         self._summary_dir = summary_dir
 
-    def parse_files(self, filenames, events_data):
+    def parse_files(self, executor, filenames, events_data):
         """
         Load files and parse files content.
 
         Args:
+            executor (Executor): The executor instance.
             filenames (list[str]): File name list.
             events_data (EventsData): The container of event data.
         """
@@ -175,7 +184,7 @@ class _Parser:
 class _PbParser(_Parser):
     """This class is used to parse pb file."""
 
-    def parse_files(self, filenames, events_data):
+    def parse_files(self, executor, filenames, events_data):
         pb_filenames = self.filter_files(filenames)
         pb_filenames = self.sort_files(pb_filenames)
         for filename in pb_filenames:
@@ -253,11 +262,12 @@ class _SummaryParser(_Parser):
         self._summary_file_handler = None
         self._events_data = None
 
-    def parse_files(self, filenames, events_data):
+    def parse_files(self, executor, filenames, events_data):
         """
         Load summary file and parse file content.
 
         Args:
+            executor (Executor): The executor instance.
             filenames (list[str]): File name list.
             events_data (EventsData): The container of event data.
         """
@@ -283,7 +293,9 @@ class _SummaryParser(_Parser):
 
             self._latest_file_size = new_size
             try:
-                self._load_single_file(self._summary_file_handler)
+                self._load_single_file(self._summary_file_handler, executor)
+                # Wait for data in this file to be processed to avoid loading multiple files at the same time.
+                executor.wait_all_tasks_finish()
             except UnknownError as ex:
                 logger.warning("Parse summary file failed, detail: %r,"
                                "file path: %s.", str(ex), file_path)
@@ -302,14 +314,14 @@ class _SummaryParser(_Parser):
             lambda filename: (re.search(r'summary\.\d+', filename)
                               and not filename.endswith("_lineage")), filenames))
 
-    def _load_single_file(self, file_handler):
+    def _load_single_file(self, file_handler, executor):
         """
         Load a log file data.
 
         Args:
             file_handler (FileHandler): A file handler.
+            executor (Executor): The executor instance.
         """
-        logger.debug("Load single summary file, file path: %s.", file_handler.file_path)
         while True:
             start_offset = file_handler.offset
             try:
@@ -317,9 +329,34 @@ class _SummaryParser(_Parser):
                 if event_str is None:
                     file_handler.reset_offset(start_offset)
                     break
-
-                event = summary_pb2.Event.FromString(event_str)
-                self._event_parse(event)
+                if len(event_str) > MAX_EVENT_STRING:
+                    logger.warning("file_path: %s, event string: %d exceeds %d and drop it.",
+                                   file_handler.file_path, len(event_str), MAX_EVENT_STRING)
+                    continue
+
+                future = executor.submit(self._event_parse, event_str, self._latest_filename)
+
+                def _add_tensor_event_callback(future_value):
+                    try:
+                        tensor_values = future_value.result()
+                        for tensor_value in tensor_values:
+                            if tensor_value.plugin_name == PluginNameEnum.GRAPH.value:
+                                try:
+                                    graph_tags = self._events_data.list_tags_by_plugin(PluginNameEnum.GRAPH.value)
+                                except KeyError:
+                                    graph_tags = []
+
+                                summary_tags = self.filter_files(graph_tags)
+                                for tag in summary_tags:
+                                    self._events_data.delete_tensor_event(tag)
+
+                            self._events_data.add_tensor_event(tensor_value)
+                    except Exception as exc:
+                        # Log exception for debugging.
+                        logger.exception(exc)
+                        raise
+
+                future.add_done_callback(_add_tensor_event_callback)
             except exceptions.CRCFailedError:
                 file_handler.reset_offset(start_offset)
                 logger.warning("Check crc faild and ignore this file, file_path=%s, "
@@ -379,19 +416,29 @@ class _SummaryParser(_Parser):
 
         return event_str
 
-    def _event_parse(self, event):
+    @staticmethod
+    def _event_parse(event_str, latest_file_name):
         """
         Transform `Event` data to tensor_event and update it to EventsData.
 
+        This method is static to avoid sending unnecessary objects to other processes.
+
         Args:
-            event (Event): Message event in summary proto, data read from file handler.
+            event (str): Message event string in summary proto, data read from file handler.
+            latest_file_name (str): Latest file name.
         """
+
         plugins = {
             'scalar_value': PluginNameEnum.SCALAR,
             'image': PluginNameEnum.IMAGE,
             'histogram': PluginNameEnum.HISTOGRAM,
+            'tensor': PluginNameEnum.TENSOR
         }
+        logger.debug("Start to parse event string. Event string len: %s.", len(event_str))
+        event = summary_pb2.Event.FromString(event_str)
+        logger.debug("Deserialize event string completed.")
 
+        ret_tensor_events = []
         if event.HasField('summary'):
             for value in event.summary.value:
                 for plugin in plugins:
@@ -399,44 +446,52 @@ class _SummaryParser(_Parser):
                         continue
                     plugin_name_enum = plugins[plugin]
                     tensor_event_value = getattr(value, plugin)
+                    logger.debug("Processing plugin value: %s.", plugin_name_enum)
 
-                    if plugin == 'histogram':
+                    if plugin == PluginNameEnum.HISTOGRAM.value:
                         tensor_event_value = HistogramContainer(tensor_event_value)
                         # Drop steps if original_buckets_count exceeds HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT
                         # to avoid time-consuming re-sample process.
-                        if tensor_event_value.original_buckets_count > HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT:
+                        if tensor_event_value.histogram.original_buckets_count > Histogram.MAX_ORIGINAL_BUCKETS_COUNT:
                             logger.info('original_buckets_count exceeds '
                                         'HistogramContainer.MAX_ORIGINAL_BUCKETS_COUNT')
                             continue
 
+                    elif plugin == PluginNameEnum.TENSOR.value:
+                        tensor_event_value = TensorContainer(tensor_event_value)
+                        tensor_count = 1
+                        for d in tensor_event_value.dims:
+                            tensor_count *= d
+                        if tensor_count > MAX_TENSOR_COUNT:
+                            logger.warning('tag: %s/tensor, tensor count: %d exceeds %d and drop it.',
+                                           value.tag, tensor_count, MAX_TENSOR_COUNT)
+                            continue
+
+                    elif plugin == PluginNameEnum.IMAGE.value:
+                        tensor_event_value = ImageContainer(tensor_event_value)
+
                     tensor_event = TensorEvent(wall_time=event.wall_time,
                                                step=event.step,
                                                tag='{}/{}'.format(value.tag, plugin_name_enum.value),
                                                plugin_name=plugin_name_enum.value,
                                                value=tensor_event_value,
-                                               filename=self._latest_filename)
-                    self._events_data.add_tensor_event(tensor_event)
+                                               filename=latest_file_name)
+                    logger.debug("Tensor event generated, plugin is %s, tag is %s, step is %s.",
+                                 plugin_name_enum, value.tag, event.step)
+                    ret_tensor_events.append(tensor_event)
 
         elif event.HasField('graph_def'):
             graph = MSGraph()
             graph.build_graph(event.graph_def)
             tensor_event = TensorEvent(wall_time=event.wall_time,
                                        step=event.step,
-                                       tag=self._latest_filename,
+                                       tag=latest_file_name,
                                        plugin_name=PluginNameEnum.GRAPH.value,
                                        value=graph,
-                                       filename=self._latest_filename)
-
-            try:
-                graph_tags = self._events_data.list_tags_by_plugin(PluginNameEnum.GRAPH.value)
-            except KeyError:
-                graph_tags = []
-
-            summary_tags = self.filter_files(graph_tags)
-            for tag in summary_tags:
-                self._events_data.delete_tensor_event(tag)
+                                       filename=latest_file_name)
+            ret_tensor_events.append(tensor_event)
 
-            self._events_data.add_tensor_event(tensor_event)
+        return ret_tensor_events
 
     @staticmethod
     def _compare_summary_file(current_file, dst_file):

mindinsight/datavisual/data_transform/reservoir.py

@@ -205,15 +205,15 @@ class HistogramReservoir(Reservoir):
             visual_range = _VisualRange()
             max_count = 0
             for sample in self._samples:
-                histogram = sample.value
-                if histogram.count == 0:
+                histogram_container = sample.value
+                if histogram_container.count == 0:
                     # ignore empty tensor
                     continue
-                max_count = max(histogram.count, max_count)
-                visual_range.update(histogram.max, histogram.min)
+                max_count = max(histogram_container.count, max_count)
+                visual_range.update(histogram_container.max, histogram_container.min)
 
             if visual_range.max == visual_range.min and not max_count:
-                logger.info("Max equals to min. Count is zero.")
+                logger.debug("Max equals to min. Count is zero.")
 
             bins = calc_histogram_bins(max_count)
 
@@ -225,7 +225,7 @@ class HistogramReservoir(Reservoir):
                 bins,
                 max_count)
             for sample in self._samples:
-                histogram = sample.value
+                histogram = sample.value.histogram
                 histogram.set_visual_range(visual_range.max, visual_range.min, bins)
 
             self._visual_range_up_to_date = True
@@ -245,6 +245,6 @@ class ReservoirFactory:
         Returns:
             Reservoir, reservoir instance for given plugin name.
         """
-        if plugin_name == PluginNameEnum.HISTOGRAM.value:
+        if plugin_name in (PluginNameEnum.HISTOGRAM.value, PluginNameEnum.TENSOR.value):
             return HistogramReservoir(size)
         return Reservoir(size)

mindinsight/datavisual/data_transform/tensor_container.py

@@ -0,0 +1,269 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tensor data container."""
+import threading
+
+import numpy as np
+
+from mindinsight.datavisual.common.log import logger
+from mindinsight.datavisual.data_transform.histogram import Histogram, Bucket
+from mindinsight.datavisual.utils.utils import calc_histogram_bins
+from mindinsight.utils.exceptions import ParamValueError
+
+F32_MIN, F32_MAX = np.finfo(np.float32).min, np.finfo(np.float32).max
+MAX_TENSOR_COUNT = 10000000
+
+
+class Statistics:
+    """Statistics data class.
+
+    Args:
+        max_value (float): max value of tensor data.
+        min_value (float): min value of tensor data.
+        avg_value (float): avg value of tensor data.
+        count (int): total count of tensor data.
+        nan_count (int): count of NAN.
+        neg_inf_count (int): count of negative INF.
+        pos_inf_count (int): count of positive INF.
+    """
+
+    def __init__(self, max_value=0, min_value=0, avg_value=0,
+                 count=0, nan_count=0, neg_inf_count=0, pos_inf_count=0):
+        self._max = max_value
+        self._min = min_value
+        self._avg = avg_value
+        self._count = count
+        self._nan_count = nan_count
+        self._neg_inf_count = neg_inf_count
+        self._pos_inf_count = pos_inf_count
+
+    @property
+    def max(self):
+        """Get max value of tensor."""
+        return self._max
+
+    @property
+    def min(self):
+        """Get min value of tensor."""
+        return self._min
+
+    @property
+    def avg(self):
+        """Get avg value of tensor."""
+        return self._avg
+
+    @property
+    def count(self):
+        """Get total count of tensor."""
+        return self._count
+
+    @property
+    def nan_count(self):
+        """Get count of NAN."""
+        return self._nan_count
+
+    @property
+    def neg_inf_count(self):
+        """Get count of negative INF."""
+        return self._neg_inf_count
+
+    @property
+    def pos_inf_count(self):
+        """Get count of positive INF."""
+        return self._pos_inf_count
+
+
+def get_statistics_from_tensor(tensors):
+    """
+    Calculates statistics data of tensor.
+
+    Args:
+        tensors (numpy.ndarray): An numpy.ndarray of tensor data.
+
+    Returns:
+         an instance of Statistics.
+    """
+    ma_value = np.ma.masked_invalid(tensors)
+    total, valid = tensors.size, ma_value.count()
+    invalids = []
+    for isfn in np.isnan, np.isposinf, np.isneginf:
+        if total - valid > sum(invalids):
+            count = np.count_nonzero(isfn(tensors))
+            invalids.append(count)
+        else:
+            invalids.append(0)
+
+    nan_count, pos_inf_count, neg_inf_count = invalids
+    if not valid:
+        logger.warning('There are no valid values in the tensors(size=%d, shape=%s)', total, tensors.shape)
+        statistics = Statistics(max_value=0,
+                                min_value=0,
+                                avg_value=0,
+                                count=total,
+                                nan_count=nan_count,
+                                neg_inf_count=neg_inf_count,
+                                pos_inf_count=pos_inf_count)
+        return statistics
+
+    # BUG: max of a masked array with dtype np.float16 returns inf
+    # See numpy issue#15077
+    if issubclass(tensors.dtype.type, np.floating):
+        tensor_min = ma_value.min(fill_value=np.PINF)
+        tensor_max = ma_value.max(fill_value=np.NINF)
+        if tensor_min < F32_MIN or tensor_max > F32_MAX:
+            logger.warning('Values(%f, %f) are too large, you may encounter some undefined '
+                           'behaviours hereafter.', tensor_min, tensor_max)
+    else:
+        tensor_min = ma_value.min()
+        tensor_max = ma_value.max()
+    tensor_sum = ma_value.sum(dtype=np.float64)
+    statistics = Statistics(max_value=tensor_max,
+                            min_value=tensor_min,
+                            avg_value=tensor_sum / valid,
+                            count=total,
+                            nan_count=nan_count,
+                            neg_inf_count=neg_inf_count,
+                            pos_inf_count=pos_inf_count)
+    return statistics
+
+
+def _get_data_from_tensor(tensor):
+    """
+    Get data from tensor and convert to tuple.
+
+    Args:
+        tensor (TensorProto): Tensor proto data.
+
+    Returns:
+        tuple, the item of tensor value.
+    """
+    return tuple(tensor.float_data)
+
+
+def calc_original_buckets(np_value, stats):
+    """
+    Calculate buckets from tensor data.
+
+    Args:
+        np_value (numpy.ndarray): An numpy.ndarray of tensor data.
+        stats (Statistics): An instance of Statistics about tensor data.
+
+    Returns:
+        list, a list of bucket about tensor data.
+
+    Raises:
+        ParamValueError, If np_value or stats is None.
+    """
+    if np_value is None or stats is None:
+        raise ParamValueError("Invalid input. np_value or stats is None.")
+    valid_count = stats.count - stats.nan_count - stats.neg_inf_count - stats.pos_inf_count
+    if not valid_count:
+        return []
+
+    bins = calc_histogram_bins(valid_count)
+    first_edge, last_edge = stats.min, stats.max
+
+    if not first_edge < last_edge:
+        first_edge -= 0.5
+        last_edge += 0.5
+
+    bins = np.linspace(first_edge, last_edge, bins + 1, dtype=np_value.dtype)
+    hists, edges = np.histogram(np_value, bins=bins)
+
+    buckets = []
+    for hist, edge1, edge2 in zip(hists, edges, edges[1:]):
+        bucket = Bucket(edge1, edge2 - edge1, hist)
+        buckets.append(bucket)
+
+    return buckets
+
+
+class TensorContainer:
+    """
+    Tensor data container.
+
+    Args:
+        tensor_message (Summary.TensorProto): Tensor message in summary file.
+    """
+
+    def __init__(self, tensor_message):
+        self._lock = threading.Lock
+        # Original dims can not be pickled to transfer to other process, so tuple is used.
+        self._dims = tuple(tensor_message.dims)
+        self._data_type = tensor_message.data_type
+        self._np_array = None
+        self._data = _get_data_from_tensor(tensor_message)
+        self._stats = get_statistics_from_tensor(self.get_or_calc_ndarray())
+        original_buckets = calc_original_buckets(self.get_or_calc_ndarray(), self._stats)
+        self._count = sum(bucket.count for bucket in original_buckets)
+        self._max = self._stats.max
+        self._min = self._stats.min
+        self._histogram = Histogram(tuple(original_buckets), self._max, self._min, self._count)
+
+    @property
+    def dims(self):
+        """Get dims of tensor."""
+        return self._dims
+
+    @property
+    def data_type(self):
+        """Get data type of tensor."""
+        return self._data_type
+
+    @property
+    def max(self):
+        """Get max value of tensor."""
+        return self._max
+
+    @property
+    def min(self):
+        """Get min value of tensor."""
+        return self._min
+
+    @property
+    def stats(self):
+        """Get statistics data of tensor."""
+        return self._stats
+
+    @property
+    def count(self):
+        """Get count value of tensor."""
+        return self._count
+
+    @property
+    def histogram(self):
+        """Get histogram data."""
+        return self._histogram
+
+    def buckets(self):
+        """Get histogram buckets."""
+        return self._histogram.buckets()
+
+    def get_or_calc_ndarray(self):
+        """Get or calculate ndarray."""
+        with self._lock():
+            if self._np_array is None:
+                self._convert_to_numpy_array()
+            return self._np_array
+
+    def _convert_to_numpy_array(self):
+        """Convert a list data to numpy array."""
+        try:
+            ndarray = np.array(self._data).reshape(self._dims)
+        except ValueError as ex:
+            logger.error("Reshape array fail, detail: %r", str(ex))
+            return
+
+        self._np_array = ndarray

mindinsight/datavisual/processors/tensor_processor.py

@@ -0,0 +1,381 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Tensor Processor APIs."""
+from urllib.parse import unquote
+
+import numpy as np
+
+from mindinsight.datavisual.utils.tools import to_int
+from mindinsight.utils.exceptions import ParamValueError, UrlDecodeError
+from mindinsight.conf.constants import MAX_TENSOR_RESPONSE_DATA_SIZE
+from mindinsight.datavisual.common.validation import Validation
+from mindinsight.datavisual.common.exceptions import StepTensorDataNotInCacheError, TensorNotExistError
+from mindinsight.datavisual.common.exceptions import ResponseDataExceedMaxValueError
+from mindinsight.datavisual.data_transform.tensor_container import TensorContainer, get_statistics_from_tensor
+from mindinsight.datavisual.processors.base_processor import BaseProcessor
+from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
+
+
+def convert_array_from_str(dims, limit=0):
+    """
+    Convert string of dims data to array.
+
+    Args:
+        dims (str): Specify dims of tensor.
+        limit (int): The max flexible dimension count, default value is 0 which means that there is no limitation.
+
+    Returns:
+        list, a string like this: "[0, 0, :, :]" will convert to this value: [0, 0, None, None].
+
+    Raises:
+        ParamValueError, If flexible dimensions exceed limit value.
+    """
+    dims = dims.replace('[', '') \
+               .replace(']', '')
+    dims_list = []
+    count = 0
+    for dim in dims.split(','):
+        dim = dim.strip()
+        if dim == ':':
+            dims_list.append(None)
+            count += 1
+        else:
+            dims_list.append(to_int(dim, "dim"))
+    if limit and count > limit:
+        raise ParamValueError("Flexible dimensions cannot exceed limit value: {}, size: {}"
+                              .format(limit, count))
+    return dims_list
+
+
+def get_specific_dims_data(ndarray, dims, tensor_dims):
+    """
+    Get specific dims data.
+
+    Args:
+        ndarray (numpy.ndarray): An ndarray of numpy.
+        dims (list): A list of specific dims.
+        tensor_dims (list): A list of tensor dims.
+
+    Returns:
+        numpy.ndarray, an ndarray of specific dims tensor data.
+
+    Raises:
+        ParamValueError, If the length of param dims is not equal to the length of tensor dims or
+                         the index of param dims out of range.
+    """
+    if len(dims) != len(tensor_dims):
+        raise ParamValueError("The length of param dims: {}, is not equal to the "
+                              "length of tensor dims: {}.".format(len(dims), len(tensor_dims)))
+    indices = []
+    for k, d in enumerate(dims):
+        if d is not None:
+            if d >= tensor_dims[k]:
+                raise ParamValueError("The index: {} of param dims out of range: {}.".format(d, tensor_dims[k]))
+            indices.append(d)
+        else:
+            indices.append(slice(0, tensor_dims[k]))
+    return ndarray[tuple(indices)]
+
+
+def get_statistics_dict(stats):
+    """
+    Get statistics dict according to statistics value.
+
+    Args:
+        stats (Statistics): An instance of Statistics.
+
+    Returns:
+        dict, a dict including 'max', 'min', 'avg', 'count', 'nan_count', 'neg_inf_count', 'pos_inf_count'.
+    """
+    statistics = {
+        "max": stats.max,
+        "min": stats.min,
+        "avg": stats.avg,
+        "count": stats.count,
+        "nan_count": stats.nan_count,
+        "neg_inf_count": stats.neg_inf_count,
+        "pos_inf_count": stats.pos_inf_count
+    }
+    return statistics
+
+
+class TensorProcessor(BaseProcessor):
+    """Tensor Processor."""
+    def get_tensors(self, train_ids, tags, step, dims, detail):
+        """
+        Get tensor data for given train_ids, tags, step, dims and detail.
+
+        Args:
+            train_ids (list): Specify list of train job ID.
+            tags (list): Specify list of tag.
+            step (int): Specify step of tag, it's necessary when detail is equal to 'data'.
+            dims (str): Specify dims of step, it's necessary when detail is equal to 'data'.
+            detail (str): Specify which data to query, available values: 'stats', 'histogram' and 'data'.
+
+        Returns:
+            dict, a dict including the `tensors`.
+
+        Raises:
+            UrlDecodeError, If unquote train id error with strict mode.
+        """
+        Validation.check_param_empty(train_id=train_ids, tag=tags)
+        for index, train_id in enumerate(train_ids):
+            try:
+                train_id = unquote(train_id, errors='strict')
+            except UnicodeDecodeError:
+                raise UrlDecodeError('Unquote train id error with strict mode')
+            else:
+                train_ids[index] = train_id
+
+        tensors = []
+        for train_id in train_ids:
+            tensors += self._get_train_tensors(train_id, tags, step, dims, detail)
+
+        return {"tensors": tensors}
+
+    def _get_train_tensors(self, train_id, tags, step, dims, detail):
+        """
+        Get tensor data for given train_id, tags, step, dims and detail.
+
+        Args:
+            train_id (str): Specify list of train job ID.
+            tags (list): Specify list of tag.
+            step (int): Specify step of tensor, it's necessary when detail is set to 'data'.
+            dims (str): Specify dims of tensor, it's necessary when detail is set to 'data'.
+            detail (str): Specify which data to query, available values: 'stats', 'histogram' and 'data'.
+
+        Returns:
+            list[dict], a list of dictionaries containing the `train_id`, `tag`, `values`.
+
+        Raises:
+            TensorNotExistError, If tensor with specific train_id and tag is not exist in cache.
+            ParamValueError, If the value of detail is not within available values:
+                            'stats', 'histogram' and 'data'.
+        """
+
+        tensors_response = []
+        for tag in tags:
+            try:
+                tensors = self._data_manager.list_tensors(train_id, tag)
+            except ParamValueError as err:
+                raise TensorNotExistError(err.message)
+
+            if tensors and not isinstance(tensors[0].value, TensorContainer):
+                raise TensorNotExistError("there is no tensor data in this tag: {}".format(tag))
+
+            if detail is None or detail == 'stats':
+                values = self._get_tensors_summary(detail, tensors)
+            elif detail == 'data':
+                Validation.check_param_empty(step=step, dims=dims)
+                step = to_int(step, "step")
+                values = self._get_tensors_data(step, dims, tensors)
+            elif detail == 'histogram':
+                values = self._get_tensors_histogram(tensors)
+            else:
+                raise ParamValueError('Can not support this value: {} of detail.'.format(detail))
+
+            tensor = {
+                "train_id": train_id,
+                "tag": tag,
+                "values": values
+            }
+            tensors_response.append(tensor)
+
+        return tensors_response
+
+    def _get_tensors_summary(self, detail, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor summary.
+
+        Args:
+            detail (str): Specify which data to query, detail value is None or 'stats' at this method.
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32"
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            } This dict is being set when detail is equal to stats.
+                        }
+                    }
+        """
+        values = []
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            value_dict = {
+                "dims": value.dims,
+                "data_type": anf_ir_pb2.DataType.Name(value.data_type)
+            }
+            if detail and detail == 'stats':
+                stats = get_statistics_dict(value.stats)
+                value_dict.update({"statistics": stats})
+
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": value_dict
+            })
+
+        return values
+
+    def _get_tensors_data(self, step, dims, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor dims data.
+
+        Args:
+            step (int): Specify step of tensor.
+            dims (str): Specify dims of tensor.
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32",
+                            "data": [[0.1]]
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            }
+                        }
+                    }
+
+        Raises:
+            ResponseDataExceedMaxValueError, If the size of response data exceed max value.
+            StepTensorDataNotInCacheError, If query step is not in cache.
+        """
+        values = []
+        step_in_cache = False
+        dims = convert_array_from_str(dims, limit=2)
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            if step != tensor.step:
+                continue
+            step_in_cache = True
+            ndarray = value.get_or_calc_ndarray()
+            res_data = get_specific_dims_data(ndarray, dims, list(value.dims))
+            flatten_data = res_data.flatten().tolist()
+            if len(flatten_data) > MAX_TENSOR_RESPONSE_DATA_SIZE:
+                raise ResponseDataExceedMaxValueError("the size of response data: {} exceed max value: {}."
+                                                      .format(len(flatten_data), MAX_TENSOR_RESPONSE_DATA_SIZE))
+
+            def transfer(array):
+                if not isinstance(array, np.ndarray):
+                    # The list is used here so that len function can be used
+                    # when the value of array is `NAN`、｀-INF｀ or ｀INF｀.
+                    array = [array]
+                transfer_data = [None] * len(array)
+                for index, data in enumerate(array):
+                    if isinstance(data, np.ndarray):
+                        transfer_data[index] = transfer(data)
+                    else:
+                        if np.isnan(data):
+                            transfer_data[index] = 'NAN'
+                        elif np.isneginf(data):
+                            transfer_data[index] = '-INF'
+                        elif np.isposinf(data):
+                            transfer_data[index] = 'INF'
+                        else:
+                            transfer_data[index] = data
+                return transfer_data
+
+            stats = get_statistics_from_tensor(res_data)
+            if stats.nan_count + stats.neg_inf_count + stats.pos_inf_count > 0:
+                tensor_data = transfer(res_data)
+            else:
+                tensor_data = res_data.tolist()
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": {
+                    "dims": value.dims,
+                    "data_type": anf_ir_pb2.DataType.Name(value.data_type),
+                    "data": tensor_data,
+                    "statistics": get_statistics_dict(stats)
+                }
+            })
+            break
+        if not step_in_cache:
+            raise StepTensorDataNotInCacheError("this step: {} data may has been dropped.".format(step))
+
+        return values
+
+    def _get_tensors_histogram(self, tensors):
+        """
+        Builds a JSON-serializable object with information about tensor histogram data.
+
+        Args:
+            tensors (list): The list of _Tensor data.
+
+        Returns:
+            dict, a dict including the `wall_time`, `step`, and `value' for each tensor.
+                    {
+                        "wall_time": 0,
+                        "step": 0,
+                        "value": {
+                            "dims": [1],
+                            "data_type": "DT_FLOAT32",
+                            "histogram_buckets": [[0.1, 0.2, 3]]
+                            "statistics": {
+                                "max": 0,
+                                "min": 0,
+                                "avg": 0,
+                                "count": 1,
+                                "nan_count": 0,
+                                "neg_inf_count": 0,
+                                "pos_inf_count": 0
+                            }
+                        }
+                    }
+        """
+        values = []
+        for tensor in tensors:
+            # This value is an instance of TensorContainer
+            value = tensor.value
+            buckets = value.buckets()
+            values.append({
+                "wall_time": tensor.wall_time,
+                "step": tensor.step,
+                "value": {
+                    "dims": value.dims,
+                    "data_type": anf_ir_pb2.DataType.Name(value.data_type),
+                    "histogram_buckets": buckets,
+                    "statistics": get_statistics_dict(value.stats)
+                }
+            })
+
+        return values

mindinsight/datavisual/processors/train_task_manager.py

@@ -83,17 +83,24 @@ class TrainTaskManager(BaseProcessor):
             plugins=plugins
         )
 
-    def query_train_jobs(self, offset=0, limit=10):
+    def query_train_jobs(self, offset=0, limit=10, request_train_id=None):
         """
         Query train jobs.
 
         Args:
             offset (int): Specify page number. Default is 0.
             limit (int): Specify page size. Default is 10.
+            request_train_id (str): Specify train id. Default is None.
 
         Returns:
             tuple, return quantity of total train jobs and list of train jobs specified by offset and limit.
         """
+        if request_train_id is not None:
+            train_job_item = self._get_train_job_item(request_train_id)
+            if train_job_item is None:
+                return 0, []
+            return 1, [train_job_item]
+
         brief_cache = self._data_manager.get_brief_cache()
         brief_train_jobs = list(brief_cache.get_train_jobs().values())
         brief_train_jobs.sort(key=lambda x: x.basic_info.update_time, reverse=True)
@@ -106,37 +113,52 @@ class TrainTaskManager(BaseProcessor):
         train_ids = [train_job.basic_info.train_id for train_job in brief_train_jobs[start:end]]
 
         for train_id in train_ids:
-            try:
-                train_job = self._data_manager.get_train_job(train_id)
-            except exceptions.TrainJobNotExistError:
-                logger.warning('Train job %s not existed', train_id)
+            train_job_item = self._get_train_job_item(train_id)
+            if train_job_item is None:
                 continue
-
-            basic_info = train_job.get_basic_info()
-            train_job_item = dict(
-                train_id=basic_info.train_id,
-                relative_path=basic_info.train_id,
-                create_time=basic_info.create_time.strftime('%Y-%m-%d %H:%M:%S'),
-                update_time=basic_info.update_time.strftime('%Y-%m-%d %H:%M:%S'),
-                profiler_dir=basic_info.profiler_dir,
-                cache_status=train_job.cache_status.value,
-            )
-
-            if train_job.cache_status == CacheStatus.CACHED:
-                plugins = self.get_plugins(train_id)
-            else:
-                plugins = dict(plugins={
-                    'graph': [],
-                    'scalar': [],
-                    'image': [],
-                    'histogram': [],
-                })
-
-            train_job_item.update(plugins)
             train_jobs.append(train_job_item)
 
         return total, train_jobs
 
+    def _get_train_job_item(self, train_id):
+        """
+        Get train job item.
+
+        Args:
+            train_id (str): Specify train id.
+
+        Returns:
+            dict, a dict of train job item.
+        """
+        try:
+            train_job = self._data_manager.get_train_job(train_id)
+        except exceptions.TrainJobNotExistError:
+            logger.warning('Train job %s not existed', train_id)
+            return None
+
+        basic_info = train_job.get_basic_info()
+        train_job_item = dict(
+            train_id=basic_info.train_id,
+            relative_path=basic_info.train_id,
+            create_time=basic_info.create_time.strftime('%Y-%m-%d %H:%M:%S'),
+            update_time=basic_info.update_time.strftime('%Y-%m-%d %H:%M:%S'),
+            profiler_dir=basic_info.profiler_dir,
+            cache_status=train_job.cache_status.value,
+        )
+
+        if train_job.cache_status == CacheStatus.CACHED:
+            plugins = self.get_plugins(train_id)
+        else:
+            plugins = dict(plugins={
+                'graph': [],
+                'scalar': [],
+                'image': [],
+                'histogram': [],
+            })
+
+        train_job_item.update(plugins)
+        return train_job_item
+
     def cache_train_jobs(self, train_ids):
         """
         Cache train jobs.

mindinsight/datavisual/utils/tools.py

@@ -21,7 +21,9 @@ from numbers import Number
 from urllib.parse import unquote
 
 from mindinsight.datavisual.common.exceptions import MaxCountExceededError
+from mindinsight.datavisual.common.log import logger
 from mindinsight.utils import exceptions
+from mindinsight.utils.exceptions import UnknownError
 
 _IMG_EXT_TO_MIMETYPE = {
     'bmp': 'image/bmp',
@@ -216,6 +218,16 @@ def if_nan_inf_to_none(name, value):
     return value
 
 
+def exception_wrapper(func):
+    def wrapper(*args, **kwargs):
+        try:
+            func(*args, **kwargs)
+        except Exception as exc:
+            logger.exception(exc)
+            raise UnknownError(str(exc))
+    return wrapper
+
+
 class Counter:
     """Count accumulator with limit checking."""
 

mindinsight/lineagemgr/cache_item_updater.py

@@ -17,7 +17,7 @@ import os
 
 from mindinsight.datavisual.data_transform.data_manager import BaseCacheItemUpdater, CachedTrainJob
 from mindinsight.lineagemgr.common.log import logger
-from mindinsight.lineagemgr.common.exceptions.exceptions import MindInsightException
+from mindinsight.lineagemgr.common.exceptions.exceptions import LineageFileNotFoundError
 from mindinsight.lineagemgr.common.validator.validate import validate_train_id, validate_added_info
 from mindinsight.lineagemgr.lineage_parser import LineageParser, LINEAGE
 from mindinsight.utils.exceptions import ParamValueError
@@ -59,19 +59,13 @@ class LineageCacheItemUpdater(BaseCacheItemUpdater):
 
         try:
             lineage_parser = self._lineage_parsing(cache_item)
-        except MindInsightException as err:
-            with cache_item.lock_key(LINEAGE):
-                try:
-                    cache_item.delete(key=LINEAGE)
-                    logger.info("Parse failed, delete the tran job %s. Detail: %s.", relative_path, str(err))
-                except ParamValueError:
-                    logger.debug("Parse failed, no need to delete the train job %s. "
-                                 "Detail: %s.", relative_path, str(err))
+        except LineageFileNotFoundError:
+            self._delete_lineage_in_cache(cache_item, LINEAGE, relative_path)
             return
 
         super_lineage_obj = lineage_parser.super_lineage_obj
         if super_lineage_obj is None:
-            logger.debug("There is no lineage to update in tran job %s.", relative_path)
+            logger.debug("There is no lineage to update in train job %s.", relative_path)
             return
 
         cache_item.set(key=LINEAGE, value=lineage_parser)
@@ -91,3 +85,12 @@ class LineageCacheItemUpdater(BaseCacheItemUpdater):
                 lineage_parser.load()
 
         return lineage_parser
+
+    def _delete_lineage_in_cache(self, cache_item, key, relative_path):
+        with cache_item.lock_key(key):
+            try:
+                cache_item.delete(key=key)
+                logger.info("Parse failed, delete the tran job %s.", relative_path)
+            except ParamValueError:
+                logger.debug("Parse failed, and it is not in cache, "
+                             "no need to delete the train job %s.", relative_path)

mindinsight/lineagemgr/lineage_parser.py

@@ -100,7 +100,15 @@ class LineageParser:
                 continue
 
             self._latest_file_size = new_size
-            self._parse_summary_log()
+            try:
+                self._parse_summary_log()
+            except (LineageSummaryAnalyzeException,
+                    LineageEventNotExistException,
+                    LineageEventFieldNotExistException) as error:
+                logger.debug("Parse file failed, file_path is %s. Detail: %s.", file_path, str(error))
+            except MindInsightException as error:
+                logger.exception(error)
+                logger.debug("Parse file failed, file_path is %s.", file_path)
 
     def _init_if_files_deleted(self, file_list):
         """Init variables if files deleted."""
@@ -189,13 +197,6 @@ class LineageOrganizer:
                     self._super_lineage_objs.update({abs_summary_dir: super_lineage_obj})
             except LineageFileNotFoundError:
                 no_lineage_count += 1
-            except (LineageSummaryAnalyzeException,
-                    LineageEventNotExistException,
-                    LineageEventFieldNotExistException) as error:
-                logger.warning("Parse file failed under summary_dir %s. Detail: %s.", relative_dir, str(error))
-            except MindInsightException as error:
-                logger.exception(error)
-                logger.warning("Parse file failed under summary_dir %s.", relative_dir)
 
         if no_lineage_count == len(relative_dirs):
             logger.info('There is no summary log file under summary_base_dir.')

mindinsight/profiler/README.md

@@ -11,11 +11,11 @@ The Profiler enables users to:
 
 To enable profiling on MindSpore, the MindInsight Profiler apis should be added to the script:
 
-1. Import MindInsight Profiler
+1. Import the Profiler
     ```
-    from mindinsight.profiler import Profiler
+    from mindspore.profiler import Profiler
     ```
-2. Initialize the Profiler after set context, and before the network initialization.  
+2. Initialize the Profiler after set context, and before the network and hccl initialization.  
 
     Example:
         

mindinsight/profiler/__init__.py

@@ -12,16 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""
-Profiler Module Introduction.
-
-This module provides Python APIs to enable the profiling of MindSpore neural networks.
-Users can import the mindinsight.profiler.Profiler, initialize the Profiler object to start profiling,
-and use Profiler.analyse() to stop profiling and analyse the results.
-To visualize the profiling results, users can open MindInsight Web, find the corresponding run
-and click the profile link.
-Now, Profiler supports the AICore operator analysis.
-"""
-from mindinsight.profiler.profiling import Profiler
-
-__all__ = ["Profiler"]
+"""Profiler Module Introduction."""

mindinsight/profiler/analyser/analyser_factory.py

@@ -15,7 +15,7 @@
 """The analyser factory."""
 import threading
 
-import mindinsight.profiler.analyser as analyser_module
+from mindinsight.profiler import analyser as analyser_module
 from mindinsight.profiler.common.exceptions.exceptions import \
     ProfilerAnalyserNotExistException
 

mindinsight/profiler/analyser/timeline_analyser.py

@@ -17,7 +17,6 @@ import json
 import os
 
 from mindinsight.profiler.analyser.base_analyser import BaseAnalyser
-from mindinsight.profiler.parser.container import TimelineContainer
 from mindinsight.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
     ProfilerIOException
 from mindinsight.profiler.common.log import logger
@@ -27,6 +26,48 @@ from mindinsight.profiler.common.validator.validate_path import validate_and_nor
 SIZE_LIMIT = 20 * 1024 * 1024  # 20MB
 
 
+class TimelineContainer:
+    """
+    A container of operator computation metadata.
+
+    Args:
+        split_list (list): The split list of metadata in op_compute output file.
+    """
+    def __init__(self, split_list):
+        self._op_name = split_list[0]
+        self._stream_id = int(split_list[1])
+        self._start_time = float(split_list[2])
+        self._duration = float(split_list[3])
+        self._pid = None
+        if len(split_list) == 5:
+            self._pid = int(split_list[4])
+
+    @property
+    def op_name(self):
+        """Get the name of the operator."""
+        return self._op_name
+
+    @property
+    def stream_id(self):
+        """Get the stream id of the operator."""
+        return self._stream_id
+
+    @property
+    def start_time(self):
+        """Get the execution start time of the operator."""
+        return self._start_time
+
+    @property
+    def duration(self):
+        """Get the duration of the operator execution."""
+        return self._duration
+
+    @property
+    def pid(self):
+        """Get the pid of the operator execution."""
+        return self._pid
+
+
 class TimelineAnalyser(BaseAnalyser):
     """
     Analyse timeline data from file.
@@ -62,9 +103,7 @@ class TimelineAnalyser(BaseAnalyser):
         Returns:
             json, the content of timeline data.
         """
-        # Search timeline json file under profiling dir.
         display_filename = self._display_filename.format(self._device_id)
-        # Check if there is a timeline json file for display
         file_path = os.path.join(self._profiling_dir, display_filename)
         file_path = validate_and_normalize_path(
             file_path, raise_key='Invalid timeline json path.'
@@ -90,11 +129,8 @@ class TimelineAnalyser(BaseAnalyser):
         Returns:
             json, the content of timeline summary information.
         """
-        file_path = None
-        summary_file_name = 'timeline_summary_{}.json'.format(self._device_id)
-        if summary_file_name in os.listdir(self._profiling_dir):
-            file_path = os.path.join(self._profiling_dir, summary_file_name)
-
+        summary_filename = self._timeline_summary_filename.format(self._device_id)
+        file_path = os.path.join(self._profiling_dir, summary_filename)
         file_path = validate_and_normalize_path(
             file_path, raise_key='Invalid timeline summary path.'
         )
@@ -107,6 +143,8 @@ class TimelineAnalyser(BaseAnalyser):
             except (IOError, OSError, json.JSONDecodeError) as err:
                 logger.error('Error occurred when read timeline summary file: %s', err)
                 raise ProfilerIOException
+        else:
+            logger.info('No timeline summary file. Please check the output path.')
 
