The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks.
DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging.
DIGITS is available as a free download to the members of the NVIDIA Developer Program. If you are not already a member, clicking “Download” will ask you join the program.
Learn more about DIGITS 5 in the following blog posts:
Image segmentation neural network trained with DIGITS 5 to partition epithelium regions that contribute to identification of tumor
DIGITS 6 adds support for TensorFlow deep learning framework. Engineers and Data Scientists can improve productivity by designing TensorFlow models within DIGITS and using it’s convenient interactive workflow to manage datasets, training, and monitor model accuracy in realtime.
For an overview of DIGITS 6 with TensorFlow, read the following blog post on Generative Adversarial Network using DIGITS.
DIGITS 6 Release Candidate is available now on Docker Hub for testing and feedback. General availability will be in September as a free download for the members of the NVIDIA Developer Program. Sign up below to be notified when it is ready for download.
DIGITS is an open source project. Customize and extend DIGITS to suit your applications and share your experience using DIGITS on the DIGITS user group.
Import data for image classification and object detection neural networks
Download pre-trained models such as AlexNet, GoogLeNet and others from the DIGITS Model Store
Visualize deep neural network architectures
Schedule, monitor, and manage neural network training jobs
Analyze accuracy and loss in real time
Visualization of inference results