Distributed Training Guide

This guide aims at a comprehensive guide on best practices for distributed training, diagnosing errors, and fully utilize all resources available.

Questions this guide answers:

• How do I update a single gpu training/fine tuning script to run on multiple GPUs or multiple nodes?
• How do I diagnose hanging/errors that happen during training?
• My model/optimizer is too big for a single gpu - how do I train/fine tune it on my cluster?
• How do I schedule/launch training on a cluster?
• How do I scale my hyperparameters when increasing the number of workers?

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Best practices for logging stdout/stderr and wandb are also included, as logging is vitally important in diagnosing/debugging training runs on a cluster.

How to read

This guide is organized into sequential chapters, each with a README.md and a train_llm.py script in them. The readme will discuss the changes introduced in that chapter, and go into more details.

Each of the training scripts is aimed at training a causal language model (i.e. gpt).

Set up

Clone this repo

git clone https://github.com/LambdaLabsML/distributed-training-guide.git

Virtual Environment

cd distributed-training-guide
python3 -m venv venv
source venv/bin/activate
python -m pip install -U pip
pip install -U setuptools wheel
pip install -r requirements.txt

wandb

This tutorial uses wandb as an experiment tracker.

wandb login

🦄 Other exciting ML projects at Lambda: ML Times, Text2Video, GPU Benchmark.