metaflow-torchrun
A torchrun decorator for Metaflow
Decision gist · record as of 2026-08-14
Yes, if you use Metaflow and PyTorch distributed training together. The package has no runtime dependencies, low install friction, active maintenance, and solves a real integration gap. License treatment is unclear—verify Apache License compliance before production use. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Metaflow to be installed and configured; GPU/compute environment must support the resource decorators used (e.g., @kubernetes or @batch).
- Installation is straightforward with no runtime dependencies.
- The package is actively maintained with a recent release, though Python version support is unspecified.
License · maintenance · safety
(unclear) — License treatment is unclear; the description mentions Apache License but SPDX metadata is not present in the package record, so verify the actual license terms before use.
last release 2026-07-15 (30 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 82,474 downloads/mo, #14,161 on PyPI
Alternatives
Verify before relying
pip install metaflow-torchrun
from metaflow import FlowSpec, step, torchrun
class MyFlow(FlowSpec):
@kubernetes(gpu=1)
@torchrun
@step
def train(self):
current.torch.run(
entrypoint="main.py",
entrypoint_args={"main-arg-1": "123"},
nproc_per_node=1
)- Exact Python version requirements and compatibility range
- Whether Metaflow itself is a required dependency or assumed to be pre-installed
- GPU/CUDA version requirements for torchrun integration
What it is and what it does
metaflow-torchrun is a plugin that bridges Metaflow workflows and PyTorch's torchrun distributed training framework. It provides a @torchrun decorator that lets you run existing PyTorch distributed programs (like DDP training) as parallel steps in a Metaflow DAG without modifying your training code. The decorator automatically handles torchrun argument selection based on Metaflow compute resource requests (CPU, GPU, memory), network discovery, and subprocess orchestration.
You use it by stacking the @torchrun decorator on a Metaflow step, then calling current.torch.run() with your training entrypoint and arguments. This pattern is useful for multi-node training jobs submitted to AWS Batch or Kubernetes, where you want Metaflow to manage task orchestration and torchrun to manage the distributed torch processes within each task.
Use it for
- Run multi-node PyTorch DDP training as a step in a Metaflow workflow without subprocess boilerplate
- Orchestrate distributed GPT or transformer training across parallel Metaflow tasks on Kubernetes or AWS Batch
- Execute existing torchrun scripts inside Metaflow without rewriting training code for the workflow framework
- Automate network discovery and torchrun argument configuration based on Metaflow resource decorators
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Metaflow and PyTorch distributed training together.
The package has no runtime dependencies, low install friction, active maintenance, and solves a real integration gap. License treatment is unclear—verify Apache License compliance before production use. No known vulnerabilities.
Install
metaflow-torchrun on PyPI
Before you install
Installation is straightforward with no runtime dependencies. The package is actively maintained with a recent release, though Python version support is unspecified.
Requires Metaflow to be installed and configured; GPU/compute environment must support the resource decorators used (e.g., @kubernetes or @batch).
License in practice
License treatment is unclear; the description mentions Apache License but SPDX metadata is not present in the package record, so verify the actual license terms before use.
Quickstart
pip install metaflow-torchrun
from metaflow import FlowSpec, step, torchrun
class MyFlow(FlowSpec):
@kubernetes(gpu=1)
@torchrun
@step
def train(self):
current.torch.run(
entrypoint="main.py",
entrypoint_args={"main-arg-1": "123"},
nproc_per_node=1
)Verify before relying
- Exact Python version requirements and compatibility range
- Whether Metaflow itself is a required dependency or assumed to be pre-installed
- GPU/CUDA version requirements for torchrun integration
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 30 days since the last release |
| First released | |
| Downloads | 82,474 / month, #14,161 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: metaflow_torchrun-0.2.2-py3-none-any.whl
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See also metaflow-prebuilt · torchtitan · torchft-nightly · torchx · torchrunx · torchtnt · metaflow-checkpoint · torch · fairscale · metaflow