Nvidia's RAPIDS.ai: Massively Accelerated Modern Data-Science | AISC

Channel:
Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
Subscribers:
20,600
Published on ● Video Link: https://www.youtube.com/watch?v=EBCtwWbbamw


Duration: 57:28
445 views
17

Speaker(s): Griffin Lacey, Mukundhan Srinivasan
Facilitator(s): Alireza Darbehani

Find the recording, slides, and more info at https://ai.science/e/rapids-massively-accelerated-modern-data-science-with-rapids-ai--OsYG3YCNrAEXwPxecIRV

Motivation / Abstract
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.
- 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.
- Seamlessly scale from GPU workstations to multi-GPU servers and multi-node clusters with Dask.
- Accelerate your Python data science toolchain with minimal code changes and no new tools to learn.
- Increase machine learning model accuracy by iterating on models faster and deploying them more frequently.
- Drastically improve your productivity with more interactive data science tools like XGBoost.
- RAPIDS is an open-source project. Supported by NVIDIA, it also relies on numba, apache arrow, and many more open source projects.

What was discussed?
- 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.

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#AISC hosts 3-5 live sessions like this on various AI research, engineering, and product topics every week! Visit https://ai.science for more details



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Tags:
nvidia
rapids
data science
machine learning
deep learning