         return timeline_summary
 

mindinsight/profiler/parser/aicpu_data_parser.py

@@ -1,182 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-The parser for AI CPU preprocess data.
-"""
-import os
-
-from tabulate import tabulate
-
-from mindinsight.profiler.common._utils import fwrite_format, get_file_join_name
-from mindinsight.profiler.common.log import logger
-
-
-class DataPreProcessParser:
-    """
-    The Parser for AI CPU preprocess data.
-
-    Args:
-         input_path(str): The profiling job path.
-         output_filename(str): The output data path and name.
-
-    """
-
-    _source_file_target = 'DATA_PREPROCESS.dev.AICPU.'
-    _dst_file_title = 'title:DATA_PREPROCESS AICPU'
-    _dst_file_column_title = ['serial_number', 'node_type_name', 'total_time(ms)',
-                              'dispatch_time(ms)', 'run_start', 'run_end']
-    _ms_unit = 1000
-
-    def __init__(self, input_path, output_filename):
-        self._input_path = input_path
-        self._output_filename = output_filename
-        self._source_file_name = self._get_source_file()
-        self._ms_kernel_flag = 3
-        self._other_kernel_flag = 6
-        self._thread_flag = 7
-        self._ms_kernel_run_end_index = 2
-        self._other_kernel_run_end_index = 5
-        self._result_list = []
-        self._min_cycle_counter = float('inf')
-
-    def _get_source_file(self):
-        """Get log file name, which was created by ada service."""
-        file_name = get_file_join_name(self._input_path, self._source_file_target)
-        if not file_name:
-            data_path = os.path.join(self._input_path, "data")
-            file_name = get_file_join_name(data_path, self._source_file_target)
-        return file_name
-
-    def _get_kernel_result(self, number, node_list, thread_list):
-        """Get the profiling data form different aicpu kernel"""
-        try:
-            if len(node_list) == self._ms_kernel_flag and len(thread_list) == self._thread_flag:
-                node_type_name = node_list[0].split(':')[-1]
-                run_end_index = self._ms_kernel_run_end_index
-            elif len(node_list) == self._other_kernel_flag and len(thread_list) == self._thread_flag:
-                node_type_name = node_list[0].split(':')[-1].split('/')[-1].split('-')[0]
-                run_end_index = self._other_kernel_run_end_index
-            else:
-                logger.warning("the data format can't support 'node_list':%s", str(node_list))
-                return None
-
-            run_start = node_list[1].split(':')[-1].split(' ')[0]
-            run_end = node_list[run_end_index].split(':')[-1].split(' ')[0]
-            total_time = float(thread_list[-1].split('=')[-1].split()[0]) / self._ms_unit
-            dispatch_time = float(thread_list[-2].split('=')[-1].split()[0]) / self._ms_unit
-
-            return [number, node_type_name, total_time, dispatch_time,
-                    run_start, run_end]
-        except IndexError as e:
-            logger.exception(e)
-            return None
-
-    def execute(self):
-        """Execute the parser, get result data, and write it to the output file."""
-
-        if not os.path.exists(self._source_file_name):
-            logger.info("Did not find the aicpu profiling source file")
-            return
-
-        with open(self._source_file_name, 'rb') as ai_cpu_data:
-            ai_cpu_str = str(ai_cpu_data.read().replace(b'\n\x00', b' ___ ')
-                             .replace(b'\x00', b' ___ '))[2:-1]
-            ai_cpu_lines = ai_cpu_str.split(" ___ ")
-
-        result_list = list()
-        ai_cpu_total_time_summary = 0
-        # Node serial number.
-        serial_number = 1
-        for i in range(len(ai_cpu_lines) - 1):
-            node_line = ai_cpu_lines[i]
-            thread_line = ai_cpu_lines[i + 1]
-            result = []
-            if "Node" in node_line and "Thread" in thread_line:
-                # Get the node data from node_line
-                node_list = node_line.split(',')
-                thread_list = thread_line.split(',')
-                result = self._get_kernel_result(serial_number, node_list, thread_list)
-
-                if result is None:
-                    continue
-
-                result_list.append(result)
-                # Calculate the total time.
-                total_time = result[2]
-                ai_cpu_total_time_summary += total_time
-                # Increase node serial number.
-                serial_number += 1
-            elif "Node" in node_line and "Thread" not in thread_line:
-                node_type_name = node_line.split(',')[0].split(':')[-1]
-                logger.warning("The node type:%s cannot find thread data", node_type_name)
-
-        if result_list:
-            ai_cpu_total_time = format(ai_cpu_total_time_summary, '.6f')
-            result_list.append(["AI CPU Total Time(ms):", ai_cpu_total_time])
-            fwrite_format(self._output_filename, data_source=self._dst_file_title, is_print=True,
-                          is_start=True)
-            fwrite_format(self._output_filename,
-                          data_source=tabulate(result_list, self._dst_file_column_title,
-                                               tablefmt='simple'),
-                          is_start=True, is_print=True)
-
-        # For timeline display.
-        self._result_list = result_list
-
-    def query_aicpu_data(self):
-        """
-        Get execution time of AI CPU operator.
-
-        Returns:
-            a dict, the metadata of AI CPU operator execution time.
-        """
-        stream_id = 0  # Default stream id for AI CPU.
-        pid = 9000  # Default pid for AI CPU.
-        factor = 1000  # Convert time unit from 1us to 1ms
-        total_time = 0
-        min_cycle_counter = float('inf')
-        aicpu_info = []
-        op_count_list = []
-        for aicpu_item in self._result_list:
-            if "AI CPU Total Time(ms):" in aicpu_item:
-                total_time = aicpu_item[-1]
-                continue
-
-            op_name = aicpu_item[1]
-            start_time = float(aicpu_item[4]) / factor
-            min_cycle_counter = min(min_cycle_counter, start_time)
-            end_time = float(aicpu_item[5]) / factor
-            duration = end_time - start_time
-            aicpu_info.append([op_name, stream_id, start_time, duration, pid])
-
-            # Record the number of operator types.
-            if op_name not in op_count_list:
-                op_count_list.append(op_name)
-
-        self._min_cycle_counter = min_cycle_counter
-        aicpu_dict = {
-            'info': aicpu_info,
-            'total_time': float(total_time),
-            'op_exe_times': len(aicpu_info),
-            'num_of_ops': len(op_count_list),
-            'num_of_streams': 1
-        }
-
-        return aicpu_dict
-
-    @property
-    def min_cycle_counter(self):
-        """Get minimum cycle counter in AI CPU."""
-        return self._min_cycle_counter

mindinsight/profiler/parser/container.py

@@ -1,99 +0,0 @@
-"""The container of metadata used in profiler parser."""
-
-
-class HWTSContainer:
-    """
-    HWTS output container.
-
-    Args:
-        split_list (list): The split list of metadata in HWTS output file.
-    """
-    def __init__(self, split_list):
-        self._op_name = ''
-        self._duration = None
-        self._status = split_list[0]
-        self._task_id = split_list[6]
-        self._cycle_counter = float(split_list[7])
-        self._stream_id = split_list[8]
-
-    @property
-    def status(self):
-        """Get the status of the operator, i.e. Start or End."""
-        return self._status
-
-    @property
-    def task_id(self):
-        """Get the task id of the operator."""
-        return self._task_id
-
-    @property
-    def cycle_counter(self):
-        """Get the cycle counter."""
-        return self._cycle_counter
-
-    @property
-    def stream_id(self):
-        """Get the stream id of the operator."""
-        return self._stream_id
-
-    @property
-    def op_name(self):
-        """Get the name of the operator."""
-        return self._op_name
-
-    @op_name.setter
-    def op_name(self, name):
-        """Set the name of the operator."""
-        self._op_name = name
-
-    @property
-    def duration(self):
-        """Get the duration of the operator execution."""
-        return self._duration
-
-    @duration.setter
-    def duration(self, value):
-        """Set the duration of the operator execution."""
-        self._duration = value
-
-
-class TimelineContainer:
-    """
-    A container of operator computation metadata.
-
-    Args:
-        split_list (list): The split list of metadata in op_compute output file.
-    """
-    def __init__(self, split_list):
-        self._op_name = split_list[0]
-        self._stream_id = int(split_list[1])
-        self._start_time = float(split_list[2])
-        self._duration = float(split_list[3])
-        self._pid = None
-        if len(split_list) == 5:
-            self._pid = int(split_list[4])
-
-    @property
-    def op_name(self):
-        """Get the name of the operator."""
-        return self._op_name
-
-    @property
-    def stream_id(self):
-        """Get the stream id of the operator."""
-        return self._stream_id
-
-    @property
-    def start_time(self):
-        """Get the execution start time of the operator."""
-        return self._start_time
-
-    @property
-    def duration(self):
-        """Get the duration of the operator execution."""
-        return self._duration
-
-    @property
-    def pid(self):
-        """Get the pid of the operator execution."""
-        return self._pid

mindinsight/profiler/parser/framework_parser.py

@@ -1,598 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Thr parser for parsing framework files."""
-import csv
-import enum
-import json
-import os
-import re
-
-from marshmallow import ValidationError
-
-from mindinsight.profiler.common.exceptions.exceptions import \
-    ProfilerPathErrorException, ProfilerDirNotFoundException, \
-    ProfilerFileNotFoundException, ProfilerDeviceIdMismatchException, \
-    ProfilerRawFileException, ProfilerParamValueErrorException
-from mindinsight.profiler.common.validator.validate_path import \
-    validate_and_normalize_path
-
-
-class VmDataType(enum.IntEnum):
-    """Definition of vm data type."""
-    NUMBER_TYPE_BEGIN = 26
-    NUMBER_TYPE_BOOL = 27
-    NUMBER_TYPE_INT = 28
-    NUMBER_TYPE_INT8 = 29
-    NUMBER_TYPE_INT16 = 30
-    NUMBER_TYPE_INT32 = 31
-    NUMBER_TYPE_INT64 = 32
-    NUMBER_TYPE_UINT = 33
-    NUMBER_TYPE_UINT8 = 34
-    NUMBER_TYPE_UINT16 = 35
-    NUMBER_TYPE_UINT32 = 36
-    NUMBER_TYPE_UINT64 = 37
-    NUMBER_TYPE_FLOAT = 38
-    NUMBER_TYPE_FLOAT16 = 39
-    NUMBER_TYPE_FLOAT32 = 40
-    NUMBER_TYPE_FLOAT64 = 41
-    NUMBER_TYPE_END = 42
-
-    @classmethod
-    def get_data_type_name(cls, num):
-        """
-        Get the name of data type by enum number.
-
-        Args:
-            num (int): Enum number.
-
-        Returns:
-            str, the name of data type.
-        """
-        data_type = cls._value2member_map_.get(num)
-        return 'UNKNOWN' if data_type is None else data_type.name
-
-
-class GeDataType(enum.IntEnum):
-    """Definition of ge data type."""
-    DT_FLOAT = 0
-    DT_FLOAT16 = 1
-    DT_INT8 = 2
-    DT_INT16 = 6
-    DT_UINT16 = 7
-    DT_UINT8 = 4
-    DT_INT32 = 3
-    DT_INT64 = 9
-    DT_UINT32 = 8
-    DT_UINT64 = 10
-    DT_BOOL = 12
-    DT_DOUBLE = 11
-    DT_STRING = 13
-    DT_DUAL_SUB_INT8 = 14
-    DT_DUAL_SUB_UINT8 = 15
-    DT_COMPLEX64 = 16
-    DT_COMPLEX128 = 17
-    DT_QINT8 = 18
-    DT_QINT16 = 19
-    DT_QINT32 = 20
-    DT_QUINT8 = 21
-    DT_QUINT16 = 22
-    DT_RESOURCE = 23
-    DT_STRING_REF = 24
-    DT_DUAL = 25
-    DT_UNDEFINED = 26
-
-    @classmethod
-    def get_data_type_name(cls, num):
-        """
-        Get the name of data type by enum number.
-
-        Args:
-            num (int): Enum number.
-
-        Returns:
-            str, the name of data type.
-        """
-        data_type = cls._value2member_map_.get(num)
-        return 'UNKNOWN' if data_type is None else data_type.name
-
-
-class GeFormat(enum.IntEnum):
-    """Definition of ge format type."""
-    FORMAT_NCHW = 0
-    FORMAT_NHWC = 1
-    FORMAT_ND = 2
-    FORMAT_NC1HWC0 = 3
-    FORMAT_FRACTAL_Z = 4
-    FORMAT_NC1C0HWPAD = 5
-    FORMAT_NHWC1C0 = 6
-    FORMAT_FSR_NCHW = 7
-    FORMAT_FRACTAL_DECONV = 8
-    FORMAT_C1HWNC0 = 9
-    FORMAT_FRACTAL_DECONV_TRANSPOSE = 10
-    FORMAT_FRACTAL_DECONV_SP_STRIDE_TRANS = 11
-    FORMAT_NC1HWC0_C04 = 12
-    FORMAT_FRACTAL_Z_C04 = 13
-    FORMAT_CHWN = 14
-    FORMAT_FRACTAL_DECONV_SP_STRIDE8_TRANS = 15
-    FORMAT_HWCN = 16
-    FORMAT_NC1KHKWHWC0 = 17
-    FORMAT_BN_WEIGHT = 18
-    FORMAT_FILTER_HWCK = 19
-    FORMAT_HASHTABLE_LOOKUP_LOOKUPS = 20
-    FORMAT_HASHTABLE_LOOKUP_KEYS = 21
-    FORMAT_HASHTABLE_LOOKUP_VALUE = 22
-    FORMAT_HASHTABLE_LOOKUP_OUTPUT = 23
-    FORMAT_HASHTABLE_LOOKUP_HITS = 24
-    FORMAT_C1HWNCOC0 = 25
-    FORMAT_MD = 26
-    FORMAT_NDHWC = 27
-    FORMAT_FRACTAL_ZZ = 28
-    FORMAT_FRACTAL_NZ = 29
-    FORMAT_NCDHW = 30
-    FORMAT_DHWCN = 31
-    FORMAT_NDC1HWC0 = 32
-    FORMAT_FRACTAL_Z_3D = 33
-    FORMAT_CN = 34
-    FORMAT_NC = 35
-    FORMAT_DHWNC = 36
-    FORMAT_FRACTAL_Z_3D_TRANSPOSE = 37
-    FORMAT_RESERVED = 38
-    FORMAT_ALL = 39
-
-    @classmethod
-    def get_format_name(cls, num):
-        """
-        Get the name of format type by enum number.
-
-        Args:
-            num (int): Enum number.
-
-        Returns:
-            str, the name of format type.
-        """
-        format_type = cls._value2member_map_.get(num)
-        return 'UNKNOWN' if format_type is None else format_type.name
-
-
-class FrameworkParser:
-    """
-    Thr parser for parsing framework files.
-
-    Args:
-        profiling_id (str): The profiling ID.
-        device_id (str): The device ID.
-        output_path (str): The directory of the parsed file. Default: `./`.
-    """
-    _raw_data_dir = '/var/log/npu/profiling'
-    _regex_framework = r'Framework\.host\.(?P<data_type>.+)\.(?P<device_id>\d).+'
-    _regex_framework_in_data = r'Framework\.host\.(?P<data_type>.+)\.' \
-                               r'(?P<device_id>\d)\.(?P<profiling_id>[a-zA-Z0-9]+).+'
-    _col_names = [
-        'task_id', 'stream_id', 'block_dim', 'full_op_name', 'op_name',
-        'op_type', 'subgraph', 'op_info'
-    ]
-    _graph_attr_name = [
-        'input_format', 'input_data_type', 'input_shape', 'output_format',
-        'output_data_type', 'output_shape'
-    ]
-
-    # if the task id is less than the task id threshold, The combination of
-    # task id and Stream id represents one operator, else the task id represents
-    # one operator
-    _task_id_threshold = 25000
-
-    def __init__(self, profiling_id, device_id, output_path='./'):
-        self._profiling_path = self._get_raw_profiling_path(profiling_id)
-        self._backend_type = None
-        self._framework_path = {'graph': [], 'task': [], 'point': []}
-        self._search_file(profiling_id, device_id)
-        self._device_id = device_id
-        self._save_path = self._get_save_path(device_id, output_path)
-        self._task_id_full_op_name_dict = {}
-        self._task_cache = {}
-        self._point_info = {}
-        self._parse_task_files()
-        self._parse_point_files()
-
-    @property
-    def save_path(self):
-        """
-        The property of save path.
-
-        Returns:
-            str, the save path.
-        """
-        return self._save_path
-
-    @property
-    def point_info(self):
-        """
-        The property of the framework point information.
-
-        Returns:
-            dict, the framework point information.
-        """
-        return self._point_info
-
-    def to_task_id_full_op_name_dict(self):
-        """
-        Get the task id and full operator name dict.
-
-        Returns:
-            dict, the task id and full operator name dict.
-        """
-        return self._task_id_full_op_name_dict
-
-    def parse(self):
-        """Parse the framework files."""
-        self._parse_graph_files_and_save(self._task_cache)
-        del self._task_cache
-
-    def check_op_name(self, op_name, is_prefix=True):
-        """
-        Check whether the operator name exists.
-
-        Args:
-            op_name (str): The operator name or operator name prefix.
-            is_prefix (bool): `True` if the op_name is prefix, else `False`.
-                Default: True.
-
-        Returns:
-            bool, `True` if the operator name does exist in framework file, else
-            `False`.
-        """
-        if not op_name:
-            raise ProfilerParamValueErrorException('The op_name should exist.')
-        for full_op_name in self._task_id_full_op_name_dict.values():
-            if full_op_name:
-                if is_prefix and full_op_name.startswith(op_name):
-                    return True
-                if not is_prefix and op_name == full_op_name:
-                    return True
-        return False
-
-    def _get_raw_profiling_path(self, profiling_id):
-        """
-        Get raw profiling path.
-
-        Args:
-            profiling_id (str): The profiling ID.
-
-        Returns:
-            str, the raw profiling path.
-
-        Raises:
-            ProfilerPathErrorException: If the profiling path is invalid.
-            ProfilerDirNotFoundException: If the profiling dir is not found.
-        """
-        profiling_path = os.path.join(self._raw_data_dir, profiling_id)
-        try:
-            profiling_path = validate_and_normalize_path(
-                profiling_path, 'profiler'
-            )
-        except ValidationError:
-            raise ProfilerPathErrorException('Profiling path is invalid.')
-        if not os.path.isdir(profiling_path):
-            raise ProfilerDirNotFoundException(profiling_path)
-        return profiling_path
-
-    def _search_file(self, profiling_id, device_id):
-        """
-        Search all framework files in raw profiling path.
-
-        Args:
-            profiling_id (str): The profiling ID.
-            device_id (str): The device ID.
-
-        Raises:
-            ProfilerFileNotFoundException: If the framework files are not found.
-        """
-        # first search in the JOB dir, and if not, search in the sub directory
-        # in the JOB
-        self._search_file_from_job_path(device_id, search_in_sub_path=False)
-        if self._backend_type is None:
-            self._search_file_from_job_path(device_id, search_in_sub_path=True)
-        self._search_file_from_data_path(profiling_id, device_id)
-
-        if self._backend_type is None:
-            raise ProfilerFileNotFoundException('Framework')
-        self._framework_path['graph'].sort()
-        self._framework_path['task'].sort()
-
-    def _search_file_from_job_path(self, device_id, search_in_sub_path=False):
-        """
-        Search framework files from job path.
-
-        Args:
-            device_id (str): The device ID.
-            search_in_sub_path (bool): `True` if search file in profiling dir,
-                else search in profiling sub dir. Default: False.
-
-        Raises:
-            ProfilerRawFileException: If the framework file type is inconsistent.
-            ProfilerDeviceIdMismatchException: If the device id is mismatch
-                with framework in the raw dir.
-        """
-        profiling_dir = os.path.join(self._profiling_path, 'data') \
-            if search_in_sub_path else self._profiling_path
-        if not os.path.isdir(profiling_dir):
-            return
-
-        files = os.listdir(profiling_dir)
-        for file in files:
-            pattern = re.search(self._regex_framework, file)
-            if not pattern or file.endswith('.done'):
-                continue
-            attrs = pattern.groupdict()
-
-            device_id_in_path = attrs.get('device_id')
-            if device_id_in_path != device_id:
-                raise ProfilerDeviceIdMismatchException()
-
-            data_type = attrs.get('data_type')
-            if data_type.startswith('vm.'):
-                if self._backend_type and self._backend_type != 'vm':
-                    raise ProfilerRawFileException('Backend type is inconsistent.')
-                self._backend_type = 'vm'
-                data_type = data_type.split('.')[1]
-            else:
-                if self._backend_type and self._backend_type != 'ge':
-                    raise ProfilerRawFileException('Backend type is inconsistent.')
-                self._backend_type = 'ge'
-            if data_type.startswith('graph_desc_info'):
-                self._framework_path['graph'].append(
-                    os.path.join(profiling_dir, file)
-                )
-            elif data_type.startswith('task_desc_info'):
-                self._framework_path['task'].append(
-                    os.path.join(profiling_dir, file)
-                )
-            elif data_type.startswith('point'):
-                self._framework_path['point'].append(
-                    os.path.join(profiling_dir, file)
-                )
-
-    def _search_file_from_data_path(self, profiling_id, device_id):
-        """
-        Search framework files from data path.
-
-        Args:
-            profiling_id (str): The profiling ID.
-            device_id (str): The device ID.
-
-        Raises:
-            ProfilerRawFileException: If the framework file type is inconsistent.
-            ProfilerDeviceIdMismatchException: If the device id is mismatch
-                with framework in the raw dir.
-        """
-        profiling_data_path = os.path.join(
-            self._raw_data_dir, 'container', device_id, 'data'
-        )
-        if not os.path.isdir(profiling_data_path):
-            return
-
-        files = os.listdir(profiling_data_path)
-        for file in files:
-            pattern = re.search(self._regex_framework_in_data, file)
-            if not pattern or file.endswith('.done') or file.endswith('.zip'):
-                continue
-            attrs = pattern.groupdict()
-
-            profiling_id_in_path = attrs.get('profiling_id')
-            if profiling_id_in_path != profiling_id:
-                continue
-
-            device_id_in_path = attrs.get('device_id')
-            if device_id_in_path != device_id:
-                raise ProfilerDeviceIdMismatchException()
-
-            data_type = attrs.get('data_type')
-            if data_type.startswith('vm.'):
-                if self._backend_type and self._backend_type != 'vm':
-                    raise ProfilerRawFileException('Backend type is inconsistent.')
-                self._backend_type = 'vm'
-                data_type = data_type.split('.')[1]
-            else:
-                if self._backend_type and self._backend_type != 'ge':
-                    raise ProfilerRawFileException('Backend type is inconsistent.')
-                self._backend_type = 'ge'
-            if data_type.startswith('graph_desc_info'):
-                self._framework_path['graph'].append(
-                    os.path.join(profiling_data_path, file)
-                )
-            elif data_type.startswith('task_desc_info'):
-                self._framework_path['task'].append(
-                    os.path.join(profiling_data_path, file)
-                )
-            elif data_type.startswith('point'):
-                self._framework_path['point'].append(
-                    os.path.join(profiling_data_path, file)
-                )
-
-    def _get_save_path(self, device_id, output_path):
-        """
-        Get the save path.
-
-        Args:
-            device_id (str): The device ID.
-            output_path (str): The output dir.
-
-        Returns:
-            str, the save path.
-
-        Raises:
-            ProfilerPathErrorException: If the output path is invalid.
-            ProfilerDirNotFoundException: If the output dir is not found.
-        """
-        try:
-            output_dir = validate_and_normalize_path(output_path, 'profiler')
-        except ValidationError:
-            raise ProfilerPathErrorException('Output path is invalid.')
-        if not os.path.isdir(output_dir):
-            raise ProfilerDirNotFoundException(output_dir)
-        return os.path.join(
-            output_dir, '_'.join(['framework', 'raw', device_id]) + '.csv'
-        )
-
-    def _parse_task_files(self):
-        """Parse the framework task files."""
-        for path in self._framework_path['task']:
-            with open(path, 'r') as file:
-                for task_info in file:
-                    infos = task_info.strip('\n').split(' ')
-                    # key is op name, values is task id, stream id, block_dim
-                    self._task_cache[infos[0]] = [infos[2], infos[3], infos[1]]
-
-                    # if the task id is less than the task id threshold, the
-                    # stream id and task id correspond to an operator
-                    task_id = infos[2]
-                    if int(task_id) < self._task_id_threshold:
-                        task_id = '_'.join([infos[3], task_id])
-                    self._task_id_full_op_name_dict[task_id] = infos[0]
-
-    def _parse_graph_files_and_save(self, task_cache):
-        """
-        Parse the framework graph files and save the framework information.
-
-        Args:
-            task_cache (dict): The task information cache.
-        """
-        with open(self._save_path, 'w') as save_file:
-            csv_writer = csv.writer(save_file)
-            csv_writer.writerow(self._col_names)
-            for path in self._framework_path['graph']:
-                with open(path, 'r') as graph_file:
-                    for graph_info in graph_file:
-                        result = self._parse_one_row_graph_info(graph_info)
-                        task_info = task_cache.get(result[0])
-                        if task_info:
-                            task_info.extend(result)
-                            csv_writer.writerow(task_info)
-                            del task_cache[result[0]]
-                        else:
-                            save_info = [None, None, None]
-                            save_info.extend(result)
-                            csv_writer.writerow(save_info)
-
-            none_list = [None, None, None, None]
-            for key, value in task_cache.items():
-                value.append(key)
-                value.extend(none_list)
-                csv_writer.writerow(value)
-
-    def _parse_one_row_graph_info(self, row_info):
-        """
-        Parse the graph information in one row.
-
-        Args:
-            row_info (str): One row graph information.
-
-        Returns:
-            list[str], the parsed graph information.
-        """
-        full_op_name = None
-        op_name = None
-        subgraph_name = None
-        op_type = None
-        op_info = dict()
-        cur_op_info_key = None
-
-        infos = row_info.strip('\n').split(' ')
-        for info in infos:
-            attr_name, attr_value = info.split(':', 1)
-            if attr_name == 'op_name':
-                full_op_name = attr_value
-                subgraph_name = self._get_subgraph_name(full_op_name)
-                op_name = self._get_op_name(full_op_name, subgraph_name)
-            elif attr_name == 'op_type':
-                op_type = attr_value
-            elif attr_name in ['input_id', 'output_id']:
-                cur_op_info_key = '{}_{}'.format(
-                    attr_name.split('_')[0], attr_value
-                )
-                op_info[cur_op_info_key] = dict()
-            elif attr_name in self._graph_attr_name:
-                op_attr = attr_name.split('_', 1)[1]
-                if op_attr == 'shape':
-                    attr_value = attr_value.strip('"')
-                if self._backend_type == 'vm':
-                    if op_attr == 'data_type':
-                        attr_value = VmDataType.get_data_type_name(
-                            int(attr_value)
-                        )
-                else:
-                    if op_attr == 'data_type':
-                        attr_value = GeDataType.get_data_type_name(
-                            int(attr_value)
-                        )
-                    elif op_attr == 'format':
-                        attr_value = GeFormat.get_format_name(int(attr_value))
-
-                op_info[cur_op_info_key][op_attr] = attr_value
-
-        # the list info are full_op_name, op_name, op_type, subgraph, op_info
-        return [full_op_name, op_name, op_type, subgraph_name,
-                json.dumps(op_info)]
-
-    def _get_subgraph_name(self, full_op_name):
-        """
-        Get subgraph name.
-
-        Args:
-            full_op_name (str): The full operator name.
-
-        Returns:
-            str, the subgraph name.
-        """
-        subgraph_name = full_op_name.split('/', 1)[0]
-        if subgraph_name in ['Default', 'Gradients']:
-            return subgraph_name
-        return None
-
-    def _get_op_name(self, full_op_name, subgraph_name):
-        """
-        Get operator name.
-
-        Args:
-            full_op_name (str): The full operator name.
-            subgraph_name (str): The subgraph name.
-
-        Returns:
-            str, the operator name.
-        """
-        if subgraph_name is None:
-            return full_op_name
-
-        if self._backend_type == 'vm':
-            return full_op_name.split('/')[-1]
-
-        strs = full_op_name.split(subgraph_name + '/')
-        op_name = None
-        for name_str in strs:
-            if not name_str:
-                continue
-            if op_name is None:
-                op_name = name_str.split('/')[-1]
-            else:
-                op_name = '+'.join([op_name, name_str.split('/')[-1]])
-        return op_name
-
-    def _parse_point_files(self):
-        """Parse the framework point files."""
-        for path in self._framework_path['point']:
-            with open(path, 'r') as file:
-                for point_info in file:
-                    infos = point_info.strip('\n').split(' ')
-                    self._point_info[int(infos[0])] = infos[1]

mindinsight/profiler/parser/hwts_log_parser.py

@@ -1,109 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""The parser for hwts log file."""
-import os
-import struct
-from mindinsight.profiler.common._utils import fwrite_format, get_file_join_name
-from mindinsight.profiler.common.log import logger
-
-
-class HWTSLogParser:
-    """
-    The Parser for hwts log files.
-
-    Args:
-         _input_path (str): The profiling job path. Such as: '/var/log/npu/profiling/JOBAIFGJEJFEDCBAEADIFJAAAAAAAAAA".
-         output_filename (str): The output data path and name. Such as: './output_format_data_hwts_0.txt'.
-    """
-
-    _source_file_target = 'hwts.log.data.45.dev.profiler_default_tag'
-    _dst_file_title = 'title:45 HWTS data'
-    _dst_file_column_title = 'Type           cnt  Core_ID  Block_ID  Task_ID  Cycle_counter   Stream_ID'
-
-    def __init__(self, input_path, output_filename):
-        self._input_path = input_path
-        self._output_filename = output_filename
-        self._source_flie_name = self._get_source_file()
-
-    def _get_source_file(self):
-        """Get hwts log file name, which was created by ada service."""
-
-        file_name = get_file_join_name(self._input_path, self._source_file_target)
-        if not file_name:
-            data_path = os.path.join(self._input_path, "data")
-            file_name = get_file_join_name(data_path, self._source_file_target)
-            if not file_name:
-                msg = ("Fail to find hwts log file, under profiling directory")
-                raise RuntimeError(msg)
-
-        return file_name
-
-    def execute(self):
-        """
-        Execute the parser, get result data, and write it to the output file.
-
-        Returns:
-            bool, whether succeed to analyse hwts log.
-        """
-
-        content_format = ['QIIIIIIIIIIII', 'QIIQIIIIIIII', 'IIIIQIIIIIIII']
-        log_type = ['Start of task', 'End of task', 'Start of block', 'End of block', 'Block PMU']
-
-        result_data = ""
-
-        with open(self._source_flie_name, 'rb') as hwts_data:
-            while True:
-                line = hwts_data.read(64)
-                if line:
-                    if not line.strip():
-                        continue
-                else:
-                    break
-                byte_first_four = struct.unpack('BBHHH', line[0:8])
-                byte_first = bin(byte_first_four[0]).replace('0b', '').zfill(8)
-                ms_type = byte_first[-3:]
-                is_warn_res0_ov = byte_first[4]
-                cnt = int(byte_first[0:4], 2)
-                core_id = byte_first_four[1]
-                blk_id, task_id = byte_first_four[3], byte_first_four[4]
-                if ms_type in ['000', '001', '010']:  # log type 0,1,2
-                    result = struct.unpack(content_format[0], line[8:])
-                    syscnt = result[0]
-                    stream_id = result[1]
-                elif ms_type == '011':  # log type 3
-                    result = struct.unpack(content_format[1], line[8:])
-                    syscnt = result[0]
-                    stream_id = result[1]
-                elif ms_type == '100':  # log type 4
-                    result = struct.unpack(content_format[2], line[8:])
-                    stream_id = result[2]
-                    if is_warn_res0_ov == '0':
-                        syscnt = result[4]
-                    else:
-                        syscnt = None
-                else:
-                    logger.info("Profiling: invalid hwts log record type %s", ms_type)
-                    continue
-
-                if int(task_id) < 25000:
-                    task_id = str(stream_id) + "_" + str(task_id)
-                result_data += ("%-14s %-4s %-8s %-9s %-8s %-15s %s\n" %(log_type[int(ms_type, 2)], cnt, core_id,
-                                                                         blk_id, task_id, syscnt, stream_id))
-
-        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
-        fwrite_format(self._output_filename, data_source=self._dst_file_column_title)
-        fwrite_format(self._output_filename, data_source=result_data)
-
-        return True

mindinsight/profiler/parser/minddata_parser.py

@@ -1,93 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Minddata aicpu parser."""
-import os
-
-from tabulate import tabulate
-
-from mindinsight.profiler.common._utils import get_file_join_name, fwrite_format
-from mindinsight.profiler.common.log import logger
-
-
-class MinddataParser:
-    """Minddata Aicpu Parser."""
-    @staticmethod
-    def parse_minddata_aicpu_data(minddata_aicpu_source_path):
-        """
-        Parse minddata get_next info which contains queue size and execute time.
-
-        Args:
-            minddata_aicpu_source_path (str): the source file path.
-
-        Returns:
-            list[Union[str, float]], the converted data.
-        """
-        result = list()
-        try:
-            with open(minddata_aicpu_source_path) as source_data_file:
-                source_data = source_data_file.read()
-                step_data = source_data.split("\x00")
-                for one_step in step_data:
-                    if one_step:
-                        node_info = one_step.split(", ")
-                        node_name, node_start, node_end, queue_size = "", 0, 0, 0
-                        if node_info:
-                            node_name = node_info[0].replace("Node:", "")
-                        if len(node_info) > 2:
-                            node_start = node_info[1].replace("Run start:", "")
-                            if node_start.isdigit():
-                                node_start = int(node_start)
-                            node_end = node_info[2].replace("Run end:", "")
-                            if node_end.isdigit():
-                                node_end = int(node_end)
-                        if len(node_info) > 3:
-                            queue_size = node_info[3].replace("queue size:", "")
-                            if queue_size.isdigit():
-                                queue_size = int(queue_size)
-
-                        one_step_list = [node_name, node_start, node_end, queue_size]
-                        result.append(one_step_list)
-        except OSError:
-            logger.error("Open get_next profiling file error.")
-
-        return result
-
-    @staticmethod
-    def execute(source_path, output_path, device_id):
-        """
-        Execute the parser.
-
-        Args:
-            source_path (str): the source file path.
-            output_path (str): the output file path.
-            device_id (str): the device id.
-        """
-        col_names = ["node_name", "start_time", "end_time", "queue_size"]
-        minddata_aicpu_source_path = get_file_join_name(
-            input_path=source_path, file_name='DATA_PREPROCESS.dev.AICPUMI')
-        if not minddata_aicpu_source_path:
-            minddata_aicpu_source_path = get_file_join_name(
-                input_path=os.path.join(source_path, "data"), file_name='DATA_PREPROCESS.dev.AICPUMI')
-            if not minddata_aicpu_source_path:
-                return
-        minddata_aicpu_output_path = os.path.join(output_path, "minddata_aicpu_" + device_id + ".txt")
-
-        minddata_aicpu_data = MinddataParser.parse_minddata_aicpu_data(minddata_aicpu_source_path)
-        if minddata_aicpu_data:
-            fwrite_format(
-                minddata_aicpu_output_path,
-                tabulate(minddata_aicpu_data, col_names, tablefmt='simple'),
-                is_start=True
-            )

mindinsight/profiler/parser/minddata_pipeline_parser.py

@@ -1,289 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Thr parser for parsing minddata pipeline files."""
-import csv
-import json
-import os
-from queue import Queue
-
-from marshmallow import ValidationError
-
-from mindinsight.profiler.common.exceptions.exceptions import \
-    ProfilerPathErrorException, ProfilerFileNotFoundException, \
-    ProfilerDirNotFoundException, ProfilerRawFileException
-from mindinsight.profiler.common.log import logger
-from mindinsight.profiler.common.validator.validate_path import \
-    validate_and_normalize_path
-
-
-class MinddataPipelineParser:
-    """
-    Thr parser for parsing minddata pipeline files.
-
-    Args:
-        source_dir (str): The minddata pipeline source dir.
-        device_id (str): The device ID.
-        output_path (str): The directory of the parsed file. Default: `./`.
-
-    Raises:
-        ProfilerPathErrorException: If the minddata pipeline file path or
-            the output path is invalid.
-        ProfilerFileNotFoundException: If the minddata pipeline file or
-            the output dir does not exist.
-    """
-    _raw_pipeline_file_name = 'pipeline_profiling_{}.json'
-    _parsed_pipeline_file_name = 'minddata_pipeline_raw_{}.csv'
-    _col_names = [
-        'op_id', 'op_type', 'num_workers', 'output_queue_size',
-        'output_queue_average_size', 'output_queue_length',
-        'output_queue_usage_rate', 'sample_interval', 'parent_id', 'children_id'
-    ]
-
-    def __init__(self, source_dir, device_id, output_path='./'):
-        self._device_id = device_id
-        self._pipeline_path = self._get_pipeline_path(source_dir)
-        self._save_path = self._get_save_path(output_path)
-
-    @property
-    def save_path(self):
-        """
-        The property of save path.
-
-        Returns:
-            str, the save path.
-        """
-        return self._save_path
-
-    def parse(self):
-        """
-        Parse the minddata pipeline files.
-
-        Raises:
-            ProfilerRawFileException: If fails to parse the raw file of
-                minddata pipeline or the file is empty.
-        """
-        with open(self._pipeline_path, 'r') as file:
-            try:
-                pipeline_info = json.load(file)
-            except (json.JSONDecodeError, TypeError) as err:
-                logger.exception(err)
-                raise ProfilerRawFileException(
-                    'Fail to parse minddata pipeline file.'
-                )
-        if not pipeline_info:
-            logger.warning('The minddata pipeline file is empty.')
-            raise ProfilerRawFileException(
-                'The minddata pipeline file is empty.'
-            )
-
-        self._parse_and_save(pipeline_info)
-
-    def _get_pipeline_path(self, source_dir):
-        """
-        Get the minddata pipeline file path.
-
-        Args:
-            source_dir (str): The minddata pipeline source dir.
-
-        Returns:
-            str, the minddata pipeline file path.
-        """
-        pipeline_path = os.path.join(
-            source_dir,
-            self._raw_pipeline_file_name.format(self._device_id)
-        )
-
-        try:
-            pipeline_path = validate_and_normalize_path(pipeline_path, 'profiler')
-        except ValidationError:
-            logger.warning('Minddata pipeline file is invalid.')
-            raise ProfilerPathErrorException('Minddata pipeline file is invalid.')
-        if not os.path.isfile(pipeline_path):
-            logger.warning(
-                'The minddata pipeline file <%s> not found.', pipeline_path
-            )
-            raise ProfilerFileNotFoundException(pipeline_path)
-
-        return pipeline_path
-
-    def _get_save_path(self, output_path):
-        """
-        Get the save path.
-
-        Args:
-            output_path (str): The output dir.
-
-        Returns:
-            str, the save path.
-        """
-        try:
-            output_dir = validate_and_normalize_path(output_path, 'profiler')
-        except ValidationError:
-            logger.warning('Output path is invalid.')
-            raise ProfilerPathErrorException('Output path is invalid.')
-        if not os.path.isdir(output_dir):
-            logger.warning('The output dir <%s> not found.', output_dir)
-            raise ProfilerDirNotFoundException(output_dir)
-        return os.path.join(
-            output_dir, self._parsed_pipeline_file_name.format(self._device_id)
-        )
-
-    def _parse_and_save(self, pipeline_info):
-        """
-        Parse and save the parsed minddata pipeline file.
-
-        Args:
-            pipeline_info (dict): The pipeline info reads from the raw file of
-                the minddata pipeline.
-
-        Raises:
-            ProfilerRawFileException: If the format of minddata pipeline raw
-                file is wrong.
-        """
-        sample_interval = pipeline_info.get('sampling_interval')
-        op_info = pipeline_info.get('op_info')
-        if sample_interval is None or not op_info:
-            raise ProfilerRawFileException(
-                'The format of minddata pipeline raw file is wrong.'
-            )
-
-        op_id_info_cache = {}
-        for item in op_info:
-            op_id_info_cache[item.get('op_id')] = item
-
-        with open(self._save_path, 'w') as save_file:
-            csv_writer = csv.writer(save_file)
-            csv_writer.writerow(self._col_names)
-            self._parse_and_save_op_info(
-                csv_writer, op_id_info_cache, sample_interval
-            )
-
-    def _parse_and_save_op_info(self, csv_writer, op_id_info_cache,
-                                sample_interval):
-        """
-        Parse and save the minddata pipeline operator information.
-
-        Args:
-            csv_writer (csv.writer): The csv writer.
-            op_id_info_cache (dict): The operator id and information cache.
-            sample_interval (int): The sample interval.
-
-        Raises:
-            ProfilerRawFileException: If the operator that id is 0 does not exist.
-        """
-        queue = Queue()
-        root_node = op_id_info_cache.get(0)
-        if not root_node:
-            raise ProfilerRawFileException(
-                'The format of minddata pipeline raw file is wrong, '
-                'the operator that id is 0 does not exist.'
-            )
-        root_node['parent_id'] = None
-        queue.put_nowait(root_node)
-
-        while not queue.empty():
-            node = queue.get_nowait()
-            self._update_child_node(node, op_id_info_cache)
-            csv_writer.writerow(self._get_op_info(node, sample_interval))
-
-            op_id = node.get('op_id')
-            children_ids = node.get('children')
-            if not children_ids:
-                continue
-            for child_op_id in children_ids:
-                sub_node = op_id_info_cache.get(child_op_id)
-                sub_node['parent_id'] = op_id
-                queue.put_nowait(sub_node)
-
-    def _update_child_node(self, node, op_id_info_cache):
-        """
-        Updates the child node information of the operator.
-
-        Args:
-            node (dict): The node represents an operator.
-            op_id_info_cache (dict): The operator id and information cache.
-        """
-        child_op_ids = node.get('children')
-        if not child_op_ids:
-            return
-
-        queue = Queue()
-        self._cp_list_item_to_queue(child_op_ids, queue)
-
-        new_child_op_ids = []
-        while not queue.empty():
-            child_op_id = queue.get_nowait()
-            child_node = op_id_info_cache.get(child_op_id)
-            if child_node is None:
-                continue
-            metrics = child_node.get('metrics')
-            if not metrics or not metrics.get('output_queue'):
-                op_ids = child_node.get('children')
-                if op_ids:
-                    self._cp_list_item_to_queue(op_ids, queue)
-            else:
-                new_child_op_ids.append(child_op_id)
-
-        node['children'] = new_child_op_ids
-
-    def _get_op_info(self, op_node, sample_interval):
-        """
-        Get the operator information.
-
-        Args:
-            op_node (dict): The node represents an operator.
-            sample_interval (int): The sample interval.
-
-        Returns:
-            list[str, int, float], the operator information.
-        """
-        queue_size = None
-        queue_average_size = None
-        queue_length = None
-        queue_usage_rate = None
-        metrics = op_node.get('metrics')
-        if metrics:
-            output_queue = metrics.get('output_queue')
-            if output_queue:
-                queue_size = output_queue.get('size')
-                queue_average_size = sum(queue_size) / len(queue_size)
-                queue_length = output_queue.get('length')
-                queue_usage_rate = queue_average_size / queue_length
-
-        children_id = op_node.get('children')
-        op_info = [
-            op_node.get('op_id'),
-            op_node.get('op_type'),
-            op_node.get('num_workers'),
-            queue_size,
-            queue_average_size,
-            queue_length,
-            queue_usage_rate,
-            sample_interval,
-            op_node.get('parent_id'),
-            children_id if children_id else None
-        ]
-        return op_info
-
-    def _cp_list_item_to_queue(self, inner_list, queue):
-        """
-        Copy the contents of a list to a queue.
-
-        Args:
-            inner_list (list): The list.
-            queue (Queue): The target queue.
-        """
-        for item in inner_list:
-            queue.put_nowait(item)

mindinsight/profiler/parser/optime_parser.py

@@ -1,247 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Op compute time files parser."""
-import os
-from mindinsight.profiler.common._utils import fwrite_format
-from mindinsight.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
-    ProfilerIOException
-from mindinsight.profiler.common.log import logger
-from mindinsight.profiler.common.validator.validate_path import validate_and_normalize_path
-from mindinsight.profiler.parser.container import HWTSContainer
-
-TIMELINE_FILE_COLUMN_TITLE = 'op_name, stream_id, start_time(ms), duration(ms)'
-
-class OPComputeTimeParser:
-    """
-    Join hwts info and framework info, get op time info, and output to the result file.
-
-    Args:
-         hwts_output_file (str): The file path of hwts_output_file. Such as: './output_format_data_hwts_0.txt".
-         output_filename (str): The output data file path and name. Such as: './output_op_compute_time_0.txt'.
-         op_task_info (dict): The task and op relation info. The format: {task_id, [opname, stream_id, block dim]}.
-    """
-
-    _dst_file_title = 'title:op compute time'
-    _dst_file_column_title = 'op_name       compute_time(ms) stream_id'
-    _dst_file_column_title += '\n------------  ---------------  ---------'
-
-    def __init__(self, hwts_output_file, output_filename, op_task_info,
-                 output_path, device_id):
-        hwts_output_file = validate_and_normalize_path(
-            hwts_output_file, raise_key='Invalid hwts output file path.'
-        )
-        self._hwts_output_file = hwts_output_file
-        self._output_filename = output_filename
-        self._op_task_info = op_task_info
-        self._output_path = output_path
-        self._device_id = device_id
-        self._min_cycle_counter = float("inf")
-
-    def _get_op_task_id_map(self):
-        """
-        Read hwts data file, get the task time info.
-
-        Returns:
-           list: all hwts task time info.
-        """
-
-        op_map_result = []
-        hwts_list = []
-
-        if not os.path.exists(self._hwts_output_file):
-            logger.error('The hwts output file does not exist.')
-            raise ProfilerFileNotFoundException('hwts output file')
-
-        with open(self._hwts_output_file, 'r') as data_file:
-            lines = data_file.readlines()
-            for line in lines:
-                if line.startswith("Start of task") or line.startswith("End of task"):
-                    line_split = line.split()
-                    container = HWTSContainer(line_split)
-                    hwts_list.append(container)
-
-        # hwts op map by taskId
-        for hwts in hwts_list:
-            if hwts.task_id in self._op_task_info.keys():
-                hwts.op_name = self._op_task_info[hwts.task_id]
-                op_map_result.append(hwts)
-
-        return op_map_result
-
-    def execute(self):
-        """Execute the parser, compute all op, get op time, and write it to the output file."""
-        # Calculate the execution time of operators,
-        # and update the minimum cycle counter.
-        tmp_result_data = self._calculate_op_execution_time()
-
-        # Convert time units from nanoseconds to milliseconds.
-        # The unit of the cycle counter is 10 nanoseconds.
-        op_name_time_dict = {}
-        op_name_stream_dict = {}
-        op_name_count_dict = {}
-        op_name_task_dict = {}
-        op_name_start_time = {}
-        self._convert_op_time_unit(
-            tmp_result_data, op_name_time_dict, op_name_stream_dict,
-            op_name_count_dict, op_name_task_dict, op_name_start_time
-        )
-
-        result_data = ""
-        total_time = 0
-        for op_name, time in op_name_time_dict.items():
-            if op_name in op_name_stream_dict.keys():
-                stream_id = op_name_stream_dict[op_name]
-                avg_time = time / op_name_count_dict[op_name]
-                total_time += avg_time
-                result_data += ("%s %s  %s\n" %(op_name, str(avg_time), stream_id))
-        result_data += ("total op  %s 0" %(str(total_time)))
-
-        timeline_data = []
-        for op_name, time in op_name_time_dict.items():
-            if op_name in op_name_stream_dict.keys():
-                stream_id = op_name_stream_dict[op_name]
-                start_time_list = op_name_start_time.get(op_name)
-                for (start_time, duration) in start_time_list:
-                    timeline_data.append([op_name, stream_id, start_time, duration])
-
-        # Write the metadata of operators into the file,
-        # including operator name, average time, and stream id.
-        self._write_op_time_into_file(result_data)
-        # Write the timeline data into file,
-        # including operator name, stream id, start time, and duration.
-        self._write_timeline_data_into_file(timeline_data)
-
-    def _write_op_time_into_file(self, result_data):
-        """
-        Write the metadata of operators into the file, including
-            op name, average time, and stream id.
-
-        Args:
-            result_data (str): The metadata to be written into the file.
-                    'op_name_1', 'avg_time_1', 'stream_id_1',
-                    'op_name_2', 'avg_time_2', 'stream_id_2',
-                    ...
-        """
-
-        fwrite_format(self._output_filename, data_source=self._dst_file_title, is_start=True)
-        fwrite_format(self._output_filename, data_source=self._dst_file_column_title)
-        fwrite_format(self._output_filename, data_source=result_data)
-
-    def _write_timeline_data_into_file(self, timeline_data):
-        """
-        Write the timeline information into the file, including
-            operator name, stream id, start time and duration.
-
-        Args:
-            timeline_data (list): The metadata to be written into the file.
-                [
-                    ['op_name_1', 'stream_id_1', 'start_time_1', 'durarion_1'],
-                    ['op_name_2', 'stream_id_2', 'start_time_2', 'durarion_2'],
-                    [...]
-                ]
-        """
-        # sorted by start times
-        timeline_data.sort(key=lambda x: float(x[2]))
-        filename = 'output_timeline_data_{}.txt'.format(self._device_id)
-        file_path = os.path.join(self._output_path, filename)
-        file_path = validate_and_normalize_path(file_path, raise_key='Invalid file path of timeline data.')
-
-        # write to file
-        try:
-            with open(file_path, 'w') as f_obj:
-                f_obj.write(TIMELINE_FILE_COLUMN_TITLE + '\n')
-                for timeline in timeline_data:
-                    timeline = [str(item) for item in timeline]
-                    f_obj.write(','.join(timeline) + '\n')
-        except (IOError, OSError) as err:
-            logger.error('Error occurred when writing intermediate timeline file: %s', err)
-            raise ProfilerIOException
-
-    def _calculate_op_execution_time(self):
-        """
-        Calculate the execution time of each operator.
-
-        Returns:
-            list, including the intermediate data of op execution time.
-        """
-        tmp_result_data = []
-        op_map_list = self._get_op_task_id_map()
-
-        cur_index = 0
-        length = len(op_map_list)
-        min_cycle_counter = float("inf")
-        while cur_index < length:
-            if cur_index + 1 == length:
-                break
-
-            op_start = op_map_list[cur_index]
-            op_end = op_map_list[cur_index + 1]
-            if op_start.status == "Start" and op_end.status == "End" \
-                    and op_start.op_name == op_end.op_name:
-                op_start.duration = op_end.cycle_counter - op_start.cycle_counter
-                tmp_result_data.append(op_start)
-                cur_index += 2
-                if not op_start.op_name.startswith("assign"):
-                    min_cycle_counter = min(min_cycle_counter, op_start.cycle_counter)
-            else:
-                cur_index += 1
-
-        # Update the value of minimum cycle counter.
-        self._min_cycle_counter = min_cycle_counter / 1e5  # Convert the time unit from 10ns to 1ms
-
-        return tmp_result_data
-
-    def _convert_op_time_unit(self, op_data_list, op_name_time_dict, op_name_stream_dict,
-                              op_name_count_dict, op_name_task_dict, op_name_start_time):
-        """
-        Calculate the execution time of operator and convert it into millisecond.
-
-        Args:
-            op_data_list (list): The list of operator metadata.
-            op_name_time_dict (dict): The mapping relation of operator name and its execution time.
-            op_name_stream_dict (dict): The mapping relation of operator name and its stream id.
-            op_name_count_dict (dict): The mapping relation of operator name and its count.
-            op_name_task_dict (dict): The mapping relation of operator name and its task id.
-            op_name_start_time (dict): The mapping relation of operator name and its start time.
-        """
-        factor = 1e5
-        for item in op_data_list:
-            op_name = item.op_name
-            # Unit conversion: converting the cycle counter into ms.
-            op_start_time_str = str(item.cycle_counter / factor)
-            op_duration = item.duration / factor
-            op_duration_str = str(item.duration / factor)
-            if op_name in op_name_time_dict.keys():
-                op_name_time_dict[op_name] += op_duration
-                if item.task_id == op_name_task_dict[op_name]:
-                    op_name_count_dict[op_name] += 1
-                op_name_start_time[op_name].append(
-                    (op_start_time_str, op_duration_str)
-                )
-
-            else:
-                op_name_time_dict[op_name] = op_duration
-                op_name_stream_dict[op_name] = item.stream_id
-                op_name_task_dict[op_name] = item.task_id
-                op_name_count_dict[op_name] = 1
-                op_name_start_time[op_name] = []
-                op_name_start_time[op_name].append(
-                    (op_start_time_str, op_duration_str)
-                )
-
-    @property
-    def min_cycle_counter(self):
-        """Get minimum cycle counter."""
-        return self._min_cycle_counter

