Add support for distributed Metrics Visualization

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
25/100
Issue type
Feature
Clarity
Needs clarification
Activity status
Stale
Tech stack
python, pytorch
Domain
distributed-systems, machine-learning, observability

Research direction

Start with the README sample visualization for live loss metrics from a PyTorch worker on Ray. Clarify the interface needed for live loss and custom per-model metrics, including how it should work from the PyTorch API on Ray. Done means distributed ML workflows can publish and view those metrics in real time.

Written by the indexing model from the issue text.

Description

good first issue

The README includes a sample visualization which showcases live loss metrics for a given pytorch worker on ray.

Implement a more general interface for the live publishing of pytorch metrics on raydar, which can help users understand the quality/progress of their distributed ML workflows on Ray in real time. A wishlist of supported features might include:

  • Real time loss metrics as distributed training is happening
  • Real time custom metrics per-model as distributed training is happening
  • Support from within the pytorch api on ray
Dominant language
Python
Stars
64
Forks
8
Avg merge
14h 24m
Merged PRs (30d)
2

Getting set up

  • No Dockerfile or Docker Compose file
  • Has a pull request template
  • No contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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