Fabrik is an online collaborative platform to build, visualize and train deep learning models via a simple drag-and-drop interface. It allows researchers to collectively develop and debug models using a web GUI that supports importing, editing and exporting networks to popular frameworks like Caffe, Keras, and TensorFlow.

This app is presently under active development and we welcome contributions. Please check out our issues thread to find things to work on, or ping us on Gitter.

Installation Instructions

Setting up Fabrik on your local machine is very easy. You can setup Fabrik using two methods:

Using Docker

Docker Installation

If you haven't installed Docker already:

To install Docker for Windows click here.

To install Docker for Mac click here.

Fabrik Installation

1. Get the source code on to your machine via git.

   git clone https://github.com/Cloud-CV/Fabrik.git && cd Fabrik
   

2. Rename settings/dev.sample.py as dev.py.

   cp settings/dev.sample.py settings/dev.py
   

3. Build and run the Docker containers. This might take a while. You should now be able to access Fabrik at http://0.0.0.0:8000.

   docker-compose up --build
   

Setup Authentication for Docker Environment

1. Go to Github Developer Applications and create a new application. here

2. For local deployments,the following should be used in the options:

   • Application name: Fabrik
   • Homepage URL: http://0.0.0.0:8000
   • Application description: Fabrik
   • Authorization callback URL: http://0.0.0.0:8000/accounts/github/login/callback/

3. Github will provide you with a Client ID and a Secret Key. Save these.

4. Create a superuser in django service of docker container

   docker-compose run django python manage.py createsuperuser
   

   Note: Before creating the superuser, make sure that django service of docker image is running. This can be done by executing docker-compose up followed by Ctrl + C to save docker configuration.

5. Open http://0.0.0.0:8000/admin and login with the credentials from step 4.

6. Setting up Social Accounts in django admin

   • Under Social Accounts, open Social applications and click on Add Social Application.

   • Choose the Provider of social application as Github and name it Github.

   • Add the sites available to the right side, so github is allowed for the current site.

   • Copy and paste your Client ID and Secret Key into the apppropriate fields and Save.

7. Go to Sites tab and update the Domain name to 0.0.0.0:8000.

Using Virtual Environment

1. First set up a virtualenv. Fabrik runs on Python2.7.

   sudo apt-get install python-pip python-dev python-virtualenv
   virtualenv --system-site-packages ~/Fabrik --python=python2.7
   source ~/Fabrik/bin/activate
   

2. Clone the repository via git

   git clone --recursive https://github.com/Cloud-CV/Fabrik.git && cd Fabrik
   

3. Rename settings/dev.sample.py as settings/dev.py and change credentials in settings/dev.py

   cp settings/dev.sample.py settings/dev.py
   

   • Change the hostname to localhost in settings/dev.py line 15. It should now look like this:

   'HOST': os.environ.get("POSTGRES_HOST", 'localhost'), 
   

4. Install redis server

   sudo apt-get install redis-server
   

   • Change the hostname to localhost in settings/common.py line 115.

     "CONFIG": {
         # replace redis hostname to localhost if running on local system
         "hosts": [("localhost", 6379)],
         "prefix": u'fabrik:',
         },
     

   • Replace celery result backend in settings/common.py line 122 with localhost.

     CELERY_RESULT_BACKEND = 'redis://localhost:6379/0'
     

   • Change celery broker URL and result backend hostname to localhost in ide/celery_app.py, line 8.

     app = Celery('app', broker='redis://localhost:6379/0', backend='redis://localhost:6379/0', include=['ide.tasks'])
     

5. If you already have Caffe, Keras and TensorFlow installed on your computer, skip this step.

• For Linux users

  • Install Caffe, Keras and Tensorflow

    cd Fabrik/requirements
    yes Y | sh caffe_tensorflow_keras_install.sh
    

  • Open your ~/.bashrc file and append this line to the end

    export PYTHONPATH=~/caffe/caffe/python:$PYTHONPATH
    

  • Save, exit and then run

    source ~/.bash_profile
    cd ..
    