mindinsight/profiler/parser/step_trace_parser.py

@@ -1,312 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""The parser for step trace data."""
-import csv
-import json
-import os
-import stat
-import struct
-from collections import namedtuple
-from decimal import Decimal
-
-from mindinsight.profiler.common.exceptions.exceptions import ProfilerPathErrorException, \
-    JobIdMismatchException, ProfilerIOException
-from mindinsight.profiler.common.log import logger as log
-from mindinsight.profiler.common.util import get_summary_for_step_trace
-
-StepTraceStruct = namedtuple(
-    'TrainingTraceStruct', ['tag_id', 'task_id', 'stream_id', 'sys_count']
-)
-
-
-class StepTraceParser:
-    """
-    The parser for step trace data.
-
-    Args:
-        input_dir (str): The directory that contains original step trace data.
-        output_file_path (str): The output file path.
-        job_id (int): The job id used to define the start of new step. Default: 0.
-        skip_first_step (bool): Whether skip the first step or not.
-    """
-    _event_size = 20
-    _fp_tag = 1
-    _bp_tag = 2
-
-    def __init__(self, input_dir, output_file_path, job_id=0, skip_first_step=False):
-        self._input_dir = input_dir
-        self._output_path = output_file_path
-        self._job_id = job_id
-        self._skip_first_step = skip_first_step
-        self._result = []
-        self._header = []
-        self._step_num = 0
-
-    @property
-    def output_file(self):
-        """The property of step trace header."""
-        file_name = self._output_path.rsplit('/', 2)
-        return file_name[-1] if len(file_name) == 3 else ''
-
-    def show(self):
-        """The property of step trace info."""
-        summary_info = {}
-        if self._result:
-            summary_info = get_summary_for_step_trace(self._result[-1], self._header)
-            summary_info['total_steps'] = len(self._result) - 1
-        print('\nStep trace summary info (unit: syscnt):')
-        print(summary_info)
-        print('\nThe step trace parse result saves under ${summary_dir}/profiler/%s'
-              % self.output_file)
-
-    def parse_and_save(self):
-        """Parse step trace files and save the result."""
-        try:
-            source_files = self._get_step_trace_files()
-            self._parse(source_files)
-            self._save()
-        except IOError as err:
-            log.exception(err)
-            raise ProfilerIOException()
-        else:
-            log.info("Finish to save intermediate result for step trace file.")
-
-    def record_point_info(self, point_info, output_path):
-        """
-        Record point info into json.
-
-        Args:
-            point_info (dict): The point info about tag id and relative op name.
-            output_path (str): The output path for saving point info.
-
-        Returns:
-            dict, parsed point info.
-        """
-        points = {
-            'fp_start': point_info.get(self._fp_tag, ''),
-            'bp_end': point_info.get(self._bp_tag, '')
-        }
-        try:
-            with open(output_path, 'w') as json_file:
-                json.dump(points, json_file)
-            os.chmod(output_path, stat.S_IREAD)
-        except (IOError, OSError) as err:
-            log.warning('Failed to save point info. %s', err)
-            raise ProfilerIOException
-        return points
-
-    def _get_step_trace_files(self):
-        """Get step trace files."""
-        # step trace files may under $profiler_dir or $profiler_dir/data
-        profiler_dir = self._input_dir
-        step_trace_files = self._search_file(profiler_dir)
-        if not step_trace_files:
-            # try to find step trace files under $profiler_dir/data
-            profiler_dir = os.path.join(profiler_dir, 'data')
-            step_trace_files = self._search_file(profiler_dir)
-        if not step_trace_files:
-            raise ProfilerPathErrorException('Training trace file does not exist.')
-
-        return step_trace_files
-
-    @staticmethod
-    def _search_file(input_dir):
-        """Search step trace file under specific input directory."""
-        # validate input_dir
-        if not os.path.isdir(input_dir):
-            raise ProfilerPathErrorException(
-                '{} does not exist or is not a dir'.format(input_dir)
-            )
-        # get step trace files
-        files = os.listdir(input_dir)
-        step_trace_files = list(
-            filter(
-                lambda file: file.startswith('training_trace') and not file.endswith('.done'),
-                files
-            )
-        )
-        # validate result
-        if len(step_trace_files) > 1:
-            # the format of file name is like
-            # `training_trace.46.dev.profiler_default_tag.$id.slice_$number`
-            # use the $number as the sorted key
-            try:
-                step_trace_files.sort(key=lambda path: int(path.rsplit('_', 1)[-1]))
-            except ValueError as err:
-                log.warning("Unable to parse file names: %s. %s", step_trace_files, err)
-                step_trace_files = []
-
-        file_paths = [os.path.join(input_dir, file) for file in step_trace_files]
-        log.info("Find %d step trace files.", len(file_paths))
-        return file_paths
-
-    def _parse(self, source_files):
-        """Parse source step trace files."""
-        log.info("Start to parse step trace file.")
-        event_info = {}
-        for source_file in source_files:
-            with open(source_file, 'rb') as handler:
-                content = handler.read()
-                for step_trace in self._get_next_step_trace(content, event_info):
-                    if self._skip_first_step:
-                        self._skip_first_step = False
-                        continue
-                    self._record_trace_event(step_trace)
-        self._record_average_info()
-        log.info("Finish to parse step trace file.")
-
-    def _get_next_step_trace(self, content, event_info):
-        """
-        Get next step trace info.
-
-        Args:
-            content (bytes): The input step trace info.
-            event_info (dict): The event info.
-
-        Returns:
-            Generator, return the step trace one by one.
-        """
-        for pos in range(0, len(content), 20):
-            next_event = self._get_trace_struct(content[pos:pos + self._event_size])
-            self._construct_event_info(next_event, event_info)
-            if event_info.get('end'):
-                yield event_info
-
-    def _get_trace_struct(self, bin_info):
-        """Translate event info to StepTraceStruct."""
-        if len(bin_info) == self._event_size:
-            parsed_info = struct.unpack('=QHHQ', bin_info)
-            return StepTraceStruct(*parsed_info)
-        return None
-
-    def _construct_event_info(self, next_event, event_info):
-        """Construct event info according to next_event."""
-        min_job_id = 255
-        step_flag: bool = lambda tag: tag > min_job_id or tag == 0
-        end_flag: bool = lambda tag: tag == min_job_id
-        fp_flag: bool = lambda tag: tag == self._fp_tag
-        bp_flag: bool = lambda tag: tag == self._bp_tag
-
-        def _on_step_event():
-            """Handle step event."""
-            self._validate_tag_id(tag_id)
-            start_time = event_info.get('end', '-')
-            event_info.clear()
-            event_info['start'] = start_time
-            event_info['reduce'] = {}
-
-        def _on_reduce_event():
-            """Handle reduce event."""
-            stream_id = next_event.stream_id
-            if event_info['reduce'].get(stream_id):
-                event_info['reduce'][stream_id].append(sys_count)
-            else:
-                event_info['reduce'][stream_id] = [sys_count]
-
-        tag_id = next_event.tag_id
-        sys_count = next_event.sys_count
-        if end_flag(tag_id):
-            event_info['end'] = sys_count
-        elif step_flag(tag_id):
-            _on_step_event()
-        elif fp_flag(tag_id):
-            event_info['fp'] = sys_count
-        elif bp_flag(tag_id):
-            event_info['bp'] = sys_count
-        else:
-            _on_reduce_event()
-
-    def _validate_tag_id(self, job_id):
-        """Check the job id in source step trace file is same os user set."""
-        if not self._job_id:
-            self._job_id = job_id
-        elif self._job_id != job_id:
-            raise JobIdMismatchException()
-
-    def _record_trace_event(self, step_trace):
-        """Record trace event."""
-        self._step_num += 1
-        start_time = step_trace.get('start')
-        end_time = step_trace.get('end')
-        fp_time = step_trace.get('fp')
-        bp_time = step_trace.get('bp')
-        if not (start_time and end_time and fp_time and bp_time):
-            log.warning("The step %d is missing basic time.", self._step_num)
-            return
-        if start_time == '-':
-            start_time = fp_time
-        row_data = {
-            'step_num': self._step_num,
-            'start_point': start_time,
-            'end_point': end_time,
-            'total': end_time - start_time,
-            'fp_point': fp_time,
-            'bp_point': bp_time,
-            'iteration_interval': fp_time - start_time,
-            'fp_and_bp': bp_time - fp_time,
-            'tail': end_time - bp_time
-        }
-        # update reduce info
-        self._update_reduce_info(step_trace, row_data)
-        # save the row data
-        if not self._header:
-            self._header = list(row_data.keys())
-        row_data_list = [row_data.get(header_name, 0) for header_name in self._header]
-        self._result.append(row_data_list)
-
-    @staticmethod
-    def _update_reduce_info(step_trace, row_data):
-        """Extract reduce info."""
-        reduce_time = step_trace.get('reduce', {})
-        for stream_id, time_points in reduce_time.items():
-            time_point_num = len(time_points)
-            if time_point_num % 2:
-                log.warning("Stream %d has %d reduce time points.", stream_id, time_point_num)
-                continue
-            for index, point_id in enumerate(range(0, time_point_num, 2)):
-                field_name = f'stream_{stream_id}_parallel_{index}'
-                row_data[field_name + '_start_point'] = time_points[point_id]
-                row_data[field_name + '_end_point'] = time_points[point_id + 1]
-                row_data[field_name] = time_points[point_id + 1] - time_points[point_id]
-
-    def _record_average_info(self):
-        """Calculate average info."""
-        result_size = len(self._result)
-        # calculate average data for each column in result data
-        average_data = [0] * len(self._header)
-        if result_size >= 2:
-            for row_info in self._result[1:]:
-                average_data = [
-                    Decimal(i) + Decimal(j) for i, j in zip(row_info, average_data)
-                ]
-            average_data = [
-                round((item / (result_size - 1))) for item in average_data
-            ]
-            # change step num info in average_data to None
-            step_num_index = self._header.index('step_num')
-            average_data[step_num_index] = '-'
-        self._result.append(average_data)
-        log.info("Finish add average info for step trace.")
-
-    def _save(self):
-        log.info("Start to save step trace file.")
-        if not self._header:
-            return
-        with open(self._output_path, 'w') as file_handle:
-            csv_writer = csv.writer(file_handle)
-            csv_writer.writerow(self._header)
-            for row_data in self._result:
-                csv_writer.writerow(row_data)
-        os.chmod(self._output_path, stat.S_IREAD)

mindinsight/profiler/profiling.py

@@ -1,460 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Profiling api file."""
-import os
-import time
-
-from marshmallow import ValidationError
-from tabulate import tabulate
-
-from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
-from mindinsight.profiler.analyser.integrator import Integrator
-from mindinsight.profiler.common._utils import get_file_names, fwrite_format
-from mindinsight.profiler.common.exceptions.exceptions import ProfilerFileNotFoundException, \
-    ProfilerIOException
-from mindinsight.profiler.common.log import logger
-from mindinsight.profiler.common.validator.checkparam import \
-    check_bool, check_subgraph
-from mindinsight.profiler.common.validator.validate_path import \
-    validate_and_normalize_path
-from mindinsight.profiler.parser.aicpu_data_parser import DataPreProcessParser
-from mindinsight.profiler.parser.framework_parser import FrameworkParser
-from mindinsight.profiler.parser.hwts_log_parser import HWTSLogParser
-from mindinsight.profiler.parser.minddata_parser import MinddataParser
-from mindinsight.profiler.parser.minddata_pipeline_parser import \
-    MinddataPipelineParser
-from mindinsight.profiler.parser.optime_parser import OPComputeTimeParser
-from mindinsight.profiler.parser.step_trace_parser import StepTraceParser
-from mindinsight.utils.exceptions import MindInsightException
-
-PROFILING_LOG_BASE_PATH = "/var/log/npu/profiling"
-INIT_OP_NAME = 'Default/InitDataSetQueue'
-
-
-class Profiler:
-    """
-    Performance profiling API.
-
-    Enable MindSpore users to profile the performance of neural network.
-
-    Args:
-        subgraph (str): Define which subgraph to monitor and analyse, can be 'all', 'Default', 'Gradients'.
-        is_detail (bool): Whether to show profiling data for op_instance level, only show optype level if False.
-        is_show_op_path (bool): Whether to save the full path for each op instance.
-        output_path (str): Output data path.
-        optypes_to_deal (str): Op type names, the data of which optype should be collected and analysed,
-            will deal with all op if null; Different op types should be seperated by comma.
-        optypes_not_deal (str): Op type names, the data of which optype will not be collected and analysed;
-            Different op types should be seperated by comma.
-
-    Examples:
-        >>> from mindinsight.profiler import Profiler
-        >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
-        >>>                     device_id=int(os.environ["DEVICE_ID"]))
-        >>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
-        >>> model = Model(train_network)
-        >>> dataset = get_dataset()
-        >>> model.train(2, dataset)
-        >>> profiler.analyse()
-    """
-
-    _base_profiling_container_path = "/var/log/npu/profiling/container"
-    _hwts_output_filename_target = "output_format_data_hwts_"
-    _opcompute_output_filename_target = "output_op_compute_time_"
-    _aicpu_op_output_filename_target = "output_data_preprocess_aicpu_"
-
-    def __init__(self, subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data',
-                 optypes_to_deal='', optypes_not_deal='Variable', job_id=""):
-        # get device_id and device_target
-        self._get_devid_and_devtarget()
-        self._container_path = os.path.join(self._base_profiling_container_path, self._dev_id)
-        data_path = os.path.join(self._container_path, "data")
-        if not os.path.exists(data_path):
-            os.makedirs(data_path, exist_ok=True)
-        self._output_path = validate_and_normalize_path(output_path,
-                                                        'Profiler output path (' + output_path + ')')
-        self._output_path = os.path.join(self._output_path, "profiler")
-        if not os.path.exists(self._output_path):
-            os.makedirs(self._output_path, exist_ok=True)
-
-        os.environ['PROFILING_MODE'] = 'true'
-        os.environ['PROFILING_OPTIONS'] = 'training_trace:task_trace'
-        os.environ['MINDDATA_PROFILING_DIR'] = self._output_path
-        os.environ['DEVICE_ID'] = self._dev_id
-        # use context interface to open profiling, for the new mindspore version(after 2020.5.21)
-        try:
-            import mindspore.context as context
-            context.set_context(enable_profiling=True, profiling_options="training_trace:task_trace")
-        except ImportError:
-            logger.error("Profiling: fail to import context from mindspore.")
-        except ValueError:
-            logger.error("Profiling: fail to set context enable_profiling")
-
-        os.environ['AICPU_PROFILING_MODE'] = 'true'
-        os.environ['PROFILING_DIR'] = str(self._container_path)
-        self._subgraph = check_subgraph(subgraph)
-        self._valid_optype_name = optypes_to_deal.split(",") if optypes_to_deal else []
-        self._filt_optype_names = optypes_not_deal.split(",") if optypes_not_deal else []
-        self._detail = check_bool(is_detail, 'is_detail')
-        self._withfullpath = check_bool(is_show_op_path, 'is_show_op_path')
-        self._profiling_job_id = job_id
-        # add job id env through user input later
-        self._job_id_env = 0
-        self._start_time = int(time.time() * 10000000)
-        logger.info("Profiling: profiling start time: %d", self._start_time)
-
-    def analyse(self):
-        """
-        Collect and analyse performance data, called after training or during training.
-
-        Examples:
-            >>> from mindinsight.profiler import Profiler
-            >>> context.set_context(mode=context.GRAPH_MODE, device_target="Ascend",
-            >>>                     device_id=int(os.environ["DEVICE_ID"]))
-            >>> profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False, output_path='./data')
-            >>> model = Model(train_network)
-            >>> dataset = get_dataset()
-            >>> model.train(2, dataset)
-            >>> profiler.analyse()
-        """
-
-        try:
-            from mindspore.communication.management import release
-            release()
-        except ImportError:
-            logger.error("Profiling: fail to import release from mindspore.")
-
-        job_id = self._get_profiling_job_id()
-        logger.info("Profiling: job id is %s ", job_id)
-
-        source_path = os.path.join(PROFILING_LOG_BASE_PATH, job_id)
-        # parse hwts.log.data.45.dev file, and get task profiling data
-        hwts_output_filename = self._hwts_output_filename_target + self._dev_id + ".txt"
-        hwts_output_filename = os.path.join(self._output_path, hwts_output_filename)
-        hwtslog_parser = HWTSLogParser(source_path, hwts_output_filename)
-        result = hwtslog_parser.execute()
-        if not result:
-            logger.error("Profiling: fail to parse hwts log file.")
-            return
-
-        # parse Framework file, and get the relation of op and tasks
-        framework_parser = FrameworkParser(job_id, self._dev_id, self._output_path)
-        framework_parser.parse()
-        op_task_dict = framework_parser.to_task_id_full_op_name_dict()
-        if not op_task_dict:
-            logger.error("Profiling: fail to parse framework files.")
-            return
-
-        # get op compute time from hwts data and framework data, write output_op_compute_time.txt
-        opcompute_output_filename = self._opcompute_output_filename_target + self._dev_id + ".txt"
-        opcompute_output_filename = os.path.join(self._output_path, opcompute_output_filename)
-        optime_parser = OPComputeTimeParser(
-            hwts_output_filename, opcompute_output_filename,
-            op_task_dict, self._output_path, self._dev_id
-        )
-        optime_parser.execute()
-
-        # parse DATA_PREPROCESS.dev.AICPU file, write output_data_preprocess_aicpu_x.txt
-        output_data_preprocess_aicpu = self._aicpu_op_output_filename_target + self._dev_id + ".txt"
-        output_data_preprocess_aicpu = os.path.join(self._output_path, output_data_preprocess_aicpu)
-        aicpu_data_parser = DataPreProcessParser(source_path, output_data_preprocess_aicpu)
-        aicpu_data_parser.execute()
-
-        # Parsing minddata AICPU profiling
-        MinddataParser.execute(source_path, self._output_path, self._dev_id)
-
-        # parse minddata pipeline operator and queue
-        try:
-            pipeline_parser = MinddataPipelineParser(self._output_path, self._dev_id, self._output_path)
-            pipeline_parser.parse()
-        except MindInsightException as err:
-            logger.warning(err.message)
-
-        # analyse op compute time info
-        try:
-            self._analyser_op_info()
-        except MindInsightException as err:
-            logger.warning(err.message)
-
-        # analyse step trace info
-        try:
-            self._analyse_step_trace(source_path, framework_parser)
-        except MindInsightException as err:
-            logger.warning(err.message)
-
-        # analyse timeline info
-        try:
-            self._analyse_timeline(aicpu_data_parser, optime_parser)
-        except (ProfilerIOException, ProfilerFileNotFoundException, ValidationError) as err:
-            logger.warning('Fail to write timeline data: %s', err)
-
-    def _analyse_step_trace(self, source_path, framework_parser):
-        """
-        Analyse step trace data and save the result.
-
-        Args:
-            source_path (str): The directory that contains the step trace original data.
-            framework_parser (FrameworkParser): The framework parse instance.
-        """
-        logger.info("Begin to parse step trace.")
-        # construct output path
-        step_trace_intermediate_file_path = os.path.join(
-            self._output_path,
-            f'step_trace_raw_{self._dev_id}_detail_time.csv'
-        )
-        point_info_file_path = os.path.join(
-            self._output_path,
-            'step_trace_point_info.json'
-        )
-        # whether keep the first step
-        skip_first_step_flag = framework_parser.check_op_name(INIT_OP_NAME)
-        point_info = framework_parser.point_info
-        # parser the step trace files and save the result to disk
-        parser = StepTraceParser(input_dir=source_path,
-                                 output_file_path=step_trace_intermediate_file_path,
-                                 job_id=self._job_id_env,
-                                 skip_first_step=skip_first_step_flag)
-        parser.parse_and_save()
-        point_info = parser.record_point_info(point_info, point_info_file_path)
-        # print parser result
-        parser.show()
-        logger.info("Finish saving the intermediate result: %s", step_trace_intermediate_file_path)
-        logger.info("The point info is: %s", point_info)
-
-    def _analyse_timeline(self, aicpu_parser, optime_parser):
-        """
-        Analyse and parse timeline info.
-
-        Args:
-            aicpu_parser (DataPreProcessParser): The parser instance for AI CPU operator
-                execution time calculation.
-            optime_parser (OPComputeTimeParserParser): The parser instance for AI Core
-                operator execution time calculation.
-        """
-        timeline_analyser = AnalyserFactory.instance().get_analyser(
-            'timeline', self._output_path, self._dev_id
-        )
-
-        # Get framework info
-        aicoredetail_analyser = AnalyserFactory.instance().get_analyser(
-            'aicore_detail', self._output_path, self._dev_id
-        )
-        framework_info = aicoredetail_analyser.query()
-
-        # Get all reduce info
-        step_trace_analyser = AnalyserFactory.instance().get_analyser(
-            'step_trace', self._output_path, self._dev_id
-        )
-        all_reduce_info = step_trace_analyser.query_for_all_reduce()
-
-        # Get timeline info
-        logger.info('Start writing timeline info...')
-        logger.info('Warm Prompt: It could take a few minutes if you are training '
-                    'with a complex network or more than 10 steps.')
-        # Add info into timeline, such as AI CPU, AllReduce, framework info.
-        aicpu_info = aicpu_parser.query_aicpu_data()
-        min_cycle_counter = min(aicpu_parser.min_cycle_counter, optime_parser.min_cycle_counter)
-        timeline_analyser.init_timeline(all_reduce_info, framework_info, aicpu_info, min_cycle_counter)
-        timeline_analyser.write_timeline()
-        timeline_analyser.write_timeline_summary()
-
-    def __del__(self):
-        """Disable the profiling collection service, called after training."""
-
-        os.environ['PROFILING_MODE'] = str("false")
-        try:
-            import mindspore.context as context
-            context.set_context(enable_profiling=False)
-        except ImportError:
-            pass
-
-    def _get_profiling_job_id(self):
-        """Get profiling job id, which was generated by ada service.
-
-        Returns:
-            str: profiling jon id.
-        """
-
-        if self._profiling_job_id:
-            return self._profiling_job_id
-
-        job_id = ""
-        cmd = "ls -t " + PROFILING_LOG_BASE_PATH + "|grep JOB|awk '{print $1}'"
-        r = os.popen(cmd)
-        profiling_job_dirs = r.readlines()
-        r.close()
-        for item in profiling_job_dirs:
-            path = os.path.join(PROFILING_LOG_BASE_PATH, item.strip())
-            log_file = get_file_names(path, "host_start.log")
-            if not log_file:
-                logger.error("Profiling: job path %s, host_start.log not exist.", path)
-                continue
-
-            log_file = os.path.join(path, log_file[0])
-            item_dict = self._parse_host_start_log(log_file)
-
-            if not item_dict:
-                logger.error("Profiling: job path %s, fail to get job start info.", path)
-                continue
-            if self._start_time > int(item_dict["start_time"]):
-                logger.info("Profiling: job path %s, start_time %s, training start_time %d.",
-                            path, item_dict["start_time"], self._start_time)
-                break
-
-            if self._dev_id != item_dict["device_id"]:
-                logger.info("Profiling: job path %s, dev id %s, training device id %s.",
-                            path, item_dict["device_id"], self._dev_id)
-                continue
-
-            job_id = item.strip()
-            break
-
-        if not job_id:
-            msg = ("Fail to get profiling job, please check whether job dir was generated")
-            raise RuntimeError(msg)
-
-        return job_id
-
-    def _parse_host_start_log(self, input_file):
-        """
-        Parse host start log file, get the device id and start time of the job.
-
-        Args:
-             input_file (str): The file path of the host start log file.
-
-        Returns:
-            dict, job start time and device id.
-        """
-
-        item_dict = {}
-        for line in open(input_file):
-            if "Device" in line:
-                item_dict["device_id"] = line[7:len(line)-2]
-            elif "clock_realtime" in line:
-                item_dict["start_time"] = line[16:len(line)-3]
-
-        return item_dict
-
-    def _analyser_op_info(self):
-        """Analyse the operator information."""
-        integrator = Integrator(self._output_path, self._dev_id)
-        integrator.integrate()
-
-        aicore_type_result = self._query_op_type_info()
-        detail_file_path = os.path.join(
-            self._output_path,
-            'output_op_compute_time_detail_{}.txt'.format(self._dev_id)
-        )
-        fwrite_format(detail_file_path, data_source='title:op compute time')
-        display_names = [
-            'optype_name', 'compute_time(ms, per-step)',
-            'called_times(per-step)', 'percent'
-        ]
-        data_source = tabulate(aicore_type_result, display_names)
-        fwrite_format(detail_file_path, data_source=data_source, is_print=True)
-
-        if self._detail:
-            op_type_order = [item[0] for item in aicore_type_result]
-            aicore_detail_result = self._query_op_detail_info(op_type_order)
-            fwrite_format(detail_file_path, data_source='', is_print=True)
-            fwrite_format(detail_file_path, data_source='Detail:', is_print=True)
-            data_source = tabulate(
-                aicore_detail_result.get('object'),
-                aicore_detail_result.get('col_name')
-            )
-            fwrite_format(detail_file_path, data_source=data_source, is_print=True)
-
-    def _query_op_type_info(self):
-        """
-        Query AICORE operator type information.
-
-        Returns:
-            list[list], the AICORE operator type and execution time information.
-        """
-        condition = {
-            'sort_condition': {
-                'name': 'execution_time',
-                'type': 'descending'
-            }
-        }
-        analyser = AnalyserFactory.instance().get_analyser(
-            'aicore_type', self._output_path, self._dev_id
-        )
-        result = analyser.query(condition)
-        return result.get('object')
-
-    def _query_op_detail_info(self, op_type_order):
-        """
-        Query AICORE operator detail information.
-
-        Args:
-            op_type_order(list): The name of the op type in order.
-
-        Returns:
-            dict, the AICORE operator detail information.
-        """
-
-        op_type_condition = {}
-        if self._valid_optype_name:
-            op_type_condition['in'] = self._valid_optype_name
-        if self._filt_optype_names:
-            op_type_condition['not_in'] = self._filt_optype_names
-
-        subgraph_condition = {}
-        if self._subgraph != 'all':
-            subgraph_condition['in'] = [self._subgraph]
-
-        filter_condition = {
-            'op_type': op_type_condition,
-            'subgraph': subgraph_condition,
-            'is_display_detail': False,
-            'is_display_full_op_name': self._withfullpath
-        }
-        analyser = AnalyserFactory.instance().get_analyser(
-            'aicore_detail', self._output_path, self._dev_id
-        )
-        result = analyser.query_and_sort_by_op_type(
-            filter_condition, op_type_order
-        )
-        return result
-
-
-    def _get_devid_and_devtarget(self):
-        """Get device id and target of this training."""
-
-        device_target = ""
-        dev_id = ""
-        try:
-            import mindspore.context as context
-            dev_id = str(context.get_context("device_id"))
-            device_target = context.get_context("device_target")
-        except ImportError:
-            logger.error("Profiling: fail to import context from mindspore.")
-        except ValueError as err:
-            logger.error("Profiling: fail to get context, %s", err)
-
-        if not dev_id or not dev_id.isdigit():
-            dev_id = os.getenv('DEVICE_ID')
-        if not dev_id or not dev_id.isdigit():
-            dev_id = "0"
-            logger.error("Fail to get DEVICE_ID, use 0 instead.")
-
-        if device_target and device_target != "Davinci" \
-            and device_target != "Ascend":
-            msg = ("Profiling: unsupport backend: %s" \
-                   % device_target)
-            raise RuntimeError(msg)
-
-        self._dev_id = dev_id

mindinsight/scripts/stop.py

@@ -103,21 +103,17 @@ class Command(BaseCommand):
         self.logfile.info('Stop mindinsight with port %s and pid %s.', port, pid)
 
         process = psutil.Process(pid)
-        child_pids = [child.pid for child in process.children()]
+        processes_to_kill = [process]
+        # Set recursive to True to kill grand children processes.
+        for child in process.children(recursive=True):
+            processes_to_kill.append(child)
 
-        # kill gunicorn master process
-        try:
-            os.kill(pid, signal.SIGKILL)
-        except PermissionError:
-            self.console.info('kill pid %s failed due to permission error', pid)
-            sys.exit(1)
-
-        # cleanup gunicorn worker processes
-        for child_pid in child_pids:
+        for proc in processes_to_kill:
+            self.logfile.info('Stopping mindinsight process %s.', proc.pid)
             try:
-                os.kill(child_pid, signal.SIGKILL)
-            except ProcessLookupError:
-                pass
+                proc.send_signal(signal.SIGKILL)
+            except psutil.Error as ex:
+                self.logfile.warning("Stop process %s failed. Detail: %s.", proc.pid, str(ex))
 
         for hook in HookUtils.instance().hooks():
             hook.on_shutdown(self.logfile)
@@ -154,7 +150,19 @@ class Command(BaseCommand):
             if user != process.username():
                 continue
 
-            pid = process.pid if process.ppid() == 1 else process.ppid()
+            gunicorn_master_process = process
+
+            # The gunicorn master process might have grand children (eg forked by process pool).
+            while True:
+                parent_process = gunicorn_master_process.parent()
+                if parent_process is None or parent_process.pid == 1:
+                    break
+                parent_cmd = parent_process.cmdline()
+                if ' '.join(parent_cmd).find(self.cmd_regex) == -1:
+                    break
+                gunicorn_master_process = parent_process
+
+            pid = gunicorn_master_process.pid
 
             for open_file in process.open_files():
                 if open_file.path.endswith(self.access_log_path):

mindinsight/sysmetric/collector/__init__.py

@@ -0,0 +1,41 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""The metrics collector."""
+from ._collect_cpu import collect_cpu
+from ._collect_mem import collect_mem
+from ._collect_npu import collect_npu
+
+__all__ = ['collect_cpu', 'collect_mem', 'collect_npu', 'get_metrics']
+
+
+def get_metrics():
+    mem = collect_mem()
+    mem_total = mem.get('total')
+    mem_available = mem.get('available')
+    mem_used = mem.get('used')
+    return {
+        'npu': collect_npu(),
+        'cpu': {
+            'overall': collect_cpu(percent=True),
+            'percpu': collect_cpu(percpu=True, percent=True)
+        },
+        'memory': {
+            'virtual': {
+                'available': mem_available,
+                'used': mem_used,
+                'others': max(mem_total - mem_available - mem_used, 0)
+            }
+        }
+    }

mindinsight/sysmetric/collector/_collect_cpu.py

@@ -0,0 +1,37 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""The cpu collector."""
+
+import psutil
+
+
+def collect_cpu(percpu=False, percent=False):
+    """
+    Collect the cpu info.
+
+    Args:
+        percpu (bool): To return a list of cpu info for each logical CPU on the system.
+        percent (bool): Represent the sized in percentage.
+
+    Returns:
+        Union[dict, List[dict]], the CPUs info.
+    """
+    if percent:
+        times = psutil.cpu_times_percent(percpu=percpu)
+    else:
+        times = psutil.cpu_times(percpu=percpu)
+    if not percpu:
+        return dict(times._asdict())
+    return [dict(time._asdict()) for time in times]

mindinsight/sysmetric/collector/_collect_mem.py

@@ -0,0 +1,27 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""The memory collector."""
+
+import psutil
+
+
+def collect_mem():
+    """
+    Collect the virtual memory info.
+
+    Returns:
+        dict, the virtual memory info.
+    """
+    return dict(psutil.virtual_memory()._asdict())

mindinsight/sysmetric/collector/_collect_npu.py

@@ -0,0 +1,420 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""The npu collector."""
+
+import inspect
+from collections import defaultdict
+from ctypes import CDLL, Structure, byref, c_char, c_int, c_uint, c_ulong, c_ushort
+from functools import lru_cache, wraps
+from threading import Lock, Thread
+
+from mindinsight.sysmetric.common.exceptions import DsmiQueryingException
+from mindinsight.sysmetric.common.log import logger
+
+
+def _timeout(seconds, default):
+    """
+    The timeout decorator wait for specified seconds or return the default value.
+
+    Args:
+        seconds (float): The specified seconds.
+        default (Any): The default value.
+    """
+
+    def outer(fn):
+        cached, lockdict = {}, defaultdict(Lock)
+
+        def target(*args):
+            lock = lockdict[args]
+            if lock.acquire(blocking=False):
+                try:
+                    cached[args] = fn(*args)
+                finally:
+                    lock.release()
+            else:
+                logger.debug('%s%r skipped.', fn.__name__, args)
+
+        @wraps(fn)
+        def inner(*args):
+            thread = Thread(target=target, args=args, daemon=True)
+            thread.start()
+            thread.join(seconds)
+            if thread.is_alive():
+                logger.debug('%s%r timeouted.', fn.__name__, args)
+            return cached.get(args, default)
+
+        return inner
+
+    return outer
+
+
+def _fallback_to_prev_result(fn):
+    """Fallback to previous successful result when failing."""
+    prev_result = None
+
+    @wraps(fn)
+    def wrap(*args):
+        nonlocal prev_result
+        sucess, result = fn(*args)
+        if sucess:
+            prev_result = result
+            return sucess, result
+        if prev_result is not None:
+            return sucess, prev_result
+        raise RuntimeError(f'{fn.__name__} querying failed and no previous successful result.')
+
+    return wrap
+
+
+def _libsmicall(*args):
+    """
+    Call the lib function to querying NPU metrics.
+
+    Returns:
+        bool, True when success of querying, False otherwise.
+    """
+    if not libsmi:
+        logger.error('Trying to call the libdrvdsmi_host which is not loaded.')
+        raise ValueError('Trying to call the libdrvdsmi_host which is not loaded.')
+    fname = inspect.stack()[1].function
+    error_code = getattr(libsmi, fname)(*args)
+    if error_code != 0:
+        logger.error('%s querying failed with error code %d.', fname, error_code)
+    return error_code == 0
+
+
+@lru_cache(maxsize=4)
+def dsmi_get_device_count():
+    """
+    Get device count.
+
+    Returns:
+        int, the device count.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    device_count = c_int()
+
+    if _libsmicall(byref(device_count)):
+        return device_count.value
+    raise RuntimeError('Querying device count failed.')
+
+
+@lru_cache(maxsize=4)
+def dsmi_list_device(count):
+    """
+    List the device IDs.
+
+    Args:
+        count (int): The device count.
+
+    Returns:
+        List[int], the device IDs.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    device_id_array = c_int * count
+    device_id_list = device_id_array()
+    count = c_int(count)
+
+    if _libsmicall(device_id_list, count):
+        return list(device_id_list)
+    raise RuntimeError('Querying device id list failed.')
+
+
+@lru_cache(maxsize=8)
+@_fallback_to_prev_result
+def dsmi_get_chip_info(device_id):
+    """
+    Get chip info.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        dict, the chip info:
+            - chip_type (str): The chip type.
+            - chip_name (str): The chip name.
+            - chip_ver (str): The chip name.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+
+    class ChipInfoStruct(Structure):
+        _fields_ = [('chip_type', c_char * 32), ('chip_name', c_char * 32), ('chip_ver', c_char * 32)]
+
+    device_id = c_int(device_id)
+    chip_info = ChipInfoStruct()
+    success = _libsmicall(device_id, byref(chip_info))
+    return success, {
+        'chip_type': chip_info.chip_type.decode('utf-8'),
+        'chip_name': chip_info.chip_name.decode('utf-8'),
+        'chip_ver': chip_info.chip_ver.decode('utf-8')
+    }
+
+
+@_fallback_to_prev_result
+def dsmi_get_device_health(device_id):
+    """
+    Get device health.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        int, 0 indicats normal, 1 minor alarm, 2 major alarm, 3 critical alarm, 0xffffffff device not found.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    device_id = c_int(device_id)
+    health = c_uint()
+
+    success = _libsmicall(device_id, byref(health))
+
+    return success, health.value
+
+
+@lru_cache(maxsize=8)
+@_fallback_to_prev_result
+def dsmi_get_device_ip_address(device_id):
+    """
+    Get device IP address.
+
+    Args:
+        device_id (int): The specific device ID.
+    Returns:
+        dict, the device IP address:
+            - ip_address (str): the IP address.
+            - mask_address (str): the mask address.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    is_ipv6, port_type, port_id = False, 1, 0
+
+    class Ipaddrstruct(Structure):
+        _fields_ = [('u_addr', c_char * (16 if is_ipv6 else 4)), ('ip_type', c_int)]
+
+    ip_type = c_int(1 if is_ipv6 else 0)
+
+    device_id = c_int(device_id)
+    ip_address = Ipaddrstruct(b'', ip_type)
+    mask_address = Ipaddrstruct(b'', ip_type)
+
+    success = _libsmicall(device_id, port_type, port_id, byref(ip_address), byref(mask_address))
+
+    def pad(u_addr):
+        for i in range(4):
+            if i < len(u_addr):
+                yield u_addr[i]
+            else:
+                yield 0
+
+    return success, {
+        'ip_address': '.'.join(str(c) for c in pad(ip_address.u_addr)),
+        'mask_address': '.'.join(str(c) for c in pad(mask_address.u_addr))
+    }
+
+
+@_fallback_to_prev_result
+def dsmi_get_hbm_info(device_id):
+    """
+    Get the HBM info.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        dict, the HBM info:
+            memory_size (int), The total HBM memory, in KB.
+            frep (int), The HBM frequency, in MHZ.
+            memory_usage (int), The used HBM memory, in KB.
+            temp (int), The HBM temperature, in °C.
+            bandwith_util_rate (int): The bandwith util rate, in %.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+
+    class HbmInfoStruct(Structure):
+        _fields_ = [('memory_size', c_ulong), ('freq', c_uint), ('memory_usage', c_ulong), ('temp', c_int),
+                    ('bandwith_util_rate', c_uint)]
+
+    device_id = c_int(device_id)
+    hbm_info = HbmInfoStruct()
+
+    success = _libsmicall(device_id, byref(hbm_info))
+
+    return success, {
+        'memory_size': hbm_info.memory_size,
+        'freq': hbm_info.freq,
+        'memory_usage': hbm_info.memory_usage,
+        'temp': hbm_info.temp,
+        'bandwith_util_rate': hbm_info.bandwith_util_rate
+    }
+
+
+@_timeout(0.2, -1)
+def dsmi_get_device_utilization_rate(device_id, device_type):
+    """
+    Get device utilization rate, %.
+
+    Note: Query AI Core when profiling turns on will return failure.
+
+    Args:
+        device_id (int): The specific device id
+        device_type (int): The device type, 1 for memory, 2 AI Core, 5 memory bandwidth, 6 HBM, 10 HBM bandwidth.
+    Returns:
+        int, the utilization rate, returning -1 to indicate querying failed.
+    """
+    device_id = c_int(device_id)
+    device_type = c_int(device_type)
+    utilization_rate = c_uint()
+    if _libsmicall(device_id, device_type, byref(utilization_rate)):
+        return utilization_rate.value
+    return -1
+
+
+@_fallback_to_prev_result
+def dsmi_get_device_power_info(device_id):
+    """
+    Get the device power.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        dict, the device power info.
+            - power, the device power, in Watt.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+
+    class PowerInfoStruct(Structure):
+        _fields_ = [('power', c_ushort)]
+
+    power_info = PowerInfoStruct()
+    device_id = c_int(device_id)
+
+    success = _libsmicall(device_id, byref(power_info))
+    return success, {'power': round(power_info.power * 0.1, 2)}
+
+
+@_fallback_to_prev_result
+def dsmi_get_device_temperature(device_id):
+    """
+    Get the device temperature.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        int, the device temperature, in °C.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    device_id = c_int(device_id)
+    temperature = c_uint()
+
+    success = _libsmicall(device_id, byref(temperature))
+
+    return success, temperature.value
+
+
+def collect_npu():
+    """Collect the metrics for each NPUs.
+
+    Returns:
+        List[dict], the metrics of each NPUs.
+
+    Raises:
+        DsmiQueryingException, when querying dsmi returning non-zero.
+    """
+    try:
+        return _collect_npus()
+    except RuntimeError as e:
+        logger.warning(e.args[0])
+        raise DsmiQueryingException(e.args[0])
+
+
+def _collect_npus():
+    """Collect the metrics for each NPUs.
+
+    Returns:
+        List[dict], the metrics of each NPUs.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    if not libsmi:
+        return None
+    count = dsmi_get_device_count()
+    device_ids = dsmi_list_device(count)
+    npus = []
+    for device_id in device_ids:
+        npu = _collect_one(device_id)
+        npus.append(npu)
+    return npus
+
+
+def _collect_one(device_id):
+    """
+    Collect NPU info by the device_id.
+
+    Args:
+        device_id (int): The specific device id.
+
+    Returns:
+        dict, the NPU info.
+
+    Raises:
+        RuntimeError, when querying dsmi returning non-zero.
+    """
+    kb_to_mb, memory_threshold, success = 1024, 4, [True] * 6
+    success[0], health = dsmi_get_device_health(device_id)
+    success[1], hbm_info = dsmi_get_hbm_info(device_id)
+    success[2], chip_info = dsmi_get_chip_info(device_id)
+    success[3], ip_addr = dsmi_get_device_ip_address(device_id)
+    success[4], power_info = dsmi_get_device_power_info(device_id)
+    success[5], temperature = dsmi_get_device_temperature(device_id)
+    aicore_rate = dsmi_get_device_utilization_rate(device_id, 2)
+    return {
+        'chip_name': chip_info.get('chip_name'),
+        'device_id': device_id,
+        'available': all(success) and health == 0 and hbm_info.get('memory_usage', 0) // kb_to_mb < memory_threshold,
+        'health': health,
+        'ip_address': ip_addr.get('ip_address'),
+        'aicore_rate': aicore_rate,
+        'hbm_info': {
+            'memory_size': hbm_info.get('memory_size') // kb_to_mb,
+            'memory_usage': hbm_info.get('memory_usage') // kb_to_mb
+        },
+        'power': power_info.get('power'),
+        'temperature': temperature,
+        'success': all(success)
+    }
+
+
+try:
+    libsmi = CDLL('libdrvdsmi_host.so')
+except OSError:
+    logger.info('Failed to load libdrvdsmi_host.so.')
+    libsmi = None

mindinsight/sysmetric/common/exceptions.py

@@ -0,0 +1,25 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Define custom exception."""
+
+from mindinsight.utils.exceptions import MindInsightException
+from mindinsight.utils.constant import SysmetricErrors
+
+
+class DsmiQueryingException(MindInsightException):
+    """Dsmi Querying Failure"""
+
+    def __init__(self, message):
+        super(DsmiQueryingException, self).__init__(SysmetricErrors.DSMI_QUERYING_NONZERO, message)

tests/ut/datavisual/common/__init__.py → mindinsight/sysmetric/common/log.py

@@ -1,4 +1,4 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
+# Copyright 2020 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -12,3 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+"""Resource logger."""
+from mindinsight.utils.log import setup_logger
+
+logger = setup_logger(sub_module='sysmetric', log_name='sysmetric')

mindinsight/ui/src/assets/images/cpu-bg.svg

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg width="1920px" height="1080px" viewBox="0 0 1920 1080" version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+    <!-- Generator: Sketch 63.1 (92452) - https://sketch.com -->
+    <title>矩形</title>
+    <desc>Created with Sketch.</desc>
+    <defs>
+        <polygon id="path-1" points="0 0 1920 0 1920 1080 0 1080"></polygon>
+        <pattern id="pattern-3" width="16.4850993" height="16.4850993" x="-16.4850993" y="-16.4850993" patternUnits="userSpaceOnUse">
+            <use xlink:href="#image-4" transform="scale(0.34343957,0.34343957)"></use>
+        </pattern>
+        <image id="image-4" width="48" height="48" xlink:href="data:image/png;base64,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"></image>
+    </defs>
+    <g id="硬件资源可视-特性文档" stroke="none" stroke-width="1" fill="none" fill-rule="evenodd">
+        <mask id="mask-2" fill="white">
+            <use xlink:href="#path-1"></use>
+        </mask>
+        <g id="矩形">
+            <use fill="#F2F5FC" xlink:href="#path-1"></use>
+            <use fill-opacity="0.2" fill="url(#pattern-3)" style="mix-blend-mode: multiply;" xlink:href="#path-1"></use>
+        </g>
+    </g>
+</svg>

mindinsight/ui/src/components/autocomplete.vue

@@ -22,7 +22,6 @@ limitations under the License.
        :aria-expanded="suggestionVisible"
        :aria-owns="id">
     <el-input ref="input"
-              class="rtl-item"
               v-bind="[$props, $attrs]"
               @input="handleChange"
               @focus="handleFocus"

mindinsight/ui/src/components/gridTableSimple.vue

@@ -296,7 +296,9 @@ export default {
      */
     accuracyChange(value) {
       this.formateGridArray();
-      this.updateGrid();
+      if (!this.requestError && !this.incorrectData) {
+        this.updateGrid();
+      }
     },
     /**
      * Dimension selection changed
@@ -369,7 +371,9 @@ export default {
         }
         this.formateGridArray();
         this.formateColumnsData();
-        this.updateGrid();
+        if (!this.incorrectData) {
+          this.updateGrid();
+        }
       });
     },
     /**

mindinsight/ui/src/components/header.vue

@@ -32,6 +32,7 @@ limitations under the License.
           <el-menu-item index="/model-traceback">{{$t("summaryManage.modelTraceback")}}</el-menu-item>
           <el-menu-item index="/data-traceback">{{$t("summaryManage.dataTraceback")}}</el-menu-item>
           <el-menu-item index="/compare-plate">{{$t("summaryManage.comparePlate")}}</el-menu-item>
+          <el-menu-item index="/hardware-visual">{{$t("summaryManage.hardwareVisual")}}</el-menu-item>
         </el-menu>
       </div>
     </div>
@@ -42,28 +43,62 @@ limitations under the License.
          || this.$route.path.indexOf('/histogram') > 0
          || this.$route.path.indexOf('/tensor') > 0
          || this.$route.path.indexOf('/training-dashboard') > 0
-         || !this.$route.path.indexOf('/compare-plate')">
-      <!-- automatic refresh switch -->
-      <el-switch v-model="isTimeReload"
-                 :active-text="$t('header.timeReload')+$t('symbols.leftbracket')+
-                 timeReloadValue+$t('header.timeSecond')+$t('symbols.rightbracket')"
-                 @change="timeReload"></el-switch>
-      <i class="el-icon-edit"
-         :title="$t('header.timeReloadScope')"
-         v-if="isTimeReload && !isShowInp"
-         @click="editTime"></i>
+         || !this.$route.path.indexOf('/compare-plate')
+         || !this.$route.path.indexOf('/hardware-visual')">
+      <div class="reload-training"
+          v-if="this.$route.path.indexOf('/scalar') > 0
+          || this.$route.path.indexOf('/image') > 0
+          || this.$route.path.indexOf('/histogram') > 0
+          || this.$route.path.indexOf('/tensor') > 0
+          || this.$route.path.indexOf('/training-dashboard') > 0
+          || !this.$route.path.indexOf('/compare-plate')">
+        <!-- automatic refresh switch -->
+        <el-switch v-model="isTimeReload"
+                  :active-text="$t('header.timeReload')+$t('symbols.leftbracket')+
+                  timeReloadValue+$t('header.timeSecond')+$t('symbols.rightbracket')"
+                  @change="timeReload"></el-switch>
+        <i class="el-icon-edit"
+          :title="$t('header.timeReloadScope')"
+          v-if="isTimeReload && !isShowInp"
+          @click="editTime"></i>
 
