-
Notifications
You must be signed in to change notification settings - Fork 564
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
Document ability to export cuML RF to predict on other machines #3890
Conversation
Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
The notebook addition looks great!, Just requested a link from the main docstrings
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"Starting with cuML version 21.06, you can export cuML Random Forest models and run predictions with them on machines without an NVIDIA GPUs. The [Treelite](https://github.com/dmlc/treelite) package defines an efficient exchange format that lets you portably move the cuML Random Forest models to other machines. We will refer to the exchange format as \"checkpoints.\"\n", |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
I think adding a link to here from the main docstring of the RF classes (with a note like RF classes can be exported to Treelite for inference on machines without GPUs
or so) would be useful for people looking at the api docs
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Done
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
lgtm
rerun tests |
@gpucibot merge |
rerun tests |
Codecov Report
@@ Coverage Diff @@
## branch-21.06 #3890 +/- ##
===============================================
Coverage ? 85.42%
===============================================
Files ? 226
Lines ? 17271
Branches ? 0
===============================================
Hits ? 14754
Misses ? 2517
Partials ? 0
Flags with carried forward coverage won't be shown. Click here to find out more. Continue to review full report at Codecov.
|
…dsai#3890) See rapidsai#3853 (comment) Authors: - Philip Hyunsu Cho (https://github.com/hcho3) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: rapidsai#3890
See #3853 (comment)