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README.md

Train ViT on CIFAR-10 from scratch

🚀 Quick Start

This example provides a training script, which provides an example of training ViT 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.

Install requirements

pip install -r requirements.txt

Train

# train with torch DDP with fp32
colossalai run --nproc_per_node 4 train.py -c ./ckpt-fp32

# train with torch DDP with mixed precision training
colossalai run --nproc_per_node 4 train.py -c ./ckpt-fp16 -p torch_ddp_fp16

# train with low level zero
colossalai run --nproc_per_node 4 train.py -c ./ckpt-low_level_zero -p low_level_zero

Expected accuracy performance will be:

Model	Single-GPU Baseline FP32	Booster DDP with FP32	Booster DDP with FP16	Booster Low Level Zero
ViT	83.00%	84.03%	84.00%	84.43%