-      <el-input v-if="isTimeReload && isShowInp"
-                v-model="newReloadValue"
-                type="text"
-                @input="timeValueChange"></el-input>
+        <el-input v-if="isTimeReload && isShowInp"
+                  v-model="newReloadValue"
+                  type="text"
+                  @input="timeValueChange"></el-input>
+
+        <i class="el-icon-check"
+          v-if="isTimeReload && isShowInp"
+          @click="saveTimeValue"></i>
+        <i class="el-icon-close"
+          v-if="isTimeReload && isShowInp"
+          @click="cancelTimeValue"></i>
+      </div>
+      <div class="reload-hardware"
+        v-if="!this.$route.path.indexOf('/hardware-visual')">
+        <!-- automatic refresh switch -->
+        <el-switch v-model="isHardwareTimeReload"
+                  :active-text="$t('header.timeReload')+$t('symbols.leftbracket')+
+                  hardwareTimeReloadValue+$t('header.timeSecond')+$t('symbols.rightbracket')"
+                  @change="hardwareTimeReload"></el-switch>
+        <i class="el-icon-edit"
+          :title="$t('header.timeReloadScope')"
+          v-if="isHardwareTimeReload && !isShowHardwareInp"
+          @click="editHardwareTime"></i>
+
+        <el-input v-if="isHardwareTimeReload && isShowHardwareInp"
+                  v-model="newHardwareReloadValue"
+                  type="text"
+                  @input="hardwareTimeValueChange"></el-input>
+
+        <i class="el-icon-check"
+          v-if="isHardwareTimeReload && isShowHardwareInp"
+          @click="saveHardwareTimeValue"></i>
+        <i class="el-icon-close"
+          v-if="isHardwareTimeReload && isShowHardwareInp"
+          @click="cancelHardwareTimeValue"></i>
+      </div>
 
-      <i class="el-icon-check"
-         v-if="isTimeReload && isShowInp"
-         @click="saveTimeValue"></i>
-      <i class="el-icon-close"
-         v-if="isTimeReload && isShowInp"
-         @click="cancleTimeValue"></i>
 
       <!-- manual refresh switch -->
       <img src="../assets/images/reload.png"
@@ -90,6 +125,9 @@ export default {
       isShowInp: false,
       timeReloadValue: this.$store.state.timeReloadValue,
       newReloadValue: this.$store.state.timeReloadValue,
+      isShowHardwareInp: false,
+      hardwareTimeReloadValue: this.$store.state.hardwareTimeReloadValue,
+      newHardwareReloadValue: this.$store.state.hardwareTimeReloadValue,
     };
   },
   computed: {
@@ -104,6 +142,13 @@ export default {
       },
       set(val) {},
     },
+    // set and get isHardwareTimeReload status
+    isHardwareTimeReload: {
+      get() {
+        return this.$store.state.isHardwareTimeReload;
+      },
+      set(val) {},
+    },
   },
   watch: {},
   mounted() {},
@@ -117,7 +162,7 @@ export default {
     relPath(path) {
       this.$router.push(path);
     },
-    // save isTimeReload status
+    // training reload setting
     timeReload(val) {
       localStorage.isTimeReload = val;
       this.$store.commit('setIsTimeReload', val);
@@ -128,7 +173,7 @@ export default {
     },
 
     saveTimeValue() {
-      if (this.newReloadValue) {
+      if (this.newReloadValue >= 0) {
         this.newReloadValue =
           this.newReloadValue < 3
             ? 3
@@ -141,20 +186,65 @@ export default {
         this.$store.commit('setTimeReloadValue', timeValue);
         this.isShowInp = false;
       } else {
-        this.cancleTimeValue();
+        this.cancelTimeValue();
       }
     },
-    cancleTimeValue() {
+    cancelTimeValue() {
       this.isShowInp = false;
       this.newReloadValue = this.timeReloadValue;
     },
     timeValueChange() {
+      if (this.newReloadValue === '') {
+        return;
+      }
       this.newReloadValue = this.newReloadValue
           .toString()
           .replace(/[^\.\d]/g, '')
           .replace(/\./g, '');
       this.newReloadValue = Number(this.newReloadValue);
     },
+    // hardware reload setting
+    hardwareTimeReload(val) {
+      localStorage.isHardwareTimeReload = val;
+      this.$store.commit('setIsHardwareTimeReload', val);
+    },
+
+    editHardwareTime() {
+      this.isShowHardwareInp = true;
+    },
+
+    saveHardwareTimeValue() {
+      if (this.newHardwareReloadValue >= 0) {
+        this.newHardwareReloadValue =
+          this.newHardwareReloadValue < 3
+            ? 3
+            : this.newHardwareReloadValue > 300
+            ? 300
+            : this.newHardwareReloadValue;
+        const timeValue = this.newHardwareReloadValue;
+        this.hardwareTimeReloadValue = timeValue;
+        localStorage.hardwareTimeReloadValue = timeValue;
+        this.$store.commit('setHardwareTimeReloadValue', timeValue);
+        this.isShowHardwareInp = false;
+      } else {
+        this.cancelHardwareTimeValue();
+      }
+    },
+    cancelHardwareTimeValue() {
+      this.isShowHardwareInp = false;
+      this.newHardwareReloadValue = this.hardwareTimeReloadValue;
+    },
+    hardwareTimeValueChange() {
+      if (this.newHardwareReloadValue === '') {
+        return;
+      }
+      this.newHardwareReloadValue = this.newHardwareReloadValue
+          .toString()
+          .replace(/[^\.\d]/g, '')
+          .replace(/\./g, '');
+      this.newHardwareReloadValue = Number(this.newHardwareReloadValue);
+    },
+
     // get active menu item
     getActive() {
       const str = this.$route.path.split('/');
@@ -217,6 +307,13 @@ export default {
     .el-icon-close {
       color: #f56c6c;
     }
+    .el-input {
+      width: 45px;
+      input {
+        padding: 0;
+        text-align: center;
+      }
+    }
   }
 
   // reload style
@@ -232,16 +329,10 @@ export default {
       transform: rotate(1turn);
     }
   }
-  .cl-header-right .el-input {
-    width: 45px;
-    input {
-      padding: 0;
-      text-align: center;
-    }
-  }
+
   .cl-header-nav {
     margin-left: 50px;
-    flex: 1;
+    flex: 1.5;
 
     .el-menu {
       border-bottom: none;

mindinsight/ui/src/components/multiselectGroup.vue

@@ -164,25 +164,26 @@ export default {
     listSelectAll() {
       this.operateSelectAll = !this.operateSelectAll;
       this.multiSelectedItemNames = {};
-      this.selectedNumber = 0;
       // Setting the status of list items
       if (this.operateSelectAll) {
         if (this.isLimit) {
           const loopCount = this.checkListArr.length;
           for (let i = 0; i < loopCount; i++) {
-            if (this.selectedNumber >= this.limitNum) {
-              break;
-            }
             const listItem = this.checkListArr[i];
-            if (listItem.checked) {
-              this.selectedNumber++;
-              if (listItem.show) {
+            if (this.selectedNumber >= this.limitNum) {
+              if (listItem.checked && listItem.show) {
                 this.multiSelectedItemNames[listItem.label] = true;
               }
-            } else if (listItem.show) {
-              listItem.checked = true;
-              this.multiSelectedItemNames[listItem.label] = true;
-              this.selectedNumber++;
+            } else {
+              if (listItem.checked) {
+                if (listItem.show) {
+                  this.multiSelectedItemNames[listItem.label] = true;
+                }
+              } else if (listItem.show) {
+                listItem.checked = true;
+                this.multiSelectedItemNames[listItem.label] = true;
+                this.selectedNumber++;
+              }
             }
           }
         } else {

mindinsight/ui/src/locales/zh-cn.json

@@ -45,7 +45,8 @@
     "modelTraceback": "模型溯源",
     "dataTraceback": "数据溯源",
     "comparePlate": "对比看板",
-    "disableProfilerTip": "无profiler日志，无法查看性能分析"
+    "disableProfilerTip": "无profiler日志，无法查看性能分析",
+    "hardwareVisual": "硬件资源"
   },
   "modelTraceback": {
     "summaryPath": "训练日志路径",
@@ -99,7 +100,8 @@
     "samplingData": "数据抽样",
     "imagesampleSwitch": "切换标签",
     "invalidId": "无效的训练作业",
-    "summaryDirPath": "训练日志路径："
+    "summaryDirPath": "训练日志路径：",
+    "loadingTip": "加载中"
   },
   "scalar": {
     "titleText": "标量",
@@ -368,7 +370,7 @@
     "FPMessage": "前向起始算子：",
     "BPMessage": "反向终止算子：",
     "approximateTime": "总时长 ≈ ",
-    "stepInputTip": "请输入step值(1~{max}的正整数)",
+    "stepInputTip": "请输入step值(1~{max}的正整数，值为空时展示平均值)",
     "inputError": "输入参数异常，请输入一个1~{max}的正整数",
     "defaultTip": "默认展示平均值",
     "downloadTimeline": "下载",
@@ -384,7 +386,45 @@
       "title3": "如何使用时间线：",
       "content31": "您可以通过时间线信息分析流切分方法是否合理、迭代间隙和拖尾时间是否过长等；",
       "content32": "也可以具体定位到某个算子，查看分析它的执行时间。"
-    }
+    },
+    "unit": "ms/次"
+  },
+  "hardwareVisual": {
+    "processor": "昇腾AI处理器",
+    "ram": "内存",
+    "selectedCpu": "CPU-选中：",
+    "allCpu": "CPU-总计：",
+    "chipNameTip": "芯片名称",
+    "deviceIdTip": "芯片号",
+    "availableTip": "芯片是否空闲（仅供参考）",
+    "healthTip": "芯片健康指数",
+    "ipTip": "芯片IP地址",
+    "aicoreTip": "芯片利用率",
+    "hbmTip": "芯片已用HBM内存",
+    "powerTip": "芯片功耗",
+    "temperatureTip": "芯片温度",
+    "cpuUserTip": "运行于用户态的时间百分比",
+    "cpuSystemTip": "运行于内核态的时间百分比",
+    "cpuIdleTip": "处于空闲状态的时间百分比",
+    "cpuNiceTip": "运行低优先级进程的时间百分比",
+    "cpuIowaitTip": "等待IO的时间百分比",
+    "cpuIrqTip": "处理硬中断的时间百分比",
+    "cpuSoftirqTip": "处理软中断的时间百分比",
+    "cpuStealTip": "被其他虚拟机抢夺的时间百分比",
+    "cpuGuestTip": "运行虚拟机的时间百分比",
+    "cpuGuestniceTip": "运行低优先级虚拟机的时间百分比",
+    "cpuInterruptTip": "处理硬中断的时间百分比",
+    "cpuDpcTip": "远程调用的时间百分比",
+    "noNpuInfo": "暂无昇腾AI处理器信息",
+    "normal": "正常",
+    "generalWarn": "一般警告",
+    "importantWarn": "重要警告",
+    "emergencyWarn": "紧急警告",
+    "noChip": "芯片不存在或未启动",
+    "availableFree": "芯片空闲",
+    "availableBusy": "芯片已被占用或不可用",
+    "failQueryChip": "芯片信息查询有误",
+    "faliQuery": "查询有误"
   },
   "components": {
     "summaryTitle": "训练选择",
@@ -422,6 +462,7 @@
     "50542218": "筛选参数错误",
     "50545012": "张量数据不存在，请刷新。",
     "50545013": "请求的数据过大，请使用其他维度重试。",
-    "50545014": "查询的张量数据已被新数据替换，请刷新。"
+    "50545014": "查询的张量数据已被新数据替换，请刷新。",
+    "50548001": "昇腾AI处理器信息查询超时"
   }
-}
+}

mindinsight/ui/src/main.js

@@ -40,6 +40,9 @@ router.beforeEach((to, from, next) => {
   store.commit('setIsReload', false);
   next();
 });
+router.onError((error) => {
+  Vue.prototype.$message.error(i18n.messages[i18n.locale].public.netWorkError);
+});
 
 // forbidden showing production tip
 Vue.config.productionTip = false;

mindinsight/ui/src/router.js

@@ -102,5 +102,9 @@ export default new Router({
         },
       ],
     },
+    {
+      path: '/hardware-visual',
+      component: () => import('./views/train-manage/hardware-visual.vue'),
+    },
   ],
 });

mindinsight/ui/src/services/request-service.js

@@ -288,4 +288,10 @@ export default {
       },
     });
   },
+  getMetricsData() {
+    return axios({
+      method: 'get',
+      url: 'v1/mindinsight/sysmetric/current',
+    });
+  },
 };

mindinsight/ui/src/store.js

@@ -30,10 +30,20 @@ export default new Vuex.Store({
     timeReloadValue: localStorage.timeReloadValue
       ? localStorage.timeReloadValue
       : 3,
+    // Scheduled hardware reload flag
+    isHardwareTimeReload: localStorage.isHardwareTimeReload === 'false' ? false : true,
+    // hardware reload time
+    hardwareTimeReloadValue: localStorage.hardwareTimeReloadValue
+      ? localStorage.hardwareTimeReloadValue
+      : 5,
     // multiSelevtGroup component count
     multiSelectedGroupCount: 0,
     tableId: 0,
     componentsCount: 0,
+    summaryDirList: undefined,
+    selectedBarList: [],
+    hidenDirChecked: [],
+    customizedColumnOptions: [],
   },
   mutations: {
     // set cancelTokenArr
@@ -71,6 +81,13 @@ export default new Vuex.Store({
     setTimeReloadValue: (state, val) => {
       state.timeReloadValue = val;
     },
+    // set isHardwareTimeReload
+    setIsHardwareTimeReload: (state, val) => {
+      state.isHardwareTimeReload = val;
+    },
+    setHardwareTimeReloadValue: (state, val) => {
+      state.hardwareTimeReloadValue = val;
+    },
     multiSelectedGroupComponentNum(state) {
       state.multiSelectedGroupCount++;
     },

mindinsight/ui/src/views/train-manage/data-map.vue

@@ -172,7 +172,9 @@ export default {
       return;
     }
     this.trainJobID = this.$route.query.train_id;
-    document.title = `${decodeURIComponent(this.trainJobID)}-${this.$t('trainingDashboard.dataMap')}-MindInsight`;
+    document.title = `${decodeURIComponent(this.trainJobID)}-${this.$t(
+        'trainingDashboard.dataMap',
+    )}-MindInsight`;
     this.$nextTick(() => {
       this.queryGraphData();
     });
@@ -554,6 +556,8 @@ export default {
             const value =
               select[item] instanceof Array
                 ? select[item].join(', ')
+                : select[item] === null
+                ? 'None'
                 : select[item];
             this.selectedNode.push({key: item, value: value});
           }

mindinsight/ui/src/views/train-manage/data-traceback.vue

@@ -435,6 +435,14 @@ export default {
         'learning_rate',
         'device_num',
       ],
+      valueType: {
+        float: 'float',
+        int: 'int',
+        string: 'string',
+        model_size: 'model_size',
+        learning_rate: 'learning_rate',
+        dataset_mark: 'dataset_mark',
+      },
       table: {
         columnOptions: {
           summary_dir: {
@@ -964,10 +972,10 @@ export default {
             id: item,
             checked: true,
           };
-          if (value && value.type === 'float') {
-            obj.type = 'float';
-          } else if (value && value.type === 'int') {
-            obj.type = 'int';
+          if (value && value.type === this.valueType.float) {
+            obj.type = this.valueType.float;
+          } else if (value && value.type === this.valueType.int) {
+            obj.type = this.valueType.int;
           }
           arrayTemp.push(obj);
         });
@@ -1006,14 +1014,14 @@ export default {
           content.name === this.repeatTitle ||
           content.name === this.shuffleTitle ||
           content.id === this.deviceNum ||
-          (content.type && content.type === 'int')
+          (content.type && content.type === this.valueType.int)
         ) {
           obj.scale = true;
           obj.minInterval = 1;
           this.setColorOfSelectedBar(selectedBarList, obj);
         } else if (
           this.numberTypeIdList.includes(content.id) ||
-          (content.type && content.type === 'float')
+          (content.type && content.type === this.valueType.float)
         ) {
           obj.scale = true;
           this.setColorOfSelectedBar(selectedBarList, obj);
@@ -1024,7 +1032,7 @@ export default {
             show: false,
           };
           this.setColorOfSelectedBar(selectedBarList, obj);
-          if (content.id === 'dataset_mark') {
+          if (content.id === this.valueType.dataset_mark) {
             obj.axisLabel = {
               show: false,
             };
@@ -1073,13 +1081,16 @@ export default {
       if (this.parallelEchart) {
         this.parallelEchart.off('axisareaselected', null);
         window.removeEventListener('resize', this.resizeChart, false);
+      } else {
+        this.parallelEchart = Echarts.init(
+            document.querySelector('#data-echart'),
+        );
       }
-      this.parallelEchart = Echarts.init(
-          document.querySelector('#data-echart'),
-      );
       this.parallelEchart.setOption(option, true);
       window.addEventListener('resize', this.resizeChart, false);
-
+      this.chartEventsListen(parallelAxis);
+    },
+    chartEventsListen(parallelAxis) {
       this.parallelEchart.on('axisareaselected', (params) => {
         this.recordsNumber = 0;
         this.showNumber = 0;
@@ -1154,12 +1165,14 @@ export default {
             }
             const strs = val.split('');
             let str = '';
-            if (val.length > 100) {
-              return val.substring(0, 12) + '...';
+            const maxStringLength = 100;
+            const showStringLength = 12;
+            if (val.length > maxStringLength) {
+              return val.substring(0, showStringLength) + '...';
             } else {
               for (let i = 0, s = ''; (s = strs[i++]); ) {
                 str += s;
-                if (!(i % 12)) {
+                if (!(i % showStringLength)) {
                   str += '\n';
                 }
               }
@@ -1204,20 +1217,25 @@ export default {
       if (isNaN(value) || !value) {
         return value;
       } else {
-        if (key === 'learning_rate') {
-          let temp = value.toPrecision(4);
+        const numDigits = 4;
+        if (key === this.valueType.learning_rate) {
+          let temp = value.toPrecision(numDigits);
           let row = 0;
           while (temp < 1) {
             temp = temp * 10;
             row += 1;
           }
-          temp = this.toFixedFun(temp, 4);
+          temp = this.toFixedFun(temp, numDigits);
           return `${temp}${row ? `e-${row}` : ''}`;
-        } else if (key === 'model_size') {
+        } else if (key === this.valueType.model_size) {
           return value + 'MB';
         } else {
-          if (value < 1000) {
-            return Math.round(value * Math.pow(10, 4)) / Math.pow(10, 4);
+          const num = 1000;
+          if (value < num) {
+            return (
+              Math.round(value * Math.pow(10, numDigits)) /
+              Math.pow(10, numDigits)
+            );
           } else {
             const reg = /(?=(\B)(\d{3})+$)/g;
             return (value + '').replace(reg, ',');
@@ -1245,7 +1263,8 @@ export default {
      * @param {Object} scope
      */
     showDialogData(val, scope) {
-      if (typeof val !== 'string' || val === '{}') {
+      const emptyObjectStr = '{}';
+      if (typeof val !== this.valueType.string || val === emptyObjectStr) {
         return;
       } else {
         const isJson = this.isJSON(val);
@@ -1541,7 +1560,7 @@ export default {
     hideDataMarkTableData() {
       const result = [];
       this.selectedBarList.forEach((item) => {
-        if (item !== 'dataset_mark') {
+        if (item !== this.valueType.dataset_mark) {
           result.push(item);
         }
       });
@@ -1906,10 +1925,6 @@ export default {
 .el-color-alpha-slider {
   display: none;
 }
-.el-select > .el-input {
-  width: 280px !important;
-  max-width: 500px !important;
-}
 .select-inner-input {
   width: calc(100% - 140px);
   margin: 2px 4px;
@@ -1958,6 +1973,10 @@ export default {
   height: 100%;
   overflow-y: auto;
   position: relative;
+  .el-select > .el-input {
+    width: 280px !important;
+    max-width: 500px !important;
+  }
   .el-table th.is-leaf {
     background: #f5f7fa;
   }

mindinsight/ui/src/views/train-manage/graph.vue

@@ -467,7 +467,7 @@ export default {
         show: false,
         info: '',
       },
-      scaleRange: [0.0001, 10000], // graph zooms in and zooms out.
+      scaleRange: [0.001, 1000], // graph zooms in and zooms out.
       rightShow: true, // Check whether the right side bar is displayed.
       fullScreen: false, // Display Full Screen
       totalMemory: 16777216 * 2, // Memory size of the graph plug-in
@@ -583,29 +583,32 @@ export default {
      * @param {String} dot dot statement encapsulated in graph data
      */
     initGraph(dot) {
-      this.graphviz = d3
-          .select('#graph')
-          .graphviz({useWorker: false, totalMemory: this.totalMemory})
-          .zoomScaleExtent(this.scaleRange)
-          .dot(dot)
-          .attributer(this.attributer)
-          .render(() => {
-            this.initSvg();
-            this.afterInitGraph();
-          });
+      try {
+        this.graphviz = d3
+            .select('#graph')
+            .graphviz({useWorker: false, totalMemory: this.totalMemory})
+            .zoomScaleExtent(this.scaleRange)
+            .dot(dot)
+            .attributer(this.attributer)
+            .render(() => {
+              this.initSvg();
+              this.afterInitGraph();
+            });
+      } catch (error) {
+        const svg = document.querySelector('#graph svg');
+        if (svg) {
+          svg.remove();
+        }
+        this.initGraph(dot);
+      }
+
       // Generate the dom of the submap.
       if (!d3.select('#graphTemp').size()) {
-        d3.select('body')
-            .append('div')
-            .attr('id', 'graphTemp')
-            .attr('style', 'visibility: collapse');
+        d3.select('body').append('div').attr('id', 'graphTemp');
       }
       // Stores the dom of all the sorted subgraphs.
       if (!d3.select('#subgraphTemp').size()) {
-        d3.select('body')
-            .append('div')
-            .attr('id', 'subgraphTemp')
-            .attr('style', 'visibility: collapse');
+        d3.select('body').append('div').attr('id', 'subgraphTemp');
       }
     },
     initSvg() {
@@ -654,10 +657,7 @@ export default {
       this.$nextTick(() => {
         this.loading.show = false;
       });
-      const elements = d3
-          .select('#graph')
-          .selectAll('g.node, g.edge')
-          .nodes();
+      const elements = d3.select('#graph').selectAll('g.node, g.edge').nodes();
       elements.forEach((ele) => {
         if (!ele.hasAttribute('transform')) {
           ele.setAttribute('transform', 'translate(0,0)');
@@ -728,6 +728,7 @@ export default {
               .parentNode.id;
           name = parentId.replace('_unfold', '');
           this.allGraphData[name].index += changePage;
+          this.selectedNode.name = name;
         }
         if (unfoldFlag) {
           this.dealDoubleClick(name);
@@ -928,26 +929,39 @@ export default {
      * @param {Boolean} toUnfold Expand the namespace.
      */
     layoutNamescope(name, toUnfold) {
-      setTimeout(() => {
+      this.$nextTick(() => {
         const dotStr = this.packageNamescope(name);
-        this.graphvizTemp = d3
-            .select('#graphTemp')
-            .graphviz({useWorker: false, totalMemory: this.totalMemory})
-            .dot(dotStr)
-            .zoomScaleExtent(this.scaleRange)
-            .attributer((datum, index, nodes) => {
-              if (
-                datum.tag === 'polygon' &&
-              datum.attributes.stroke !== 'transparent'
-              ) {
-                datum.attributes.stroke = 'rgb(167, 167, 167)';
-              }
-            })
-            .render(() => {
-              this.fitGraph('graphTemp');
-              this.dealNamescopeTempGraph(name);
-            });
-      }, 20);
+        try {
+          this.graphvizTemp = d3
+              .select('#graphTemp')
+              .graphviz({useWorker: false, totalMemory: this.totalMemory})
+              .dot(dotStr)
+              .zoomScaleExtent(this.scaleRange)
+              .attributer((datum, index, nodes) => {
+                if (
+                  datum.tag === 'polygon' &&
+                datum.attributes.stroke !== 'transparent'
+                ) {
+                  datum.attributes.stroke = 'rgb(167, 167, 167)';
+                }
+              })
+              .render(() => {
+                this.fitGraph('graphTemp');
+                this.dealNamescopeTempGraph(name);
+              });
+        } catch (error) {
+          const graphTempSvg = document.querySelector('#graphTemp svg');
+          if (graphTempSvg) {
+            graphTempSvg.remove();
+          }
+          const subGraphTempSvg = document.querySelector('#subgraphTemp svg');
+          if (subGraphTempSvg) {
+            subGraphTempSvg.remove();
+          }
+
+          this.dealDoubleClick(this.selectedNode.name);
+        }
+      });
     },
     /**
      * To obtain graph data, initialize and expand the namespace or aggregate nodes.
@@ -1665,10 +1679,7 @@ export default {
           .attr('width', g.node().getBBox().width + this.frameSpace * 2)
           .attr('height', g.node().getBBox().height + this.frameSpace * 2);
 
-      boxTemp = d3
-          .select(`${idStr}g[id="${name}_unfold"]`)
-          .node()
-          .getBBox();
+      boxTemp = d3.select(`${idStr}g[id="${name}_unfold"]`).node().getBBox();
       // After the namespace dom is successfully encapsulated, set the related data of the data object.
       this.allGraphData[name].isUnfold = true;
       this.allGraphData[name].size = [boxTemp.width / 72, boxTemp.height / 72];
@@ -1680,8 +1691,9 @@ export default {
         const node = document.querySelector(`#graphTemp g[id="${name}"]`);
         const box = node.getBBox();
         const boxTemp = nodeTemp.getBBox();
-        const translateStr = `translate(${box.x - boxTemp.x},${box.y -
-          boxTemp.y})`;
+        const translateStr = `translate(${box.x - boxTemp.x},${
+          box.y - boxTemp.y
+        })`;
         nodeTemp.setAttribute('transform', translateStr);
         node.parentNode.appendChild(nodeTemp);
         document.querySelector('#subgraphTemp svg').remove();
@@ -1732,8 +1744,9 @@ export default {
           if (node && nodeTemp) {
             const box = node.getBBox();
             const boxTemp = nodeTemp.getBBox();
-            const translateStr = `translate(${box.x - boxTemp.x},${box.y -
-              boxTemp.y})`;
+            const translateStr = `translate(${box.x - boxTemp.x},${
+              box.y - boxTemp.y
+            })`;
             nodeTemp.setAttribute('transform', translateStr);
             node.parentNode.appendChild(nodeTemp);
             node.remove();
@@ -1809,10 +1822,7 @@ export default {
         this.loading.show = true;
       }
       if (name.includes('/')) {
-        const subPath = name
-            .split('/')
-            .slice(0, -1)
-            .join('/');
+        const subPath = name.split('/').slice(0, -1).join('/');
         this.layoutNamescope(subPath, true);
       } else {
         const svg = document.querySelector('#graph svg');
@@ -2029,9 +2039,13 @@ export default {
           selectedNode.type === 'name_scope' ||
           selectedNode.type === 'aggregation_scope';
         this.selectedNode.count = selectedNode.subnode_count;
-        this.selectedNode.info.attributes = JSON.parse(
-            JSON.stringify(selectedNode.attr),
-        );
+        const attrTemp = JSON.parse(JSON.stringify(selectedNode.attr || {}));
+        this.selectedNode.info.attributes = Object.keys(attrTemp).map((key) => {
+          return {
+            name: key,
+            value: attrTemp[key],
+          };
+        });
 
         Object.keys(selectedNode.input).forEach((key) => {
           const value = this.getEdgeLabel(selectedNode.input[key]);
@@ -2288,10 +2302,7 @@ export default {
       if (subPsth && this.allGraphData[subPsth]) {
         // The virtual node and its subnodes need to return their namespaces.
         if (this.allGraphData[subPsth].independent_layout) {
-          subPsth = subPsth
-              .split('/')
-              .slice(0, -1)
-              .join('/');
+          subPsth = subPsth.split('/').slice(0, -1).join('/');
         }
       }
       return subPsth;
@@ -2615,7 +2626,7 @@ export default {
         this.insideBox.height = this.smallResize.height;
         this.insideBox.top = this.insideBox.left = 0;
         this.styleSet('#inside-box', this.insideBox);
-        insideBox.style.cursor = 'not-allowed';
+        this.insideBox.style.cursor = 'not-allowed';
       } else {
         let transformString = '';
         const transTemp = this.graph.dom.attributes.transform || null;
@@ -2753,8 +2764,9 @@ export default {
         `<svg xmlns="http://www.w3.org/2000/svg" ` +
         `xmlns:xlink="http://www.w3.org/1999/xlink" width="100%" height="100%" ` +
         `viewBox="0.00 0.00 ${this.svg.originSize.width} ${this.svg.originSize.height}"` +
-        `><g id="smallGraph" class="graph" transform="translate(4,${this.svg
-            .originSize.height - 4}) scale(1)"` +
+        `><g id="smallGraph" class="graph" transform="translate(4,${
+          this.svg.originSize.height - 4
+        }) scale(1)"` +
         `>${this.graph.dom.innerHTML}</g></svg>`;
 
       smallMap.innerHTML = svgOuterHtml;
@@ -3489,4 +3501,9 @@ export default {
     padding-right: 32px;
   }
 }
+#graphTemp,
+#subgraphTemp {
+  position: absolute;
+  bottom: 0;
+}
 </style>

mindinsight/ui/src/views/train-manage/hardware-visual.vue

@@ -0,0 +1,981 @@
+<!--
+Copyright 2020 Huawei Technologies Co., Ltd.All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<template>
+  <div class="cl-hardware-visual">
+    <div class="cl-hardware-content"
+         v-if="!(chipTableData.length === 0 && cpuList.length===0)">
+      <div class="cl-hardware-top"
+           v-if="chipTableData.length">
+        <div class="cl-hardware-left">
+          <div class="cl-sub-title"
+               v-if="chipTableData.length">
+            {{$t('hardwareVisual.processor')}}
+          </div>
+          <div class="cl-chip-wrap">
+            <el-table v-if="chipTableData.length"
+                      :data="chipTableData"
+                      width="100%"
+                      height="100%"
+                      :row-class-name="tableRowClassName">
+              <el-table-column width="120">
+                <template slot="header">
+                  <span class="cl-text-center">
+                    Name
+                    <el-tooltip class="item"
+                                effect="light"
+                                :content="$t('hardwareVisual.chipNameTip')"
+                                placement="top-start">
+                      <i class="el-icon-info"></i>
+                    </el-tooltip>
+                  </span>
+                </template>
+                <template slot-scope="scope">
+                  <span class="cl-text-center">{{ scope.row.chip_name }}</span>
+                </template>
+              </el-table-column>
+              <el-table-column width="80">
+                <template slot="header">
+                  <span class="cl-text-center">
+                    NPU
+                    <el-tooltip class="item"
+                                effect="light"
+                                :content="$t('hardwareVisual.deviceIdTip')"
+                                placement="top-start">
+                      <i class="el-icon-info"></i>
+                    </el-tooltip>
+                  </span>
+                </template>
+                <template slot-scope="scope">
+                  <span class="cl-text-center">{{ scope.row.device_id }}</span>
+                </template>
+              </el-table-column>
+              <el-table-column width="110">
+                <template slot="header">
+                  <span class="cl-text-center">
+                    Available
+                    <el-tooltip class="item"
+                                effect="light"
+                                :content="$t('hardwareVisual.availableTip')"
+                                placement="top-start">
+                      <i class="el-icon-info"></i>
+                    </el-tooltip>
+                  </span>
+                </template>
+                <template slot-scope="scope">
+                  <span class="cl-text-center">
+                    <i class="el-icon-success"
+                       v-if="scope.row.available"
+                       :title="$t('hardwareVisual.availableFree')"></i>
+                    <i class="el-icon-question"
+                       :title="$t('hardwareVisual.availableBusy')"
+                       v-else></i>
+                  </span>
+                </template>
+              </el-table-column>
+              <el-table-column width="80">
+                <template slot="header">
+                  <span class="cl-text-center">
+                    Health
+                    <el-tooltip class="item"
+                                effect="light"
+                                :content="$t('hardwareVisual.healthTip')"
+                                placement="top-start">
+                      <i class="el-icon-info"></i>
+                    </el-tooltip>
+                  </span>
+                </template>
+                <template slot-scope="scope">
+                  <span class="cl-text-center">
+                    <i class="el-icon-success"
+                       v-if="scope.row.health===0"
+                       :title="$t('hardwareVisual.normal')"></i>
+                    <i class="el-icon-warning normal"
+                       v-if="scope.row.health===1"
+                       :title="$t('hardwareVisual.generalWarn')"></i>
+                    <i class="el-icon-warning important"
+                       v-if="scope.row.health===2"
+                       :title="$t('hardwareVisual.importantWarn')"></i>
+                    <i class="el-icon-warning emergency"
+                       v-if="scope.row.health===3"
+                       :title="$t('hardwareVisual.emergencyWarn')"></i>
+                    <i class="el-icon-remove"
+                       v-if="scope.row.health=== 0xffffffff"
+                       :title="$t('hardwareVisual.noChip')"></i>
+                  </span>
+                </template>
+              </el-table-column>
+              <el-table-column width="130">
+                <template slot="header">
+                  <span class="cl-text-center">
+                    IP Address
+                    <el-tooltip class="item"
+                                effect="light"
+                                :content="$t('hardwareVisual.ipTip')"
+                                placement="top-start">
+                      <i class="el-icon-info"></i>
+                    </el-tooltip>
+                  </span>
+                </template>
+                <template slot-scope="scope">
+                  <span class="cl-text-center">{{ scope.row.ip_address }}</span>
+                </template>
+              </el-table-column>
+              <el-table-column prop="aicore">
+                <template slot="header">
+                  AI Core(%)
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="$t('hardwareVisual.aicoreTip')"
+                              placement="top-start">
+                    <i class="el-icon-info"></i>
+                  </el-tooltip>
+                </template>
+                <template slot-scope="scope">
+                  <div class="core-wrap">
+                    <el-progress :percentage="scope.row.aicore_rate===-1?0:scope.row.aicore_rate"
+                                 :format="format(scope.row.aicore_rate)"></el-progress>
+                  </div>
+                </template>
+
+              </el-table-column>
+              <el-table-column prop="hbm_usage"
+                               min-width="100">
+                <template slot="header">
+                  HBM-Usage(MB)
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="$t('hardwareVisual.hbmTip')"
+                              placement="top-start">
+                    <i class="el-icon-info"></i>
+                  </el-tooltip>
+                </template>
+                <template slot-scope="scope">
+                  <div class="hbs-wrap">
+                    <el-progress :percentage="scope.row.hbm_info.memory_size?
+                    parseInt(scope.row.hbm_info.memory_usage/scope.row.hbm_info.memory_size*100):0"
+                                 :format="formatHbm(scope.row.hbm_info)"></el-progress>
+                  </div>
+                </template>
+              </el-table-column>
+              <el-table-column prop="power">
+                <template slot="header">
+                  Power(W)
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="$t('hardwareVisual.powerTip')"
+                              placement="top-start">
+                    <i class="el-icon-info"></i>
+                  </el-tooltip>
+                </template>
+                <template slot-scope="scope">
+                  <div class="power-wrap">
+                    <div class="power"
+                         :style="{width:`${scope.row.power/powerMax*100}%`}">{{scope.row.power}}</div>
+                  </div>
+                </template>
+
+              </el-table-column>
+              <el-table-column prop="temp"
+                               width="150">
+                <template slot="header">
+                  Temp(℃)
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="$t('hardwareVisual.temperatureTip')"
+                              placement="top-start">
+                    <i class="el-icon-info"></i>
+                  </el-tooltip>
+                </template>
+                <template slot-scope="scope">
+                  <div class="temp-wrap">
+                    <div class="circle"
+                         :class="{zero:!scope.row.temperature}"></div>
+                    <div class="process-wrap">
+                      <div class="process-cover"
+                           :style="{width:temperatureMax?scope.row.temperature/temperatureMax*100+'%':0}"></div>
+                    </div>
+                    <span>{{scope.row.temperature}}</span>
+                  </div>
+                </template>
+              </el-table-column>
+            </el-table>
+            <div class="image-noData"
+                 v-if="chipTableData.length === 0">
+              <div>
+                <img :src="require('@/assets/images/nodata.png')"
+                     alt="" />
+              </div>
+              <p>{{$t("hardwareVisual.noNpuInfo")}}</p>
+            </div>
+          </div>
+        </div>
+      </div>
+      <div class="cl-hardware-bottom"
+           :class="{noNpu:!chipTableData.length}">
+        <div class="cl-hardware-left">
+          <div class="cl-sub-title">
+            CPU
+          </div>
+          <div class="cl-cpu-wrap">
+            <div class="cpu-items">
+              <div class="cpu-item"
+                   v-for="(item,key) in cpuList"
+                   :key="key">
+                <div class="cpu"
+                     :class="{selected:item.selected}"
+                     :style="{backgroundColor:item.idle!==undefined?
+                     `rgba(250,152,65,${(100-item.idle).toFixed(2)/100}`:'#ccc'}"
+                     :title="item.idle!==undefined?`Core ${key}`:''"
+                     @click="viewPerCpuInfo(key)">
+                  {{ item.idle!==undefined?(100-item.idle).toFixed(2):'' }}
+                </div>
+              </div>
+            </div>
+            <div class="cpu-detail">
+              <div class="all-cpu-info">
+                <span>{{$t('hardwareVisual.allCpu')}}</span>
+                <div class="info-item"
+                     v-for="(item,index) in overallCpuInfo"
+                     :key="index">
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="item.tips"
+                              placement="top-start">
+                    <span>
+                      <span class="label">{{item.label}}</span>
+                      <span class="value">{{`${item.value}%`}}</span>
+                    </span>
+                  </el-tooltip>
+                </div>
+              </div>
+              <div class="selected-cpu-info"
+                   v-if="selectedCpuIndex!==null">
+                <span>{{$t('hardwareVisual.selectedCpu')}}</span>
+                <div class="info-item"
+                     v-for="(item,index) in selectedCpuInfo"
+                     :key="index">
+                  <el-tooltip class="item"
+                              effect="light"
+                              :content="item.tips"
+                              placement="top-start">
+                    <span>
+                      <span class="label">{{item.label}}</span>
+                      <span class="value">{{`${item.value}%`}}</span>
+                    </span>
+                  </el-tooltip>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+        <div class="cl-hardware-right">
+          <div class="cl-sub-title ram">
+            {{$t('hardwareVisual.ram')}}
+          </div>
+          <div class="cl-ram-wrap">
+            <div class="virtual-wrap">
+              <div id="virtual"></div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+    <div class="image-noData"
+         v-if="chipTableData.length === 0 && cpuList.length===0 && initOver">
+      <div>
+        <img :src="require('@/assets/images/nodata.png')"
+             alt="" />
+      </div>
+      <p>{{$t("public.noData")}}</p>
+    </div>
+  </div>
+</template>
+
+<script>
+import echarts from 'echarts';
+import RequestService from '../../services/request-service';
+export default {
+  data() {
+    return {
+      chipTableData: [],
+      powerMax: null,
+      temperatureMax: null,
+      virtualChart: {
+        id: 'virtual',
+        chartDom: null,
+        data: [],
+        legend: [],
+        totalValue: null,
+      },
+      defaultCpuNum: 96,
+      cpuList: [],
+      overallCpuInfo: [],
+      selectedCpuInfo: [],
+      selectedCpuIndex: null,
+      pieColorArr: ['#5e7ce0', '#ccc', '#a6dd82'],
+      autoUpdateTimer: null, // Automatic refresh timer
+      isReloading: false, // Manually refresh
+      legendSelected: {},
+      initOver: false,
+    };
+  },
+  computed: {
+    /**
+     * Global refresh switch
+     * @return {Boolean}
+     */
+    isReload() {
+      return this.$store.state.isReload;
+    },
+    /**
+     * Automatic hardware refresh switch
+     * @return {Boolean}
+     */
+    isHardwareTimeReload() {
+      return this.$store.state.isHardwareTimeReload;
+    },
+    /**
+     * Automatic hardware refresh value
+     * @return {Boolean}
+     */
+    hardwareTimeReloadValue() {
+      return this.$store.state.hardwareTimeReloadValue;
+    },
+  },
+  watch: {
+    /**
+     * Global refresh switch Listener
+     * @param {Boolean} newVal Value After Change
+     * @param {Boolean} oldVal Value Before Change
+     */
+    isReload(newVal, oldVal) {
+      if (newVal) {
+        this.isReloading = true;
+        if (this.isHardwareTimeReload) {
+          this.autoUpdateSamples();
+        }
+        this.init();
+      }
+    },
+    /**
+     * Automatic refresh switch Listener
+     * @param {Boolean} newVal Value After Change
+     * @param {Boolean} oldVal Value Before Change
+     */
+    isHardwareTimeReload(newVal, oldVal) {
+      if (newVal) {
+        this.autoUpdateSamples();
+      } else {
+        this.stopUpdateSamples();
+      }
+    },
+    /**
+     * The refresh time is changed.
+     */
+    hardwareTimeReloadValue() {
+      this.autoUpdateSamples();
+    },
+  },
+  destroyed() {
+    // Disable the automatic refresh function
+    if (this.autoUpdateTimer) {
+      clearInterval(this.autoUpdateTimer);
+      this.autoUpdateTimer = null;
+    }
+    // Stop Refreshing
+    if (this.isReloading) {
+      this.$store.commit('setIsReload', false);
+      this.isReloading = false;
+    }
+  },
+  mounted() {
+    document.title = this.$t('summaryManage.hardwareVisual') + '-MindInsight';
+    // Automatic refresh
+    if (this.isHardwareTimeReload) {
+      this.autoUpdateSamples();
+    }
+    this.init();
+  },
+
+  methods: {
+    /**
+     * Initialization data
+     */
+    init() {
+      RequestService.getMetricsData().then(
+          (res) => {
+            this.initOver = true;
+            if (this.isReloading) {
+              this.$store.commit('setIsReload', false);
+              this.isReloading = false;
+            }
+            if (res && res.data) {
+              this.chipTableData = res.data.npu || [];
+              if (this.chipTableData.length === 0) {
+                this.defaultCpuNum = 192;
+              }
+              this.powerMax =
+              Math.max(...this.chipTableData.map((val) => val.power)) * 1.2;
+              this.temperatureMax =
+              Math.max(...this.chipTableData.map((val) => val.temperature)) *
+              1.2;
+              // 1.2 In order to Demonstrated effect
+              if (res.data.memory && res.data.memory.virtual) {
+                this.dealChartData(this.virtualChart, res.data.memory.virtual);
+                this.setOption(this.virtualChart);
+              }
+              if (res.data.cpu) {
+                const overall = res.data.cpu.overall || {};
+                this.overallCpuInfo = Object.keys(overall).map((val) => {
+                  return {
+                    label: val,
+                    value: overall[val],
+                  };
+                });
+                this.addtips(this.overallCpuInfo);
+                this.cpuList = (res.data.cpu.percpu || []).map((val) => {
+                  return {...val, selected: false};
+                });
+                while (this.cpuList.length < this.defaultCpuNum) {
+                  this.cpuList.push({});
+                }
+                if (this.selectedCpuIndex !== null) {
+                  this.viewPerCpuInfo(this.selectedCpuIndex);
+                } else {
+                  this.selectedCpuInfo = [];
+                }
+                this.$nextTick(() => {
+                  const doms = document.querySelectorAll('.fail-row');
+                  if (doms) {
+                    for (let i = 0; i < doms.length; i++) {
+                      doms[i].setAttribute(
+                          'title',
+                          this.$t('hardwareVisual.failQueryChip'),
+                      );
+                    }
+                  }
+                });
+              }
+            }
+          },
+          (err) => {
+            this.chipTableData = [];
+            this.cpuList = [];
+            this.initOver = true;
+            if (this.isReloading) {
+              this.$store.commit('setIsReload', false);
+              this.isReloading = false;
+            }
+          },
+      );
+    },
+    tableRowClassName({row, rowIndex}) {
+      if (!row.success) {
+        return 'fail-row';
+      }
+      return '';
+    },
+    /**
+     * add tips
+     * @param {Array} arr cpu Info
+     */
+    addtips(arr) {
+      arr.forEach((val) => {
+        switch (val.label) {
+          case 'user':
+            val.tips = this.$t('hardwareVisual.cpuUserTip');
+            break;
+          case 'nice':
+            val.tips = this.$t('hardwareVisual.cpuNiceTip');
+            break;
+          case 'system':
+            val.tips = this.$t('hardwareVisual.cpuSystemTip');
+            break;
+          case 'idle':
+            val.tips = this.$t('hardwareVisual.cpuIdleTip');
+            break;
+          case 'iowait':
+            val.tips = this.$t('hardwareVisual.cpuIowaitTip');
+            break;
+          case 'irq':
+            val.tips = this.$t('hardwareVisual.cpuIrqTip');
+            break;
+          case 'softirq':
+            val.tips = this.$t('hardwareVisual.cpuSoftirqTip');
+            break;
+          case 'steal':
+            val.tips = this.$t('hardwareVisual.cpuStealTip');
+            break;
+          case 'guest':
+            val.tips = this.$t('hardwareVisual.cpuGuestTip');
+            break;
+          case 'guest_nice':
+            val.tips = this.$t('hardwareVisual.cpuGuestniceTip');
+            break;
+          case 'interrupt':
+            val.tips = this.$t('hardwareVisual.cpuInterruptTip');
+            break;
+          case 'dpc':
+            val.tips = this.$t('hardwareVisual.cpuDpcTip');
+            break;
+        }
+      });
+    },
+    /**
+     * View the information of each cpu
+     * @param {Number} index index
+     */
+    viewPerCpuInfo(index) {
+      this.cpuList.forEach((val, key) => {
+        if (val.idle !== undefined) {
+          if (index === key) {
+            this.selectedCpuIndex = key;
+            val.selected = !val.selected;
+            if (val.selected) {
+              this.selectedCpuInfo = Object.keys(this.cpuList[index]).map(
+                  (val) => {
+                    return {
+                      label: val,
+                      value: this.cpuList[index][val],
+                    };
+                  },
+              );
+              this.selectedCpuInfo.pop();
+            } else {
+              this.selectedCpuIndex = null;
+              this.selectedCpuInfo = [];
+            }
+          } else {
+            if (this.cpuList[index].idle !== undefined) {
+              val.selected = false;
+            }
+          }
+        }
+      });
+      this.addtips(this.selectedCpuInfo);
+    },
+    /**
+     * Handling pie chart data
+     * @param {Object} chart chart obejct
+     * @param {Object} data chart data
+     */
+    dealChartData(chart, data) {
+      if (data.others === 0) {
+        chart.legend = ['used', 'available'];
+      } else {
+        chart.legend = ['used', 'others', 'available'];
+      }
+      chart.data = chart.legend.map((val) => {
+        return {
+          value: data[val],
+          name: val,
+        };
+      });
+      chart.totalValue = 0;
+      chart.data.forEach((val) => {
+        chart.totalValue += val.value;
+      });
+    },
+    /**
+     * Data unit conversion
+     * @param {Number} n chart obejct
+     * @param {Boolean} type format type
+     * @return {String}
+     */
+    bytesHuman(n, type) {
+      const symbols = 'KMG'
+          .split('')
+          .map((symbol, index) => [symbol, 1 << ((index + 1) * 10)]);
+      for (const [symbol, prefix] of symbols.reverse()) {
+        if (n >= prefix) {
+          if (type) {
+            return `${n}(${(n / prefix).toFixed(1)}${symbol})`;
+          } else {
+            return `${(n / prefix).toFixed(1)}${symbol}`;
+          }
+        }
+      }
+      return `${n}`;
+    },
+    format(percentage, item) {
+      return () => {
+        return percentage === -1
+          ? this.$t('hardwareVisual.faliQuery')
+          : `${percentage}`;
+      };
+    },
+    formatHbm(hbmInfo) {
+      return function() {
+        return `${hbmInfo.memory_usage}/${hbmInfo.memory_size}`;
+      };
+    },
+    /**
+     * Enable automatic hardware refresh
+     */
+    autoUpdateSamples() {
+      if (this.autoUpdateTimer) {
+        clearInterval(this.autoUpdateTimer);
+        this.autoUpdateTimer = null;
+      }
+      this.autoUpdateTimer = setInterval(() => {
+        this.$store.commit('clearToken');
+        this.init();
+      }, this.hardwareTimeReloadValue * 1000);
+    },
+    /**
+     * Disable automatic refresh
+     */
+    stopUpdateSamples() {
+      if (this.autoUpdateTimer) {
+        clearInterval(this.autoUpdateTimer);
+        this.autoUpdateTimer = null;
+      }
+    },
+    setOption(chart) {
+      const option = {
+        tooltip: {
+          trigger: 'item',
+          formatter: (params) => {
+            return `${params.name}<br>
+            ${params.marker}${this.bytesHuman(params.value, true)}`;
+          },
+          confine: true,
+        },
+        legend: {
+          orient: 'vertical',
+          left: '50%',
+          top: '35%',
+          icon: 'circle',
+          data: chart.legend,
+          formatter: (params) => {
+            let legendStr = '';
+            for (let i = 0; i < chart.data.length; i++) {
+              if (chart.data[i].name === params) {
+                const name = chart.data[i].name;
+                legendStr = `{a|${this.bytesHuman(
+                    chart.data[i].value,
+                    true,
+                )}}\n{b|${name}}`;
+              }
+            }
+            return legendStr;
+          },
+          selected: this.legendSelected,
+          textStyle: {
+            rich: {
+              a: {
+                fontSize: 14,
+              },
+              b: {
+                color: '#aeb2bf',
+              },
+            },
+          },
+        },
+        series: [
+          {
+            name: '',
+            center: ['25%', '50%'],
+            type: 'pie',
+            radius: this.chipTableData.length ? ['40%', '60%'] : ['30%', '40%'],
+            avoidLabelOverlap: false,
+            label: {
+              show: true,
+              formatter: () => {
+                return `{a|${this.bytesHuman(chart.totalValue)}}{b|All}`;
+              },
+              position: 'center',
+              textStyle: {
+                rich: {
+                  a: {
+                    fontSize: 20,
+                    color: '#000',
+                  },
+                  b: {
+                    color: '#aeb2bf',
+                  },
+                },
+              },
+            },
+            labelLine: {
+              show: false,
+            },
+            data: chart.data,
+            itemStyle: {
+              normal: {
+                color: (params) => {
+                  return this.pieColorArr[params.dataIndex];
+                },
+              },
+            },
+          },
+        ],
+      };
+      this.$nextTick(() => {
+        const cpuDom = document.getElementById(chart.id);
+        if (cpuDom) {
+          chart.chartDom = echarts.init(cpuDom, null);
+          chart.chartDom.setOption(option, true);
+          chart.chartDom.resize();
+          chart.chartDom.on('legendselectchanged', (obj) => {
+            this.legendSelected = obj.selected;
+          });
+        }
+      });
+    },
+  },
+};
+</script>
+<style lang="scss" >
+.cl-hardware-visual {
+  height: 100%;
+  background-color: #fff;
+
+  .cl-hardware-content {
+    height: 100%;
+    padding: 0 24px 24px 24px;
+    .cl-hardware-top {
+      height: calc(100% - 372px);
+      padding-top: 16px;
+      & > div {
+        width: 100%;
+        .cl-text-center {
+          display: inline-block;
+          text-align: center;
+          width: 100%;
+        }
+        .el-table::before {
+          height: 0px;
+        }
+      }
+    }
+    .cl-hardware-bottom {
+      height: 360px;
+      .cl-hardware-left {
+        width: calc(100% - 466px);
+        margin-right: 16px;
+      }
+      .cl-hardware-right {
+        width: 450px;
+      }
+    }
+    & > div {
+      height: calc(50% - 8px);
+      margin-bottom: 16px;
+      & > div {
+        float: left;
+        height: 100%;
+        border: 1px solid #eee;
+        border-radius: 4px;
+        padding: 16px;
+        .cl-sub-title {
+          font-weight: bold;
+          font-size: 16px;
+          margin-bottom: 15px;
+        }
+        .cl-sub-title.ram {
+          margin-bottom: 10px;
+        }
+        .cl-chip-wrap {
+          height: calc(100% - 36px);
+          overflow: auto;
+          .el-icon-question::before {
+            color: #f06281;
+          }
+          .el-icon-success:before {
+            color: #57d7ac;
+          }
+          .el-icon-error:before {
+            color: #e37783;
+          }
+          .el-icon-warning.normal:before {
+            color: #6f81e4;
+          }
+          .el-icon-warning.important:before {
+            color: #faa048;
+          }
+          .el-icon-warning.emergency:before {
+            color: #f06281;
+          }
+          .el-icon-remove:before {
+            color: #8b8e95;
+          }
+          .temp-wrap {
+            .circle {
+              width: 10px;
+              height: 10px;
+              border-radius: 5px;
+              background: #ffaa00;
+              display: inline-block;
+              position: absolute;
+              left: 1px;
+              top: 50%;
+              margin-top: -4px;
+            }
+            .circle.zero {
+              background: #e6ebf5;
+            }
+            .process-wrap {
+              background: #e6ebf5;
+              width: calc(100% - 50px);
+              height: 6px;
+              display: inline-block;
+              border-top-right-radius: 50px;
+              border-bottom-right-radius: 50px;
+              margin-right: 5px;
+              .process-cover {
+                height: 6px;
+                border-top-right-radius: 50px;
+                border-bottom-right-radius: 50px;
+                background: #ff5100;
+                background-image: linear-gradient(to right, #ffaa00, #ff5100);
+              }
+            }
+          }
+          .hbs-wrap {
+            .el-progress-bar {
+              padding-right: 140px;
+              margin-right: -145px;
+            }
+          }
+          .core-wrap {
+            .el-progress-bar {
+              padding-right: 80px;
+              margin-right: -85px;
+            }
+          }
+          .power {
+            background: #e5f6f6;
+            padding-left: 10px;
+          }
+        }
+        .cl-ram-wrap {
+          height: calc(100% - 36px);
+          .virtual-wrap {
+            height: 100%;
+            overflow: auto;
+            #virtual {
+              height: 100%;
+              overflow: hidden;
+            }
+          }
+        }
+        .cl-disk-wrap {
+          height: calc(100% - 36px);
+          overflow: auto;
+        }
+        .cl-cpu-wrap {
+          height: 201px;
+          .cpu-items {
+            height: 100%;
+            overflow: auto;
+            background: url('../../assets/images/cpu-bg.svg') repeat;
+            padding: 3px 0 0 3px;
+            .cpu-item {
+              float: left;
+              width: calc(6.25% - 3px);
+              height: 30px;
+              text-align: center;
+              background: #fff;
+              margin-right: 3px;
+              margin-bottom: 3px;
+              cursor: pointer;
+              .cpu {
+                height: 100%;
+                line-height: 30px;
+              }
+              .cpu.selected {
+                line-height: 30px;
+                outline: 3px solid #00a5a7;
+              }
+            }
+          }
+          .cpu-detail {
+            & > div {
+              margin-top: 10px;
+              & > span {
+                margin-right: 5px;
+                color: #b2b4bb;
+              }
+              & > div {
+                display: inline-block;
+                padding: 0 7px;
+                border-right: 1px solid #ccc;
+                &:last-child {
+                  border-right: none;
+                }
+                .label {
+                  margin-right: 5px;
+                  cursor: pointer;
+                }
+                .value {
+                  display: inline-block;
+                  width: 40px;
+                  text-align: right;
+                  cursor: pointer;
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+    .cl-hardware-bottom.noNpu {
+      padding-top: 16px;
+      height: 570px;
+      .cl-cpu-wrap {
+        height: 399px;
+      }
+    }
+    .el-table thead tr {
+      background: #f0f3fa;
+    }
+    .el-table th.is-leaf .cell {
+      border-left: 1px solid #d4d9e6;
+    }
+    .el-table th.is-leaf:first-child .cell {
+      border-left: none;
+    }
+    .el-pagination {
+      margin: 7px 0;
+      float: right;
+    }
+  }
+  .el-table th {
+    height: 32px;
+  }
+  .image-noData {
+    width: 100%;
+    height: 100%;
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    flex-direction: column;
+    p {
+      font-size: 16px;
+      padding-top: 10px;
+    }
+  }
+  .el-icon-info:before {
+    color: #6c7280;
+  }
+  .el-table .fail-row {
+    opacity: 0.24;
+    filter: grayscale(1);
+  }
+}
+</style>

