pytorch / pytorch/kineto

Is it able to profile for Gloo backend and distributed CPU settings?

Open
#592 3 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

enhancement plugin
Dominant language
C++
Stars
992
Forks
270
Avg merge
2d 11h
Merged PRs (30d)
12

Description

The example is running on the NCCL backend for distributed GPU settings. I'm wondering if it can profile correctly on a multi-node (multiple CPU servers) distributed CPU settings with Gloo backend?

I try to change the example code: change NCCL to Gloo backend, and make the device as CPU. It could generate reports for distributed and memory view. I think that is correct? But it is only on one-machine.

I'm curious that if multiple machine settings are supported. Thanks!

Contributor guide

Open the 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.

Research direction

Start by reviewing the profiling example and the reported single-machine CPU run using the Gloo backend. Reproduce the modified example, then determine whether distributed and memory reports remain correct across multiple CPU machines; done means multi-node support is confirmed or its missing requirements are documented.

Written by the indexing model from the issue text.

Assessment

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

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.