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Making large AI models cheaper, faster and more accessible
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ColossalAI/examples/tutorial/large_batch_optimizer
Frank Lee 8b7495dd54
[example] integrate seq-parallel tutorial with CI (#2463) 		2 years ago
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README.md [example] integrate seq-parallel tutorial with CI (#2463) 2 years ago
config.py [example] updated large-batch optimizer tutorial (#2448) 2 years ago
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train.py [example] updated large-batch optimizer tutorial (#2448) 2 years ago

README.md

Large Batch Training Optimization

Table of contents

📚 Overview

This example lets you to quickly try out the large batch training optimization provided by Colossal-AI. We use synthetic dataset to go through the process, thus, you don't need to prepare any dataset. You can try out the Lamb and Lars optimizers from Colossal-AI with the following code.

from colossalai.nn.optimizer import Lamb, Lars

🚀 Quick Start

  1. Install PyTorch

  2. Install the dependencies.

pip install -r requirements.txt
  1. Run the training scripts with synthetic data.
# run on 4 GPUs
# run with lars
colossalai run --nproc_per_node 4 train.py --config config.py --optimizer lars

# run with lamb
colossalai run --nproc_per_node 4 train.py --config config.py --optimizer lamb