mindinsight/ui/src/views/train-manage/model-traceback.vue

@@ -128,7 +128,7 @@ limitations under the License.
                              :prop="key"
                              :label="table.columnOptions[key].label.substring(3)"
                              show-overflow-tooltip
-                             min-width="150"
+                             min-width="120"
                              sortable="custom">
               <template slot="header"
                         slot-scope="scope">
@@ -152,7 +152,7 @@ limitations under the License.
                              :prop="key"
                              :label="table.columnOptions[key].label.substring(3)"
                              show-overflow-tooltip
-                             min-width="150"
+                             min-width="120"
                              sortable="custom">
               <template slot="header"
                         slot-scope="scope">
@@ -426,6 +426,26 @@ export default {
         metric: 'metric/',
         userDefined: 'user_defined/',
       },
+      valueType: {
+        int: 'int',
+        str: 'str',
+        mixed: 'mixed',
+        category: 'category',
+        model_size: 'model_size',
+        dataset_mark: 'dataset_mark',
+      },
+      valueName: {
+        userDefined: 'userDefined',
+        metric: 'metric',
+        UserDefined: 'UserDefined',
+        Metric: 'Metric',
+      },
+      labelValue: {
+        loss: 'loss',
+        batch_size: 'batch_size',
+        epoch: 'epoch',
+        learning_rate: 'learning_rate',
+      },
     };
   },
   computed: {},
@@ -479,8 +499,9 @@ export default {
       }
       this.addIconBorder(row);
       this.tagDialogShow = true;
+      const dialogHeight = 130;
       document.getElementById('tag-dialog').style.top =
-        window.event.clientY - 130 + 'px';
+        window.event.clientY - dialogHeight + 'px';
     },
 
     /**
@@ -790,7 +811,7 @@ export default {
             required: true,
           },
           loss: {
-            label: 'loss',
+            label: this.labelValue.loss,
             required: true,
           },
           network: {
@@ -814,11 +835,11 @@ export default {
             required: false,
           },
           epoch: {
-            label: 'epoch',
+            label: this.labelValue.epoch,
             required: false,
           },
           batch_size: {
-            label: 'batch_size',
+            label: this.labelValue.batch_size,
             required: false,
           },
           device_num: {
@@ -909,9 +930,9 @@ export default {
         } else if (item.indexOf('user_defined/') === 0) {
           userDefinedArray.push(item);
         } else if (
-          item === 'epoch' ||
-          item === 'batch_size' ||
-          item === 'learning_rate'
+          item === this.labelValue.epoch ||
+          item === this.labelValue.batch_size ||
+          item === this.labelValue.learning_rate
         ) {
           hyperArray.push(item);
         } else {
@@ -965,7 +986,9 @@ export default {
           .then(
               (res) => {
                 if (res && res.data && res.data.object) {
-                  const list = this.setDataOfModel(res.data.object);
+                  const listTemp = this.setDataOfModel(res.data.object);
+                  const list = JSON.parse(JSON.stringify(listTemp));
+                  const tempEchartData = JSON.parse(JSON.stringify(listTemp));
                   if (allData) {
                     let customized = {};
                     if (res.data.customized) {
@@ -973,17 +996,17 @@ export default {
                       const customizedKeys = Object.keys(customized);
                       if (customizedKeys.length) {
                         customizedKeys.forEach((i) => {
-                          if (customized[i].type === 'int') {
+                          if (customized[i].type === this.valueType.int) {
                             this.keysOfIntValue.push(i);
-                          } else if (customized[i].type === 'str') {
+                          } else if (customized[i].type === this.valueType.str) {
                             this.keysOfStringValue.push(i);
-                          } else if (customized[i].type === 'mixed') {
+                          } else if (customized[i].type === this.valueType.mixed) {
                             // list of type mixed
                             this.keysOfMixed.push(i);
                             this.keysOfStringValue.push(i);
                           }
                           if (i.startsWith(this.replaceStr.userDefined)) {
-                            this.labelObj.userDefined = 'userDefined';
+                            this.labelObj.userDefined = this.valueName.userDefined;
                             customized[i].label = customized[i].label.replace(
                                 this.replaceStr.userDefined,
                                 '[U]',
@@ -997,7 +1020,7 @@ export default {
                                 this.replaceStr.metric,
                                 '[M]',
                             );
-                            this.labelObj.metric = 'metric';
+                            this.labelObj.metric = this.valueName.metric;
                             const metricObject = {value: '', label: ''};
                             metricObject.value = customized[i].label;
                             metricObject.label = customized[i].label;
@@ -1033,7 +1056,7 @@ export default {
                       ];
                       if (this.labelObj.metric) {
                         const metricTemp = {
-                          label: 'Metric',
+                          label: this.valueName.Metric,
                           options: this.metricOptions,
                         };
                         this.checkOptions.push(metricTemp);
@@ -1041,7 +1064,7 @@ export default {
                       }
                       if (this.labelObj.userDefined) {
                         const userTemp = {
-                          label: 'UserDefined',
+                          label: this.valueName.UserDefined,
                           options: this.userOptions,
                         };
                         this.checkOptions.push(userTemp);
@@ -1049,19 +1072,19 @@ export default {
                       }
                       Object.keys(this.table.columnOptions).forEach((item) => {
                         if (
-                          item !== 'epoch' &&
-                      item !== 'learning_rate' &&
-                      item !== 'batch_size'
+                          item !== this.labelValue.epoch &&
+                      item !== this.labelValue.learning_rate &&
+                      item !== this.labelValue.batch_size
                         ) {
-                          const index = this.table.optionsNotInCheckbox.indexOf(
+                          const haveItem = this.table.optionsNotInCheckbox.includes(
                               item,
                           );
-                          if (index < 0) {
+                          if (!haveItem) {
                             const otherType = {value: '', label: ''};
                             otherType.value = this.table.columnOptions[item].label;
                             otherType.label = this.table.columnOptions[item].label;
                             if (
-                              otherType.value === 'loss' ||
+                              otherType.value === this.labelValue.loss ||
                           otherType.value ===
                             this.$t('modelTraceback.network') ||
                           otherType.value ===
@@ -1119,7 +1142,6 @@ export default {
                     this.noData = !res.data.object.length;
                     this.showEchartPic = !!res.data.object.length;
                     if (this.hidenDirChecked.length) {
-                      const tempEchartData = this.setDataOfModel(res.data.object);
                       this.hidenDirChecked.forEach((dir) => {
                         tempEchartData.forEach((item, index) => {
                           if (item.summary_dir === dir) {
@@ -1230,8 +1252,9 @@ export default {
             ? item.added_info.tag
             : 0;
           const modelData = JSON.parse(JSON.stringify(item.model_lineage));
+          const byteNum = 1024;
           modelData.model_size = parseFloat(
-              ((modelData.model_size || 0) / 1024 / 1024).toFixed(2),
+              ((modelData.model_size || 0) / byteNum / byteNum).toFixed(2),
           );
           const keys = Object.keys(modelData.metric || {});
           if (keys.length) {
@@ -1512,9 +1535,9 @@ export default {
               values[i[key].toString()] = i[key].toString();
             }
           });
-          obj.type = 'category';
+          obj.type = this.valueType.category;
           obj.data = Object.keys(values);
-          if (key === 'dataset_mark') {
+          if (key === this.valueType.dataset_mark) {
             obj.axisLabel = {
               show: false,
             };
@@ -1612,15 +1635,15 @@ export default {
       if (this.echart.chart) {
         this.echart.chart.off('axisareaselected', null);
         window.removeEventListener('resize', this.resizeChart, false);
+      } else {
+        this.echart.chart = Echarts.init(document.querySelector('#echart'));
       }
-
-      this.echart.chart = Echarts.init(document.querySelector('#echart'));
       this.echart.chart.setOption(echartOption, true);
       window.addEventListener('resize', this.resizeChart, false);
-
-      // select use api
+      this.chartEventsListen(parallelAxis);
+    },
+    chartEventsListen(parallelAxis) {
       this.echart.chart.on('axisareaselected', (params) => {
-        // key of mixed item
         this.recordsNumber = 0;
         this.showNumber = 0;
         const key = params.parallelAxisId;
@@ -1649,15 +1672,16 @@ export default {
         const [axisData] = parallelAxis.filter((i) => {
           return i.id === key;
         });
-
-        if (axisData && range.length === 2) {
-          if (axisData && axisData.id === 'model_size') {
+        const lineLength = 2;
+        if (axisData && range.length === lineLength) {
+          if (axisData && axisData.id === this.valueType.model_size) {
+            const byteNum = 1024;
             range = [
-              parseInt(range[0] * 1024 * 1024, 0),
-              parseInt(range[1] * 1024 * 1024, 0),
+              parseInt(range[0] * byteNum * byteNum, 0),
+              parseInt(range[1] * byteNum * byteNum, 0),
             ];
           }
-          if (axisData.type === 'category') {
+          if (axisData.type === this.valueType.category) {
             const rangeData = {};
             for (let i = range[0]; i <= range[1]; i++) {
               rangeData[axisData.data[i]] = axisData.data[i];
@@ -1720,11 +1744,11 @@ export default {
                         ];
                         this.keysOfMixed = [];
                         customizedKeys.forEach((i) => {
-                          if (customized[i].type === 'int') {
+                          if (customized[i].type === this.valueType.int) {
                             this.keysOfIntValue.push(i);
-                          } else if (customized[i].type === 'str') {
+                          } else if (customized[i].type === this.valueType.str) {
                             this.keysOfStringValue.push(i);
-                          } else if (customized[i].type === 'mixed') {
+                          } else if (customized[i].type === this.valueType.mixed) {
                             // list of type mixed
                             this.keysOfMixed.push(i);
                             this.keysOfStringValue.push(i);
@@ -1858,20 +1882,25 @@ export default {
       if (isNaN(value) || !value) {
         return value;
       } else {
-        if (key === 'learning_rate') {
-          let temp = value.toPrecision(4);
+        const numDigits = 4;
+        if (key === this.labelValue.learning_rate) {
+          let temp = value.toPrecision(numDigits);
           let row = 0;
           while (temp < 1) {
             temp = temp * 10;
             row += 1;
           }
-          temp = this.toFixedFun(temp, 4);
+          temp = this.toFixedFun(temp, numDigits);
           return `${temp}${row ? `e-${row}` : ''}`;
-        } else if (key === 'model_size') {
+        } else if (key === this.valueType.model_size) {
           return value + 'MB';
         } else {
-          if (value < 1000) {
-            return Math.round(value * Math.pow(10, 4)) / Math.pow(10, 4);
+          const num = 1000;
+          if (value < num) {
+            return (
+              Math.round(value * Math.pow(10, numDigits)) /
+              Math.pow(10, numDigits)
+            );
           } else {
             const reg = /(?=(\B)(\d{3})+$)/g;
             return (value + '').replace(reg, ',');
@@ -1883,7 +1912,9 @@ export default {
      * Resizing Chart
      */
     resizeChart() {
-      this.echart.chart.resize();
+      if (this.echart && this.echart.chart) {
+        this.echart.chart.resize();
+      }
     },
   },
   /**
@@ -1927,11 +1958,6 @@ export default {
 .el-tag.el-tag--info .el-tag__close {
   color: #fff;
 }
-// select
-.el-select > .el-input {
-  min-width: 280px !important;
-  max-width: 500px !important;
-}
 .select-inner-input {
   width: calc(100% - 140px);
   margin: 2px 4px;
@@ -2091,7 +2117,11 @@ export default {
     -webkit-box-shadow: 0 1px 0 0 rgba(200, 200, 200, 0.5);
     box-shadow: 0 1px 0 0 rgba(200, 200, 200, 0.5);
     overflow: hidden;
-
+    // select
+    .el-select > .el-input {
+      min-width: 180px !important;
+      max-width: 500px !important;
+    }
     .top-area {
       margin: 24px 32px 12px;
       display: flex;

mindinsight/ui/src/views/train-manage/operator.vue

@@ -22,6 +22,7 @@ limitations under the License.
         <el-tab-pane label="AI CORE"
                      name="core">
           <div class="cl-profiler-top"
+               :class="{fullScreen:fullScreen}"
                v-if="coreCharts.data.length">
             <div>
               <span class="profiler-title">
@@ -46,10 +47,15 @@ limitations under the License.
             </div>
           </div>
           <div class="cl-profiler-bottom"
+               :class="{fullScreen:fullScreen}"
                v-if="coreCharts.data.length">
             <span class="profiler-title">
               {{ $t('operator.operatorStatistics') }}
             </span>
+            <img src="../../assets/images/full-screen.png"
+                 :title="$t('graph.fullScreen')"
+                 class="fullScreen"
+                 @click="fullScreenControl()">
             <div>
               <el-radio-group v-model="statisticType"
                               @change="coreTableChange"
@@ -103,7 +109,7 @@ limitations under the License.
                                        :width="(ele==='avg_execution_time'|| ele==='subgraph' ||
                                         ele==='op_name'|| ele==='op_type')?'220':''"
                                        show-overflow-tooltip
-                                       :label="ele==='avg_execution_time'?`${ele} （ms)`:ele">
+                                       :label="ele==='avg_execution_time'?`${ele} (${$t('profiling.unit')})`:ele">
                       </el-table-column>
                     </el-table>
                     <el-pagination :current-page="props.row.opDetailPage.offset + 1"
@@ -120,7 +126,7 @@ limitations under the License.
                                :property="item"
                                :key="$index"
                                sortable
-                               :label="item==='execution_time'?`${item} (ms)`:item">
+                               :label="item==='execution_time'?`${item} (${$t('profiling.unit')})`:item">
               </el-table-column>
             </el-table>
             <el-table v-show="statisticType && opAllTypeList.opDetailCol && opAllTypeList.opDetailCol.length"
@@ -135,7 +141,7 @@ limitations under the License.
               <el-table-column v-for="(item, $index) in opAllTypeList.opDetailCol"
                                :property="item"
                                :key="$index"
-                               :label="item==='avg_execution_time'?`${item} (ms)`:item"
+                               :label="item==='avg_execution_time'?`${item} (${$t('profiling.unit')})`:item"
                                :sortable="item === 'op_info' ? false : 'custom'"
                                :width="(item==='avg_execution_time'|| item==='subgraph' ||
                                 item==='op_name'|| item==='op_type')?'220':''"
@@ -198,7 +204,8 @@ limitations under the License.
               <el-table-column v-for="(item, $index) in opCpuList.opDetailCol"
                                :property="item"
                                :key="$index"
-                               :label="(item==='total_time' || item==='dispatch_time')?`${item} (ms)`:item"
+                               :label="(item==='total_time' || item==='dispatch_time')?
+                               `${item} (${$t('profiling.unit')})`:item"
                                sortable="custom"
                                show-overflow-tooltip>
               </el-table-column>
@@ -318,6 +325,7 @@ export default {
         childProp: null,
         childOrder: null,
       },
+      fullScreen: false,
     };
   },
   watch: {
@@ -347,6 +355,14 @@ export default {
         }, 300);
       }
     },
+    fullScreenControl() {
+      this.fullScreen = !this.fullScreen;
+      if (this.coreCharts.chartDom && !this.fullScreen) {
+        this.$nextTick(() => {
+          this.coreCharts.chartDom.resize();
+        });
+      }
+    },
     /**
      * Current device change
      */
@@ -1106,13 +1122,25 @@ export default {
   .cl-search-box {
     float: right;
     margin-bottom: 10px;
+    margin-right: 20px;
   }
   .cl-profiler-top {
     height: 45%;
   }
+  .cl-profiler-top.fullScreen {
+    display: none;
+  }
   .cl-profiler-bottom {
     height: 55%;
     padding-top: 10px;
+    .fullScreen {
+      float: right;
+      margin-top: 5px;
+      cursor: pointer;
+    }
+  }
+  .cl-profiler-bottom.fullScreen {
+    height: 100%;
   }
   .cpu-tab {
     .cl-profiler-top {

mindinsight/ui/src/views/train-manage/profiling-dashboard.vue

@@ -73,8 +73,8 @@ limitations under the License.
                         markerHeight="8"
                         orient="auto">
                   <path d="M1,1 L1,7 L9,4 z"
-                        fill="#E6EBF5"
-                        stroke="#E6EBF5"></path>
+                        fill="#6c7280"
+                        stroke="#6c7280"></path>
                 </marker>
                 <marker id="marker_start"
                         refX="5"
@@ -83,14 +83,14 @@ limitations under the License.
                         markerHeight="8"
                         orient="auto">
                   <path d="M9,1 L9,7 L1,4 z"
-                        fill="#E6EBF5"
-                        stroke="#E6EBF5"></path>
+                        fill="#6c7280"
+                        stroke="#6c7280"></path>
                 </marker>
               </defs>
             </svg>
           </div>
           <div class="image-noData"
-               v-if="svg.noData">
+               v-if="svg.noData && svg.initOver">
             <div>
               <img :src="require('@/assets/images/nodata.png')"
                    alt="" />
@@ -236,7 +236,7 @@ limitations under the License.
           </div>
         </div>
         <div class="image-noData"
-             v-if="processSummary.noData">
+             v-if="processSummary.noData && processSummary.initOver">
           <div>
             <img :src="require('@/assets/images/nodata.png')"
                  alt="" />
@@ -257,7 +257,7 @@ limitations under the License.
           </div>
         </div>
         <div class="image-noData"
-             v-if="pieChart.noData && pieChart.data.length === 0">
+             v-if="pieChart.noData && pieChart.data.length === 0 && pieChart.initOver">
           <div>
             <img :src="require('@/assets/images/nodata.png')"
                  alt="" />
@@ -281,7 +281,7 @@ limitations under the License.
                 <span class="time">
                   <span class="bar"
                         :style="{width: item.time / pieChart.topN[0].time * 100 + '%'}"></span>
-                  <span class="value">{{item.time}}ms</span>
+                  <span class="value">{{item.time + $t('profiling.unit')}}</span>
                 </span>
               </li>
             </ul>
@@ -337,7 +337,7 @@ limitations under the License.
 
         </div>
         <div class="image-noData"
-             v-if="timelineInfo.noData">
+             v-if="timelineInfo.noData && timelineInfo.initOver">
           <div>
             <img :src="require('@/assets/images/nodata.png')"
                  alt="" />
@@ -372,19 +372,27 @@ export default {
         svgPadding: 20,
         totalWidth: 0,
         totalTime: 0,
-        rowHeight: 60,
+        cellHeight: 40,
+        cellPadding: 0,
+        rowPadding: 20,
+        rowMargin: 10,
+        totalHeight: 0,
         markerPadding: 4,
-        minRate: 0.05,
+        minRate: 0.1,
+        minTime: 0,
+        minWidth: 1,
+        fontSize: 12,
+        textMargin: 21,
         namespaceURI: 'http://www.w3.org/2000/svg',
         resizeTimer: null,
-        colorList: [
-          ['#A6DD82', '#edf8e6'],
-          ['#6CBFFF', '#e2f2ff'],
-          ['#fa8e5b', '#fff4de'],
-          ['#01a5a7', '#cceded'],
-        ],
-        colorIndex: 0,
+        colors: {
+          iteration_interval: ['#A6DD82', '#edf8e6'],
+          fp_and_bp: ['#6CBFFF', '#e2f2ff'],
+          tail: ['#fa8e5b', '#fff4de'],
+          stream_parallel: ['#01a5a7', '#cceded'],
+        },
         noData: false,
+        initOver: false,
       },
       trainingJobId: this.$route.query.id,
       summaryPath: this.$route.query.dir,
@@ -396,6 +404,7 @@ export default {
         noData: false,
         topN: [],
         colorList: ['#6C92FA', '#6CBFFF', '#4EDED2', '#7ADFA0', '#A6DD82'],
+        initOver: false,
       },
       timeLine: {
         data: null,
@@ -407,6 +416,7 @@ export default {
         opNum: 0,
         opTimes: 0,
         noData: true,
+        initOver: false,
       },
       processSummary: {
         noData: true,
@@ -422,6 +432,7 @@ export default {
           full: 0,
           total: 0,
         },
+        initOver: false,
       },
     };
   },
@@ -474,6 +485,7 @@ export default {
         device_id: this.currentCard,
       };
       RequestService.queryProcessSummary(params).then((resp) => {
+        this.processSummary.initOver = true;
         if (resp && resp.data) {
           const data = JSON.parse(JSON.stringify(resp.data));
           this.processSummary.count = Object.keys(data).length;
@@ -498,6 +510,7 @@ export default {
           }
         } else {
           this.dealProcess(null);
+          this.processSummary.initOver = true;
         }
       });
     },
@@ -573,6 +586,7 @@ export default {
       };
       RequestService.getProfilerOpData(params)
           .then((res) => {
+            this.pieChart.initOver = true;
             if (res && res.data) {
               if (res.data.object) {
                 this.pieChart.data = [];
@@ -614,6 +628,7 @@ export default {
           })
           .catch(() => {
             this.pieChart.noData = true;
+            this.pieChart.initOver = true;
           });
     },
     queryTrainingTrace() {
@@ -624,21 +639,20 @@ export default {
       };
       RequestService.queryTrainingTrace(params).then(
           (res) => {
+            this.svg.initOver = true;
             if (
               res.data &&
             res.data.training_trace_graph &&
             res.data.training_trace_graph.length
             ) {
               this.svg.noData = false;
-              document.querySelector('#trace').style.height = `${res.data
-                  .training_trace_graph.length * this.svg.rowHeight}px`;
-              this.svg.data = JSON.parse(
-                  JSON.stringify(res.data.training_trace_graph),
-              );
               this.removeTrace();
-              setTimeout(() => {
-                this.dealTraceData();
-              }, 100);
+              this.$nextTick(() => {
+                this.packageTraceData(
+                    JSON.parse(JSON.stringify(res.data.training_trace_graph)),
+                );
+              });
+
               if (res.data.summary) {
                 this.fp_and_bp_percent = res.data.summary.fp_and_bp_percent;
                 this.iteration_interval_percent =
@@ -664,44 +678,86 @@ export default {
             document.querySelector('#trace').style.height = '0px';
             this.svg.noData = true;
             this.svg.data = [];
+            this.svg.initOver = true;
             this.removeTrace();
           },
       );
     },
 
+    packageTraceData(traceGraph) {
+      this.svg.totalTime = 0;
+      this.svg.minTime = 0;
+      this.svg.totalHeight = 0;
+      const data = [];
+
+      if (traceGraph && traceGraph[0] && traceGraph[0][0]) {
+        this.svg.totalTime = traceGraph[0][0].duration;
+        this.svg.minTime = this.svg.minRate * this.svg.totalTime;
+
+        traceGraph.forEach((row, index) => {
+          const rowObj = {
+            rowCount: 0,
+            data: [],
+            height: 0,
+            startY: this.svg.totalHeight,
+          };
+          let obj = [];
+          for (let i = 0; i < row.length; i++) {
+            if (row[i].duration < this.svg.minTime) {
+              if (obj.length) {
+                rowObj.data.push(obj);
+                obj = [];
+                rowObj.rowCount++;
+              }
+              rowObj.data.push([row[i]]);
+              rowObj.rowCount++;
+            } else {
+              obj.push(row[i]);
+            }
+
+            if (i === row.length - 1 && obj.length) {
+              rowObj.data.push(obj);
+              obj = [];
+              rowObj.rowCount++;
+            }
+          }
+          rowObj.height =
+            rowObj.rowCount * this.svg.cellHeight +
+            (rowObj.rowCount - 1) * this.svg.cellPadding +
+            (index ? this.svg.rowPadding * 2 : 0);
+
+          this.svg.totalHeight += rowObj.height + this.svg.rowMargin;
+          data.push(rowObj);
+        });
+
+        this.svg.totalHeight += this.svg.rowPadding;
+        document.querySelector(
+            '#trace',
+        ).style.height = `${this.svg.totalHeight}px`;
+        this.svg.data = JSON.parse(JSON.stringify(data));
+
+        this.$nextTick(() => {
+          this.dealTraceData();
+        });
+      }
+    },
+
     dealTraceData() {
       const traceDom = document.querySelector('#trace');
       if (traceDom) {
         this.svg.totalWidth = traceDom.offsetWidth - this.svg.svgPadding * 2;
 
-        if (this.svg.data[0] && this.svg.data[0].length) {
+        if (this.svg.data[0] && this.svg.data[0].data.length) {
           const svg = traceDom.querySelector('svg');
-          this.svg.totalTime = this.svg.data[0][0].duration;
-
           if (this.svg.totalTime) {
-            this.svg.colorIndex = 0;
-            const minTime = this.svg.minRate * this.svg.totalTime;
-
-            this.svg.data.forEach((row, index) => {
-              if (row && row.length) {
-                const dashedLine = this.addDashedLine(index);
-                svg.insertBefore(dashedLine, svg.querySelector('g'));
-
-                row.forEach((i) => {
-                  if (i.duration) {
-                    if (i.name) {
-                      const tempDom = this.createRect(i, index);
-                      const tempStr = `g${
-                        i.duration > minTime ? '' : '.arrow'
-                      }`;
-                      svg.insertBefore(tempDom, svg.querySelector(tempStr));
-                    } else {
-                      const tempDom = this.createArrow(i, index);
-                      svg.appendChild(tempDom);
-                    }
-                  }
-                });
+            this.svg.data.forEach((item, index) => {
+              let itemDom = {};
+              if (index) {
+                itemDom = this.createMultipleRowContainer(item);
+              } else {
+                itemDom = this.createRowContainer(item.data, item.startY);
               }
+              svg.appendChild(itemDom);
             });
           }
         } else {
@@ -709,40 +765,69 @@ export default {
         }
       }
     },
-    addDashedLine(index) {
-      const x1 = this.svg.svgPadding;
-      const x2 = this.svg.svgPadding + this.svg.totalWidth;
-      const y = index * this.svg.rowHeight;
-      const line = document.createElementNS(this.svg.namespaceURI, 'line');
-      line.setAttribute('x1', x1);
-      line.setAttribute('y1', y);
-      line.setAttribute('x2', x2);
-      line.setAttribute('y2', y);
-      line.setAttribute('style', 'stroke:#E2E2E2;stroke-width:1');
-      line.setAttribute('stroke-dasharray', '5 5');
+
+    createMultipleRowContainer(item) {
+      const rectContainer = document.createElementNS(
+          this.svg.namespaceURI,
+          'g',
+      );
+      rectContainer.setAttribute('class', 'container');
+
+      const rect = document.createElementNS(this.svg.namespaceURI, 'rect');
+      rect.setAttribute('x', this.svg.svgPadding);
+      rect.setAttribute('y', item.startY + this.svg.rowPadding);
+      rect.setAttribute('height', item.height);
+      rect.setAttribute('width', this.svg.totalWidth);
+      rect.setAttribute('style', 'fill:#edf0f5;stroke:#E2E2E2;stroke-width:1');
+      rectContainer.appendChild(rect);
+
+      const temp = this.createRowContainer(
+          item.data,
+          item.startY + this.svg.rowPadding,
+      );
+      rectContainer.appendChild(temp);
+      return rectContainer;
+    },
+
+    createRowContainer(data, startY) {
       const g = document.createElementNS(this.svg.namespaceURI, 'g');
-      g.setAttribute('class', 'dashedLine');
-      g.appendChild(line);
+
+      data.forEach((row, index) => {
+        const y =
+          startY +
+          this.svg.rowPadding +
+          index * (this.svg.cellPadding + this.svg.cellHeight);
+        row.forEach((i) => {
+          if (i.duration) {
+            let temp;
+            if (i.name) {
+              temp = this.createRect(i, y);
+              g.insertBefore(temp, g.querySelector('g'));
+            } else {
+              temp = this.createArrow(i, y);
+              g.appendChild(temp);
+            }
+          }
+        });
+      });
       return g;
     },
-    createRect(data, rowIndex) {
-      const color = this.svg.colorList[
-        rowIndex > 1 ? 3 : this.svg.colorIndex++ % 4
-      ];
-      const height = 40;
-      const width = (data.duration / this.svg.totalTime) * this.svg.totalWidth;
-      const fontSize = 12;
-      const normalRect = data.duration > this.svg.minRate * this.svg.totalTime;
+
+    createRect(data, startY) {
+      const color =
+        data.name && this.svg.colors[data.name]
+          ? this.svg.colors[data.name]
+          : this.svg.colors.stream_parallel;
 
       const x1 =
         (data.start / this.svg.totalTime) * this.svg.totalWidth +
         this.svg.svgPadding;
-      const y1 =
-        rowIndex * this.svg.rowHeight + (this.svg.rowHeight - height) / 2;
 
-      const g = document.createElementNS(this.svg.namespaceURI, 'g');
-      g.setAttribute('class', 'rect');
-      const gChild = document.createElementNS(this.svg.namespaceURI, 'g');
+      const width = Math.max(
+          this.svg.minWidth,
+          (data.duration / this.svg.totalTime) * this.svg.totalWidth,
+      );
+
       let name = '';
       switch (data.name) {
         case 'iteration_interval':
@@ -759,102 +844,117 @@ export default {
           break;
       }
 
+      const textContent = `${name}: ${data.duration.toFixed(4)}ms`;
+      const textWidth = this.getTextWidth(textContent);
+      const normalSize = data.duration >= this.svg.minTime;
+
+      const g = document.createElementNS(this.svg.namespaceURI, 'g');
+      g.setAttribute('class', 'rect');
+
       const rect = document.createElementNS(this.svg.namespaceURI, 'rect');
       rect.setAttribute('x', x1);
-      rect.setAttribute('y', y1);
-      rect.setAttribute('height', height);
+      rect.setAttribute('y', startY);
+      rect.setAttribute('height', this.svg.cellHeight);
       rect.setAttribute('width', width);
-      rect.setAttribute('style', `fill:${color[1]};stroke:${color[1]};`);
+      rect.setAttribute('style', `fill:${color[1]};stroke:${color[0]};`);
 
       const foreignObject = document.createElementNS(
           this.svg.namespaceURI,
           'foreignObject',
       );
-      foreignObject.textContent = `${name}: ${data.duration.toFixed(4)}ms`;
-      const textWidth = this.getTextWidth(foreignObject.textContent);
-
+      foreignObject.textContent = textContent;
       foreignObject.setAttribute(
           'x',
-        normalRect
+        normalSize
           ? x1
           : Math.min(
-              this.svg.svgPadding * 2 + this.svg.totalWidth - textWidth,
-              Math.max(0, x1 + width / 2 - textWidth / 2),
+              this.svg.svgPadding * 2 +
+                this.svg.totalWidth -
+                textWidth -
+                this.svg.textMargin,
+              Math.max(this.svg.textMargin, x1 + width / 2 - textWidth / 2),
           ),
       );
 
-      foreignObject.setAttribute(
-          'y',
-          y1 + (height - fontSize) / 2 + (normalRect ? 0 : fontSize),
-      );
-      foreignObject.setAttribute('height', fontSize);
+      foreignObject.setAttribute('y', startY);
+      foreignObject.setAttribute('height', this.svg.cellHeight);
       foreignObject.setAttribute('width', width);
       foreignObject.setAttribute('style', `color:${color[0]}`);
       foreignObject.setAttribute(
           'class',
-          `content${normalRect ? '' : ' content-mini'}`,
+          `content${normalSize ? '' : ' content-mini'}`,
       );
 
       const title = document.createElementNS(this.svg.namespaceURI, 'title');
-      title.textContent = `${name}: ${data.duration.toFixed(4)}ms`;
+      title.textContent = textContent;
 
-      gChild.appendChild(rect);
-      gChild.appendChild(foreignObject);
-      gChild.appendChild(title);
-      g.appendChild(gChild);
+      g.appendChild(rect);
+      g.appendChild(foreignObject);
+      g.appendChild(title);
       return g;
     },
 
-    createArrow(data, rowIndex) {
+    createArrow(data, startY) {
       const width = (data.duration / this.svg.totalTime) * this.svg.totalWidth;
       const x1 =
         (data.start / this.svg.totalTime) * this.svg.totalWidth +
-        this.svg.markerPadding +
         this.svg.svgPadding;
-      const x2 = x1 + width - this.svg.markerPadding * 2;
-      const y = rowIndex * this.svg.rowHeight + this.svg.rowHeight / 2;
+      const centerY = startY + this.svg.cellHeight / 2;
+
       const g = document.createElementNS(this.svg.namespaceURI, 'g');
       g.setAttribute('class', 'arrow');
 
       const line = document.createElementNS(this.svg.namespaceURI, 'line');
-      line.setAttribute('x1', x1);
-      line.setAttribute('y1', y);
-      line.setAttribute('x2', x2);
-      line.setAttribute('y2', y);
-      line.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
-      line.setAttribute('marker-end', 'url(#marker_end)');
-      line.setAttribute('marker-start', 'url(#marker_start)');
+      line.setAttribute('y1', centerY);
+      line.setAttribute('y2', centerY);
+      line.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
+      if (width > this.svg.markerPadding) {
+        line.setAttribute('x1', x1 + this.svg.markerPadding);
+        line.setAttribute('x2', x1 + width - this.svg.markerPadding);
+        line.setAttribute('marker-end', 'url(#marker_end)');
+        line.setAttribute('marker-start', 'url(#marker_start)');
+      } else {
+        line.setAttribute('x1', x1);
+        line.setAttribute('x2', x1 + width);
+      }
 
       const text = document.createElementNS(this.svg.namespaceURI, 'text');
       text.textContent = `${
-        rowIndex === 0 ? this.$t('profiling.approximateTime') : ''
+        data.duration === this.svg.totalTime
+          ? this.$t('profiling.approximateTime')
+          : ''
       }${data.duration.toFixed(4)}ms`;
-      const textWidth = this.getTextWidth(text.textContent);
+      const textWidth = text.textContent
+        ? this.getTextWidth(text.textContent)
+        : 0;
       text.setAttribute(
           'x',
           Math.min(
-              this.svg.svgPadding * 2 + this.svg.totalWidth - textWidth,
-              Math.max(0, (x2 - x1) / 2 + x1 - textWidth / 2),
+              this.svg.svgPadding * 2 +
+            this.svg.totalWidth -
+            textWidth -
+            this.svg.textMargin,
+              Math.max(this.svg.textMargin, width / 2 + x1 - textWidth / 2),
           ),
       );
-      text.setAttribute('y', y - 6);
-      text.setAttribute('font-size', 12);
-      text.setAttribute('fill', '#6c7280');
+      text.setAttribute('y', centerY - this.svg.fontSize / 2);
+      text.setAttribute('font-size', this.svg.fontSize);
+      text.setAttribute('fill', 'black');
 
       const startLine = document.createElementNS(this.svg.namespaceURI, 'line');
-      startLine.setAttribute('x1', x1 - this.svg.markerPadding);
-      startLine.setAttribute('y1', y - this.svg.rowHeight / 4);
-      startLine.setAttribute('x2', x1 - this.svg.markerPadding);
-      startLine.setAttribute('y2', y + this.svg.rowHeight / 4);
-      startLine.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
+      startLine.setAttribute('x1', x1);
+      startLine.setAttribute('y1', startY);
+      startLine.setAttribute('x2', x1);
+      startLine.setAttribute('y2', startY + this.svg.cellHeight);
+      startLine.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
       g.appendChild(startLine);
 
       const endLine = document.createElementNS(this.svg.namespaceURI, 'line');
-      endLine.setAttribute('x1', x1 + width - this.svg.markerPadding);
-      endLine.setAttribute('y1', y - this.svg.rowHeight / 4);
-      endLine.setAttribute('x2', x1 + width - this.svg.markerPadding);
-      endLine.setAttribute('y2', y + this.svg.rowHeight / 4);
-      endLine.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
+      endLine.setAttribute('x1', x1 + width);
+      endLine.setAttribute('y1', startY);
+      endLine.setAttribute('x2', x1 + width);
+      endLine.setAttribute('y2', startY + this.svg.cellHeight);
+      endLine.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
       g.appendChild(endLine);
       g.appendChild(line);
       g.appendChild(text);
@@ -901,12 +1001,14 @@ export default {
       return new Uint8Array(arr);
     },
     queryTimeline() {
+      this.timeLine.waiting = true;
       const params = {
         dir: this.relativePath,
         device_id: this.currentCard,
       };
       RequestService.queryTimlineInfo(params)
           .then((res) => {
+            this.timelineInfo.initOver = true;
             if (res && res.data) {
               this.timelineInfo.noData = false;
               this.timelineInfo.totalTime = res.data.total_time.toFixed(4);
@@ -919,8 +1021,8 @@ export default {
           })
           .catch(() => {
             this.timelineInfo.noData = true;
+            this.timelineInfo.initOver = true;
           });
-      this.timeLine.waiting = true;
       RequestService.queryTimeline(params)
           .then((res) => {
             if (res && res.data && res.data.length) {
@@ -1093,7 +1195,7 @@ export default {
       margin-bottom: 15px;
       .trace-container {
         width: 100%;
-        height: calc(100% - 50px);
+        height: calc(100% - 54px);
         overflow: auto;
         .training-trace {
           position: relative;
@@ -1104,7 +1206,7 @@ export default {
             text-overflow: ellipsis;
             white-space: nowrap;
             font-size: 12px;
-            line-height: 12px;
+            line-height: 40px;
           }
           .content-mini {
             overflow: visible;

mindinsight/ui/src/views/train-manage/scalar.vue

@@ -222,6 +222,31 @@ limitations under the License.
       </span>
     </el-dialog>
 