• For Mac users

  • Install Caffe
  • Install Tensorflow
  • Install Keras

6. Install dependencies

• For developers:

  pip install -r requirements/dev.txt
  

• For others:

  pip install -r requirements/common.txt
  

7. Install postgres >= 9.5

• Setup postgres database

  • Start postgresql by typing sudo service postgresql start

  • Now login as user postgres by running sudo -u postgres psql and type the commands below:

    CREATE DATABASE fabrik;
    CREATE USER admin WITH PASSWORD 'fabrik';
    ALTER ROLE admin SET client_encoding TO 'utf8';
    ALTER ROLE admin SET default_transaction_isolation TO 'read committed';
    ALTER ROLE admin SET timezone TO 'UTC';
    ALTER USER admin CREATEDB;
    

  • Exit psql by typing in \q and hitting enter.

• Migrate

  python manage.py makemigrations caffe_app
  python manage.py migrate
  

8. Install node modules

   npm install
   npm install --save-dev json-loader
   sudo npm install -g webpack@1.15.0
   

   • Run the command below in a separate terminal for hot-reloading, i.e. see the changes made to the UI in real time.

   webpack --progress --watch --colors
   

9. Start celery worker

   celery -A ide worker --app=ide.celery_app  --loglevel=info
   

   The celery worker needs to be run in parallel to the django server in a separate terminal.

10. Start django application

    python manage.py runserver
    

    You should now be able to access Fabrik at http://localhost:8000.

Setup Authentication for Virtual Environment

1. Go to Github Developer Applications and create a new application. here

2. For local deployments, the following should be used in the options:

   • Application name: Fabrik
   • Homepage URL: http://localhost:8000
   • Application description: Fabrik
   • Authorization callback URL: http://localhost:8000/accounts/github/login/callback/

3. Github will provide you with a client ID and secret Key, save these.

4. Create a superuser in django

   python manage.py createsuperuser
   

5. Start the application

   python manage.py runserver
   

6. Open http://localhost:8000/admin

7. Login with the credentials from step 4.

8. Setting up Social Accounts in django admin :

   • Under Social Accounts open Social applications, click on Add Social Application.

   • Choose the Provider of social application as Github & name it Github.

   • Add the sites available to the right side, so github is allowed for the current site. This should be localhost:8000 for local deployment.

   • Copy and paste your Client ID and Secret Key into the appropriate fields and Save.

9. From the django admin home page, go to Sites under the Sites category and update Domain name to localhost:8000.

Note: For testing, you will only need one authentication backend. However, if you want to try out Google's authentication, then, you will need to follow the same steps as above, but switch out the Github for Google.

Usage

python manage.py runserver

Example

• Use example/tensorflow/GoogleNet.pbtxt for TensorFlow import
• Use example/caffe/GoogleNet.prototxt for Caffe import
• Use example/keras/vgg16.json for Keras import

Tested models

The model conversion between currently supported frameworks is tested on some models.

Models	Caffe	Keras	Tensorflow
Inception V3	√	√	√
Inception V4	√	√	√
ResNet 101	√	√	√
VGG 16	√	√	√
GoogLeNet	√	×	×
SqueezeNet	√	×	×
DenseNet	√	×	×
AllCNN	√	×	×
AlexNet	√	√	√
FCN32 Pascal	√	×	×
YoloNet	√	√	√
Pix2Pix	√	×	×
VQA	√	√	√
Denoising Auto-Encoder	×	√	√

Note: For models that use a custom LRN layer (Alexnet), Keras expects the custom layer to be passed when it is loaded from json. LRN.py is located in keras_app/custom_layers. Alexnet import for Keras

Documentation

• Using a Keras model exported from Fabrik
• Loading a Keras model exported from Fabrik and printing its summary
• Using an Exported Caffe Model
• Loading a Caffe model in python and printing its parameters and output size
• List of models tested with Fabrik
• Adding model to the Fabrik model zoo
• Adding new layers
• Using custom layers with Keras
• Linux installation walk-through

License

This software is licensed under GNU GPLv3. Please see the included License file. All external libraries, if modified, will be mentioned below explicitly.