Metaflow torchrun decorator

Introduction

This repository implements a plugin to run parallel Metaflow tasks as nodes in a torchrun job which can be submitted to AWS Batch or a Kubernetes cluster.

Features

• Automatic torchrun integration: This extension provides a simple and intuitive way to incorporate PyTorch distributed programs in your Metaflow workflows using the @torchrun decorator
• No changes to model code: The @torchrun decorator exposes a new method on the Metaflow current object, so you can run your existing torch distributed programs inside Metaflow tasks with no changes in the research code.
• Run one command: You don't need to log into many nodes and run commands on each. Instead, the @torchrun decorator will select arguments for the torchrun command based on the requests in Metaflow compute decorators like number of GPUs. Network addresses are automatically discoverable.
• No user-facing subprocess calls: At the end of the day, @torchrun is calling a subprocess inside a Metaflow task. Although many Metaflow users do this, it can make code difficult to read for beginners. One major goal of this plugin is to motivate hardening and automating a pattern for submitting subprocess calls inside Metaflow tasks.

Installation

You can install it with:

pip install metaflow-torchrun

Getting Started

And then you can import it and use in parallel steps:

from metaflow import FlowSpec, step, torchrun

...
class MyGPT(FlowSpec):

    @step
    def start(self):
        self.next(self.torch_multinode, num_parallel=N_NODES)

    @kubernetes(cpu=N_CPU, gpu=N_GPU, memory=MEMORY)
    @torchrun
    @step
    def torch_multinode(self):
        ...
        current.torch.run(
            entrypoint="main.py", # No changes made to original script.
            entrypoint_args = {"main-arg-1": "123", "main-arg-2": "777"},
            nproc_per_node=1,     # edge case of a torchrun arg user-facing.
        )
        ...
    ...

Examples

License

metaflow-torchrun is distributed under the Apache License.