+    <el-dialog :title="$t('scalar.info')"
+               :visible.sync="delThresholdVisible"
+               custom-class="delDialog"
+               :close-on-click-modal="false"
+               @close="delThresholdCancel"
+               top="35vh"
+               width="425px">
+      <div class="delThresholdItem">
+        <span class="delThresholdIcon el-icon-warning"></span>
+        <span class="delThresholdInfo">{{$t('scalar.isDelete')}}</span>
+      </div>
+      <div class="delThresholdItem">
+        <span class="delThresholdIcon">
+          <el-switch v-model="delThresholdSwitch"></el-switch>
+        </span>
+        <span class="delThresholdInfo">{{$t('scalar.applyAllSelectTag')}}</span>
+      </div>
+      <span slot="footer"
+            class="dialog-footer">
+        <el-button @click="delThresholdCancel">{{$t('public.cancel')}}</el-button>
+        <el-button type="primary"
+                   @click="delThresholdCommit">{{$t('public.sure')}}</el-button>
+      </span>
+    </el-dialog>
+
   </div>
 </template>
 <script>
@@ -262,9 +287,9 @@ export default {
       yAxisScaleTimer: null, // yAxis scale timer
       compare: false, // Comparison Page
       scalarCompare: this.$t('scalar')['comparison'],
-      abort: false, // charts that have not been drawn.
       trainingJobId: this.$route.query.train_id, // ID of the current training job
       thresholdDialogVisible: false,
+      delThresholdVisible: false,
       currentTagName: '',
       currentSample: {},
       thresholdErrorMsg: '',
@@ -285,6 +310,7 @@ export default {
       ],
       thresholdLocal: null,
       thresholdSwitch: false,
+      delThresholdSwitch: false,
       thresholdColor: '#f00',
       decodeTrainingJobId: '',
     };
@@ -579,6 +605,10 @@ export default {
             .then((res) => {
             // error
               if (!res || !res.data || !res.data.metadatas) {
+              // canceled
+                if (res.toString() === 'false') {
+                  return;
+                }
                 if (sampleObject.charObj) {
                   sampleObject.charObj.clear();
                   sampleObject.onePoint = false;
@@ -666,8 +696,6 @@ export default {
                 // Draw chart
                 if (!this.compare) {
                   this.updateOrCreateChar(sampleIndex, true);
-                } else {
-                  this.abort = true;
                 }
               });
             })
@@ -844,7 +872,6 @@ export default {
           scale: true,
           nameGap: 30,
           minInterval: this.isActive === 0 ? 1 : 0,
-
           axisLine: {
             lineStyle: {
               color: '#E6EBF5',
@@ -1263,68 +1290,65 @@ export default {
         clearTimeout(this.axisBenchChangeTimer);
         this.axisBenchChangeTimer = null;
       }
-      switch (val) {
-        case this.$t('scalar.step'):
-          this.curBenchX = 'stepData';
-          this.curAxisName = this.$t('scalar.step');
-          this.isActive = 0;
-          break;
-        case this.$t('scalar.relativeTime'):
-          this.curBenchX = 'relativeData';
-          this.curAxisName = this.$t('scalar.relativeTime');
-          this.isActive = 1;
-          break;
-        case this.$t('scalar.absoluteTime'):
-          this.curBenchX = 'absData';
-          this.curAxisName = this.$t('scalar.absoluteTime');
-          this.isActive = 2;
-          break;
-        default:
-          this.curBenchX = 'stepData';
-          this.curAxisName = this.$t('scalar.step');
-          this.isActive = 0;
-          break;
-      }
       this.axisBenchChangeTimer = setTimeout(() => {
+        switch (val) {
+          case this.$t('scalar.step'):
+            this.curBenchX = 'stepData';
+            this.curAxisName = this.$t('scalar.step');
+            this.isActive = 0;
+            break;
+          case this.$t('scalar.relativeTime'):
+            this.curBenchX = 'relativeData';
+            this.curAxisName = this.$t('scalar.relativeTime');
+            this.isActive = 1;
+            break;
+          case this.$t('scalar.absoluteTime'):
+            this.curBenchX = 'absData';
+            this.curAxisName = this.$t('scalar.absoluteTime');
+            this.isActive = 2;
+            break;
+          default:
+            this.curBenchX = 'stepData';
+            this.curAxisName = this.$t('scalar.step');
+            this.isActive = 0;
+            break;
+        }
         // Update the horizontal benchmark of the default data
         this.curPageArr.forEach((sampleObject) => {
           if (sampleObject.charObj) {
             sampleObject.charData.oriData.forEach((originData, index) => {
+              const seriesData = sampleObject.charData.charOption.series;
+              const oriIndexData = sampleObject.charData.oriData[index];
               if (sampleObject.log) {
-                sampleObject.charData.charOption.series[
-                    index * 2
-                ].data = this.formateSmoothData(
-                    sampleObject.charData.oriData[index].logData[this.curBenchX],
+                seriesData[index * 2].data = this.formateSmoothData(
+                    oriIndexData.logData[this.curBenchX],
                 );
-                sampleObject.charData.charOption.series[index * 2 + 1].data =
-                  sampleObject.charData.oriData[index].logData[this.curBenchX];
+                seriesData[index * 2 + 1].data =
+                  oriIndexData.logData[this.curBenchX];
               } else {
-                sampleObject.charData.charOption.series[
-                    index * 2
-                ].data = this.formateSmoothData(
-                    sampleObject.charData.oriData[index].valueData[this.curBenchX],
+                seriesData[index * 2].data = this.formateSmoothData(
+                    oriIndexData.valueData[this.curBenchX],
                 );
-                sampleObject.charData.charOption.series[index * 2 + 1].data =
-                  sampleObject.charData.oriData[index].valueData[
-                      this.curBenchX
-                  ];
+                seriesData[index * 2 + 1].data =
+                  oriIndexData.valueData[this.curBenchX];
               }
             });
-            sampleObject.charData.charOption.xAxis.minInterval =
-              this.isActive === 0 ? 1 : 0;
-            sampleObject.charData.charOption.xAxis.axisLabel.rotate =
-              this.isActive === 2 ? 0 : 90;
+
+            const optionxAxis = sampleObject.charData.charOption.xAxis;
+            const seriesData = sampleObject.charData.charOption.series[0];
+            optionxAxis.minInterval = this.isActive === 0 ? 1 : 0;
+            optionxAxis.axisLabel.rotate = this.isActive === 2 ? 0 : 90;
             sampleObject.updateFlag = true;
             sampleObject.charObj.clear();
 
-            if (sampleObject.charData.charOption.series[0].data.length === 1) {
-              sampleObject.charData.charOption.series[0].showSymbol = true;
+            if (seriesData.data.length === 1) {
+              seriesData.showSymbol = true;
               sampleObject.onePoint = true;
             } else {
-              sampleObject.charData.charOption.series[0].showSymbol = false;
+              seriesData.showSymbol = false;
               sampleObject.onePoint = false;
             }
-            this.updateOrCreateChar(sampleObject.sampleIndex);
+            this.updateOrCreateChar(sampleObject.sampleIndex, true);
           }
         });
       }, 500);
@@ -1763,40 +1787,35 @@ export default {
         return;
       }
       this.yAxisScaleTimer = setTimeout(() => {
+        const tempOption = sampleObject.charData.charOption;
+        const tempOriData = sampleObject.charData.oriData;
         const log = !sampleObject.log;
         if (log) {
-          sampleObject.charData.charOption.toolbox.feature.myTool2.iconStyle.borderColor =
-            '#3E98C5';
-          sampleObject.charData.charOption.yAxis.type = 'log';
+          tempOption.toolbox.feature.myTool2.iconStyle.borderColor = '#3E98C5';
+          tempOption.yAxis.type = 'log';
         } else {
-          sampleObject.charData.charOption.yAxis.type = 'value';
-          sampleObject.charData.charOption.toolbox.feature.myTool2.iconStyle.borderColor =
-            '#666';
+          tempOption.yAxis.type = 'value';
+          tempOption.toolbox.feature.myTool2.iconStyle.borderColor = '#666';
         }
-        sampleObject.charData.oriData.forEach((originData, index) => {
+        tempOriData.forEach((originData, index) => {
           if (log) {
-            sampleObject.charData.charOption.series[
-                index * 2
-            ].data = this.formateSmoothData(
-                sampleObject.charData.oriData[index].logData[this.curBenchX],
+            tempOption.series[index * 2].data = this.formateSmoothData(
+                tempOriData[index].logData[this.curBenchX],
             );
-            sampleObject.charData.charOption.series[index * 2 + 1].data =
-              sampleObject.charData.oriData[index].logData[this.curBenchX];
+            tempOption.series[index * 2 + 1].data =
+              tempOriData[index].logData[this.curBenchX];
           } else {
-            sampleObject.charData.charOption.series[
-                index * 2
-            ].data = this.formateSmoothData(
-                sampleObject.charData.oriData[index].valueData[this.curBenchX],
+            tempOption.series[index * 2].data = this.formateSmoothData(
+                tempOriData[index].valueData[this.curBenchX],
             );
-            sampleObject.charData.charOption.series[index * 2 + 1].data =
-              sampleObject.charData.oriData[index].valueData[this.curBenchX];
+            tempOption.series[index * 2 + 1].data =
+              tempOriData[index].valueData[this.curBenchX];
           }
         });
         sampleObject.log = log;
         sampleObject.updateFlag = true;
         sampleObject.charObj.clear();
 
-        const tempOption = sampleObject.charData.charOption;
         const dataObj = tempOption.series[0];
 
         // one point
@@ -1834,19 +1853,12 @@ export default {
       } else {
         this.scalarCompare = this.$t('scalar.comparison');
 
-        if (this.abort) {
-          this.curPageArr.forEach((sampleObject) => {
-            this.$nextTick(() => {
-              // Draw chart
-              if (!this.compare) {
-                this.updateOrCreateChar(sampleObject.sampleIndex);
-              } else {
-                this.abort = true;
-              }
-            });
-          });
-          this.abort = false;
-        }
+        this.curPageArr.forEach((sampleObject) => {
+          // Draw chart
+          if (!sampleObject.charObj) {
+            this.updateOrCreateChar(sampleObject.sampleIndex);
+          }
+        });
 
         this.$nextTick(() => {
           this.resizeCallback();
@@ -1896,49 +1908,9 @@ export default {
 
     delThreshold(sampleItem) {
       this.stopUpdateSamples();
-      this.$confirm(this.$t('scalar.isDelete'), this.$t('scalar.info'), {
-        confirmButtonText: this.$t('public.sure'),
-        cancelButtonText: this.$t('public.cancel'),
-        type: 'warning',
-      })
-          .then(() => {
-            this.getCache();
-            if (
-              this.thresholdLocal &&
-            this.thresholdLocal[this.decodeTrainingJobId] &&
-            this.thresholdLocal[this.decodeTrainingJobId][sampleItem.tagName]
-            ) {
-              delete this.thresholdLocal[this.decodeTrainingJobId][
-                  sampleItem.tagName
-              ];
-              this.clearCache();
-              localStorage.setItem(
-                  'thresholdCache',
-                  JSON.stringify(this.thresholdLocal),
-              );
-            }
-            sampleItem.pieceStr = '';
-            const tempCharOption = sampleItem.charData.charOption;
-
-            if (
-              tempCharOption.visualMap &&
-            tempCharOption.visualMap['pieces'] &&
-            tempCharOption.visualMap['pieces'].length > 0
-            ) {
-              tempCharOption.visualMap = null;
-              tempCharOption.series[0].markLine = null;
-              tempCharOption.series[0].lineStyle['color'] = sampleItem.colors;
-            }
-            sampleItem.charObj.setOption(tempCharOption, false);
-            if (this.isTimeReload) {
-              this.autoUpdateSamples();
-            }
-          })
-          .catch(() => {
-            if (this.isTimeReload) {
-              this.autoUpdateSamples();
-            }
-          });
+      this.currentTagName = sampleItem.tagName;
+      this.currentSample = sampleItem;
+      this.delThresholdVisible = true;
     },
 
     /**
@@ -2325,6 +2297,90 @@ export default {
         this.autoUpdateSamples();
       }
     },
+
+    /**
+     * delete threshold cancel
+     */
+
+    delThresholdCancel() {
+      this.currentTagName = '';
+      this.currentSample = {};
+      this.delThresholdSwitch = false;
+      this.delThresholdVisible = false;
+      if (this.isTimeReload) {
+        this.autoUpdateSamples();
+      }
+    },
+
+    /**
+     * delete threshold commit
+     */
+
+    delThresholdCommit() {
+      this.getCache();
+      if (this.delThresholdSwitch) {
+        this.originDataArr.forEach((sampleObject) => {
+          if (this.multiSelectedTagNames[sampleObject.tagName]) {
+            if (
+              this.thresholdLocal &&
+              this.thresholdLocal[this.decodeTrainingJobId] &&
+              this.thresholdLocal[this.decodeTrainingJobId][
+                  sampleObject.tagName
+              ]
+            ) {
+              delete this.thresholdLocal[this.decodeTrainingJobId][
+                  sampleObject.tagName
+              ];
+              sampleObject.pieceStr = '';
+              const tempCharOption = sampleObject.charData.charOption;
+              if (
+                tempCharOption.visualMap &&
+                tempCharOption.visualMap['pieces'] &&
+                tempCharOption.visualMap['pieces'].length > 0
+              ) {
+                tempCharOption.visualMap = null;
+                tempCharOption.series[0].markLine = null;
+                tempCharOption.series[0].lineStyle['color'] =
+                  sampleObject.colors;
+              }
+              if (sampleObject.charObj) {
+                sampleObject.charObj.setOption(tempCharOption, false);
+              }
+            }
+          }
+        });
+      } else {
+        if (
+          this.thresholdLocal &&
+          this.thresholdLocal[this.decodeTrainingJobId] &&
+          this.thresholdLocal[this.decodeTrainingJobId][this.currentTagName]
+        ) {
+          delete this.thresholdLocal[this.decodeTrainingJobId][
+              this.currentTagName
+          ];
+          this.currentSample.pieceStr = '';
+          const tempCharOption = this.currentSample.charData.charOption;
+          if (
+            tempCharOption.visualMap &&
+            tempCharOption.visualMap['pieces'] &&
+            tempCharOption.visualMap['pieces'].length > 0
+          ) {
+            tempCharOption.visualMap = null;
+            tempCharOption.series[0].markLine = null;
+            tempCharOption.series[0].lineStyle[
+                'color'
+            ] = this.currentSample.colors;
+          }
+          this.currentSample.charObj.setOption(tempCharOption, false);
+        }
+      }
+      this.clearCache();
+      localStorage.setItem(
+          'thresholdCache',
+          JSON.stringify(this.thresholdLocal),
+      );
+      this.delThresholdVisible = false;
+    },
   },
   components: {
     ScalarButton,
@@ -2601,7 +2657,7 @@ export default {
 
         span {
           cursor: pointer;
-          width: 80px;
+          width: auto;
           height: 39px;
           display: inline-block;
         }
@@ -2702,7 +2758,7 @@ export default {
   .fs16 {
     font-size: 16px;
     color: #6c7280;
-    width: 160px;
+    width: 180px;
   }
 }
 .tooltip-show-content {
@@ -2711,4 +2767,34 @@ export default {
 .cl-title-right {
   padding-right: 20px;
 }
+
+.delDialog {
+  .el-dialog__header {
+    padding: 15px 15px 10px;
+  }
+  .el-dialog__title {
+    font-weight: normal;
+    line-height: 18px;
+  }
+  .el-dialog__body {
+    padding: 10px 15px;
+  }
+
+  .delThresholdItem {
+    display: flex;
+    margin-bottom: 10px;
+  }
+
+  .delThresholdIcon {
+    color: #e6a23c;
+    font-size: 24px;
+    width: 40px;
+    margin-right: 10px;
+  }
+
+  .delThresholdInfo {
+    line-height: 24px;
+    height: 24px;
+  }
+}
 </style>

mindinsight/ui/src/views/train-manage/step-trace.vue

@@ -59,10 +59,6 @@ limitations under the License.
       <div id="trace-container">
         <div id="trace"
              class="training-trace">
-          <div :title="$t('graph.downloadPic')"
-               class="download-button"
-               @click="downloadSVG">
-          </div>
           <svg version="1.1"
                xmlns="http://www.w3.org/2000/svg"
                height="100%"
@@ -75,8 +71,8 @@ limitations under the License.
                       markerHeight="8"
                       orient="auto">
                 <path d="M1,1 L1,7 L9,4 z"
-                      fill="#E6EBF5"
-                      stroke="#E6EBF5"></path>
+                      fill="#6c7280"
+                      stroke="#6c7280"></path>
               </marker>
               <marker id="marker_start"
                       refX="5"
@@ -85,14 +81,14 @@ limitations under the License.
                       markerHeight="8"
                       orient="auto">
                 <path d="M9,1 L9,7 L1,4 z"
-                      fill="#E6EBF5"
-                      stroke="#E6EBF5"></path>
+                      fill="#6c7280"
+                      stroke="#6c7280"></path>
               </marker>
             </defs>
           </svg>
         </div>
         <div class="image-noData svg"
-             v-if="svg.data.length === 0">
+             v-if="svg.data.length === 0 && svg.initOver">
           <div>
             <img :src="require('@/assets/images/nodata.png')"
                  alt="" />
@@ -162,19 +158,27 @@ export default {
         svgPadding: 20,
         totalWidth: 0,
         totalTime: 0,
-        rowHeight: 60,
+        cellHeight: 40,
+        cellPadding: 0,
+        rowPadding: 20,
+        rowMargin: 10,
+        totalHeight: 0,
         markerPadding: 4,
-        minRate: 0.05,
+        minRate: 0.1,
+        minTime: 0,
+        minWidth: 1,
+        fontSize: 12,
+        textMargin: 21,
         namespaceURI: 'http://www.w3.org/2000/svg',
         resizeTimer: null,
-        colorList: [
-          ['#A6DD82', '#edf8e6'],
-          ['#6CBFFF', '#e2f2ff'],
-          ['#fa8e5b', '#fff4de'],
-          ['#01a5a7', '#cceded'],
-        ],
-        colorIndex: 0,
+        colors: {
+          iteration_interval: ['#A6DD82', '#edf8e6'],
+          fp_and_bp: ['#6CBFFF', '#e2f2ff'],
+          tail: ['#fa8e5b', '#fff4de'],
+          stream_parallel: ['#01a5a7', '#cceded'],
+        },
         noData: false,
+        initOver: false,
       },
       deviceId: 0,
       radio: this.$t('profiling.lterationGap'),
@@ -270,7 +274,7 @@ export default {
       this.queryTrainingTrace(0);
     },
     changeStep(value) {
-      if (value === 0) {
+      if (value === 0 || (!this.steps.step && this.steps.step !== 0)) {
         this.steps.step = null;
         this.steps.trueStep = null;
         this.queryTrainingTrace(0);
@@ -421,6 +425,7 @@ export default {
       };
       RequestService.queryTrainingTrace(params).then(
           (res) => {
+            this.svg.initOver = true;
             if (
               res.data &&
             res.data.training_trace_graph &&
@@ -438,15 +443,13 @@ export default {
                 this.fp_start = '--';
                 this.bp_end = '--';
               }
-              document.querySelector('#trace').style.height = `${res.data
-                  .training_trace_graph.length * this.svg.rowHeight}px`;
-              this.svg.data = JSON.parse(
-                  JSON.stringify(res.data.training_trace_graph),
-              );
+
               this.removeTrace();
-              setTimeout(() => {
-                this.dealTraceData();
-              }, 100);
+              this.$nextTick(() => {
+                this.packageTraceData(
+                    JSON.parse(JSON.stringify(res.data.training_trace_graph)),
+                );
+              });
             } else {
               this.fp_start = '--';
               this.bp_end = '--';
@@ -460,44 +463,85 @@ export default {
             this.bp_end = '--';
             this.svg.data = [];
             this.svg.noData = true;
+            this.svg.initOver = true;
             this.removeTrace();
           },
       );
     },
+    packageTraceData(traceGraph) {
+      this.svg.totalTime = 0;
+      this.svg.minTime = 0;
+      this.svg.totalHeight = 0;
+      const data = [];
+
+      if (traceGraph && traceGraph[0] && traceGraph[0][0]) {
+        this.svg.totalTime = traceGraph[0][0].duration;
+        this.svg.minTime = this.svg.minRate * this.svg.totalTime;
+
+        traceGraph.forEach((row, index) => {
+          const rowObj = {
+            rowCount: 0,
+            data: [],
+            height: 0,
+            startY: this.svg.totalHeight,
+          };
+          let obj = [];
+          for (let i = 0; i < row.length; i++) {
+            if (row[i].duration < this.svg.minTime) {
+              if (obj.length) {
+                rowObj.data.push(obj);
+                obj = [];
+                rowObj.rowCount++;
+              }
+              rowObj.data.push([row[i]]);
+              rowObj.rowCount++;
+            } else {
+              obj.push(row[i]);
+            }
+
+            if (i === row.length - 1 && obj.length) {
+              rowObj.data.push(obj);
+              obj = [];
+              rowObj.rowCount++;
+            }
+          }
+          rowObj.height =
+            rowObj.rowCount * this.svg.cellHeight +
+            (rowObj.rowCount - 1) * this.svg.cellPadding +
+            (index ? this.svg.rowPadding * 2 : 0);
+
+          this.svg.totalHeight += rowObj.height + this.svg.rowMargin;
+          data.push(rowObj);
+        });
+
+        this.svg.totalHeight += this.svg.rowPadding;
+        document.querySelector(
+            '#trace',
+        ).style.height = `${this.svg.totalHeight}px`;
+        this.svg.data = JSON.parse(JSON.stringify(data));
+
+        this.$nextTick(() => {
+          this.dealTraceData();
+        });
+      }
+    },
 
     dealTraceData() {
       const traceDom = document.querySelector('#trace');
       if (traceDom) {
         this.svg.totalWidth = traceDom.offsetWidth - this.svg.svgPadding * 2;
 
-        if (this.svg.data[0] && this.svg.data[0].length) {
+        if (this.svg.data[0] && this.svg.data[0].data.length) {
           const svg = traceDom.querySelector('svg');
-          this.svg.totalTime = this.svg.data[0][0].duration;
-
           if (this.svg.totalTime) {
-            this.svg.colorIndex = 0;
-            const minTime = this.svg.minRate * this.svg.totalTime;
-
-            this.svg.data.forEach((row, index) => {
-              if (row && row.length) {
-                const dashedLine = this.addDashedLine(index);
-                svg.insertBefore(dashedLine, svg.querySelector('g'));
-
-                row.forEach((i) => {
-                  if (i.duration) {
-                    if (i.name) {
-                      const tempDom = this.createRect(i, index);
-                      const tempStr = `g${
-                        i.duration > minTime ? '' : '.arrow'
-                      }`;
-                      svg.insertBefore(tempDom, svg.querySelector(tempStr));
-                    } else {
-                      const tempDom = this.createArrow(i, index);
-                      svg.appendChild(tempDom);
-                    }
-                  }
-                });
+            this.svg.data.forEach((item, index) => {
+              let itemDom = {};
+              if (index) {
+                itemDom = this.createMultipleRowContainer(item);
+              } else {
+                itemDom = this.createRowContainer(item.data, item.startY);
               }
+              svg.appendChild(itemDom);
             });
           }
         } else {
@@ -505,40 +549,69 @@ export default {
         }
       }
     },
-    addDashedLine(index) {
-      const x1 = this.svg.svgPadding;
-      const x2 = this.svg.svgPadding + this.svg.totalWidth;
-      const y = index * this.svg.rowHeight;
-      const line = document.createElementNS(this.svg.namespaceURI, 'line');
-      line.setAttribute('x1', x1);
-      line.setAttribute('y1', y);
-      line.setAttribute('x2', x2);
-      line.setAttribute('y2', y);
-      line.setAttribute('style', 'stroke:#E2E2E2;stroke-width:1');
-      line.setAttribute('stroke-dasharray', '5 5');
+
+    createMultipleRowContainer(item) {
+      const rectContainer = document.createElementNS(
+          this.svg.namespaceURI,
+          'g',
+      );
+      rectContainer.setAttribute('class', 'container');
+
+      const rect = document.createElementNS(this.svg.namespaceURI, 'rect');
+      rect.setAttribute('x', this.svg.svgPadding);
+      rect.setAttribute('y', item.startY + this.svg.rowPadding);
+      rect.setAttribute('height', item.height);
+      rect.setAttribute('width', this.svg.totalWidth);
+      rect.setAttribute('style', 'fill:#edf0f5;stroke:#E2E2E2;stroke-width:1');
+      rectContainer.appendChild(rect);
+
+      const temp = this.createRowContainer(
+          item.data,
+          item.startY + this.svg.rowPadding,
+      );
+      rectContainer.appendChild(temp);
+      return rectContainer;
+    },
+
+    createRowContainer(data, startY) {
       const g = document.createElementNS(this.svg.namespaceURI, 'g');
-      g.setAttribute('class', 'dashedLine');
-      g.appendChild(line);
+
+      data.forEach((row, index) => {
+        const y =
+          startY +
+          this.svg.rowPadding +
+          index * (this.svg.cellPadding + this.svg.cellHeight);
+        row.forEach((i) => {
+          if (i.duration) {
+            let temp;
+            if (i.name) {
+              temp = this.createRect(i, y);
+              g.insertBefore(temp, g.querySelector('g'));
+            } else {
+              temp = this.createArrow(i, y);
+              g.appendChild(temp);
+            }
+          }
+        });
+      });
       return g;
     },
-    createRect(data, rowIndex) {
-      const color = this.svg.colorList[
-        rowIndex > 1 ? 3 : this.svg.colorIndex++ % 4
-      ];
-      const height = 40;
-      const width = (data.duration / this.svg.totalTime) * this.svg.totalWidth;
-      const fontSize = 12;
-      const normalRect = data.duration > this.svg.minRate * this.svg.totalTime;
+
+    createRect(data, startY) {
+      const color =
+        data.name && this.svg.colors[data.name]
+          ? this.svg.colors[data.name]
+          : this.svg.colors.stream_parallel;
 
       const x1 =
         (data.start / this.svg.totalTime) * this.svg.totalWidth +
         this.svg.svgPadding;
-      const y1 =
-        rowIndex * this.svg.rowHeight + (this.svg.rowHeight - height) / 2;
 
-      const g = document.createElementNS(this.svg.namespaceURI, 'g');
-      g.setAttribute('class', 'rect');
-      const gChild = document.createElementNS(this.svg.namespaceURI, 'g');
+      const width = Math.max(
+          this.svg.minWidth,
+          (data.duration / this.svg.totalTime) * this.svg.totalWidth,
+      );
+
       let name = '';
       switch (data.name) {
         case 'iteration_interval':
@@ -555,102 +628,117 @@ export default {
           break;
       }
 
+      const textContent = `${name}: ${data.duration.toFixed(4)}ms`;
+      const textWidth = this.getTextWidth(textContent);
+      const normalSize = data.duration >= this.svg.minTime;
+
+      const g = document.createElementNS(this.svg.namespaceURI, 'g');
+      g.setAttribute('class', 'rect');
+
       const rect = document.createElementNS(this.svg.namespaceURI, 'rect');
       rect.setAttribute('x', x1);
-      rect.setAttribute('y', y1);
-      rect.setAttribute('height', height);
+      rect.setAttribute('y', startY);
+      rect.setAttribute('height', this.svg.cellHeight);
       rect.setAttribute('width', width);
-      rect.setAttribute('style', `fill:${color[1]};stroke:${color[1]};`);
+      rect.setAttribute('style', `fill:${color[1]};stroke:${color[0]};`);
 
       const foreignObject = document.createElementNS(
           this.svg.namespaceURI,
           'foreignObject',
       );
-      foreignObject.textContent = `${name}: ${data.duration.toFixed(4)}ms`;
-      const textWidth = this.getTextWidth(foreignObject.textContent);
-
+      foreignObject.textContent = textContent;
       foreignObject.setAttribute(
           'x',
-        normalRect
+        normalSize
           ? x1
           : Math.min(
-              this.svg.svgPadding * 2 + this.svg.totalWidth - textWidth,
-              Math.max(0, x1 + width / 2 - textWidth / 2),
+              this.svg.svgPadding * 2 +
+                this.svg.totalWidth -
+                textWidth -
+                this.svg.textMargin,
+              Math.max(this.svg.textMargin, x1 + width / 2 - textWidth / 2),
           ),
       );
 
-      foreignObject.setAttribute(
-          'y',
-          y1 + (height - fontSize) / 2 + (normalRect ? 0 : fontSize),
-      );
-      foreignObject.setAttribute('height', fontSize);
+      foreignObject.setAttribute('y', startY);
+      foreignObject.setAttribute('height', this.svg.cellHeight);
       foreignObject.setAttribute('width', width);
       foreignObject.setAttribute('style', `color:${color[0]}`);
       foreignObject.setAttribute(
           'class',
-          `content${normalRect ? '' : ' content-mini'}`,
+          `content${normalSize ? '' : ' content-mini'}`,
       );
 
       const title = document.createElementNS(this.svg.namespaceURI, 'title');
-      title.textContent = `${name}: ${data.duration.toFixed(4)}ms`;
+      title.textContent = textContent;
 
-      gChild.appendChild(rect);
-      gChild.appendChild(foreignObject);
-      gChild.appendChild(title);
-      g.appendChild(gChild);
+      g.appendChild(rect);
+      g.appendChild(foreignObject);
+      g.appendChild(title);
       return g;
     },
 
-    createArrow(data, rowIndex) {
+    createArrow(data, startY) {
       const width = (data.duration / this.svg.totalTime) * this.svg.totalWidth;
       const x1 =
         (data.start / this.svg.totalTime) * this.svg.totalWidth +
-        this.svg.markerPadding +
         this.svg.svgPadding;
-      const x2 = x1 + width - this.svg.markerPadding * 2;
-      const y = rowIndex * this.svg.rowHeight + this.svg.rowHeight / 2;
+      const centerY = startY + this.svg.cellHeight / 2;
+
       const g = document.createElementNS(this.svg.namespaceURI, 'g');
       g.setAttribute('class', 'arrow');
 
       const line = document.createElementNS(this.svg.namespaceURI, 'line');
-      line.setAttribute('x1', x1);
-      line.setAttribute('y1', y);
-      line.setAttribute('x2', x2);
-      line.setAttribute('y2', y);
-      line.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
-      line.setAttribute('marker-end', 'url(#marker_end)');
-      line.setAttribute('marker-start', 'url(#marker_start)');
+      line.setAttribute('y1', centerY);
+      line.setAttribute('y2', centerY);
+      line.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
+      if (width > this.svg.markerPadding) {
+        line.setAttribute('x1', x1 + this.svg.markerPadding);
+        line.setAttribute('x2', x1 + width - this.svg.markerPadding);
+        line.setAttribute('marker-end', 'url(#marker_end)');
+        line.setAttribute('marker-start', 'url(#marker_start)');
+      } else {
+        line.setAttribute('x1', x1);
+        line.setAttribute('x2', x1 + width);
+      }
 
       const text = document.createElementNS(this.svg.namespaceURI, 'text');
       text.textContent = `${
-        rowIndex === 0 ? this.$t('profiling.approximateTime') : ''
+        data.duration === this.svg.totalTime
+          ? this.$t('profiling.approximateTime')
+          : ''
       }${data.duration.toFixed(4)}ms`;
-      const textWidth = this.getTextWidth(text.textContent);
+      const textWidth = text.textContent
+        ? this.getTextWidth(text.textContent)
+        : 0;
       text.setAttribute(
           'x',
           Math.min(
-              this.svg.svgPadding * 2 + this.svg.totalWidth - textWidth,
-              Math.max(0, (x2 - x1) / 2 + x1 - textWidth / 2),
+              this.svg.svgPadding * 2 +
+            this.svg.totalWidth -
+            textWidth -
+            this.svg.textMargin,
+              Math.max(this.svg.textMargin, width / 2 + x1 - textWidth / 2),
           ),
       );
-      text.setAttribute('y', y - 6);
-      text.setAttribute('font-size', 12);
-      text.setAttribute('fill', '#6c7280');
+      text.setAttribute('y', centerY - this.svg.fontSize / 2);
+      text.setAttribute('font-size', this.svg.fontSize);
+      text.setAttribute('fill', 'black');
 
       const startLine = document.createElementNS(this.svg.namespaceURI, 'line');
-      startLine.setAttribute('x1', x1 - this.svg.markerPadding);
-      startLine.setAttribute('y1', y - this.svg.rowHeight / 4);
-      startLine.setAttribute('x2', x1 - this.svg.markerPadding);
-      startLine.setAttribute('y2', y + this.svg.rowHeight / 4);
-      startLine.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
+      startLine.setAttribute('x1', x1);
+      startLine.setAttribute('y1', startY);
+      startLine.setAttribute('x2', x1);
+      startLine.setAttribute('y2', startY + this.svg.cellHeight);
+      startLine.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
       g.appendChild(startLine);
 
       const endLine = document.createElementNS(this.svg.namespaceURI, 'line');
-      endLine.setAttribute('x1', x1 + width - this.svg.markerPadding);
-      endLine.setAttribute('y1', y - this.svg.rowHeight / 4);
-      endLine.setAttribute('x2', x1 + width - this.svg.markerPadding);
-      endLine.setAttribute('y2', y + this.svg.rowHeight / 4);
-      endLine.setAttribute('style', 'stroke:#E6EBF5;stroke-width:1');
+      endLine.setAttribute('x1', x1 + width);
+      endLine.setAttribute('y1', startY);
+      endLine.setAttribute('x2', x1 + width);
+      endLine.setAttribute('y2', startY + this.svg.cellHeight);
+      endLine.setAttribute('style', 'stroke:#6c7280;stroke-width:1');
       g.appendChild(endLine);
       g.appendChild(line);
       g.appendChild(text);
@@ -689,15 +777,6 @@ export default {
         this.svg.resizeTimer = null;
       }, 500);
     },
-    downloadSVG() {
-      const svgDom = document.querySelector('svg').outerHTML;
-      const src = `data:image/svg+xml;base64,
-      ${window.btoa(unescape(encodeURIComponent(svgDom)))}`;
-      const a = document.createElement('a');
-      a.href = src;
-      a.download = new Date().valueOf();
-      a.click();
-    },
   },
   destroyed() {
     window.removeEventListener('resize', this.resizeTrace, false);
@@ -747,8 +826,8 @@ export default {
     }
   }
   .step-message {
-    height: 24px;
-    line-height: 24px;
+    height: 32px;
+    line-height: 16px;
     margin-top: 6px;
     margin-left: 14px;
     overflow-y: auto;
@@ -765,7 +844,7 @@ export default {
     font-weight: bold;
   }
   .pf-content-middle {
-    padding: 15px 15px 0;
+    padding: 10px 15px 0;
     height: calc(100% - 62px);
     #trace-container {
       width: 100%;
@@ -775,23 +854,13 @@ export default {
       .training-trace {
         position: relative;
         height: 0;
-        .download-button {
-          display: none;
-          position: absolute;
-          width: 12px;
-          height: 12px;
-          right: 10px;
-          top: 10px;
-          cursor: pointer;
-          background-image: url('../../assets/images/download.png');
-        }
         .content {
           overflow: hidden;
           text-align: center;
           text-overflow: ellipsis;
           white-space: nowrap;
           font-size: 12px;
-          line-height: 12px;
+          line-height: 40px;
         }
         .content-mini {
           overflow: visible;

mindinsight/ui/src/views/train-manage/tensor.vue

@@ -707,13 +707,14 @@ export default {
       this.$nextTick(() => {
         elementItem = this.$refs[sampleItem.ref];
         if (elementItem) {
-          elementItem[0].updateGridData();
           if (showLimitError) {
             elementItem[0].showRequestErrorMessage(
                 errorMsg,
                 sampleItem.formateData.value.dims,
                 sampleItem.filterStr,
             );
+          } else {
+            elementItem[0].updateGridData();
           }
         }
       });

mindinsight/ui/src/views/train-manage/training-dashboard.vue

@@ -21,6 +21,9 @@ limitations under the License.
       <div class="cl-dashboard-top-title">
         {{$t('trainingDashboard.trainingDashboardTitle')}}
       </div>
+      <div :title="$t('trainingDashboard.loadingTip')"
+           v-if="trainJobCached"
+           class="el-icon-loading loading-icon"></div>
       <div class="path-message">
         <span>{{$t('symbols.leftbracket')}}</span>
         <span>{{$t('trainingDashboard.summaryDirPath')}}</span>
@@ -234,6 +237,12 @@ export default {
         mousedown: 'mousedown',
         mouseup: 'mouseup',
       },
+      trainJobCached: false,
+      cacheKey: {
+        notInCache: 'NOT_IN_CACHE',
+        caching: 'CACHING',
+        cached: 'CACHED',
+      },
     };
   },
   computed: {
@@ -262,6 +271,7 @@ export default {
         }
         this.getDatavisualPlugins(true);
         this.queryDatasetGraph();
+        this.queryTrainJobCacheState();
         setTimeout(() => {
           this.$store.commit('setIsReload', false);
         }, this.reloadStopTime);
@@ -349,6 +359,7 @@ export default {
         this.startAutoUpdate();
       }
       this.queryDatasetGraph();
+      this.queryTrainJobCacheState();
     },
 
     /**
@@ -504,6 +515,7 @@ export default {
         if (!Object.keys(this.allDatasetGraphData).length) {
           this.queryDatasetGraph();
         }
+        this.queryTrainJobCacheState();
       }, this.timeReloadValue * 1000);
     },
     /**
@@ -990,9 +1002,10 @@ export default {
             this.$nextTick(() => {
               const elementItem = this.$refs.tensorChart;
               if (elementItem) {
-                elementItem.updateGridData();
                 if (showLimitError) {
                   elementItem.showRequestErrorMessage(errorMsg);
+                } else {
+                  elementItem.updateGridData();
                 }
               }
             });
@@ -1940,6 +1953,31 @@ export default {
           .selectAll('title')
           .remove();
     },
+    /**
+    * Query the cachee status of training jonb
+    */
+    queryTrainJobCacheState() {
+      const params = {
+        train_id: this.trainingJobId,
+      };
+      RequestService.querySummaryList(params, true).then((response) => {
+        if (
+          response &&
+          response.data &&
+          response.data.train_jobs &&
+          response.data.train_jobs.length
+        ) {
+          const curTrain = response.data.train_jobs[0];
+          if (curTrain.cache_status !== this.cacheKey.cached) {
+            this.trainJobCached = true;
+          } else {
+            this.trainJobCached = false;
+          }
+        } else {
+          this.trainJobCached = false;
+        }
+      });
+    },
   },
 };
 </script>
@@ -1962,6 +2000,9 @@ export default {
       padding: 18px 16px;
       font-weight: bold;
     }
+    .loading-icon {
+      margin-left: 5px;
+    }
     .cl-dashboard-top-title {
       float: left;
       color: #000000;

mindinsight/utils/computing_resource_mgr.py

@@ -0,0 +1,265 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Compute resource manager."""
+import fractions
+import math
+import threading
+import multiprocessing
+from concurrent import futures
+
+from mindinsight.utils.log import utils_logger as logger
+from mindinsight.utils.constant import GeneralErrors
+from mindinsight.utils.exceptions import MindInsightException
+
+
+_MP_CONTEXT = multiprocessing.get_context(method="forkserver")
+
+
+class ComputingResourceManager:
+    """
+    Manager for computing resources.
+
+    This class provides executors for computing tasks. Executors can only be used once.
+
+    Args:
+        executors_cnt (int): Number of executors to be provided by this class.
+        max_processes_cnt (int): Max number of processes to be used for computing.
+    """
+    def __init__(self, executors_cnt, max_processes_cnt):
+        self._max_processes_cnt = max_processes_cnt
+        self._executors_cnt = executors_cnt
+        self._lock = threading.Lock()
+        self._executors = {
+            ind: Executor(
+                self, executor_id=ind,
+                available_workers=fractions.Fraction(self._max_processes_cnt, self._executors_cnt))
+            for ind in range(self._executors_cnt)
+        }
+        self._remaining_executors = len(self._executors)
+        self._backend = futures.ProcessPoolExecutor(max_workers=max_processes_cnt, mp_context=_MP_CONTEXT)
+        logger.info("Initialized ComputingResourceManager with executors_cnt=%s, max_processes_cnt=%s.",
+                    executors_cnt, max_processes_cnt)
+
+    def __enter__(self):
+        """This method is not thread safe."""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """
+        This should not block because every executor have waited. If it blocks, there may be some problem.
+
+        This method is not thread safe.
+        """
+        self._backend.shutdown()
+
+    def get_executor(self):
+        """
+        Get an executor.
+
+        Returns:
+            Executor, which can be used for submitting tasks.
+
+        Raises:
+            ComputeResourceManagerException: when no more executor is available.
+        """
+        with self._lock:
+            self._remaining_executors -= 1
+            if self._remaining_executors < 0:
+                raise ComputingResourceManagerException("No more executors.")
+            return self._executors[self._remaining_executors]
+
+    def destroy_executor(self, executor_id):
+        """
+        Destroy an executor to reuse it's workers.
+
+        Args:
+            executor_id (int): Id of the executor to be destroyed.
+        """
+        with self._lock:
+            released_workers = self._executors[executor_id].available_workers
+            self._executors.pop(executor_id)
+
+            remaining_executors = len(self._executors)
+            logger.info("Destroy executor %s. Will release %s worker(s). Remaining executors: %s.",
+                        executor_id, released_workers, remaining_executors)
+            if not remaining_executors:
+                return
+
+            for executor in self._executors.values():
+                executor.add_worker(
+                    fractions.Fraction(
+                        released_workers.numerator,
+                        released_workers.denominator * remaining_executors))
+
+    def submit(self, *args, **kwargs):
+        """
+        Submit a task.
+
+        See concurrent.futures.Executor.submit() for details.
+
+        This method should only be called by Executor. Users should not call this method directly.
+        """
+        with self._lock:
+            return self._backend.submit(*args, **kwargs)
+
+
+class ComputingResourceManagerException(MindInsightException):
+    """
+    Indicates a computing resource error has occurred.
+
+    This exception should not be presented to end users.
+
+    Args:
+        msg (str): Exception message.
+    """
+    def __init__(self, msg):
+        super().__init__(error=GeneralErrors.COMPUTING_RESOURCE_ERROR, message=msg)
+
+
+class WrappedFuture:
+    """
+    Wrap Future objects with custom logics to release compute slots.
+
+    Args:
+         executor (Executor): The executor which generates this future.
+         original_future (futures.Future): Original future object.
+    """
+    def __init__(self, executor, original_future: futures.Future):
+        self._original_future = original_future
+        self._executor = executor
+
+    def add_done_callback(self, callback):
+        """
+        Add done callback.
+
+        See futures.Future.add_done_callback() for details.
+        """
+        def _wrapped_callback(*args, **kwargs):
+            logger.debug("Future callback called.")
+            try:
+                return callback(*args, **kwargs)
+            finally:
+                self._executor.release_slot()
+                self._executor.remove_done_future(self._original_future)
+        self._original_future.add_done_callback(_wrapped_callback)
+
+
+class Executor:
+    """
+    Task executor.
+
+    Args:
+        mgr (ComputingResourceManager): The ComputingResourceManager that generates this executor.
+        executor_id (int): Executor id.
+        available_workers (fractions.Fraction): Available workers.
+    """
+    def __init__(self, mgr: ComputingResourceManager, executor_id, available_workers):
+        self._mgr = mgr
+        self.closed = False
+        self._available_workers = available_workers
+        self._effective_workers = self._calc_effective_workers(self._available_workers)
+        self._slots = threading.Semaphore(value=self._effective_workers)
+        self._id = executor_id
+        self._futures = set()
+
+        self._lock = threading.Lock()
+
+        logger.debug("Available workers: %s.", available_workers)
+
+    def __enter__(self):
+        """This method is not thread safe."""
+        if self.closed:
+            raise ComputingResourceManagerException("Can not reopen closed executor.")
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """This method is not thread safe."""
+        self._close()
+
+    def submit(self, *args, **kwargs):
+        """
+        Submit task.
+
+        See concurrent.futures.Executor.submit() for details. This method is not thread safe.
+        """
+        logger.debug("Task submitted to executor %s.", self._id)
+
+        if self.closed:
+            raise ComputingResourceManagerException("Cannot submit task to a closed executor.")
+
+        # Thread will wait on acquire().
+        self._slots.acquire()
+        future = self._mgr.submit(*args, **kwargs)
+
+        # set.add is atomic in c-python.
+        self._futures.add(future)
+        return WrappedFuture(self, future)
+
+    def release_slot(self):
+        """
+        Release a slot for new tasks to be submitted.
+
+        Semaphore is itself thread safe, so no lock is needed.
+
+        This method should only be called by ExecutorFuture.
+        """
+        self._slots.release()
+
+    def remove_done_future(self, future):
+        """
+        Remove done futures so the executor will not track them.
+
+        This method should only be called by WrappedFuture.
+        """
+        # set.remove is atomic in c-python so no lock is needed.
+        self._futures.remove(future)
+
+    @staticmethod
+    def _calc_effective_workers(available_workers):
+        return 1 if available_workers <= 1 else math.floor(available_workers)
+
+    def _close(self):
+        self.closed = True
+        logger.debug("Executor is being closed, futures to wait: %s", self._futures)
+        futures.wait(self._futures)
+        logger.debug("Executor wait futures completed.")
+        self._mgr.destroy_executor(self._id)
+        logger.debug("Executor is closed.")
+
+    @property
+    def available_workers(self):
+        """Get available workers."""
+        with self._lock:
+            return self._available_workers
+
+    def add_worker(self, added_available_workers):
+        """This method should only be called by ComputeResourceManager."""
+        logger.debug("Add worker: %s", added_available_workers)
+        with self._lock:
+            self._available_workers += added_available_workers
+            new_effective_workers = self._calc_effective_workers(self._available_workers)
+            if new_effective_workers > self._effective_workers:
+                for _ in range(new_effective_workers - self._effective_workers):
+                    self._slots.release()
+
+            self._effective_workers = new_effective_workers
+
+    def wait_all_tasks_finish(self):
+        """
+        Wait all tasks finish.
+
+        This method is not thread safe.
+        """
+        futures.wait(self._futures)

mindinsight/utils/constant.py

@@ -31,6 +31,7 @@ class MindInsightModules(Enum):
     DATAVISUAL = 5
     PROFILERMGR = 6
     SCRIPTCONVERTER = 7
+    SYSMETRIC = 8
 
 
 class GeneralErrors(Enum):
@@ -43,6 +44,7 @@ class GeneralErrors(Enum):
     FILE_SYSTEM_PERMISSION_ERROR = 8
     PORT_NOT_AVAILABLE_ERROR = 9
     URL_DECODE_ERROR = 10
+    COMPUTING_RESOURCE_ERROR = 11
 
 
 class ProfilerMgrErrors(Enum):
@@ -71,7 +73,14 @@ class DataVisualErrors(Enum):
     HISTOGRAM_NOT_EXIST = 15
     TRAIN_JOB_DETAIL_NOT_IN_CACHE = 16
     QUERY_STRING_CONTAINS_NULL_BYTE = 17
+    TENSOR_NOT_EXIST = 18
+    MAX_RESPONSE_DATA_EXCEEDED_ERROR = 19
+    STEP_TENSOR_DATA_NOT_IN_CACHE = 20
 
 
 class ScriptConverterErrors(Enum):
     """Enum definition for mindconverter errors."""
+
+class SysmetricErrors(Enum):
+    """Enum definition for sysmetric errors."""
+    DSMI_QUERYING_NONZERO = 1

mindinsight/utils/log.py

@@ -224,3 +224,6 @@ def setup_logger(sub_module, log_name, **kwargs):
         logger.addHandler(logfile_handler)
 
     return logger
+
+
+utils_logger = setup_logger("utils", "utils")

tests/st/func/profiler/conftest.py

@@ -15,31 +15,11 @@
 """The st config."""
 
