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# 快速开始 |
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ModelScope Library目前支持tensorflow,pytorch深度学习框架进行模型训练、推理, 在Python 3.7+, Pytorch 1.8+, Tensorflow1.15,Tensorflow 2.x上测试可运行。 |
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ModelScope Library目前支持tensorflow,pytorch深度学习框架进行模型训练、推理, 在Python 3.7+, Pytorch 1.8+, Tensorflow1.15,Tensorflow 2.x上测试可运行。 |
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注: `语音相关`的功能仅支持 python3.7,tensorflow1.15的`linux`环境使用。 其他功能可以在windows、mac上安装使用。 |
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**注: **`**语音相关**`**的功能仅支持 python3.7,tensorflow1.15的**`**linux**`**环境使用。 其他功能可以在windows、mac上安装使用。** |
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## python环境配置 |
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## python环境配置 |
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首先,参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境 |
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首先,参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境。 |
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安装完成后,执行如下命令为modelscope library创建对应的python环境。 |
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安装完成后,执行如下命令为modelscope library创建对应的python环境。 |
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```shell |
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```shell |
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conda create -n modelscope python=3.7 |
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conda create -n modelscope python=3.7 |
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conda activate modelscope |
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conda activate modelscope |
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``` |
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``` |
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## 安装深度学习框架 |
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## 安装深度学习框架 |
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* 安装pytorch[参考链接](https://pytorch.org/get-started/locally/) |
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- 安装pytorch[参考链接](https://pytorch.org/get-started/locally/)。 |
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```shell |
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```shell |
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pip install torch torchvision |
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pip3 install torch torchvision torchaudio |
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``` |
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``` |
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* 安装Tensorflow[参考链接](https://www.tensorflow.org/install/pip) |
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- 安装Tensorflow[参考链接](https://www.tensorflow.org/install/pip)。 |
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```shell |
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```shell |
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pip install --upgrade tensorflow |
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pip install --upgrade tensorflow |
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``` |
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``` |
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## ModelScope library 安装 |
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## ModelScope library 安装 |
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注: 如果在安装过程中遇到错误,请前往[常见问题](faq.md)查找解决方案。 |
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注: 如果在安装过程中遇到错误,请前往[常见问题](faq.md)查找解决方案。 |
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### pip安装 |
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### pip安装 |
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执行如下命令: |
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执行如下命令可以安装所有领域依赖: |
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```shell |
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```shell |
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pip install "modelscope[all]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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pip install "modelscope[cv,nlp,audio,multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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``` |
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如需体验`语音功能`,请`额外`执行如下命令: |
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如仅需体验`语音功能`,请执行如下命令: |
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```shell |
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```shell |
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pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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pip install "modelscope[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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``` |
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如仅需体验CV功能,可执行如下命令安装依赖: |
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```shell |
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pip install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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如仅需体验NLP功能,可执行如下命令安装依赖: |
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```shell |
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pip install "modelscope[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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如仅需体验多模态功能,可执行如下命令安装依赖: |
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```shell |
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pip install "modelscope[multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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**注**: |
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1. `**语音相关**`**的功能仅支持 python3.7,tensorflow1.15的**`**linux**`**环境使用。 其他功能可以在windows、mac上安装使用。** |
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2. 语音领域中一部分模型使用了三方库SoundFile进行wav文件处理,**在Linux系统上用户需要手动安装SoundFile的底层依赖库libsndfile**,在Windows和MacOS上会自动安装不需要用户操作。详细信息可参考[SoundFile官网](https://github.com/bastibe/python-soundfile#installation)。以Ubuntu系统为>例,用户需要执行如下命令: |
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```shell |
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sudo apt-get update |
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sudo apt-get install libsndfile1 |
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``` |
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3. **CV功能使用需要安装mmcv-full, 请参考mmcv**[**安装手册**](https://github.com/open-mmlab/mmcv#installation)**进行安装** |
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### 使用源码安装 |
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### 使用源码安装 |
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适合本地开发调试使用,修改源码后可以直接执行 |
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下载源码可以直接clone代码到本地 |
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适合本地开发调试使用,修改源码后可以直接执行。 |
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ModelScope的源码可以直接clone到本地: |
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```shell |
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```shell |
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git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git modelscope |
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git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git modelscope |
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cd modelscope |
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git fetch origin master |
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git fetch origin master |
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git checkout master |
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git checkout master |
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cd modelscope |
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``` |
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``` |
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安装依赖并设置PYTHONPATH |
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安装依赖 |
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如需安装所有依赖,请执行如下命令 |
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```shell |
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```shell |
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pip install -e ".[all]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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export PYTHONPATH=`pwd` |
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pip install -e ".[cv,nlp,audio,multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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``` |
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注: 6.30版本需要把cv、nlp、multi-modal领域依赖都装上,7.30号各个领域依赖会作为选装,用户需要使用哪个领域安装对应领域依赖即可。 |
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如需使用语音功能,请执行如下命令安装语音功能所需依赖 |
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如需体验`语音功能`,请单独执行如下命令: |
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```shell |
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```shell |
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pip install -e ".[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo |
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pip install -e ".[audio]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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``` |
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### 安装验证 |
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安装成功后,可以执行如下命令进行验证安装是否正确 |
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如仅需体验CV功能,可执行如下命令安装依赖: |
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```shell |
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```shell |
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python -c "from modelscope.pipelines import pipeline;print(pipeline('word-segmentation')('今天天气不错,适合 出去游玩'))" |
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{'output': '今天 天气 不错 , 适合 出去 游玩'} |
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pip install -e ".[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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如仅需体验NLP功能,可执行如下命令安装依赖: |
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```shell |
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pip install -e ".[nlp]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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``` |
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## 推理 |
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pipeline函数提供了简洁的推理接口,相关介绍和示例请参考[pipeline使用教程](tutorials/pipeline.md) |
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如仅需体验多模态功能,可执行如下命令安装依赖: |
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```shell |
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pip install -e ".[multi-modal]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html |
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``` |
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### |
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### 安装验证 |
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## 训练 & 评估 |
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安装成功后,可以执行如下命令进行验证安装是否正确: |
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Trainer类提供了简洁的Finetuning和评估接口,相关介绍和示例请参考[Trainer使用教程](tutorials/trainer.md) |
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```shell |
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python -c "from modelscope.pipelines import pipeline;print(pipeline('word-segmentation')('今天天气不错,适合 出去游玩'))" |
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``` |