/gans-from-theory-to-production

Material for the tutorial: "Deep Diving into GANs: from theory to production"

Primary LanguageJupyter NotebookMozilla Public License 2.0MPL-2.0

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

    • Generator
    • Discriminator
    • Intuitive explaination
    • Non saturating value function
    • Models definition
    • Training phase
    • Types of GANs
    • Conditional GANs
    • Applications
      • Unconditional GAN
      • Conditional GAN

  • GANs in TensorFlow 2.0:

    • What does a GAN learn?
    • Input data
    • Generator and discriminator networks: Keras functional API
    • Define input and instantiate networks
    • The loss function and the training procedure
    • Discriminator loss function
    • Generator loss function
    • Gradient ascent
    • Visualize training
    • Advantages and disadvantages
    • Bonus exercise: converting it to a Conditional GAN

  • Writing a GAN using AshPy and TensorFlow Datasets

    • AshPy Essentials
    • tfds and AshPy input format
    • Getting the data ready to use
    • DCGAN Theory and Practice
      • Generator: from noise to insight
      • Deconvolution
      • Batch Normalization
      • Discriminator
      • Loss function: a bridge between two networks
    • Training
    • Tensorboard
    • Towards Serving

  • Production:

    • Google Cloud ML
    • Serving at scale

Requirements

This tutorial requires the following packages:

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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