Monarch 🦋#
Monarch is a distributed execution engine for PyTorch. Our overall goal is to deliver the high-quality user experience that people get from single-GPU PyTorch, but at cluster scale.
⚠️ Early Development Warning Monarch is currently in an experimental stage. You should expect bugs, incomplete features, and APIs that may change in future versions. The project welcomes bugfixes, but to make sure things are well coordinated you should discuss any significant change before starting the work. It’s recommended that you signal your intention to contribute in the issue tracker, either by filing a new issue or by claiming an existing one.
What is Monarch?#
Monarch extends PyTorch’s capabilities to efficiently run on distributed systems. It maintains the familiar PyTorch API while handling the complexities of distributed execution, making it easier to scale your deep learning workloads across multiple GPUs and nodes.
Key features:
Familiar PyTorch API - Use the same PyTorch code you’re already familiar with
Efficient Distribution - Scale your models across multiple GPUs and nodes
Simplified Communication - Built-in primitives for distributed communication
Performance Optimized - Designed for high performance at scale
Note: Monarch is currently only supported on Linux systems.
Getting Started#
Here are some suggested steps to get started with Monarch:
Learn the Basics: Check out the Getting Started guide to learn the basics of Monarch
Explore Examples: Review the Examples to see Monarch in action
Dive Deeper: Explore the API Documentation for more detailed information
License#
Monarch is BSD-3 licensed, as found in the LICENSE file.
Community#
We welcome contributions from the community! If you’re interested in contributing, please:
Check the GitHub repository
Review existing issues or create a new one
Discuss your proposed changes before starting work
Submit a pull request with your changes
