DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. The currently supported frameworks are: Caffe, Torch, and Tensorflow. In addition to submitting pull requests, feel free to submit and vote on feature requests via our ideas portal. Current and most updated document is availabel at NVIDIA Accelerated Computing, Deep Learning Documentation, NVIDIA DIGITS. Official DIGITS container is available at nvcr.io via docker pull command. Once you have installed DIGITS, visit docs/GettingStarted.md for an introductory walkthrough.

Google announced in its recent I/O conference that it has developed Tensor Processing Unit (TPU), an application specific processor designed for deep learning. Google states that TPU demonstrates faster intelligence capabilities to manage AI workloads. Google proudly claims that TPUs are high-tech chips that drive forward chip-technology seven years into the future. This custom IC (Integrated Circuit) finds its application in Google's TensorFlow machine learning software. Thousands of them are being used in its own data centres for over a year now.

Deep learning users can now access pre-configured NVIDIA DIGITS Titan-X GPU instance starting at 49 cents per hour on Nimbix! Data Scientists and Deep learning users can now try the single-click solution on Cloud Service Provider Nimbix to launch an instance configured to run NVIDIA DIGITS on Titan-X GPUs at as low as 0.49/hour, the most affordable high performance GPUs compared to anywhere in the cloud, powered by Bitfusion Boost's Software Defined Supercompute technology. The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning in the hands of data scientists and researchers. Best of all, DIGITS is a complete system so you don't have to write any code. You can train neural network models, including pre-built AlexNet and GoogleNet models.