Skip to content

LBFGS Logistic regression accelerated with OneDAL (builds against Random Forest PR #6364) #6373

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

Closed
wants to merge 7 commits into from

Conversation

rgesteve
Copy link
Contributor

We are excited to review your PR.

So we can do the best job, please check:

  • There's a descriptive title that will make sense to other developers some time from now.
  • There's associated issues (N/A)
  • Your change description explains what the change does, why you chose your approach, and anything else that reviewers should know. (please see below)
  • You have included any necessary tests in the same PR.

Provides an accelerated (under certain circumstances) path to do logistic regression with the LBFGS optimizer. This assumes the algorithm is running on a suitable platform (non-ARM CPU) and is enabled by setting the environment variable MLNET_BACKEND to "ONEDAL" (a few extra checks are done against the shape of the dataset to ensure acceleration). Beyond this "knob", no change is required in user code.

This change builds on the infrastructure provided by PR #6364 in terms of OneDAL build and runtime support.

@rgesteve
Copy link
Contributor Author

rgesteve commented Dec 8, 2022

Closed in favor of #6521

@rgesteve rgesteve closed this Dec 8, 2022
@ghost ghost locked as resolved and limited conversation to collaborators Jan 7, 2023
Sign up for free to subscribe to this conversation on GitHub. Already have an account? Sign in.
Projects
None yet
Development

Successfully merging this pull request may close these issues.

1 participant