DeepForge is an open-source visual development environment for deep learning providing end-to-end support for creating deep learning models. This is achieved through providing the ability to design architectures, create training pipelines, and then execute these pipelines over a cluster. Using a notebook-esque api, users can get real-time feedback about the status of any of their executions including compare them side-by-side in real-time.

Additional features include:

Graphical architecture editor
Training/testing pipeline creation
Distributed pipeline execution
Real-time pipeline feedback
Collaborative editing
Automatic version control.
Facilitates defining custom layers

Quick Start

Simply run the following command to install deepforge with its dependencies:

curl -o- https://raw.githubusercontent.com/dfst/deepforge/master/install.sh | bash

Or, if you already have NodeJS (v6) installed, simply run

npm install -g deepforge

Finally, start deepforge with deepforge startand navigate to http://localhost:8888 to start using DeepForge! For more, detailed instructions, check out the wiki.

Note: running deepforge w/ deepforge start will also require MongoDB to be installed locally.

Also, be sure to check out the other available features of the deepforge cli; it can be used to update, manage your torch installation, uninstall deepforge and run individual components!

Additional Resources

Intro to DeepForge Slides
wiki containing overview, installation, configuration and developer information
Starter Kit containing example pipelines demonstrating various deepforge features
Examples
Datamodel Developer Slides

Interested in contributing?

Contributions are welcome! There are a couple different ways to contribute to DeepForge:

Provide user feedback!
- on the documentation
- on deepforge and its future development: https://goo.gl/forms/2pDdCPXoUvkQhVzQ2
Contribute to the project directly by submitting some PR's!

If you have any questions, check out the wiki or drop me a line on the gitter!