Troubleshooting and Support
This guide helps you with usage issues or bugs that you might encounter using this product.
DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models. DIGITS currently supports the TensorFlow framework. DIGITS puts the power of deep learning into the hands of engineers and data scientists.
DIGITS is not a framework. DIGITS is a wrapper for TensorFlow; which provides a graphical web interface to those frameworks rather than dealing with them directly on the command-line.
DIGITS can be used to rapidly train highly accurate deep neural network (DNNs) for image classification, segmentation, object detection tasks, and more. 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 through multiple channels such as:
- GitHub download
- NVIDIA’s Docker repository,
DIGITS uses environment variables for configuration.
Note: Prior to #1091 (up to DIGITS 4.0), DIGITS used configuration files instead of environment variables.
Note:DIGITS is not designed to be run as an exposed external web service.
|Location where job files are stored. Default is $DIGITS_ROOT/digits/jobs.
|File for saving log messages. Default is $DIGITS_ROOT/digits/digits.log.
|Minimum log message level to be saved (DEBUG/INFO/WARNING/ERROR/CRITICAL). Default is INFO.
|The Big One
|The name of the server (accessible in the UI under "Info"). Default is the system hostname.
|A list of URL's, separated by comma. Default is the official NVIDIA store.
|A path to prepend before every URL. Sets the home-page to be at "http://localhost/custom-prefix" instead of "http://localhost/"/
- Nvidia DIGITS Installation Guide
- Installing CUDA and the NVIDIA Driver for DIGITS
- Nvidia DIGITS Container Getting Started Guide
- Consult the User Group forum: https://groups.google.com/forum/#!forum/digits-users
If you have another server running on port 80 already, you may need to reconfigure DIGITS to use a different port.
sudo dpkg-reconfigure digits
All other configuration is done with environment variables. See Configuration.md for detailed information about which variables you can change.
- Ubuntu 14.04
- Edit /etc/init/digits.conf
- Add/remove/edit lines that start with env
- Restart with sudo service digits restart
- Ubuntu 16.04
- Edit /lib/systemd/system/digits.service
- Add/remove/edit lines that start with Environment= in the [Service] section
- Restart with sudo systemctl daemon-reload && sudo systemctl restart digits
- Ubuntu 14.04:
sudo service digits stop # (install driver) sudo service digits start
- Ubuntu 16.04:
sudo systemctl stop digits # (install driver) sudo systemctl start digits
The DIGITS server runs as www-data, so keep in mind that prebuilt LMDB datasets used for generic models need to be readable by the www-data user. In particular, the entire chain of directories from / to your data must be readable by www-data.
To file a new issue go to: https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA%2FDIGITS%2Fissues%2Fnew
To contribute by opening a pull request: https://help.github.com/articles/about-pull-requests/
You will need to send a signed copy of the Contributor License Agreement to firstname.lastname@example.org before your change can be accepted.
For the latest Release Notes, see the DIGITS Release Notes Documentation website (http://docs.nvidia.com/deeplearning/digits/digits-release-notes/index.html ). For more information about DIGITS, see:
- DIGITS website (https://developer.nvidia.com/digits )
- DIGITS project (https://github.com/NVIDIA/DIGITS/blob/digits-5.0/README.md )
- GitHub documentation (https://github.com/NVIDIA/nvidia-docker/wiki/DIGITS )
Note: There may be slight variations between the NVIDIA-docker images and this image.
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