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Erpim
3b8020f69c
|
7 months ago | |
|---|---|---|
| .. | ||
| README.md | 9 months ago | |
| auto_dp_main.py | 6 months ago | |
| run_auto_dp_training.sh | 9 months ago | |
README.md
NetWork Description
This example describes how to train a ResNet-50 network by using a ImageNet dataset on a GPU/Ascend processor hardware platform through MSAdapter and auto parallelism mode.
Dataset
Dataset used: imagenet
- Dataset size: ~125G, 224*224 colorful images in 1000 classes
- Train: 120G, 1281167 images
- Test: 5G, 50000 images
- Data format: RGB images.
Environment Requirements
- Hardware（Ascend/GPU）
Model Source
In this case, the source of the model definition or training script corresponding to the implementation of PyTorch is as follows:
https://github.com/pytorch/examples/tree/main/imagenet
Quick Start
You can start training as follows:
bash run_auto_dp_training.sh
No Description
Jupyter Notebook Text Python C++ Shell other
Contributors (17)
zhangych02@pcl.ac.cn
jiaronghan@outlook.com
yangge8@huawei.com
yongchizyc@gmail.com
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root@h975ef02e93a4226bc646df114683342-task0-0.h975ef02e93a4226bc646df114683342.fccb038c23234b9e80105d4ccd152117.svc.cluster.local
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