picotron: Train Llama-Like Models with Distributed Parallelism

Overview

Picotron is an educational framework for pre-training Llama-like models across multiple GPUs. It brings together four forms of distributed parallelism, data, tensor, pipeline, and context, in a small codebase intended to be easier to inspect and modify than production-oriented training systems.

It suits learners and researchers who want to understand distributed training techniques by running and changing working code. It is not positioned as a top-performance alternative to larger training frameworks, and the project notes that performance is still under active development.

Key Features

• Combines data, tensor, pipeline, and context parallelism for distributed training.
• Keeps core training and parallelism modules compact and readable.
• Supports configuration generation for model and training settings.
• Provides a local multi-process launch path with torchrun.
• Includes an example workflow for submitting jobs to Slurm.
• Offers CPU execution for small experiments, with a warning that it will be slow.
• Links to a tutorial playlist and a companion tutorial codebase.

Use Cases

• Students studying distributed machine learning can trace how parallelism strategies are implemented in a compact training framework.
• ML engineers can build small experiments to compare or modify data, tensor, and pipeline parallel training setups.
• Researchers can use the code as a learning-oriented starting point when exploring distributed pre-training techniques.
• Instructors can pair the framework with its tutorial materials for hands-on lessons about multi-GPU training.

Getting Started

Install the package in editable mode:

pip install -e .

For configuration, model access, and GPU or Slurm launch examples, see the README.

Considerations

• The project prioritizes education and hackability, not best-in-class performance.
• Meaningful distributed training requires suitable GPU resources; CPU runs are possible but expected to be slow.
• Downloading models from Hugging Face may require an access token, depending on the model.
• The README describes performance work as active development and says benchmarks will come soon.