We've generally focused on enabling cuML with other HPO tools, but we're always looking to better understand user needs. Could you file a Github issue on cuML describing how built-in HPO tooling would affect your work? github.com/rapidsai/cuml/iss…

SVMs are a perfect example of this scenario (results will vary based on data size, CPU, and GPU). In this example, my CPU is relatively weak so I can get benefits even with just 10K rows. gist.github.com/beckernick/f…

Using scikit-learn's GridSearchCV with cuML models should generally give you a large speedup if the model training itself is the bottleneck. If the cuML speedup is > the number of parallel models you can run via n_jobs without sacrificing performance, you'll come out ahead.

Sorry to hear about your experience with PyTorch! As a heads up, you can use cuML estimators with a variety of HPO toolkits including Optuna, Ray/Tune, and most of scikit-learn's model_selection capabilities like GridSearchCV and RandomSearchCV (if you use CPU data)

It looks like this may work smoothly in the cuDF 23.04 nightlies. Please let us know if things work for you!

@bilzard which version does wandb require? cuDF has relaxed its protobuf constraint to >=4.21.6,<4.22 in the 23.04 nightly packages

You can now pip install cuDF, cuML, and cugraph on Colab, which should be much faster!

@BenHarlander XGBoost now supports categorical features via optimal partitioning. If you haven't revisited categorical handling in XGBoost in a while, it's worth a look developer.nvidia.com/blog/ca…

You can use a GPU by clicking "Runtime -> Change runtime type" from the dropdown menu. You can then install and use XGBoost and other GPU-accelerated ML libraries like cuML rapids.ai/pip.html

Are you experiencing a specific error or issue?

You should now be able to smoothly combine the standard RAPIDS and PyTorch installation commands using the CUDA Toolkit version PyTorch wants. We now run tests every RAPIDS release to verify creating a joint environment works. This blog is separate, exciting new functionality!