Nick Becker

Building an accelerated data science and data engineering ecosystem with @rapidsai

[Nick Becker @becker_data]

@DaPiePiece 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? https://t.co/tJ4l5UXkHX

[Nick Becker @becker_data]

@DaPiePiece 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.
https://t.co/PN1XiC4CFb

[Nick Becker @becker_data]

@DaPiePiece 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.

[James Dolezal @JamesDolezal]

🚀 Slideflow 2.0 is here! Take your #digitalpathology research to the next level with extended MIL support, expanded feature generators, and enhanced stain normalization. Deploy & visualize models with Slideflow Studio. 🔬💻 Check it out at https://t.co/rJEafYUqRl

[Nick Becker @becker_data]

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

[Nick Becker @becker_data]

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

[Nick Becker @becker_data]

@StonewrightAI @RAPIDSai Hi @StonewrightAI , we've just updated the RAPIDS website and it includes new installation instructions and advanced install resources. You can learn more at https://t.co/n15uC94Iog

[Nick Becker @becker_data]

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

[Nick Becker @becker_data]

@AIMLDS XGBoost now provides built-in support for categorical features. If you haven't revisited categorical handling in XGBoost in a while, it's worth a look. https://t.co/EtFp29kph7

[Nick Becker @becker_data]

@PtrPomorski @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

https://t.co/ZHR5YVfgoV

[Nick Becker @becker_data]

@zaialamm 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 https://t.co/6HiOXjy4hk

[Jacob Tomlinson @_JacobTomlinson]

Here are the CLI 5 commands it takes to get a multi-node multi-GPU Data Science cluster running in the cloud with RAPIDS and Dask.

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[RAPIDS AI @RAPIDSai]

Moving between CPU and GPU environments just got a whole lot easier. Find out more in this new blog by @_rjzamora and @quasiben: https://t.co/OEhBXojLLx

[RAPIDS AI @RAPIDSai]

Working with BERTopic and not meeting your New Year's wait loss goals? This blog by @MaartenGron and the RAPDIS team on speeding up BERTopic on CPU, and going the next mile with GPU, will meet you where you are at to keep those resolutions strong. https://t.co/aVoIVxov8Z

[Nick Becker @becker_data]

@SNbarbat Are you experiencing a specific error or issue?

[Nick Becker @becker_data]

@tom_gxt @RAPIDSai @PyTorch 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!

[RAPIDS AI @RAPIDSai]

Anyone interested in topic modeling or #NLP should check out BERTopic. The upcoming release will include deeper support for #RAPIDS and cuML to help you get results faster and process larger datasets (among other amazing updates)!

[Maarten Grootendorst @MaartenGr]

Preview #5: Heavyweight BERTopic!

Last week was a lightweight update... this week a heavyweight💪 Better and more native integration of cuML's HDBSCAN in BERTopic! Use the full force of GPU acceleration to scale topic modeling to millions of documents.

A preview thread👇🧵