Codes for ACM MM Open Source Software Competition

This repo summarizes the codes that mention in "TensorLayer: A Versatile Library for Efficient Deep Learning Development".

Set Up Environment

Before you start, you need to set up the environment for all examples in this repo.

Layer and Model Modules
- These are the basic modules of TensorLayer, you can run all single machine examples with this module only.
- TensorFlow 1.0+
  - pip install tensorflow-gpu or follow the official installation guide
- TensorLayer
  - It is self-contained
  - You can also get the latest version in Github
Deep Reinforcement Learning Environment
- To run the DRL example, you will need to install OpenAI gym for game environment, lz4 for data compression and the dataset module.
- pip install gym lz4
- sudo apt-get install swig cmake
- pip install gym[atari] or pip install gym[all]
Dataset and Workflow Modules
- For Deep Reinforcement Learning and Hyper-parameter selection and cross-validation you may want to use dataset and workflow modules.
- Install MongoDB
  - Follow MongoDB docs
  - We recommend to use one machine as dataset server.
- Install eAE (Optional)
  - You may need this environement to distribute the different jobs. An installation process is available at that address: https://github.com/aoehmichen/eae-docker/blob/master/install_eae_hybrid.txt

Raw Performance

You only need to install TensorFlow to run these examples. This a raw performance comparsion between TensorLayer and original TensorFlow engine, to prove TensorLayer's simplicity would not sacrifice the performance.

CIFAR-10 classification
- TensorFlow Implementation
- TensorLayer Implementation, Optimized Version (5x faster, optimize CPU/GPU operation, tested on Titan X Pascal)
PTB language modelling
- TensorFlow Implementation
- TensorLayer Implementation
Word2vec
- TensorFlow Implementation
- TensorLayer Implementation

Deep Reinforcement Learning

You need to install all modules to run this example. This is a simple asynchronous DRL example, you can run this example in one machine or multiple machines with dataset module.

About the codes
- tutorial_tensordb_atari_pong_generator.py is the data generator.
- tutorial_tensordb_atari_pong_trainer.py is the GPU trainer.
Run the experiment
- Before you run it in distributed mode, we higly recommend you to run one trainer with multiple data generators in a standalone machine.
- For distributed mode, one machine run one trainer with GPU, all other machines run data generator.
  - Start multiple agents that generate training samples for the trainer. Run the following script uses to start multiple generators. Note: all of the generated data will be stored in MongoDB, which will be fetched by the trainer
    - python submit_job_eae.py
  - After start the generators, run the following script to train a model.
    - python tutorial_tensordb_atari_pong_trainer.py
  - To terminate all of the generators, run the following scripts.
    - python terminate_running_jobs.py

Hyper-parameter selection and cross-validation

You need to install all modules to run this example.

DeepSleepNet

Generative Adversarial Network

You only need to install TensorFlow to run these examples.

DCGAN
- TensorFlow Implementation
- TensorLayer Implementation
Text to image synthesis
- TensorFlow Implementation
- TensorLayer Implementation