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GTC ON-DEMAND

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Abstract:

We'll discuss cuML, a GPU-Accelerated library of machine learning algorithms within the RAPIDS data science ecosystem. The cuML library allows data scientists, researchers, and software engineers to run traditional ML tasks on GPUs without going into the details of CUDA programming. We'll show you how to get tremendous speed-up for traditional machine learning workloads by using APIs like Scikit-Learn with Python. We'll also provide code examples, benchmarks, and the latest news.

We'll discuss cuML, a GPU-Accelerated library of machine learning algorithms within the RAPIDS data science ecosystem. The cuML library allows data scientists, researchers, and software engineers to run traditional ML tasks on GPUs without going into the details of CUDA programming. We'll show you how to get tremendous speed-up for traditional machine learning workloads by using APIs like Scikit-Learn with Python. We'll also provide code examples, benchmarks, and the latest news.

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Topics:
Accelerated Data Science, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9817
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