 import os
-import shutil
 import sys
-import tempfile
-
-import pytest
 
+from tests.st.func.profiler import RAW_DATA_BASE
 from tests.utils import mindspore
 
 sys.modules['mindspore'] = mindspore
 
-BASE_SUMMARY_DIR = tempfile.mkdtemp(prefix='test_profiler_summary_dir_base_')
-
-
-@pytest.fixture(scope="session")
-def create_summary_dir():
-    """Create summary directory for profiler module."""
-
-    try:
-        if os.path.exists(BASE_SUMMARY_DIR):
-            shutil.rmtree(BASE_SUMMARY_DIR)
-        permissions = os.R_OK | os.W_OK | os.X_OK
-        mode = permissions << 6
-        if not os.path.exists(BASE_SUMMARY_DIR):
-            os.mkdir(BASE_SUMMARY_DIR, mode=mode)
-        yield
-    finally:
-        if os.path.exists(BASE_SUMMARY_DIR):
-            shutil.rmtree(BASE_SUMMARY_DIR)
+BASE_SUMMARY_DIR = os.path.realpath(os.path.join(RAW_DATA_BASE, "run_1"))

tests/st/func/profiler/test_analyse.py

@@ -21,19 +21,16 @@ Usage:
 """
 
 import os
-from unittest import mock, TestCase
+from unittest import TestCase
 
 import pytest
 
 from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
 from mindinsight.profiler.common.exceptions.exceptions import StepNumNotSupportedException, \
     ProfilerParamValueErrorException
-from mindinsight.profiler.profiling import Profiler, FrameworkParser
-from tests.st.func.profiler import RAW_DATA_BASE
 from tests.st.func.profiler.conftest import BASE_SUMMARY_DIR
 
 
-@pytest.mark.usefixtures('create_summary_dir')
 class TestProfilerAnalyse(TestCase):
     """Test Converter module."""
     JOB_ID = 'JOB3'
@@ -42,26 +39,14 @@ class TestProfilerAnalyse(TestCase):
     def setup_class(cls):
         """Generate parsed files."""
         cls.step_trace_file = 'step_trace_raw_1_detail_time.csv'
-        cls.generate_parsed_files()
+        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
+        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
 
     def setUp(self):
         """Setup before each test."""
         self.step_trace_analyser = AnalyserFactory.instance().get_analyser(
             'step_trace', self.profiler, '1')
 
-    @classmethod
-    def generate_parsed_files(cls):
-        """Test parse raw info about profiler."""
-        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
-        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
-        FrameworkParser._raw_data_dir = RAW_DATA_BASE
-        if not os.path.exists(cls.summary_dir):
-            os.makedirs(cls.summary_dir)
-        Profiler._base_profiling_container_path = os.path.join(RAW_DATA_BASE, 'container')
-        with mock.patch('mindinsight.profiler.profiling.PROFILING_LOG_BASE_PATH', RAW_DATA_BASE):
-            profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False,
-                                output_path=cls.summary_dir, job_id=cls.JOB_ID)
-            profiler.analyse()
 
     @pytest.mark.level0
     @pytest.mark.env_single

tests/st/func/profiler/test_minddata_pipeline_analyser.py

@@ -19,19 +19,13 @@ Usage:
     pytest tests/st/func/profiler
 """
 import os
-import shutil
-from unittest import mock
 
 import pytest
 
-from mindinsight.profiler import Profiler
 from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
-from mindinsight.profiler.parser.framework_parser import FrameworkParser
 from tests.st.func.profiler.conftest import BASE_SUMMARY_DIR
-from tests.ut.profiler import RAW_DATA_BASE
 
 
-@pytest.mark.usefixtures('create_summary_dir')
 class TestMinddataPipelineAnalyser:
     """Test minddata pipeline analyser module."""
     JOB_ID = 'JOB3'
@@ -39,29 +33,14 @@ class TestMinddataPipelineAnalyser:
     @classmethod
     def setup_class(cls):
         """Generate parsed files."""
-        cls.generate_parsed_files()
+        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
+        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
 
     def setup_method(self):
         """Create analyser."""
         self._analyser = AnalyserFactory.instance().get_analyser(
             'minddata_pipeline', self.profiler, '1')
 
-    @classmethod
-    def generate_parsed_files(cls):
-        """Test parse raw info about profiler."""
-        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
-        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
-        FrameworkParser._raw_data_dir = RAW_DATA_BASE
-        if not os.path.exists(cls.summary_dir):
-            os.makedirs(cls.summary_dir)
-        os.makedirs(cls.profiler, exist_ok=True)
-        pipeline_path = os.path.join(RAW_DATA_BASE, 'profiler', 'pipeline_profiling_1.json')
-        shutil.copy(pipeline_path, cls.profiler)
-        Profiler._base_profiling_container_path = os.path.join(RAW_DATA_BASE, 'container')
-        with mock.patch('mindinsight.profiler.profiling.PROFILING_LOG_BASE_PATH', RAW_DATA_BASE):
-            profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False,
-                                output_path=cls.summary_dir, job_id=cls.JOB_ID)
-            profiler.analyse()
 
     @pytest.mark.level0
     @pytest.mark.env_single

tests/st/func/profiler/test_op_analyser.py

@@ -19,16 +19,11 @@ Usage:
     pytest tests/st/func/profiler
 """
 import os
-from unittest import mock
 
 import pytest
 
-from mindinsight.profiler import Profiler
 from mindinsight.profiler.analyser.analyser_factory import AnalyserFactory
-from mindinsight.profiler.parser.framework_parser import FrameworkParser
 from tests.st.func.profiler.conftest import BASE_SUMMARY_DIR
-from tests.ut.profiler import RAW_DATA_BASE
-
 
 OP_GATHER_V2_INFO = {
     'col_name': [
@@ -84,7 +79,6 @@ OP_GATHER_V2_INFO = {
 }
 
 
-@pytest.mark.usefixtures('create_summary_dir')
 class TestOpAnalyser:
     """Test AICORE and AICPU analyser module."""
     JOB_ID = 'JOB3'
@@ -92,7 +86,8 @@ class TestOpAnalyser:
     @classmethod
     def setup_class(cls):
         """Generate parsed files."""
-        cls.generate_parsed_files()
+        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
+        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
 
     def setup_method(self):
         """Create analyser."""
@@ -101,20 +96,6 @@ class TestOpAnalyser:
         self._analyser_aicore_detail = AnalyserFactory.instance().get_analyser(
             'aicore_detail', self.profiler, '1')
 
-    @classmethod
-    def generate_parsed_files(cls):
-        """Test parse raw info about profiler."""
-        cls.summary_dir = os.path.join(BASE_SUMMARY_DIR, 'normal_run')
-        cls.profiler = os.path.join(cls.summary_dir, 'profiler')
-        FrameworkParser._raw_data_dir = RAW_DATA_BASE
-        if not os.path.exists(cls.summary_dir):
-            os.makedirs(cls.summary_dir)
-        Profiler._base_profiling_container_path = os.path.join(RAW_DATA_BASE, 'container')
-        with mock.patch('mindinsight.profiler.profiling.PROFILING_LOG_BASE_PATH', RAW_DATA_BASE):
-            profiler = Profiler(subgraph='all', is_detail=True, is_show_op_path=False,
-                                output_path=cls.summary_dir, job_id=cls.JOB_ID)
-            profiler.analyse()
-
     @pytest.mark.level0
     @pytest.mark.env_single
     @pytest.mark.platform_x86_cpu

tests/ut/backend/datavisual/conftest.py

@@ -15,28 +15,15 @@
 """
 Description: This file is used for some common util.
 """
-from unittest.mock import Mock
-
 import pytest
 from flask import Response
 
-from mindinsight.backend import datavisual
-from mindinsight.datavisual.utils import tools
+from mindinsight.backend.application import APP
 
 
 @pytest.fixture
 def client():
     """This fixture is flask client."""
-    mock_data_manager = Mock()
-    mock_data_manager.start_load_data = Mock()
-    datavisual.DATA_MANAGER = mock_data_manager
-
-    packages = ["mindinsight.backend.data_visual"]
-
-    mock_obj = Mock(return_value=packages)
-    tools.find_app_package = mock_obj
-
-    from mindinsight.backend.application import APP
     APP.response_class = Response
     app_client = APP.test_client()
 

tests/ut/datavisual/common/test_error_handler.py → tests/ut/backend/datavisual/test_request_error.py

@@ -22,12 +22,10 @@ from unittest.mock import patch
 
 from werkzeug.exceptions import MethodNotAllowed, NotFound
 
-from mindinsight.datavisual.processors import scalars_processor
 from mindinsight.datavisual.processors.scalars_processor import ScalarsProcessor
 
 from ....utils.tools import get_url
 from ...backend.datavisual.conftest import TRAIN_ROUTES
-from ..mock import MockLogger
 
 
 class TestErrorHandler:
@@ -36,7 +34,6 @@ class TestErrorHandler:
     @patch.object(ScalarsProcessor, 'get_metadata_list')
     def test_handle_http_exception_error_not_found(self, mock_scalar_processor, client):
         """Test handle http exception error not found."""
-        scalars_processor.logger = MockLogger
         text = 'Test Message'
 
         # NotFound
@@ -59,7 +56,6 @@ class TestErrorHandler:
     @patch.object(ScalarsProcessor, 'get_metadata_list')
     def test_handle_http_exception_error_method_not_allowed(self, mock_scalar_processor, client):
         """Test handling http exception error method not allowed."""
-        scalars_processor.logger = MockLogger
         text = 'Test Message'
 
         # MethodNotAllowed
@@ -82,7 +78,6 @@ class TestErrorHandler:
     @patch.object(ScalarsProcessor, 'get_metadata_list')
     def test_handle_http_exception_error_method_other_errors(self, mock_scalar_processor, client):
         """Test handling http exception error method other errors."""
-        scalars_processor.logger = MockLogger
         text = 'Test Message'
 
         # Other errors

tests/ut/datavisual/conftest.py

@@ -1,45 +0,0 @@
-# Copyright 2019 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""
-Description: This file is used for some common util.
-"""
-from unittest.mock import Mock
-
-import pytest
-from flask import Response
-
-from mindinsight.backend import datavisual
-from mindinsight.datavisual import utils
-
-
-@pytest.fixture
-def client():
-    """This fixture is flask client."""
-    mock_data_manager = Mock()
-    mock_data_manager.start_load_data = Mock()
-    datavisual.DATA_MANAGER = mock_data_manager
-
-    packages = ["mindinsight.backend.raw_dataset",
-                "mindinsight.backend.train_dataset",
-                "mindinsight.backend.data_visual"]
-
-    mock_obj = Mock(return_value=packages)
-    utils.find_app_package = mock_obj
-
-    from mindinsight.backend.application import APP
-    APP.response_class = Response
-    app_client = APP.test_client()
-
-    yield app_client

tests/ut/datavisual/data_transform/test_data_loader.py

@@ -27,6 +27,7 @@ import pytest
 from mindinsight.datavisual.common.exceptions import SummaryLogPathInvalid
 from mindinsight.datavisual.data_transform import data_loader
 from mindinsight.datavisual.data_transform.data_loader import DataLoader
+from mindinsight.utils.computing_resource_mgr import ComputingResourceManager
 
 from ..mock import MockLogger
 
@@ -57,7 +58,7 @@ class TestDataLoader:
         """Test loading method with empty file list."""
         loader = DataLoader(self._summary_dir)
         with pytest.raises(SummaryLogPathInvalid):
-            loader.load()
+            loader.load(ComputingResourceManager(1, 1))
         assert 'No valid files can be loaded' in str(MockLogger.log_msg['warning'])
 
     def test_load_with_invalid_file_list(self):
@@ -66,7 +67,7 @@ class TestDataLoader:
         self._generate_files(self._summary_dir, file_list)
         loader = DataLoader(self._summary_dir)
         with pytest.raises(SummaryLogPathInvalid):
-            loader.load()
+            loader.load(ComputingResourceManager(1, 1))
         assert 'No valid files can be loaded' in str(MockLogger.log_msg['warning'])
 
     def test_load_success(self):
@@ -77,6 +78,6 @@ class TestDataLoader:
         file_list = ['summary.001', 'summary.002']
         self._generate_files(dir_path, file_list)
         dataloader = DataLoader(dir_path)
-        dataloader.load()
+        dataloader.load(ComputingResourceManager(1, 1))
         assert dataloader._loader is not None
         shutil.rmtree(dir_path)

tests/ut/datavisual/data_transform/test_data_manager.py

@@ -81,8 +81,9 @@ class TestDataManager:
     def test_start_load_data_success(self):
         """Test start_load_data method success."""
         summary_base_dir = tempfile.mkdtemp()
+        dir_num = 3
         train_ids = []
-        for i in range(3):
+        for i in range(dir_num):
             log_path = os.path.join(summary_base_dir, f'dir{i}')
             self._make_path_and_file_list(log_path)
             train_ids.append(f'./dir{i}')
@@ -215,7 +216,7 @@ class TestDataManager:
         expected_loader_ids = expected_loader_ids[-MAX_DATA_LOADER_SIZE:]
 
         # Make sure to finish loading, make it init.
-        mock_data_manager._status = DataManagerStatus.INIT
+        mock_data_manager._detail_cache._status = DataManagerStatus.INIT.value
         mock_generate_loaders.return_value = loader_dict
         mock_data_manager.start_load_data(reload_interval=0)
         check_loading_done(mock_data_manager)

tests/ut/datavisual/data_transform/test_histogram_container.py

@@ -29,9 +29,9 @@ class TestHistogram:
         mocked_bucket.width = 1
         mocked_bucket.count = 1
         mocked_input.buckets = [mocked_bucket]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=1, min_val=0, bins=1)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=1, min_val=0, bins=1)
+        buckets = histogram_container.buckets()
         assert buckets == ((0.0, 1.0, 1),)
 
     def test_re_sample_buckets_split_original(self):
@@ -42,9 +42,9 @@ class TestHistogram:
         mocked_bucket.width = 1
         mocked_bucket.count = 1
         mocked_input.buckets = [mocked_bucket]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=1, min_val=0, bins=3)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=1, min_val=0, bins=3)
+        buckets = histogram_container.buckets()
         assert buckets == ((0.0, 0.3333333333333333, 1), (0.3333333333333333, 0.3333333333333333, 1),
                            (0.6666666666666666, 0.3333333333333333, 1))
 
@@ -60,9 +60,9 @@ class TestHistogram:
         mocked_bucket2.width = 1
         mocked_bucket2.count = 2
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=3, min_val=-1, bins=4)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=3, min_val=-1, bins=4)
+        buckets = histogram_container.buckets()
         assert buckets == ((-1.0, 1.0, 0), (0.0, 1.0, 1), (1.0, 1.0, 2), (2.0, 1.0, 0))
 
     def test_re_sample_buckets_merge_bucket(self):
@@ -77,9 +77,9 @@ class TestHistogram:
         mocked_bucket2.width = 1
         mocked_bucket2.count = 10
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=3, min_val=-1, bins=5)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=3, min_val=-1, bins=5)
+        buckets = histogram_container.buckets()
         assert buckets == (
             (-1.0, 0.8, 0), (-0.19999999999999996, 0.8, 1), (0.6000000000000001, 0.8, 5), (1.4000000000000004, 0.8, 6),
             (2.2, 0.8, 0))
@@ -96,9 +96,9 @@ class TestHistogram:
         mocked_bucket2.width = 0
         mocked_bucket2.count = 2
         mocked_input.buckets = [mocked_bucket, mocked_bucket2]
-        histogram = hist.HistogramContainer(mocked_input)
-        histogram.set_visual_range(max_val=2, min_val=0, bins=3)
-        buckets = histogram.buckets()
+        histogram_container = hist.HistogramContainer(mocked_input)
+        histogram_container.histogram.set_visual_range(max_val=2, min_val=0, bins=3)
+        buckets = histogram_container.buckets()
         assert buckets == (
             (0.0, 0.6666666666666666, 1),
             (0.6666666666666666, 0.6666666666666666, 3),

tests/ut/datavisual/data_transform/test_ms_data_loader.py

@@ -30,6 +30,7 @@ from mindinsight.datavisual.data_transform.ms_data_loader import MSDataLoader
 from mindinsight.datavisual.data_transform.ms_data_loader import _PbParser
 from mindinsight.datavisual.data_transform.events_data import TensorEvent
 from mindinsight.datavisual.common.enums import PluginNameEnum
+from mindinsight.utils.computing_resource_mgr import ComputingResourceManager
 
 from ..mock import MockLogger
 from ....utils.log_generators.graph_pb_generator import create_graph_pb_file
@@ -85,7 +86,7 @@ class TestMsDataLoader:
         write_file(file1, SCALAR_RECORD)
         ms_loader = MSDataLoader(summary_dir)
         ms_loader._latest_summary_filename = 'summary.00'
-        ms_loader.load()
+        ms_loader.load(ComputingResourceManager(1, 1))
         shutil.rmtree(summary_dir)
         tag = ms_loader.get_events_data().list_tags_by_plugin('scalar')
         tensors = ms_loader.get_events_data().tensors(tag[0])
@@ -98,7 +99,7 @@ class TestMsDataLoader:
         file2 = os.path.join(summary_dir, 'summary.02')
         write_file(file2, SCALAR_RECORD)
         ms_loader = MSDataLoader(summary_dir)
-        ms_loader.load()
+        ms_loader.load(ComputingResourceManager(1, 1))
         shutil.rmtree(summary_dir)
         assert 'Check crc faild and ignore this file' in str(MockLogger.log_msg['warning'])
 
@@ -124,7 +125,7 @@ class TestMsDataLoader:
         summary_dir = tempfile.mkdtemp()
         create_graph_pb_file(output_dir=summary_dir, filename=filename)
         ms_loader = MSDataLoader(summary_dir)
-        ms_loader.load()
+        ms_loader.load(ComputingResourceManager(1, 1))
         events_data = ms_loader.get_events_data()
         plugins = events_data.list_tags_by_plugin(PluginNameEnum.GRAPH.value)
         shutil.rmtree(summary_dir)

tests/ut/datavisual/processors/test_train_task_manager.py

@@ -69,7 +69,7 @@ class TestTrainTaskManager:
     def load_data(self):
         """Load data."""
         log_operation = LogOperations()
-        self._plugins_id_map = {'image': [], 'scalar': [], 'graph': [], 'histogram': []}
+        self._plugins_id_map = {'image': [], 'scalar': [], 'graph': [], 'histogram': [], 'tensor': []}
         self._events_names = []
         self._train_id_list = []
 

tests/ut/profiler/parser/test_aicpu_parser.py

@@ -1,74 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Test the aicpu parser."""
-import os
-import tempfile
-import shutil
-
-from unittest import TestCase
-
-from mindinsight.profiler.parser.aicpu_data_parser import DataPreProcessParser
-
-
-def get_result(file_path):
-    """
-    Get result from the aicpu file.
-
-    Args:
-        file_path (str): The aicpu file path.
-
-    Returns:
-        list[list], the parsed aicpu information.
-    """
-    result = []
-    try:
-        file = open(file_path, 'r')
-        result.append(file.read())
-        return result
-    finally:
-        if file:
-            file.close()
-
-
-class TestAicpuParser(TestCase):
-    """Test the class of Aicpu Parser."""
-
-    def setUp(self) -> None:
-        """Initialization before test case execution."""
-        self.profiling_dir = os.path.realpath(os.path.join(os.path.dirname(__file__),
-                                                           '../../../utils/resource/'
-                                                           'JOB_AICPU/data'))
-        self.expect_dir = os.path.realpath(os.path.join(os.path.dirname(__file__),
-                                                        '../../../utils/resource/'
-                                                        'JOB_AICPU/expect'))
-        self.output_path = tempfile.mkdtemp(prefix='output_data_preprocess_aicpu_')
-        self.output_file = os.path.join(self.output_path, 'output_data_preprocess_aicpu_0.txt')
-        self.expect_file = os.path.join(self.expect_dir, 'output_data_preprocess_aicpu_0.txt')
-
-    def test_aicpu_parser(self):
-        """Test the class of Aicpu Parser."""
-        data = DataPreProcessParser(self.profiling_dir, self.output_file)
-        data.execute()
-        expect_result = get_result(self.expect_file)
-        result = get_result(self.output_file)
-        shutil.rmtree(self.output_path)
-        assert expect_result == result
-
-    def test_aicpu_parser_file_not_exist(self):
-        """Test the class of Aicpu Parser."""
-        profiling_dir = os.path.realpath(os.path.join(self.profiling_dir, 'data'))
-        data = DataPreProcessParser(profiling_dir, self.output_file)
-        data.execute()
-        shutil.rmtree(self.output_path)

tests/ut/profiler/parser/test_framework_parser.py

@@ -1,158 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Test the framework parser module."""
-import csv
-import os
-import shutil
-import tempfile
-from unittest import mock
-
-import pytest
-from marshmallow import ValidationError
-
-from mindinsight.profiler.common.exceptions.exceptions import \
-    ProfilerPathErrorException, ProfilerDirNotFoundException, \
-    ProfilerFileNotFoundException
-from mindinsight.profiler.parser.framework_parser import FrameworkParser
-from tests.ut.profiler import PROFILER_DIR, RAW_DATA_BASE
-
-
-def get_framework_result(file_path):
-    """
-    Get framework result from the framework file.
-
-    Args:
-        file_path (str): The framework file path.
-
-    Returns:
-        list[list], the parsed framework information.
-    """
-    result = []
-    with open(file_path, 'r') as file:
-        csv_reader = csv.reader(file)
-        for row in csv_reader:
-            result.append(row)
-    return result
-
-
-class TestFrameworkParser:
-    """Test the class of `FrameworkParser`."""
-    def setup_method(self):
-        """Initialization before test case execution."""
-        FrameworkParser._raw_data_dir = RAW_DATA_BASE
-
-        self._output_path_1 = tempfile.mkdtemp(prefix='test_framework_parser_')
-        self._parser_1 = FrameworkParser('JOB1', '0', self._output_path_1)
-
-        self._output_path_2 = tempfile.mkdtemp(prefix='test_framework_parser_')
-        self._parser_2 = FrameworkParser('JOB2', '0', self._output_path_2)
-
-        self._output_path_4 = tempfile.mkdtemp(prefix='test_framework_parser_')
-        self._parser_4 = FrameworkParser('JOB4', '0', self._output_path_4)
-
-    def teardown_method(self) -> None:
-        """Clear up after test case execution."""
-        shutil.rmtree(self._output_path_1)
-        shutil.rmtree(self._output_path_2)
-        shutil.rmtree(self._output_path_4)
-        FrameworkParser._raw_data_dir = '/var/log/npu/profiling'
-
-    def test_save_path(self):
-        """Test the querying save path function."""
-        expect_result = os.path.join(self._output_path_1, 'framework_raw_0.csv')
-        assert expect_result == self._parser_1.save_path
-
-        expect_result = os.path.join(self._output_path_2, 'framework_raw_0.csv')
-        assert expect_result == self._parser_2.save_path
-
-    def test_point_info(self):
-        """Test the querying point info function."""
-        expect_result = {
-            1: 'Default/Cast-op6',
-            2: 'Default/TransData-op7'
-        }
-        assert expect_result == self._parser_4.point_info
-
-    def test_to_task_id_full_op_name_dict(self):
-        """Test the querying task id and full operator name dict function."""
-        expect_result = {
-            '51517': 'Default/Cast-op6',
-            '51518': 'Default/TransData-op7',
-            '51519': 'Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5',
-            '51522': 'Default/network-WithLossCell/_backbone-ResNet/'
-                     'layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28'
-        }
-        assert expect_result == self._parser_1.to_task_id_full_op_name_dict()
-        assert expect_result == self._parser_2.to_task_id_full_op_name_dict()
-
-        expect_result = {
-            '0_1': 'Default/Cast-op6',
-            '0_2': 'Default/TransData-op7',
-            '0_3': 'Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5',
-            '0_4': 'Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/'
-                   '0-ResidualBlock/conv1-Conv2d/Cast-op28'
-        }
-        assert expect_result == self._parser_4.to_task_id_full_op_name_dict()
-
-    def test_parse(self):
-        """Test the parse function."""
-        expect_framework_file = os.path.join(PROFILER_DIR, 'framework_raw_0.csv')
-        expect_framework_file = os.path.realpath(expect_framework_file)
-        expect_result = get_framework_result(expect_framework_file)
-
-        self._parser_1.parse()
-        framework_file = os.path.join(self._output_path_1, 'framework_raw_0.csv')
-        result = get_framework_result(framework_file)
-        assert expect_result == result
-
-        self._parser_2.parse()
-        framework_file = os.path.join(self._output_path_2, 'framework_raw_0.csv')
-        result = get_framework_result(framework_file)
-        assert expect_result == result
-
-    @mock.patch('mindinsight.profiler.parser.framework_parser.validate_and_normalize_path')
-    def test_create_framework_parser_fail_1(self, *args):
-        """Test the function of fail to create framework parser."""
-        args[0].side_effect = ValidationError({'profiler': {"The path is invalid!"}})
-
-        with pytest.raises(ProfilerPathErrorException) as exc_info:
-            FrameworkParser('JOB1', '0')
-        assert exc_info.value.error_code == '50546081'
-        assert exc_info.value.message == 'Path error. Profiling path is invalid.'
-
-    @mock.patch('os.path.isdir')
-    def test_create_framework_parser_fail_2(self, *args):
-        """Test the function of fail to create framework parser."""
-        args[0].return_value = False
-        FrameworkParser._raw_data_dir = '/var/log/npu/profiling'
-
-        with pytest.raises(ProfilerDirNotFoundException) as exc_info:
-            FrameworkParser('JOB1', '0')
-        assert exc_info.value.error_code == '50546083'
-        assert exc_info.value.message == \
-               'The dir </var/log/npu/profiling/JOB1> not found.'
-
-    @mock.patch('os.listdir')
-    @mock.patch('os.path.isdir')
-    def test_create_framework_parser_fail_3(self, *args):
-        """Test the function of fail to create framework parser."""
-        args[0].return_value = True
-        args[1].return_value = []
-        FrameworkParser._raw_data_dir = '/var/log/npu/profiling'
-
-        with pytest.raises(ProfilerFileNotFoundException) as exc_info:
-            FrameworkParser('JOB1', '0')
-        assert exc_info.value.error_code == '50546084'
-        assert exc_info.value.message == 'The file <Framework> not found.'

tests/ut/profiler/parser/test_minddata_pipeline_parser.py

@@ -1,93 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""Test the minddata pipeline parser module."""
-import csv
-import os
-import shutil
-import tempfile
-
-from mindinsight.profiler.parser.minddata_pipeline_parser import \
-    MinddataPipelineParser
-from tests.ut.profiler import PROFILER_DIR, RAW_DATA, RAW_DATA_JOB2
-
-
-def get_minddata_pipeline_result(file_path):
-    """
-    Get minddata pipeline result from the minddata pipeline file.
-
-    Args:
-        file_path (str): The minddata pipeline file path.
-
-    Returns:
-        list[list], the parsed minddata pipeline information.
-    """
-    result = []
-    with open(file_path, 'r') as file:
-        csv_reader = csv.reader(file)
-        for row in csv_reader:
-            result.append(row)
-    return result
-
-
-class TestMinddataPipelineParser:
-    """Test the class of `MinddataPipelineParser`."""
-    def setup_method(self):
-        """Initialization before test case execution."""
-        self._output_path_1 = tempfile.mkdtemp(
-            prefix='test_minddata_pipeline_parser_'
-        )
-        self._parser_1 = MinddataPipelineParser(
-            RAW_DATA, '0', self._output_path_1
-        )
-
-        self._output_path_2 = tempfile.mkdtemp(
-            prefix='test_minddata_pipeline_parser_'
-        )
-        self._parser_2 = MinddataPipelineParser(
-            RAW_DATA_JOB2, '0', self._output_path_2
-        )
-
-    def teardown_method(self) -> None:
-        """Clear up after test case execution."""
-        shutil.rmtree(self._output_path_1)
-        shutil.rmtree(self._output_path_2)
-
-    def test_save_path(self):
-        """Test the querying save path function."""
-        expect_result = os.path.join(
-            self._output_path_1, 'minddata_pipeline_raw_0.csv'
-        )
-        assert expect_result == self._parser_1.save_path
-
-    def test_parse(self):
-        """Test the parse function."""
-        expect_pipeline_file = os.path.join(
-            PROFILER_DIR, 'minddata_pipeline_raw_0.csv'
-        )
-        expect_result = get_minddata_pipeline_result(expect_pipeline_file)
-
-        self._parser_1.parse()
-        pipeline_file = os.path.join(
-            self._output_path_1, 'minddata_pipeline_raw_0.csv'
-        )
-        result = get_minddata_pipeline_result(pipeline_file)
-        assert expect_result == result
-
-        self._parser_2.parse()
-        pipeline_file = os.path.join(
-            self._output_path_2, 'minddata_pipeline_raw_0.csv'
-        )
-        result = get_minddata_pipeline_result(pipeline_file)
-        assert expect_result == result

tests/ut/profiler/parser/__init__.py → tests/ut/sysmetric/__init__.py

@@ -1,14 +1,15 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Test the system metrics."""

tests/ut/sysmetric/metrics_collector.py

@@ -0,0 +1,42 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Test the metrics collector."""
+from os import cpu_count
+
+from mindinsight.sysmetric.collector import collect_cpu, collect_mem, collect_npu
+
+
+def test_collect_cpu():
+    overall = collect_cpu(percent=True)
+    assert isinstance(overall, dict)
+    for value in overall.values():
+        assert 0 <= value <= 100
+    for key in 'user', 'system', 'idle':
+        assert key in overall
+    cores = collect_cpu(percpu=True)
+    assert isinstance(cores, list) and len(cores) == cpu_count()
+
+
+def test_collect_mem():
+    mem = collect_mem()
+    assert 'total' in mem
+    assert 'available' in mem
+    assert mem['total'] > mem['available']
+
+
+def test_collect_npu():
+    npu = collect_npu()
+    if npu is not None:
+        assert len(npu) == 8

tests/utils/log_generators/tensor_log_generator.py

@@ -0,0 +1,110 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Log generator for tensor data."""
+import time
+
+from operator import mul
+from functools import reduce
+import numpy as np
+from mindinsight.datavisual.proto_files import mindinsight_anf_ir_pb2 as anf_ir_pb2
+from mindinsight.datavisual.proto_files import mindinsight_summary_pb2 as summary_pb2
+
+from .log_generator import LogGenerator
+
+
+class TensorLogGenerator(LogGenerator):
+    """
+    Log generator for tensor data.
+
+    This is a log generator writing tensor data. User can use it to generate fake
+    summary logs about tensor.
+    """
+
+    def generate_event(self, values):
+        """
+        Method for generating tensor event.
+
+        Args:
+            values (dict): A dict contains:
+                {
+                    wall_time (float): Timestamp.
+                    step (int): Train step.
+                    value (float): Tensor value.
+                    tag (str): Tag name.
+                }
+
+       Returns:
+            summary_pb2.Event.
+
+        """
+        tensor_event = summary_pb2.Event()
+        tensor_event.wall_time = values.get('wall_time')
+        tensor_event.step = values.get('step')
+
+        value = tensor_event.summary.value.add()
+        value.tag = values.get('tag')
+        tensor = values.get('value')
+
+        value.tensor.dims[:] = tensor.get('dims')
+        value.tensor.data_type = tensor.get('data_type')
+        value.tensor.float_data[:] = tensor.get('float_data')
+        print(tensor.get('float_data'))
+
+        return tensor_event
+
+    def generate_log(self, file_path, steps_list, tag_name):
+        """
+        Generate log for external calls.
+
+        Args:
+            file_path (str): Path to write logs.
+            steps_list (list): A list consists of step.
+            tag_name (str): Tag name.
+
+        Returns:
+            list[dict], generated tensor metadata.
+            list, generated tensors.
+
+        """
+        tensor_metadata = []
+        tensor_values = dict()
+        for step in steps_list:
+            tensor = dict()
+
+            wall_time = time.time()
+            tensor.update({'wall_time': wall_time})
+            tensor.update({'step': step})
+            tensor.update({'tag': tag_name})
+            dims = list(np.random.randint(1, 10, 4))
+            mul_value = reduce(mul, dims)
+            tensor.update({'value': {
+                "dims": dims,
+                "data_type": anf_ir_pb2.DataType.DT_FLOAT32,
+                "float_data": np.random.randn(mul_value)
+            }})
+            tensor_metadata.append(tensor)
+            tensor_values.update({step: tensor})
+
+            self._write_log_one_step(file_path, tensor)
+
+        return tensor_metadata, tensor_values
+
+
+if __name__ == "__main__":
+    tensor_log_generator = TensorLogGenerator()
+    test_file_name = '%s.%s.%s' % ('tensor', 'summary', str(time.time()))
+    test_steps = [1, 3, 5]
+    test_tag = "test_tensor_tag_name"
+    tensor_log_generator.generate_log(test_file_name, test_steps, test_tag)

tests/utils/log_operations.py

@@ -25,12 +25,14 @@ from .log_generators.graph_log_generator import GraphLogGenerator
 from .log_generators.images_log_generator import ImagesLogGenerator
 from .log_generators.scalars_log_generator import ScalarsLogGenerator
 from .log_generators.histogram_log_generator import HistogramLogGenerator
+from .log_generators.tensor_log_generator import TensorLogGenerator
 
 log_generators = {
     PluginNameEnum.GRAPH.value: GraphLogGenerator(),
     PluginNameEnum.IMAGE.value: ImagesLogGenerator(),
     PluginNameEnum.SCALAR.value: ScalarsLogGenerator(),
-    PluginNameEnum.HISTOGRAM.value: HistogramLogGenerator()
+    PluginNameEnum.HISTOGRAM.value: HistogramLogGenerator(),
+    PluginNameEnum.TENSOR.value: TensorLogGenerator()
 }
 
 

tests/utils/resource/JOB3/Framework.host.vm.point.1.slice_0

@@ -1,4 +1,8 @@
 1 Default/Cast-op6
 2 Default/TransData-op7
-3 Default/network-WithLossCell/_backbone-ResNet/conv1-Conv2d/Cast-op5
-4 Default/network-WithLossCell/_backbone-ResNet/layer1-SequentialCell/0-ResidualBlock/conv1-Conv2d/Cast-op28
+3 Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/AllGather-op136
+4 Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/AllGather-op136
+5 Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/ReduceScatter-op145
+6 Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/ReduceScatter-op145
+7 Gradients/Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/gradReduceScatter/AllGather-op147
+8 Gradients/Default/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/gradReduceScatter/AllGather-op147

tests/utils/resource/run_1/normal_run/profiler/aicore_intermediate_1_detail.csv

@@ -0,0 +1,200 @@
+full_op_name,execution_time
+Default/AssignAdd-op414,0.001688
+Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op29,0.0012020000000000002
+Default/network-TrainStepWrap/optimizer_d-Adam/Assign-op30,0.0013606666666666667
+Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op31,0.0011659999999999997
+Default/network-TrainStepWrap/optimizer_d-Adam/Assign-op32,0.001116
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op33,0.9352293333333332
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Mul-op35,0.010222666666666666
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/ReduceSum-op36,0.015073333333333333
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/TensorAdd-op37,0.003832666666666666
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_5-DenseLayer/Cast-op34,0.001396666666666667
+Default/TransData-op216,0.006697333333333332
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Square-op38,0.009799333333333334
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Split-op39,0.09720533333333335
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Concat-op40,0.08841666666666667
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/StridedSlice-op41,0.012427333333333335
+Default/AtomicAddrClean-op418,0.001378666666666667
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/ReduceSum-op42,0.009832666666666665
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradRealDiv/ReduceSum-op48,0.001400666666666667
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/RealDiv-op44,0.0014346666666666666
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Mul-op28,0.001468
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_4-DenseLayer/Cast-op47,0.004459333333333333
+Default/TransData-op281,0.0027733333333333334
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/Cast-op46,0.004600000000000001
+Default/TransData-op278,0.004403333333333333
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/Cast-op45,0.00711
+Default/TransData-op275,0.005461333333333334
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/Cast-op52,0.023115999999999994
+Default/TransData-op272,0.009749333333333332
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradRealDiv/ReduceSum-op43,0.0013153333333333335
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradReduceMean/Tile-op53,0.004243333333333333
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradReduceMean/RealDiv-op54,0.004824666666666667
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradReduceMean/Tile-op49,0.003735
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradReduceMean/RealDiv-op50,0.0045564285714285715
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/grad_VirtualDiv/RealDiv-op51,0.004516428571428571
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op55,42.220212142857136
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradReduceMean/Tile-op56,0.00871357142857143
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradStridedSlice/StridedSliceGrad-op57,0.15243714285714288
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op58,0.9626657142857143
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op59,1.0643285714285715
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op60,0.9675764285714286
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op61,0.9675435714285714
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op62,1.0075085714285714
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op63,0.9250400000000002
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op64,1.1294107142857144
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradConcat/Slice-op65,1.0091157142857143
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradSplit/Concat-op66,0.051030714285714276
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Split-op67,2.617072142857143
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Concat-op68,3.084827142857143
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/StridedSlice-op69,0.3331414285714285
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Mul-op70,0.37437785714285715
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/ReLU-op71,0.32776857142857135
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/Mul-op72,0.33151499999999995
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/Cast-op73,0.2518214285714286
+Default/TransData-op271,0.14980214285714283
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/MatMul-op74,0.45218500000000006
+Default/TransData-op240,0.09184714285714284
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/RealDiv-op76,0.10391071428571431
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/BiasAdd-op77,0.11015571428571427
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/ReLU-op78,0.10085142857142855
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/Mul-op79,0.10943071428571426
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/Cast-op80,0.04727285714285715
+Default/TransData-op274,0.03735642857142857
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/MatMul-op81,0.09832214285714284
+Default/TransData-op245,0.037176428571428576
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/RealDiv-op83,0.036798571428571424
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/BiasAdd-op84,0.04016857142857143
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/ReLU-op85,0.027936428571428574
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/Mul-op86,0.039065
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/Cast-op87,0.02587642857142857
+Default/TransData-op277,0.01939
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/MatMul-op88,0.03152
+Default/TransData-op250,0.020935000000000002
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/RealDiv-op90,0.025487142857142854
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/BiasAdd-op91,0.021720714285714288
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_4-DenseLayer/ReLU-op92,0.016717857142857142
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_4-DenseLayer/Mul-op93,0.021017857142857147
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+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_4-DenseLayer/gradRealDiv/RealDiv-op135,0.013412857142857143
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+Default/TransData-op233,0.020412142857142858
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+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_4-DenseLayer/gradReLU/ReluGrad-op145,0.03936785714285714
+Default/AtomicAddrClean-op429,0.0014571428571428572
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/gradBiasAdd/BiasAddGrad-op147,0.012325714285714285
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/gradRealDiv/RealDiv-op148,0.021508571428571432
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+Default/TransData-op247,0.012454999999999997
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+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/gradMatMul/MatMul-op152,0.03651357142857143
+Default/TransData-op231,0.03276571428571429
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/gradMul/Mul-op156,0.037129999999999996
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_3-DenseLayer/gradReLU/ReluGrad-op158,0.09050499999999999
+Default/AtomicAddrClean-op430,0.001497142857142857
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_2-DenseLayer/gradBiasAdd/BiasAddGrad-op160,0.017480714285714283
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+Default/TransData-op242,0.019189285714285714
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+Default/AtomicAddrClean-op431,0.0014599999999999997
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_1-DenseLayer/gradBiasAdd/BiasAddGrad-op173,0.030029999999999998
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+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op202,0.08427071428571428

tests/utils/resource/run_1/normal_run/profiler/aicore_intermediate_1_type.csv

@@ -0,0 +1,30 @@
+op_type,execution_time,execution_frequency,percent
+AssignAdd,0.001688,1,0.00
+Mul,1.9029486666666665347,32,1.51
+Assign,0.0024766666666666667,2,0.00
+GatherV2,43.1554414761904692,2,34.13
+ReduceSum,0.0307648571428571411,5,0.02
+TensorAdd,0.0092183809523809521,3,0.01
+Cast,0.4846848571428571735,15,0.38
+TransData,1.1151575238095237340,30,0.88
+Square,0.009799333333333334,1,0.01
+Split,2.71427747619047635,2,2.15
+Concat,5.808453809523809946,4,4.59
+StridedSlice,0.345568761904761835,2,0.27
+AtomicAddrClean,0.0193686666666666662,8,0.02
+RealDiv,0.4228071904761904831,15,0.33
+Tile,0.044158333333333339,4,0.03
+StridedSliceGrad,1.50683428571428578,2,1.19
+Slice,20.3763149999999997,16,16.12
+ReLU,0.483282142857142759,5,0.38
+MatMul,1.936681428571428733,15,1.53
+BiasAdd,0.189662857142857130,5,0.15
+SigmoidCrossEntropyWithLogits,0.004808571428571428,1,0.00
+SigmoidCrossEntropyWithLogitsGrad,0.009582142857142858,2,0.01
+ReduceMean,0.004534999999999999,1,0.00
+BiasAddGrad,0.0716814285714285667,5,0.06
+UnsortedSegmentSum,44.607826428571423,2,35.28
+ReluGrad,0.85406857142857138,5,0.68
+AddN,0.012836428571428572,1,0.01
+ApplyFtrl,0.0254535714285714273,2,0.02
+Adam,0.2859357142857142737,11,0.23

tests/utils/resource/run_1/normal_run/profiler/framework_raw_1.csv

@@ -0,0 +1,200 @@
+task_id,stream_id,block_dim,full_op_name,op_name,op_type,subgraph,op_info
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+30093,3,1,Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op29,Mul-op29,Mul,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}}"
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+30096,3,1,Default/network-TrainStepWrap/optimizer_d-Adam/Assign-op32,Assign-op32,Assign,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}}"
+30103,3,32,Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op33,GatherV2-op33,GatherV2,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""184696,1""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_INT32"", ""shape"": ""16000,39""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""16000,39,1""}}"
+30104,3,32,Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Mul-op35,Mul-op35,Mul,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""16000,39,1""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""16000,39""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""16000,39,1""}}"
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+32445,25,31,Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op179,Adam-op179,Adam,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""input_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""input_3"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_4"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_5"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_6"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_7"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_8"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_9"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""output_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}, ""output_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1024,512""}}"
+32446,25,31,Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op166,Adam-op166,Adam,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""input_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""input_3"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_4"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_5"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_6"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_7"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_8"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_9"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""output_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}, ""output_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""512,256""}}"
+32447,25,16,Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op153,Adam-op153,Adam,Default,"{""input_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""input_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""input_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""input_3"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_4"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""1""}, ""input_5"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_6"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_7"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_8"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": """"}, ""input_9"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""output_0"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""output_1"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}, ""output_2"": {""format"": ""DefaultFormat"", ""data_type"": ""NUMBER_TYPE_FLOAT32"", ""shape"": ""256,128""}}"
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tests/utils/resource/run_1/normal_run/profiler/min_cycle_counter_1.txt

