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GTC ON-DEMAND

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Abstract:
In addition to the new production deployment-oriented capabilities included in the 1.0 release of PyTorch, the deep learning framework also added improved distributed training, allowing researchers and developers to easily parallelize computations across processes and clusters of machines. The PyTorch dev team at Facebook has continued to up the performance, and will be walking through new benchmarks, and how developers can readily take advantage of distributed training in PyTorch and NVIDIA GPUs to train their models faster.
In addition to the new production deployment-oriented capabilities included in the 1.0 release of PyTorch, the deep learning framework also added improved distributed training, allowing researchers and developers to easily parallelize computations across processes and clusters of machines. The PyTorch dev team at Facebook has continued to up the performance, and will be walking through new benchmarks, and how developers can readily take advantage of distributed training in PyTorch and NVIDIA GPUs to train their models faster.  Back
 
Topics:
Deep Learning & AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9830
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