mirror of https://github.com/hpcaitech/ColossalAI
57 lines
2.1 KiB
Markdown
57 lines
2.1 KiB
Markdown
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# Train ResNet on CIFAR-10 from scratch
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## 🚀 Quick Start
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This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch.
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- Training Arguments
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- `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`.
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- `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming.
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- `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`.
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- `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved.
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- `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`.
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- Eval Arguments
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- `-e`, `--epoch`: select the epoch to evaluate
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- `-c`, `--checkpoint`: the folder where checkpoints are found
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### Install requirements
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```bash
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pip install -r requirements.txt
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```
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### Train
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```bash
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# train with torch DDP with fp32
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colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32
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# train with torch DDP with mixed precision training
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colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 -p torch_ddp_fp16
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# train with low level zero
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colossalai run --nproc_per_node 2 train.py -c ./ckpt-low_level_zero -p low_level_zero
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```
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### Eval
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```bash
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# evaluate fp32 training
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python eval.py -c ./ckpt-fp32 -e 80
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# evaluate fp16 mixed precision training
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python eval.py -c ./ckpt-fp16 -e 80
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# evaluate low level zero training
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python eval.py -c ./ckpt-low_level_zero -e 80
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```
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Expected accuracy performance will be:
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| Model | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero |
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| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- |
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| ResNet-18 | 85.85% | 84.91% | 85.46% | 84.50% |
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**Note: the baseline is adapted from the [script](https://pytorch-tutorial.readthedocs.io/en/latest/tutorial/chapter03_intermediate/3_2_2_cnn_resnet_cifar10/) to use `torchvision.models.resnet18`**
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