Deep Diving into GANs: from theory to production

With our accrued experience with GANs, we would like to guide you through the required steps to go from theory to production with this revolutionary technology.

Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine.

This is the ZURU Tech way of making GANs: enjoy it.

Workshop's Table of contents

Introduction to GANs: Theory and Applications
- Unconditional GAN
- Conditional GAN
GANs in Tensorflow:
- Writing an GAN from scratch: a complete example
- Define generator with tf.estimator API
- Input pipeline with tf.data API
- How to use tf.estimator to train both generator and discriminator?
TFGAN:
- API overview
- Generator and discriminator definition
- Input pipeline definition
- Loss function: a bond between generator and discriminator
- Train end Prediction
- Export the trained model
Production:
- Google Cloud ML
- Serving at scale

Requirements

This tutorial requires the following packages:

python >= 3.6
tensorflow >=2.0: https://www.tensorflow.org/install/install_linux
jupyter
numpy
requests to run the CelebA Dataset downloader

Optional, but recommended:

virtualenv to manage a virtual environment
NVIDIA CUDA®: Compute Unified Device Architecture
cuDNN: The NVIDIA CUDA® Deep Neural Network library
NOTE: If you have an NVIDIA GPU with Compute Capability 3.0 or higher, you can install tensorflow-gpu instead of tensorflow.
Google Cloud account with access to the CloudML APIs (only needed for the serving in production section).
Google Cloud SDK (only needed for the serving in production section).
jsonlines to easily generate .ndjson files for CloudML Engine

Setting up the environment (Linux, MacOS)

Clone the repository

git clone https://github.com/zurutech/gans-from-theory-to-production
cd gans-from-theory-to-production

Prepare a virtual environment

virtualenv: virtualenv venv && source venv/bin/activate

Installing the required packages

pip install -r no-gpu-requirements.txt
# or pip install -r gpu-requirements if a GPU with Compute Capability >= 3.0 is present

Start your Jupyter server

jupyter notebook . or the newer jupyter lab ..

If you're here, you're ready to go.

Happy workshop!

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Do you just love machine learning and you're also interested in Computer Vision? Join us at ZURU Tech!

Authors

Michele "Ubik" De Simoni - https://ubik.tech/ - michele.d[at]zuru.tech
Paolo Galeone - https://pgaleone.eu/ - paolo[at]zuru.tech
Federico Di Mattia - federico.d[at]zuru.tech
Emanuele Ghelfi - emanuele[at]zuru.tech

igorfyago / gans-from-theory-to-production