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GTC ON-DEMAND

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Abstract:

Learn how RAPIDS uses Dask to scale to distributed clusters of machines. Dask, a library for scalable computing in Python, is known for scaling out popular PyData libraries like Numpy, Pandas, and Scikit-Learn. The GPU-Accelerated data science software stack RAPIDS also uses Dask to easily scale to multiple GPUs on a single node, and multiple nodes within a cluster. We'll explain how RAPIDS used Dask to scale out, discuss the challenges of integrating GPUs into the existing PyData stack, and describe how this work creates opportunities for Python users.

Learn how RAPIDS uses Dask to scale to distributed clusters of machines. Dask, a library for scalable computing in Python, is known for scaling out popular PyData libraries like Numpy, Pandas, and Scikit-Learn. The GPU-Accelerated data science software stack RAPIDS also uses Dask to easily scale to multiple GPUs on a single node, and multiple nodes within a cluster. We'll explain how RAPIDS used Dask to scale out, discuss the challenges of integrating GPUs into the existing PyData stack, and describe how this work creates opportunities for Python users.

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Topics:
Accelerated Data Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9797
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