Why This Is Interesting
Why should you attend this talk?
Using RAPIDS and GPUs users can see their data science models run 100x faster or more, with little to no code changes required.
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The RAPIDS suite of open-source software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.
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Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
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Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
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Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
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Drastically improve your productivity with more interactive data science tools like XGBoost.
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RAPIDS is an open-source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects.
Discussion Points
- Introduction to GPUs and how it is possible to get such incredible speedups with minimal code changes.
- Overview of popular RAPIDS tools such as GPU-accelerated Pandas (cuDF) and Sci-Kit Learn (cuML).
- Guidance on how and where to get started.
Takeaways
- Understand the GPU performance parallel computing metrics.
- RAPIDS performance on large scale data-sets.
- Rapids syntax is similar to Pandas syntax and it makes it really easy for data scientists to transition.
- Spark 3.0 GPU accelerated capabilities.
- Resources to learn RAPIDS and Spark 3.0