@@ -0,0 +1 @@
+43806841592.0

tests/utils/resource/run_1/normal_run/profiler/minddata_pipeline_raw_1.csv

@@ -0,0 +1,5 @@
+op_id,op_type,num_workers,output_queue_size,output_queue_average_size,output_queue_length,output_queue_usage_rate,sample_interval,parent_id,children_id
+0,Batch,4,,,,,10,,[1]
+1,Shuffle,1,"[10, 20, 30]",20.0,64,0.3125,10,0,"[2, 3]"
+2,TFReader,4,"[10, 20, 30]",20.0,64,0.3125,10,1,
+3,TFReader,4,"[10, 20, 30]",20.0,64,0.3125,10,1,

tests/utils/resource/run_1/normal_run/profiler/output_op_compute_time_1.txt

@@ -0,0 +1,203 @@
+====================op compute time====================
+op_name       compute_time(ms) stream_id
+------------  ---------------  ---------
+Default/AssignAdd-op414 0.001688  519
+Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op29 0.0012020000000000002  519
+Default/network-TrainStepWrap/optimizer_d-Adam/Assign-op30 0.0013606666666666667  519
+Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op31 0.0011659999999999997  519
+Default/network-TrainStepWrap/optimizer_d-Adam/Assign-op32 0.001116  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/GatherV2-op33 0.9352293333333332  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/Mul-op35 0.010222666666666666  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/ReduceSum-op36 0.015073333333333333  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/TensorAdd-op37 0.003832666666666666  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/dense_layer_5-DenseLayer/Cast-op34 0.001396666666666667  519
+Default/TransData-op216 0.006697333333333332  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Square-op38 0.009799333333333334  519
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Split-op39 0.09720533333333335  523
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/Concat-op40 0.08841666666666667  523
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/StridedSlice-op41 0.012427333333333335  523
+Default/AtomicAddrClean-op418 0.001378666666666667  523
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/ReduceSum-op42 0.009832666666666665  523
+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/gradRealDiv/ReduceSum-op48 0.001400666666666667  525
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+Gradients/Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/network-WideDeepModel/gradGatherV2/UnsortedSegmentSum-op199 37.25223428571428  541
+Default/network-TrainStepWrap/network-VirtualDatasetCellTriple/_backbone-NetWithLossClass/AddN-op200 0.012836428571428572  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Mul-op201 0.010897142857142855  541
+Default/network-TrainStepWrap/optimizer_w-FTRL/ApplyFtrl-op133 0.02319642857142857  541
+Default/network-TrainStepWrap/optimizer_w-FTRL/ApplyFtrl-op132 0.0022571428571428573  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op185 0.003688571428571429  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op172 0.003175714285714285  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op159 0.0029478571428571436  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op146 0.0028899999999999998  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op127 0.0022257142857142853  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op198 0.133745  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op179 0.03321571428571428  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op166 0.010665  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op153 0.006292857142857143  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op140 0.002818571428571428  541
+Default/network-TrainStepWrap/optimizer_d-Adam/Adam-op202 0.08427071428571428  541
+total op  126.43631757142849 0

tests/utils/resource/run_1/normal_run/profiler/output_op_compute_time_detail_1.txt

@@ -0,0 +1,705 @@
+====================op compute time====================
+optype_name                          compute_time(ms, per-step)    called_times(per-step)    percent
+---------------------------------  ----------------------------  ------------------------  ---------
+UnsortedSegmentSum                                    44.6078                           2      35.28
+GatherV2                                              43.1554                           2      34.13
+Slice                                                 20.3763                          16      16.12
+Concat                                                 5.80845                          4       4.59
+Split                                                  2.71428                          2       2.15
+MatMul                                                 1.93668                         15       1.53
+Mul                                                    1.90295                         32       1.51
+StridedSliceGrad                                       1.50683                          2       1.19
+TransData                                              1.11516                         30       0.88
+ReluGrad                                               0.854069                         5       0.68
+Cast                                                   0.484685                        15       0.38
+ReLU                                                   0.483282                         5       0.38
+RealDiv                                                0.422807                        15       0.33
+StridedSlice                                           0.345569                         2       0.27
+Adam                                                   0.285936                        11       0.23
+BiasAdd                                                0.189663                         5       0.15
+BiasAddGrad                                            0.071681                         5       0.06
+Tile                                                   0.044158                         4       0.03
+ReduceSum                                              0.030765                         5       0.02
+ApplyFtrl                                              0.025454                         2       0.02
+AtomicAddrClean                                        0.019369                         8       0.02
+AddN                                                   0.012836                         1       0.01
+Square                                                 0.009799                         1       0.01
+SigmoidCrossEntropyWithLogitsGrad                      0.009582                         2       0.01
+TensorAdd                                              0.009218                         3       0.01
+SigmoidCrossEntropyWithLogits                          0.004809                         1       0
+ReduceMean                                             0.004535                         1       0
+Assign                                                 0.002477                         2       0
+AssignAdd                                              0.001688                         1       0
+
+Detail:
+op_name                                  op_type                              avg_execution_time  subgraph
+---------------------------------------  ---------------------------------  --------------------  ----------
+UnsortedSegmentSum-op199                 UnsortedSegmentSum                          37.2522      Gradients
+UnsortedSegmentSum-op123                 UnsortedSegmentSum                           7.35559     Gradients
+GatherV2-op55                            GatherV2                                    42.2202      Default
+GatherV2-op33                            GatherV2                                     0.935229    Default
+Slice-op192                              Slice                                        1.56074     Gradients
+Slice-op191                              Slice                                        1.55147     Gradients
+Slice-op188                              Slice                                        1.5461      Gradients
+Slice-op190                              Slice                                        1.54024     Gradients
+Slice-op193                              Slice                                        1.53854     Gradients
+Slice-op194                              Slice                                        1.53768     Gradients
+Slice-op195                              Slice                                        1.53429     Gradients
+Slice-op189                              Slice                                        1.53405     Gradients
+Slice-op64                               Slice                                        1.12941     Gradients
+Slice-op59                               Slice                                        1.06433     Gradients
+Slice-op65                               Slice                                        1.00912     Gradients
+Slice-op62                               Slice                                        1.00751     Gradients
+Slice-op60                               Slice                                        0.967576    Gradients
+Slice-op61                               Slice                                        0.967544    Gradients
+Slice-op58                               Slice                                        0.962666    Gradients
+Slice-op63                               Slice                                        0.92504     Gradients
+Concat-op68                              Concat                                       3.08483     Default
+Concat-op196                             Concat                                       2.58418     Gradients
+Concat-op40                              Concat                                       0.0884167   Default
+Concat-op66                              Concat                                       0.0510307   Gradients
+Split-op67                               Split                                        2.61707     Default
+Split-op39                               Split                                        0.0972053   Default
+MatMul-op178                             MatMul                                       0.535181    Gradients
+MatMul-op74                              MatMul                                       0.452185    Default
+MatMul-op177                             MatMul                                       0.421444    Gradients
+MatMul-op165                             MatMul                                       0.116006    Gradients
+MatMul-op164                             MatMul                                       0.106089    Gradients
+MatMul-op81                              MatMul                                       0.0983221   Default
+MatMul-op151                             MatMul                                       0.053485    Gradients
+MatMul-op152                             MatMul                                       0.0365136   Gradients
+MatMul-op88                              MatMul                                       0.03152     Default
+MatMul-op138                             MatMul                                       0.0295893   Gradients
+MatMul-op139                             MatMul                                       0.016685    Gradients
+MatMul-op95                              MatMul                                       0.01319     Default
+MatMul-op120                             MatMul                                       0.00954643  Gradients
+MatMul-op119                             MatMul                                       0.00929929  Gradients
+MatMul-op102                             MatMul                                       0.007625    Default
+Mul-op186                                Mul                                          0.385978    Gradients
+Mul-op70                                 Mul                                          0.374378    Default
+Mul-op72                                 Mul                                          0.331515    Default
+Mul-op182                                Mul                                          0.317914    Gradients
+Mul-op79                                 Mul                                          0.109431    Default
+Mul-op169                                Mul                                          0.100926    Gradients
+Mul-op86                                 Mul                                          0.039065    Default
+Mul-op156                                Mul                                          0.03713     Gradients
+Mul-op116                                Mul                                          0.0331357   Gradients
+Mul-op93                                 Mul                                          0.0210179   Default
+Mul-op121                                Mul                                          0.0209471   Gradients
+Mul-op143                                Mul                                          0.0205921   Gradients
+Mul-op197                                Mul                                          0.0166779   Default
+Mul-op128                                Mul                                          0.0122129   Gradients
+Mul-op100                                Mul                                          0.01205     Default
+Mul-op201                                Mul                                          0.0108971   Default
+Mul-op35                                 Mul                                          0.0102227   Default
+Mul-op117                                Mul                                          0.0101321   Gradients
+Mul-op176                                Mul                                          0.007605    Default
+Mul-op163                                Mul                                          0.00625286  Default
+Mul-op130                                Mul                                          0.00571571  Gradients
+Mul-op150                                Mul                                          0.004635    Default
+Mul-op183                                Mul                                          0.00165571  Default
+Mul-op137                                Mul                                          0.00160786  Default
+Mul-op129                                Mul                                          0.00159643  Gradients
+Mul-op170                                Mul                                          0.00159571  Default
+Mul-op157                                Mul                                          0.00153143  Default
+Mul-op144                                Mul                                          0.00147357  Default
+Mul-op28                                 Mul                                          0.001468    Default
+Mul-op122                                Mul                                          0.00122071  Default
+Mul-op29                                 Mul                                          0.001202    Default
+Mul-op31                                 Mul                                          0.001166    Default
+StridedSliceGrad-op187                   StridedSliceGrad                             1.3544      Gradients
+StridedSliceGrad-op57                    StridedSliceGrad                             0.152437    Gradients
+TransData-op284                          TransData                                    0.325711    Default
+TransData-op271                          TransData                                    0.149802    Default
+TransData-op229                          TransData                                    0.0921293   Default
+TransData-op240                          TransData                                    0.0918471   Default
+TransData-op257                          TransData                                    0.0662064   Default
+TransData-op274                          TransData                                    0.0373564   Default
+TransData-op245                          TransData                                    0.0371764   Default
+TransData-op237                          TransData                                    0.0368179   Default
+TransData-op231                          TransData                                    0.0327657   Default
+TransData-op206                          TransData                                    0.0277764   Default
+TransData-op250                          TransData                                    0.020935    Default
+TransData-op233                          TransData                                    0.0204121   Default
+TransData-op277                          TransData                                    0.01939     Default
+TransData-op242                          TransData                                    0.0191893   Default
+TransData-op204                          TransData                                    0.0162743   Default
+TransData-op255                          TransData                                    0.0145864   Default
+TransData-op235                          TransData                                    0.0144157   Default
+TransData-op247                          TransData                                    0.012455    Default
+TransData-op280                          TransData                                    0.0124257   Default
+TransData-op272                          TransData                                    0.00974933  Default
+TransData-op215                          TransData                                    0.00940429  Default
+TransData-op252                          TransData                                    0.00857286  Default
+TransData-op208                          TransData                                    0.00839571  Default
+TransData-op216                          TransData                                    0.00669733  Default
+TransData-op210                          TransData                                    0.00627071  Default
+TransData-op275                          TransData                                    0.00546133  Default
+TransData-op278                          TransData                                    0.00440333  Default
+TransData-op212                          TransData                                    0.00333286  Default
+TransData-op281                          TransData                                    0.00277333  Default
+TransData-op214                          TransData                                    0.00242357  Default
+ReluGrad-op184                           ReluGrad                                     0.514471    Gradients
+ReluGrad-op171                           ReluGrad                                     0.187441    Gradients
+ReluGrad-op158                           ReluGrad                                     0.090505    Gradients
+ReluGrad-op145                           ReluGrad                                     0.0393679   Gradients
+ReluGrad-op131                           ReluGrad                                     0.0222843   Gradients
+Cast-op73                                Cast                                         0.251821    Default
+Cast-op80                                Cast                                         0.0472729   Default
+Cast-op175                               Cast                                         0.0464929   Gradients
+Cast-op87                                Cast                                         0.0258764   Default
+Cast-op162                               Cast                                         0.0248979   Gradients
+Cast-op52                                Cast                                         0.023116    Default
+Cast-op94                                Cast                                         0.01493     Default
+Cast-op149                               Cast                                         0.0125914   Gradients
+Cast-op101                               Cast                                         0.00926143  Default
+Cast-op136                               Cast                                         0.00819714  Gradients
+Cast-op45                                Cast                                         0.00711     Default
+Cast-op46                                Cast                                         0.0046      Default
+Cast-op47                                Cast                                         0.00445933  Default
+Cast-op115                               Cast                                         0.00266143  Gradients
+Cast-op34                                Cast                                         0.00139667  Default
+ReLU-op71                                ReLU                                         0.327769    Default
+ReLU-op78                                ReLU                                         0.100851    Default
+ReLU-op85                                ReLU                                         0.0279364   Default
+ReLU-op92                                ReLU                                         0.0167179   Default
+ReLU-op99                                ReLU                                         0.0100079   Default
+RealDiv-op174                            RealDiv                                      0.137046    Gradients
+RealDiv-op76                             RealDiv                                      0.103911    Default
+RealDiv-op161                            RealDiv                                      0.0425664   Gradients
+RealDiv-op83                             RealDiv                                      0.0367986   Default
+RealDiv-op90                             RealDiv                                      0.0254871   Default
+RealDiv-op148                            RealDiv                                      0.0215086   Gradients
+RealDiv-op97                             RealDiv                                      0.0157514   Default
+RealDiv-op135                            RealDiv                                      0.0134129   Gradients
+RealDiv-op104                            RealDiv                                      0.00482857  Default
+RealDiv-op54                             RealDiv                                      0.00482467  Gradients
+RealDiv-op50                             RealDiv                                      0.00455643  Gradients
+RealDiv-op113                            RealDiv                                      0.00454714  Gradients
+RealDiv-op51                             RealDiv                                      0.00451643  Gradients
+RealDiv-op118                            RealDiv                                      0.00161786  Default
+RealDiv-op44                             RealDiv                                      0.00143467  Default
+StridedSlice-op69                        StridedSlice                                 0.333141    Default
+StridedSlice-op41                        StridedSlice                                 0.0124273   Default
+Adam-op198                               Adam                                         0.133745    Default
+Adam-op202                               Adam                                         0.0842707   Default
+Adam-op179                               Adam                                         0.0332157   Default
+Adam-op166                               Adam                                         0.010665    Default
+Adam-op153                               Adam                                         0.00629286  Default
+Adam-op185                               Adam                                         0.00368857  Default
+Adam-op172                               Adam                                         0.00317571  Default
+Adam-op159                               Adam                                         0.00294786  Default
+Adam-op146                               Adam                                         0.00289     Default
+Adam-op140                               Adam                                         0.00281857  Default
+Adam-op127                               Adam                                         0.00222571  Default
+BiasAdd-op77                             BiasAdd                                      0.110156    Default
+BiasAdd-op84                             BiasAdd                                      0.0401686   Default
+BiasAdd-op91                             BiasAdd                                      0.0217207   Default
+BiasAdd-op98                             BiasAdd                                      0.013145    Default
+BiasAdd-op105                            BiasAdd                                      0.00447286  Default
+BiasAddGrad-op173                        BiasAddGrad                                  0.03003     Gradients
+BiasAddGrad-op160                        BiasAddGrad                                  0.0174807   Gradients
+BiasAddGrad-op147                        BiasAddGrad                                  0.0123257   Gradients
+BiasAddGrad-op134                        BiasAddGrad                                  0.00878357  Gradients
+BiasAddGrad-op112                        BiasAddGrad                                  0.00306143  Gradients
+Tile-op114                               Tile                                         0.0274664   Gradients
+Tile-op56                                Tile                                         0.00871357  Gradients
+Tile-op53                                Tile                                         0.00424333  Gradients
+Tile-op49                                Tile                                         0.003735    Gradients
+ReduceSum-op36                           ReduceSum                                    0.0150733   Default
+ReduceSum-op42                           ReduceSum                                    0.00983267  Default
+ReduceSum-op111                          ReduceSum                                    0.00314286  Gradients
+ReduceSum-op48                           ReduceSum                                    0.00140067  Gradients
+ReduceSum-op43                           ReduceSum                                    0.00131533  Gradients
+ApplyFtrl-op133                          ApplyFtrl                                    0.0231964   Default
+ApplyFtrl-op132                          ApplyFtrl                                    0.00225714  Default
+AtomicAddrClean-op434                    AtomicAddrClean                              0.00771929  Default
+AtomicAddrClean-op427                    AtomicAddrClean                              0.00293714  Default
+AtomicAddrClean-op425                    AtomicAddrClean                              0.001515    Default
+AtomicAddrClean-op430                    AtomicAddrClean                              0.00149714  Default
+AtomicAddrClean-op431                    AtomicAddrClean                              0.00146     Default
+AtomicAddrClean-op429                    AtomicAddrClean                              0.00145714  Default
+AtomicAddrClean-op428                    AtomicAddrClean                              0.00140429  Default
+AtomicAddrClean-op418                    AtomicAddrClean                              0.00137867  Default
+AddN-op200                               AddN                                         0.0128364   Default
+Square-op38                              Square                                       0.00979933  Default
+SigmoidCrossEntropyWithLogitsGrad-op109  SigmoidCrossEntropyWithLogitsGrad            0.00495071  Gradients
+SigmoidCrossEntropyWithLogitsGrad-op108  SigmoidCrossEntropyWithLogitsGrad            0.00463143  Gradients
+TensorAdd-op106                          TensorAdd                                    0.00392571  Default
+TensorAdd-op37                           TensorAdd                                    0.00383267  Default
+TensorAdd-op124                          TensorAdd                                    0.00146     Default
+SigmoidCrossEntropyWithLogits-op107      SigmoidCrossEntropyWithLogits                0.00480857  Default
+ReduceMean-op110                         ReduceMean                                   0.004535    Default
+Assign-op30                              Assign                                       0.00136067  Default
+Assign-op32                              Assign                                       0.001116    Default
+AssignAdd-op414                          AssignAdd                                    0.001688    Default
+====================op compute time====================
+optype_name                          compute_time(ms, per-step)    called_times(per-step)    percent
+---------------------------------  ----------------------------  ------------------------  ---------
+UnsortedSegmentSum                                    44.6078                           2      35.28
+GatherV2                                              43.1554                           2      34.13
+Slice                                                 20.3763                          16      16.12
+Concat                                                 5.80845                          4       4.59
+Split                                                  2.71428                          2       2.15
+MatMul                                                 1.93668                         15       1.53
+Mul                                                    1.90295                         32       1.51
+StridedSliceGrad                                       1.50683                          2       1.19
+TransData                                              1.11516                         30       0.88
+ReluGrad                                               0.854069                         5       0.68
+Cast                                                   0.484685                        15       0.38
+ReLU                                                   0.483282                         5       0.38
+RealDiv                                                0.422807                        15       0.33
+StridedSlice                                           0.345569                         2       0.27
+Adam                                                   0.285936                        11       0.23
+BiasAdd                                                0.189663                         5       0.15
+BiasAddGrad                                            0.071681                         5       0.06
+Tile                                                   0.044158                         4       0.03
+ReduceSum                                              0.030765                         5       0.02
+ApplyFtrl                                              0.025454                         2       0.02
+AtomicAddrClean                                        0.019369                         8       0.02
+AddN                                                   0.012836                         1       0.01
+Square                                                 0.009799                         1       0.01
+SigmoidCrossEntropyWithLogitsGrad                      0.009582                         2       0.01
+TensorAdd                                              0.009218                         3       0.01
+SigmoidCrossEntropyWithLogits                          0.004809                         1       0
+ReduceMean                                             0.004535                         1       0
+Assign                                                 0.002477                         2       0
+AssignAdd                                              0.001688                         1       0
+
+Detail:
+op_name                                  op_type                              avg_execution_time  subgraph
+---------------------------------------  ---------------------------------  --------------------  ----------
+UnsortedSegmentSum-op199                 UnsortedSegmentSum                          37.2522      Gradients
+UnsortedSegmentSum-op123                 UnsortedSegmentSum                           7.35559     Gradients
+GatherV2-op55                            GatherV2                                    42.2202      Default
+GatherV2-op33                            GatherV2                                     0.935229    Default
+Slice-op192                              Slice                                        1.56074     Gradients
+Slice-op191                              Slice                                        1.55147     Gradients
+Slice-op188                              Slice                                        1.5461      Gradients
+Slice-op190                              Slice                                        1.54024     Gradients
+Slice-op193                              Slice                                        1.53854     Gradients
+Slice-op194                              Slice                                        1.53768     Gradients
+Slice-op195                              Slice                                        1.53429     Gradients
+Slice-op189                              Slice                                        1.53405     Gradients
+Slice-op64                               Slice                                        1.12941     Gradients
+Slice-op59                               Slice                                        1.06433     Gradients
+Slice-op65                               Slice                                        1.00912     Gradients
+Slice-op62                               Slice                                        1.00751     Gradients
+Slice-op60                               Slice                                        0.967576    Gradients
+Slice-op61                               Slice                                        0.967544    Gradients
+Slice-op58                               Slice                                        0.962666    Gradients
+Slice-op63                               Slice                                        0.92504     Gradients
+Concat-op68                              Concat                                       3.08483     Default
+Concat-op196                             Concat                                       2.58418     Gradients
+Concat-op40                              Concat                                       0.0884167   Default
+Concat-op66                              Concat                                       0.0510307   Gradients
+Split-op67                               Split                                        2.61707     Default
+Split-op39                               Split                                        0.0972053   Default
+MatMul-op178                             MatMul                                       0.535181    Gradients
+MatMul-op74                              MatMul                                       0.452185    Default
+MatMul-op177                             MatMul                                       0.421444    Gradients
+MatMul-op165                             MatMul                                       0.116006    Gradients
+MatMul-op164                             MatMul                                       0.106089    Gradients
+MatMul-op81                              MatMul                                       0.0983221   Default
+MatMul-op151                             MatMul                                       0.053485    Gradients
+MatMul-op152                             MatMul                                       0.0365136   Gradients
+MatMul-op88                              MatMul                                       0.03152     Default
+MatMul-op138                             MatMul                                       0.0295893   Gradients
+MatMul-op139                             MatMul                                       0.016685    Gradients
+MatMul-op95                              MatMul                                       0.01319     Default
+MatMul-op120                             MatMul                                       0.00954643  Gradients
+MatMul-op119                             MatMul                                       0.00929929  Gradients
+MatMul-op102                             MatMul                                       0.007625    Default
+Mul-op186                                Mul                                          0.385978    Gradients
+Mul-op70                                 Mul                                          0.374378    Default
+Mul-op72                                 Mul                                          0.331515    Default
+Mul-op182                                Mul                                          0.317914    Gradients
+Mul-op79                                 Mul                                          0.109431    Default
+Mul-op169                                Mul                                          0.100926    Gradients
+Mul-op86                                 Mul                                          0.039065    Default
+Mul-op156                                Mul                                          0.03713     Gradients
+Mul-op116                                Mul                                          0.0331357   Gradients
+Mul-op93                                 Mul                                          0.0210179   Default
+Mul-op121                                Mul                                          0.0209471   Gradients
+Mul-op143                                Mul                                          0.0205921   Gradients
+Mul-op197                                Mul                                          0.0166779   Default
+Mul-op128                                Mul                                          0.0122129   Gradients
+Mul-op100                                Mul                                          0.01205     Default
+Mul-op201                                Mul                                          0.0108971   Default
+Mul-op35                                 Mul                                          0.0102227   Default
+Mul-op117                                Mul                                          0.0101321   Gradients
+Mul-op176                                Mul                                          0.007605    Default
+Mul-op163                                Mul                                          0.00625286  Default
+Mul-op130                                Mul                                          0.00571571  Gradients
+Mul-op150                                Mul                                          0.004635    Default
+Mul-op183                                Mul                                          0.00165571  Default
+Mul-op137                                Mul                                          0.00160786  Default
+Mul-op129                                Mul                                          0.00159643  Gradients
+Mul-op170                                Mul                                          0.00159571  Default
+Mul-op157                                Mul                                          0.00153143  Default
+Mul-op144                                Mul                                          0.00147357  Default
+Mul-op28                                 Mul                                          0.001468    Default
+Mul-op122                                Mul                                          0.00122071  Default
+Mul-op29                                 Mul                                          0.001202    Default
+Mul-op31                                 Mul                                          0.001166    Default
+StridedSliceGrad-op187                   StridedSliceGrad                             1.3544      Gradients
+StridedSliceGrad-op57                    StridedSliceGrad                             0.152437    Gradients
+TransData-op284                          TransData                                    0.325711    Default
+TransData-op271                          TransData                                    0.149802    Default
+TransData-op229                          TransData                                    0.0921293   Default
+TransData-op240                          TransData                                    0.0918471   Default
+TransData-op257                          TransData                                    0.0662064   Default
+TransData-op274                          TransData                                    0.0373564   Default
+TransData-op245                          TransData                                    0.0371764   Default
+TransData-op237                          TransData                                    0.0368179   Default
+TransData-op231                          TransData                                    0.0327657   Default
+TransData-op206                          TransData                                    0.0277764   Default
+TransData-op250                          TransData                                    0.020935    Default
+TransData-op233                          TransData                                    0.0204121   Default
+TransData-op277                          TransData                                    0.01939     Default
+TransData-op242                          TransData                                    0.0191893   Default
+TransData-op204                          TransData                                    0.0162743   Default
+TransData-op255                          TransData                                    0.0145864   Default
+TransData-op235                          TransData                                    0.0144157   Default
+TransData-op247                          TransData                                    0.012455    Default
+TransData-op280                          TransData                                    0.0124257   Default
+TransData-op272                          TransData                                    0.00974933  Default
+TransData-op215                          TransData                                    0.00940429  Default
+TransData-op252                          TransData                                    0.00857286  Default
+TransData-op208                          TransData                                    0.00839571  Default
+TransData-op216                          TransData                                    0.00669733  Default
+TransData-op210                          TransData                                    0.00627071  Default
+TransData-op275                          TransData                                    0.00546133  Default
+TransData-op278                          TransData                                    0.00440333  Default
+TransData-op212                          TransData                                    0.00333286  Default
+TransData-op281                          TransData                                    0.00277333  Default
+TransData-op214                          TransData                                    0.00242357  Default
+ReluGrad-op184                           ReluGrad                                     0.514471    Gradients
+ReluGrad-op171                           ReluGrad                                     0.187441    Gradients
+ReluGrad-op158                           ReluGrad                                     0.090505    Gradients
+ReluGrad-op145                           ReluGrad                                     0.0393679   Gradients
+ReluGrad-op131                           ReluGrad                                     0.0222843   Gradients
+Cast-op73                                Cast                                         0.251821    Default
+Cast-op80                                Cast                                         0.0472729   Default
+Cast-op175                               Cast                                         0.0464929   Gradients
+Cast-op87                                Cast                                         0.0258764   Default
+Cast-op162                               Cast                                         0.0248979   Gradients
+Cast-op52                                Cast                                         0.023116    Default
+Cast-op94                                Cast                                         0.01493     Default
+Cast-op149                               Cast                                         0.0125914   Gradients
+Cast-op101                               Cast                                         0.00926143  Default
+Cast-op136                               Cast                                         0.00819714  Gradients
+Cast-op45                                Cast                                         0.00711     Default
+Cast-op46                                Cast                                         0.0046      Default
+Cast-op47                                Cast                                         0.00445933  Default
+Cast-op115                               Cast                                         0.00266143  Gradients
+Cast-op34                                Cast                                         0.00139667  Default
+ReLU-op71                                ReLU                                         0.327769    Default
+ReLU-op78                                ReLU                                         0.100851    Default
+ReLU-op85                                ReLU                                         0.0279364   Default
+ReLU-op92                                ReLU                                         0.0167179   Default
+ReLU-op99                                ReLU                                         0.0100079   Default
+RealDiv-op174                            RealDiv                                      0.137046    Gradients
+RealDiv-op76                             RealDiv                                      0.103911    Default
+RealDiv-op161                            RealDiv                                      0.0425664   Gradients
+RealDiv-op83                             RealDiv                                      0.0367986   Default
+RealDiv-op90                             RealDiv                                      0.0254871   Default
+RealDiv-op148                            RealDiv                                      0.0215086   Gradients
+RealDiv-op97                             RealDiv                                      0.0157514   Default
+RealDiv-op135                            RealDiv                                      0.0134129   Gradients
+RealDiv-op104                            RealDiv                                      0.00482857  Default
+RealDiv-op54                             RealDiv                                      0.00482467  Gradients
+RealDiv-op50                             RealDiv                                      0.00455643  Gradients
+RealDiv-op113                            RealDiv                                      0.00454714  Gradients
+RealDiv-op51                             RealDiv                                      0.00451643  Gradients
+RealDiv-op118                            RealDiv                                      0.00161786  Default
+RealDiv-op44                             RealDiv                                      0.00143467  Default
+StridedSlice-op69                        StridedSlice                                 0.333141    Default
+StridedSlice-op41                        StridedSlice                                 0.0124273   Default
+Adam-op198                               Adam                                         0.133745    Default
+Adam-op202                               Adam                                         0.0842707   Default
+Adam-op179                               Adam                                         0.0332157   Default
+Adam-op166                               Adam                                         0.010665    Default
+Adam-op153                               Adam                                         0.00629286  Default
+Adam-op185                               Adam                                         0.00368857  Default
+Adam-op172                               Adam                                         0.00317571  Default
+Adam-op159                               Adam                                         0.00294786  Default
+Adam-op146                               Adam                                         0.00289     Default
+Adam-op140                               Adam                                         0.00281857  Default
+Adam-op127                               Adam                                         0.00222571  Default
+BiasAdd-op77                             BiasAdd                                      0.110156    Default
+BiasAdd-op84                             BiasAdd                                      0.0401686   Default
+BiasAdd-op91                             BiasAdd                                      0.0217207   Default
+BiasAdd-op98                             BiasAdd                                      0.013145    Default
+BiasAdd-op105                            BiasAdd                                      0.00447286  Default
+BiasAddGrad-op173                        BiasAddGrad                                  0.03003     Gradients
+BiasAddGrad-op160                        BiasAddGrad                                  0.0174807   Gradients
+BiasAddGrad-op147                        BiasAddGrad                                  0.0123257   Gradients
+BiasAddGrad-op134                        BiasAddGrad                                  0.00878357  Gradients
+BiasAddGrad-op112                        BiasAddGrad                                  0.00306143  Gradients
+Tile-op114                               Tile                                         0.0274664   Gradients
+Tile-op56                                Tile                                         0.00871357  Gradients
+Tile-op53                                Tile                                         0.00424333  Gradients
+Tile-op49                                Tile                                         0.003735    Gradients
+ReduceSum-op36                           ReduceSum                                    0.0150733   Default
+ReduceSum-op42                           ReduceSum                                    0.00983267  Default
+ReduceSum-op111                          ReduceSum                                    0.00314286  Gradients
+ReduceSum-op48                           ReduceSum                                    0.00140067  Gradients
+ReduceSum-op43                           ReduceSum                                    0.00131533  Gradients
+ApplyFtrl-op133                          ApplyFtrl                                    0.0231964   Default
+ApplyFtrl-op132                          ApplyFtrl                                    0.00225714  Default
+AtomicAddrClean-op434                    AtomicAddrClean                              0.00771929  Default
+AtomicAddrClean-op427                    AtomicAddrClean                              0.00293714  Default
+AtomicAddrClean-op425                    AtomicAddrClean                              0.001515    Default
+AtomicAddrClean-op430                    AtomicAddrClean                              0.00149714  Default
+AtomicAddrClean-op431                    AtomicAddrClean                              0.00146     Default
+AtomicAddrClean-op429                    AtomicAddrClean                              0.00145714  Default
+AtomicAddrClean-op428                    AtomicAddrClean                              0.00140429  Default
+AtomicAddrClean-op418                    AtomicAddrClean                              0.00137867  Default
+AddN-op200                               AddN                                         0.0128364   Default
+Square-op38                              Square                                       0.00979933  Default
+SigmoidCrossEntropyWithLogitsGrad-op109  SigmoidCrossEntropyWithLogitsGrad            0.00495071  Gradients
+SigmoidCrossEntropyWithLogitsGrad-op108  SigmoidCrossEntropyWithLogitsGrad            0.00463143  Gradients
+TensorAdd-op106                          TensorAdd                                    0.00392571  Default
+TensorAdd-op37                           TensorAdd                                    0.00383267  Default
+TensorAdd-op124                          TensorAdd                                    0.00146     Default
+SigmoidCrossEntropyWithLogits-op107      SigmoidCrossEntropyWithLogits                0.00480857  Default
+ReduceMean-op110                         ReduceMean                                   0.004535    Default
+Assign-op30                              Assign                                       0.00136067  Default
+Assign-op32                              Assign                                       0.001116    Default
+AssignAdd-op414                          AssignAdd                                    0.001688    Default
+====================op compute time====================
+optype_name                          compute_time(ms, per-step)    called_times(per-step)    percent
+---------------------------------  ----------------------------  ------------------------  ---------
+UnsortedSegmentSum                                    44.6078                           2      35.28
+GatherV2                                              43.1554                           2      34.13
+Slice                                                 20.3763                          16      16.12
+Concat                                                 5.80845                          4       4.59
+Split                                                  2.71428                          2       2.15
+MatMul                                                 1.93668                         15       1.53
+Mul                                                    1.90295                         32       1.51
+StridedSliceGrad                                       1.50683                          2       1.19
+TransData                                              1.11516                         30       0.88
+ReluGrad                                               0.854069                         5       0.68
+Cast                                                   0.484685                        15       0.38
+ReLU                                                   0.483282                         5       0.38
+RealDiv                                                0.422807                        15       0.33
+StridedSlice                                           0.345569                         2       0.27
+Adam                                                   0.285936                        11       0.23
+BiasAdd                                                0.189663                         5       0.15
+BiasAddGrad                                            0.071681                         5       0.06
+Tile                                                   0.044158                         4       0.03
+ReduceSum                                              0.030765                         5       0.02
+ApplyFtrl                                              0.025454                         2       0.02
+AtomicAddrClean                                        0.019369                         8       0.02
+AddN                                                   0.012836                         1       0.01
+Square                                                 0.009799                         1       0.01
+SigmoidCrossEntropyWithLogitsGrad                      0.009582                         2       0.01
+TensorAdd                                              0.009218                         3       0.01
+SigmoidCrossEntropyWithLogits                          0.004809                         1       0
+ReduceMean                                             0.004535                         1       0
+Assign                                                 0.002477                         2       0
+AssignAdd                                              0.001688                         1       0
+
+Detail:
+op_name                                  op_type                              avg_execution_time  subgraph
+---------------------------------------  ---------------------------------  --------------------  ----------
+UnsortedSegmentSum-op199                 UnsortedSegmentSum                          37.2522      Gradients
+UnsortedSegmentSum-op123                 UnsortedSegmentSum                           7.35559     Gradients
+GatherV2-op55                            GatherV2                                    42.2202      Default
+GatherV2-op33                            GatherV2                                     0.935229    Default
+Slice-op192                              Slice                                        1.56074     Gradients
+Slice-op191                              Slice                                        1.55147     Gradients
+Slice-op188                              Slice                                        1.5461      Gradients
+Slice-op190                              Slice                                        1.54024     Gradients
+Slice-op193                              Slice                                        1.53854     Gradients
+Slice-op194                              Slice                                        1.53768     Gradients
+Slice-op195                              Slice                                        1.53429     Gradients
+Slice-op189                              Slice                                        1.53405     Gradients
+Slice-op64                               Slice                                        1.12941     Gradients
+Slice-op59                               Slice                                        1.06433     Gradients
+Slice-op65                               Slice                                        1.00912     Gradients
+Slice-op62                               Slice                                        1.00751     Gradients
+Slice-op60                               Slice                                        0.967576    Gradients
+Slice-op61                               Slice                                        0.967544    Gradients
+Slice-op58                               Slice                                        0.962666    Gradients
+Slice-op63                               Slice                                        0.92504     Gradients
+Concat-op68                              Concat                                       3.08483     Default
+Concat-op196                             Concat                                       2.58418     Gradients
+Concat-op40                              Concat                                       0.0884167   Default
+Concat-op66                              Concat                                       0.0510307   Gradients
+Split-op67                               Split                                        2.61707     Default
+Split-op39                               Split                                        0.0972053   Default
+MatMul-op178                             MatMul                                       0.535181    Gradients
+MatMul-op74                              MatMul                                       0.452185    Default
+MatMul-op177                             MatMul                                       0.421444    Gradients
+MatMul-op165                             MatMul                                       0.116006    Gradients
+MatMul-op164                             MatMul                                       0.106089    Gradients
+MatMul-op81                              MatMul                                       0.0983221   Default
+MatMul-op151                             MatMul                                       0.053485    Gradients
+MatMul-op152                             MatMul                                       0.0365136   Gradients
+MatMul-op88                              MatMul                                       0.03152     Default
+MatMul-op138                             MatMul                                       0.0295893   Gradients
+MatMul-op139                             MatMul                                       0.016685    Gradients
+MatMul-op95                              MatMul                                       0.01319     Default
+MatMul-op120                             MatMul                                       0.00954643  Gradients
+MatMul-op119                             MatMul                                       0.00929929  Gradients
+MatMul-op102                             MatMul                                       0.007625    Default
+Mul-op186                                Mul                                          0.385978    Gradients
+Mul-op70                                 Mul                                          0.374378    Default
+Mul-op72                                 Mul                                          0.331515    Default
+Mul-op182                                Mul                                          0.317914    Gradients
+Mul-op79                                 Mul                                          0.109431    Default
+Mul-op169                                Mul                                          0.100926    Gradients
+Mul-op86                                 Mul                                          0.039065    Default
+Mul-op156                                Mul                                          0.03713     Gradients
+Mul-op116                                Mul                                          0.0331357   Gradients
+Mul-op93                                 Mul                                          0.0210179   Default
+Mul-op121                                Mul                                          0.0209471   Gradients
+Mul-op143                                Mul                                          0.0205921   Gradients
+Mul-op197                                Mul                                          0.0166779   Default
+Mul-op128                                Mul                                          0.0122129   Gradients
+Mul-op100                                Mul                                          0.01205     Default
+Mul-op201                                Mul                                          0.0108971   Default
+Mul-op35                                 Mul                                          0.0102227   Default
+Mul-op117                                Mul                                          0.0101321   Gradients
+Mul-op176                                Mul                                          0.007605    Default
+Mul-op163                                Mul                                          0.00625286  Default
+Mul-op130                                Mul                                          0.00571571  Gradients
+Mul-op150                                Mul                                          0.004635    Default
+Mul-op183                                Mul                                          0.00165571  Default
+Mul-op137                                Mul                                          0.00160786  Default
+Mul-op129                                Mul                                          0.00159643  Gradients
+Mul-op170                                Mul                                          0.00159571  Default
+Mul-op157                                Mul                                          0.00153143  Default
+Mul-op144                                Mul                                          0.00147357  Default
+Mul-op28                                 Mul                                          0.001468    Default
+Mul-op122                                Mul                                          0.00122071  Default
+Mul-op29                                 Mul                                          0.001202    Default
+Mul-op31                                 Mul                                          0.001166    Default
+StridedSliceGrad-op187                   StridedSliceGrad                             1.3544      Gradients
+StridedSliceGrad-op57                    StridedSliceGrad                             0.152437    Gradients
+TransData-op284                          TransData                                    0.325711    Default
+TransData-op271                          TransData                                    0.149802    Default
+TransData-op229                          TransData                                    0.0921293   Default
+TransData-op240                          TransData                                    0.0918471   Default
+TransData-op257                          TransData                                    0.0662064   Default
+TransData-op274                          TransData                                    0.0373564   Default
+TransData-op245                          TransData                                    0.0371764   Default
+TransData-op237                          TransData                                    0.0368179   Default
+TransData-op231                          TransData                                    0.0327657   Default
+TransData-op206                          TransData                                    0.0277764   Default
+TransData-op250                          TransData                                    0.020935    Default
+TransData-op233                          TransData                                    0.0204121   Default
+TransData-op277                          TransData                                    0.01939     Default
+TransData-op242                          TransData                                    0.0191893   Default
+TransData-op204                          TransData                                    0.0162743   Default
+TransData-op255                          TransData                                    0.0145864   Default
+TransData-op235                          TransData                                    0.0144157   Default
+TransData-op247                          TransData                                    0.012455    Default
+TransData-op280                          TransData                                    0.0124257   Default
+TransData-op272                          TransData                                    0.00974933  Default
+TransData-op215                          TransData                                    0.00940429  Default
+TransData-op252                          TransData                                    0.00857286  Default
+TransData-op208                          TransData                                    0.00839571  Default
+TransData-op216                          TransData                                    0.00669733  Default
+TransData-op210                          TransData                                    0.00627071  Default
+TransData-op275                          TransData                                    0.00546133  Default
+TransData-op278                          TransData                                    0.00440333  Default
+TransData-op212                          TransData                                    0.00333286  Default
+TransData-op281                          TransData                                    0.00277333  Default
+TransData-op214                          TransData                                    0.00242357  Default
+ReluGrad-op184                           ReluGrad                                     0.514471    Gradients
+ReluGrad-op171                           ReluGrad                                     0.187441    Gradients
+ReluGrad-op158                           ReluGrad                                     0.090505    Gradients
+ReluGrad-op145                           ReluGrad                                     0.0393679   Gradients
+ReluGrad-op131                           ReluGrad                                     0.0222843   Gradients
+Cast-op73                                Cast                                         0.251821    Default
+Cast-op80                                Cast                                         0.0472729   Default
+Cast-op175                               Cast                                         0.0464929   Gradients
+Cast-op87                                Cast                                         0.0258764   Default
+Cast-op162                               Cast                                         0.0248979   Gradients
+Cast-op52                                Cast                                         0.023116    Default
+Cast-op94                                Cast                                         0.01493     Default
+Cast-op149                               Cast                                         0.0125914   Gradients
+Cast-op101                               Cast                                         0.00926143  Default
+Cast-op136                               Cast                                         0.00819714  Gradients
+Cast-op45                                Cast                                         0.00711     Default
+Cast-op46                                Cast                                         0.0046      Default
+Cast-op47                                Cast                                         0.00445933  Default
+Cast-op115                               Cast                                         0.00266143  Gradients
+Cast-op34                                Cast                                         0.00139667  Default
+ReLU-op71                                ReLU                                         0.327769    Default
+ReLU-op78                                ReLU                                         0.100851    Default
+ReLU-op85                                ReLU                                         0.0279364   Default
+ReLU-op92                                ReLU                                         0.0167179   Default
+ReLU-op99                                ReLU                                         0.0100079   Default
+RealDiv-op174                            RealDiv                                      0.137046    Gradients
+RealDiv-op76                             RealDiv                                      0.103911    Default
+RealDiv-op161                            RealDiv                                      0.0425664   Gradients
+RealDiv-op83                             RealDiv                                      0.0367986   Default
+RealDiv-op90                             RealDiv                                      0.0254871   Default
+RealDiv-op148                            RealDiv                                      0.0215086   Gradients
+RealDiv-op97                             RealDiv                                      0.0157514   Default
+RealDiv-op135                            RealDiv                                      0.0134129   Gradients
+RealDiv-op104                            RealDiv                                      0.00482857  Default
+RealDiv-op54                             RealDiv                                      0.00482467  Gradients
+RealDiv-op50                             RealDiv                                      0.00455643  Gradients
+RealDiv-op113                            RealDiv                                      0.00454714  Gradients
+RealDiv-op51                             RealDiv                                      0.00451643  Gradients
+RealDiv-op118                            RealDiv                                      0.00161786  Default
+RealDiv-op44                             RealDiv                                      0.00143467  Default
+StridedSlice-op69                        StridedSlice                                 0.333141    Default
+StridedSlice-op41                        StridedSlice                                 0.0124273   Default
+Adam-op198                               Adam                                         0.133745    Default
+Adam-op202                               Adam                                         0.0842707   Default
+Adam-op179                               Adam                                         0.0332157   Default
+Adam-op166                               Adam                                         0.010665    Default
+Adam-op153                               Adam                                         0.00629286  Default
+Adam-op185                               Adam                                         0.00368857  Default
+Adam-op172                               Adam                                         0.00317571  Default
+Adam-op159                               Adam                                         0.00294786  Default
+Adam-op146                               Adam                                         0.00289     Default
+Adam-op140                               Adam                                         0.00281857  Default
+Adam-op127                               Adam                                         0.00222571  Default
+BiasAdd-op77                             BiasAdd                                      0.110156    Default
+BiasAdd-op84                             BiasAdd                                      0.0401686   Default
+BiasAdd-op91                             BiasAdd                                      0.0217207   Default
+BiasAdd-op98                             BiasAdd                                      0.013145    Default
+BiasAdd-op105                            BiasAdd                                      0.00447286  Default
+BiasAddGrad-op173                        BiasAddGrad                                  0.03003     Gradients
+BiasAddGrad-op160                        BiasAddGrad                                  0.0174807   Gradients
+BiasAddGrad-op147                        BiasAddGrad                                  0.0123257   Gradients
+BiasAddGrad-op134                        BiasAddGrad                                  0.00878357  Gradients
+BiasAddGrad-op112                        BiasAddGrad                                  0.00306143  Gradients
+Tile-op114                               Tile                                         0.0274664   Gradients
+Tile-op56                                Tile                                         0.00871357  Gradients
+Tile-op53                                Tile                                         0.00424333  Gradients
+Tile-op49                                Tile                                         0.003735    Gradients
+ReduceSum-op36                           ReduceSum                                    0.0150733   Default
+ReduceSum-op42                           ReduceSum                                    0.00983267  Default
+ReduceSum-op111                          ReduceSum                                    0.00314286  Gradients
+ReduceSum-op48                           ReduceSum                                    0.00140067  Gradients
+ReduceSum-op43                           ReduceSum                                    0.00131533  Gradients
+ApplyFtrl-op133                          ApplyFtrl                                    0.0231964   Default
+ApplyFtrl-op132                          ApplyFtrl                                    0.00225714  Default
+AtomicAddrClean-op434                    AtomicAddrClean                              0.00771929  Default
+AtomicAddrClean-op427                    AtomicAddrClean                              0.00293714  Default
+AtomicAddrClean-op425                    AtomicAddrClean                              0.001515    Default
+AtomicAddrClean-op430                    AtomicAddrClean                              0.00149714  Default
+AtomicAddrClean-op431                    AtomicAddrClean                              0.00146     Default
+AtomicAddrClean-op429                    AtomicAddrClean                              0.00145714  Default
+AtomicAddrClean-op428                    AtomicAddrClean                              0.00140429  Default
+AtomicAddrClean-op418                    AtomicAddrClean                              0.00137867  Default
+AddN-op200                               AddN                                         0.0128364   Default
+Square-op38                              Square                                       0.00979933  Default
+SigmoidCrossEntropyWithLogitsGrad-op109  SigmoidCrossEntropyWithLogitsGrad            0.00495071  Gradients
+SigmoidCrossEntropyWithLogitsGrad-op108  SigmoidCrossEntropyWithLogitsGrad            0.00463143  Gradients
+TensorAdd-op106                          TensorAdd                                    0.00392571  Default
+TensorAdd-op37                           TensorAdd                                    0.00383267  Default
+TensorAdd-op124                          TensorAdd                                    0.00146     Default
+SigmoidCrossEntropyWithLogits-op107      SigmoidCrossEntropyWithLogits                0.00480857  Default
+ReduceMean-op110                         ReduceMean                                   0.004535    Default
+Assign-op30                              Assign                                       0.00136067  Default
+Assign-op32                              Assign                                       0.001116    Default
+AssignAdd-op414                          AssignAdd                                    0.001688    Default