High performance deep learning
software infrastructure

Better models 10x faster.

Accelerate Your Deep Learning
Development Lifecycle

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AutoML at scale

Speed up model development by 100x via distributed training and best-in-class hyperparameter search.

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Seamless infrastructure

Manage and share GPU resources, on premises, in the cloud, or both.

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Broad compatibility

Run unmodified TensorFlow, Keras, and PyTorch code on Kubernetes or bare-metal.

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Reproducibility and collaboration

Track, share, and reproduce experiments and metrics automatically.

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Edge, cloud, and mobile deployment

Optimize models through automated architecture search for constrained deployments.

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One-click Jupyter notebooks

Explore and visualize results using GPU-powered notebooks.

Recent posts

MAR 16, 2020

Distributed Deep Learning That Actually Works

MAR 01, 2020

March '20 Newsletter

FEB 14, 2020

AI in the 2020s Must Get Greener—and Here’s How

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