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5 years ago | |
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| .. | ||
| citeseer | 5 years ago | |
| cora | 5 years ago | |
| README.md | 5 years ago | |
| graph_map_schema.py | 5 years ago | |
| read_citeseer.sh | 5 years ago | |
| read_cora.sh | 5 years ago | |
| reader.py | 5 years ago | |
| write_citeseer.sh | 5 years ago | |
| write_cora.sh | 5 years ago | |
| writer.py | 5 years ago | |
This example provides an efficient way to generate MindRecord. Users only need to define the parallel granularity of training data reading and the data reading function of a single task. That is, they can efficiently convert the user's training data into MindRecord.
Download and prepare the Cora dataset as required.
Edit write_cora.sh and modify the parameters
--mindrecord_file: output MindRecord file.
--mindrecord_partitions: the partitions for MindRecord.
Run the bash script
bash write_cora.sh
Assume the dataset name is 'xyz'
cd ${your_mindspore_home}/example/graph_to_mindrecord
cp -r cora xyz
Edit dictionary data generator.
cd ${your_mindspore_home}/example/graph_to_mindrecord
vi xyz/mr_api.py
Two API, 'mindrecord_task_number' and 'mindrecord_dict_data', must be implemented.
cd ${your_mindspore_home}/example/graph_to_mindrecord
python writer.py --mindrecord_script xyz [...]
You can put this command in script write_xyz.sh for easy execution
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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