parth-verma/Fabrik

:factory: Collaboratively build, visualize, and design neural nets in browser

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 collaboratively develop and debug models using a web GUI that supports importing, editing and exporting networks written in widely 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 really easy. You can setup Fabrik using two methods:

Using Docker

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 be able to access Fabrik at 0.0.0.0:8000.

   docker-compose up --build
   

Using Virtual Environment

1. First set up a virtualenv

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

2. Clone the repository

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

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

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

4. Install redis server and replace the hostname to 'localhost' in settings/common.py line 99.

   sudo apt-get install redis-server
   

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

   • For Linux users

     cd Fabrik/requirements
     yes Y | sh caffe_tensorflow_keras_install.sh
     

     Open your ~/.bashrc file and append this line at 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
  

• 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
sudo npm install -g webpack
webpack --progress --watch --colors

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	√	√	√

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
• 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.