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GTC ON-DEMAND

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Abstract:

RAPIDS is an open-source platform for GPU data science, incubated by NVIDIA. Built to look and feel like popular tools in the Python Data Science ecosystem, RAPIDS is easy to use and dramatically speeds up execution of all steps of a typical data science workflow. Intended for working data scientists, this session will be an in-depth walk through of all the stages of a model data science workflow using RAPIDS. The presentation will cover ingesting and cleaning data, feature engineering, working with strings, user-defined functions, and applying machine learning. The session will discuss the community and ecosystem around RAPIDS and future plans for the cuML library. Additionally, the session will cover how users can contribute to RAPIDS. At the end of the session, attendees will have learned RAPIDS benefits for data science, how to get started installing RAPIDS, and how to build their own workflows using RAPIDS.

RAPIDS is an open-source platform for GPU data science, incubated by NVIDIA. Built to look and feel like popular tools in the Python Data Science ecosystem, RAPIDS is easy to use and dramatically speeds up execution of all steps of a typical data science workflow. Intended for working data scientists, this session will be an in-depth walk through of all the stages of a model data science workflow using RAPIDS. The presentation will cover ingesting and cleaning data, feature engineering, working with strings, user-defined functions, and applying machine learning. The session will discuss the community and ecosystem around RAPIDS and future plans for the cuML library. Additionally, the session will cover how users can contribute to RAPIDS. At the end of the session, attendees will have learned RAPIDS benefits for data science, how to get started installing RAPIDS, and how to build their own workflows using RAPIDS.

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Topics:
Accelerated Data Science, Algorithms & Numerical Techniques
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9801
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Abstract:
Artificial Intelligence is a wide field where different types of learning models serve different kinds of tasks. Deep neural networks (DL), for example, are great for data with spatial-temporal locality like images, audio, and text. However, for data without inherent locality, non-neural network machine learning algorithms, such as random forest or gradient boosted trees, are often used instead. In this session, we will discuss our how RAPIDS machine learning libraries are optimized for non-neural-network based ML algorithms for latest GPU architectures.
Artificial Intelligence is a wide field where different types of learning models serve different kinds of tasks. Deep neural networks (DL), for example, are great for data with spatial-temporal locality like images, audio, and text. However, for data without inherent locality, non-neural network machine learning algorithms, such as random forest or gradient boosted trees, are often used instead. In this session, we will discuss our how RAPIDS machine learning libraries are optimized for non-neural-network based ML algorithms for latest GPU architectures.  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Israel
Year:
2018
Session ID:
SIL8137
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