ColossalAI/examples/images/resnet/README.md

# Train ResNet on CIFAR-10 from scratch

## 🚀 Quick Start

This example provides a training script and an evaluation script. The training script provides an example of training ResNet on CIFAR10 dataset from scratch.

- Training Arguments
  - `-p`, `--plugin`: Plugin to use. Choices: `torch_ddp`, `torch_ddp_fp16`, `low_level_zero`. Defaults to `torch_ddp`.
  - `-r`, `--resume`: Resume from checkpoint file path. Defaults to `-1`, which means not resuming.
  - `-c`, `--checkpoint`: The folder to save checkpoints. Defaults to `./checkpoint`.
  - `-i`, `--interval`: Epoch interval to save checkpoints. Defaults to `5`. If set to `0`, no checkpoint will be saved.
  - `--target_acc`: Target accuracy. Raise exception if not reached. Defaults to `None`.

- Eval Arguments
  - `-e`, `--epoch`: select the epoch to evaluate
  - `-c`, `--checkpoint`: the folder where checkpoints are found

### Install requirements

```bash
pip install -r requirements.txt
```

### Train
The folders will be created automatically.
```bash
# train with torch DDP with fp32
colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp32

# train with torch DDP with mixed precision training
colossalai run --nproc_per_node 2 train.py -c ./ckpt-fp16 -p torch_ddp_fp16

# train with low level zero
colossalai run --nproc_per_node 2 train.py -c ./ckpt-low_level_zero -p low_level_zero
```

### Eval

```bash
# evaluate fp32 training
python eval.py -c ./ckpt-fp32 -e 80

# evaluate fp16 mixed precision training
python eval.py -c ./ckpt-fp16 -e 80

# evaluate low level zero training
python eval.py -c ./ckpt-low_level_zero -e 80
```

Expected accuracy performance will be:

| Model     | Single-GPU Baseline FP32 | Booster DDP with FP32 | Booster DDP with FP16 | Booster Low Level Zero | Booster Gemini |
| --------- | ------------------------ | --------------------- | --------------------- | ---------------------- | -------------- |
| ResNet-18 | 85.85%                   | 84.91%                | 85.46%                | 84.50%                 | 84.60%         |

**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`**