Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/nvidia/digits

Deep Learning GPU Training System
https://github.com/nvidia/digits

caffe deep-learning gpu machine-learning torch

Last synced: 7 days ago
JSON representation

Deep Learning GPU Training System

Awesome Lists containing this project

README 

        
# DIGITS



DIGITS (the **D**eep Learning **G**PU **T**raining **S**ystem) is a webapp for training deep learning models.



_**Note:** We are no longer adding features, fixing bugs, or supporting the NVIDIA Deep Learning GPU Training System (DIGITS) software. You may continue to use the software if it meets your needs. However:_

* _For developers creating vision AI applications, we suggest NVIDIA TAO, an open source toolkit for AI model training and customization. Learn more about [NVIDIA TAO](https://developer.nvidia.com/tao-toolkit)._

* _For developers interested in NVIDIA Project DIGITS, to learn more, visit [NVIDIA Project DIGITS](https://www.nvidia.com/en-us/project-digits/)._



# Feedback

In addition to submitting pull requests, feel free to submit and vote on feature requests via [our ideas portal]( https://nvdigits.ideas.aha.io/).



# Documentation



Current and most updated document is available at

 [NVIDIA Accelerated Computing, Deep Learning Documentation, NVIDIA DIGITS](https://docs.nvidia.com/deeplearning/digits/index.html).



# Installation



| Installation method | Supported platform[s] | Available versions | Instructions |

| --- | --- | --- | --- |

| Source | Ubuntu 14.04, 16.04 | [GitHub tags](https://github.com/NVIDIA/DIGITS/releases) | [docs/BuildDigits.md](docs/BuildDigits.md) |



Official DIGITS container is available at nvcr.io via docker pull command.



# Usage



Once you have installed DIGITS, visit [docs/GettingStarted.md](docs/GettingStarted.md) for an introductory walkthrough.



Then, take a look at some of the other documentation at [docs/](docs/) and [examples/](examples/):



* [Getting started with TensorFlow](docs/GettingStartedTensorflow.md)

* [Getting started with Torch](docs/GettingStartedTorch.md)

* [Fine-tune a pretrained model](examples/fine-tuning/README.md)

* [Creating a dataset using data from S3 endpoint](examples/s3/README.md)

* [Train an autoencoder network](examples/autoencoder/README.md)

* [Train a regression network](examples/regression/README.md)

* [Train a Siamese network](examples/siamese/README.md)

* [Train a text classification network](examples/text-classification/README.md)

* [Train an object detection network](examples/object-detection/README.md)

* [Learn more about weight initialization](examples/weight-init/README.md)

* [Use Python layers in your Caffe networks](examples/python-layer/README.md)

* [Download a model and use it to classify an image outside of DIGITS](examples/classification/README.md)

* [Overview of the REST API](docs/API.md)



# Get help



### Installation issues

* First, check out the instructions above

* Then, ask questions on our [user group](https://groups.google.com/d/forum/digits-users)



### Usage questions

* First, check out the [Getting Started](docs/GettingStarted.md) page

* Then, ask questions on our [user group](https://groups.google.com/d/forum/digits-users)



### Bugs and feature requests

* Please let us know by [filing a new issue](https://github.com/NVIDIA/DIGITS/issues/new)

* Bonus points if you want to contribute by opening a [pull request](https://help.github.com/articles/using-pull-requests/)!

  * You will need to send a signed copy of the [Contributor License Agreement](CLA) to [email protected] before your change can be accepted.



# Notice on security

 Users shall understand that DIGITS is not designed to be run as an exposed external web service.