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.
| Installation method | Supported platform[s] | Available versions | Instructions |
|---|---|---|---|
| Source | Ubuntu 14.04, 16.04 | GitHub tags | docs/BuildDigits.md |
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.
Then, take a look at some of the other documentation at docs/ and examples/:
- Getting started with TensorFlow
- Getting started with Torch
- Fine-tune a pretrained model
- Creating a dataset using data from S3 endpoint
- Train an autoencoder network
- Train a regression network
- Train a Siamese network
- Train a text classification network
- Train an object detection network
- Learn more about weight initialization
- Use Python layers in your Caffe networks
- Download a model and use it to classify an image outside of DIGITS
- Overview of the REST API
- First, check out the instructions above
- Then, ask questions on our user group
- First, check out the Getting Started page
- Then, ask questions on our user group
- Please let us know by filing a new issue
- Bonus points if you want to contribute by opening a pull request!
- You will need to send a signed copy of the Contributor License Agreement to digits@nvidia.com before your change can be accepted.
Users shall understand that DIGITS is not designed to be run as an exposed external web service.