ColossalAI/examples/tutorial/large_batch_optimizer
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[tutorial] polish all README (#1946)
2022-11-14 19:49:32 +08:00
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README.md [tutorial] polish all README (#1946) 2022-11-14 19:49:32 +08:00
config.py [tutorial] edited hands-on practices (#1899) 2022-11-11 17:08:17 +08:00
train.py [tutorial] added synthetic data for hybrid parallel (#1919) 2022-11-12 17:49:48 +08:00

README.md

Comparison of Large Batch Training Optimization

🚀Quick Start

Run with synthetic data

colossalai run --nproc_per_node 4 train.py --config config.py -s

Prepare Dataset

We use CIFAR10 dataset in this example. You should invoke the donwload_cifar10.py in the tutorial root directory or directly run the auto_parallel_with_resnet.py. The dataset will be downloaded to colossalai/examples/tutorials/data by default. If you wish to use customized directory for the dataset. You can set the environment variable DATA via the following command.

export DATA=/path/to/data

You can also use synthetic data for this tutorial if you don't wish to download the CIFAR10 dataset by adding the -s or --synthetic flag to the command.

Run on 2*2 device mesh

# run with cifar10
colossalai run --nproc_per_node 4 train.py --config config.py

# run with synthetic dataset
colossalai run --nproc_per_node 4 train.py --config config.py -s