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GTC ON-DEMAND

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Abstract:
Many machine learning algorithms can benefit from the efficient fine-grained parallelism and high throughput of GPUs. Importantly, GPUs make it possible to complete training and inference much faster than possible on ordinary CPUs. In this talk, we will share some recent developments in implementing machine learning on GPUs and show how to leverage H2O.ai's H2O GPU edition to capture benefits from GPU acceleration. We'll also compare performance gains from GPUs with CPU performance.
Many machine learning algorithms can benefit from the efficient fine-grained parallelism and high throughput of GPUs. Importantly, GPUs make it possible to complete training and inference much faster than possible on ordinary CPUs. In this talk, we will share some recent developments in implementing machine learning on GPUs and show how to leverage H2O.ai's H2O GPU edition to capture benefits from GPU acceleration. We'll also compare performance gains from GPUs with CPU performance.  Back
 
Topics:
Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9250
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Abstract:

Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs. We introduce H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust python API that is a drop dead replacement for scikit-learn. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.

Deep learning algorithms have benefited greatly from the recent performance gains of GPUs. However, it has been unclear whether GPUs can speed up machine learning algorithms such as generalized linear modeling, random forests, gradient boosting machines, and clustering. H2O.ai, the leading open source AI company, is bringing the best-of-breed data science and machine learning algorithms to GPUs. We introduce H2O4GPU, a fully featured machine learning library that is optimized for GPUs with a robust python API that is a drop dead replacement for scikit-learn. We'll demonstrate benchmarks for the most common algorithms relevant to enterprise AI and showcase performance gains as compared to running on CPUs.

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Topics:
Algorithms & Numerical Techniques, AI Startup, Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8523
Streaming:
Download:
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