Getting Started¶

GaMorNet is written in Python and uses the Keras and TFLearn deep learning libraries to perform all of the machine learning operations. Both these aforementioned libraries in turn use TensorFlow for their underlying tensor operations. GaMorNet was originally written using TFLearn, but the Keras module was added later as we expect Keras to be better supported and developed going forward.

GaMorNet has two separate packages available via pip. One happens to be the standard gamornet package and the other one is a gamornet-cpu package meant for users who don’t have access to a GPU.

Ways to Use GaMorNet¶

If you have access to a GPU,
- We recommend installing the gamornet package using the instructions in Installation
- However, if you are not familiar with how to enable GPU support for TensorFlow and want to get started quickly, you may consider using Google Colab like we have done in the Tutorials
If you don’t have access to a GPU,
- and want to use our models for predictions
  You can install the gamornet-cpu package using the instructions in Installation
  
  You can use Google Colab like we have done in the Tutorials
- and want to train your own models
  Use the GPUs available via Google Colab as we have done in the Tutorials

Installation¶

It is highly recommended to have a separate Python virtual environment for running GaMorNet as the package has many specific version oriented dependencies on other Python packages. The following instructions are shown using Anaconda, but feel free to go ahead and use any other virtual environment tool you are comfortable using. Note that GaMorNet only runs on Python >= 3.3 and is recommended to be run on Python 3.6

Using pip
- Install Anaconda if you don’t have it already using the instructions here
- Create a new Anaconda environment using conda create -n gamornetenv python=3.6
- Activate the above environment using conda activate gamornetenv
- Install GaMorNet using pip install gamornet or pip install gamornet-cpu depending on your requirements
- For the GPU installation, if you don’t have the proper CUDA libraries, please see GPU Support
- To test the installation, open up a Python shell and type from gamornet.keras_module import *. If this doesn’t raise any errors, it means you have installed GaMorNet successfully.
- To exit the virtual environment, type conda deactivate
From Source
- Install Anaconda if you don’t have it already using the instructions here
- Create a new Anaconda environment using conda create -n gamornetenv python=3.6
- Activate the above environment using conda activate gamornetenv
- Clone GaMorNet repository from GitHub using git clone https://github.com/aritraghsh09/GaMorNet.git
- To install, do the following based on the package you want
  For GPU installation,
  
  cd GaMorNet
  
  python setup.py install
  
  For CPU version,
  
  cd GaMorNet
  
  git fetch --all
  
  git checkout cpu_version
  
  python setup.py install
- For the GPU installation, if you don’t have the proper CUDA libraries, please see GPU Support
- To test the installation, open up a Python shell and type from gamornet.keras_module import *. If this doesn’t raise any errors, it means you have installed GaMorNet successfully.
- To exit the virtual environment, type conda deactivate

GPU Support¶

If you are using a GPU, then you would need to make sure that the appropriate CUDA and cuDNN versions are installed. The appropriate version is decided by the versions of your installed Python libraries. For detailed instructions on how to enable GPU support for Tensorflow, please see this link.

We tested GaMorNet using the following configurations:-

Python	Keras	TFLearn	Tensorflow	CUDA	cuDNN
3.6.10	2.2.4	0.3.2	1.13.1	10.0.130	7.6.0
3.6.10	2.3.1	0.3.2	1.15.3	10.0.130	7.6.2

For more build configurations tested out by the folks at TensorFlow, please see this link