Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[tune] Track live trials in a set in the TrialRunner to reduce linear scans #15811

Merged
merged 3 commits into from
Jun 17, 2021

Conversation

krfricke
Copy link
Contributor

Why are these changes needed?

We're scanning through all trials several times on each invocation of TrialRunner.step(). To optimize this in the case of many trials, we can reduce this by tracking live (non-terminated) trials in a separate set and looping through this.

Related issue number

Closes #15504

Checks

  • I've run scripts/format.sh to lint the changes in this PR.
  • I've included any doc changes needed for https://docs.ray.io/en/master/.
  • I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
  • Testing Strategy
    • Unit tests
    • Release tests
    • This PR is not tested :(

@krfricke
Copy link
Contributor Author

We should add a test to make sure bookkeeping works.

@krfricke
Copy link
Contributor Author

cc @richardliaw @max0x7ba

@krfricke
Copy link
Contributor Author

cc @richardliaw can you take a look?

Copy link
Contributor

@richardliaw richardliaw left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

nice!

@richardliaw richardliaw merged commit e547a27 into ray-project:master Jun 17, 2021
@krfricke krfricke deleted the tune-live-trials branch June 17, 2021 08:45
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Ray Tune doesn't scale, scheduling performance degrades to less than 25% worker utilization with 32 workers.
2 participants