High performance deep learning
software infrastructure

Better models 10x faster.

Accelerate Your Deep Learning
Development Lifecycle


AutoML at scale

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


Seamless infrastructure

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


Broad compatibility

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


Reproducibility and collaboration

Track, share, and reproduce experiments and metrics automatically.


Edge, cloud, and mobile deployment

Optimize models through automated architecture search for constrained deployments.


One-click Jupyter notebooks

Explore and visualize results using GPU-powered notebooks.

Recent posts

AUG 19, 2019

Specialized AI chips hold both promise and peril for developers

AUG 13, 2019

[Product feature series] One-click access to TensorBoard for model development and experimentation

JUN 04, 2019

The cloud giants have an AI problem

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