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Licence: MIT license
A high-level framework for advanced deep learning with TensorFlow

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CHI – A high-level framework for advanced deep learning with TensorFlow

Chi provides high-level operations for writing and visualizing experiments


Getting started

Experiment Scripts can also be defined via decorator. When run from the command line the function arguments are translated into command line parameters.

@chi.experiment
def my_experiment(logdir, a=0.5):
  print(logdir)
  ...

If no logdir is specified it will generate a new one. See examples/experiments.py for the full example

For a more interesting experiment see the Wasserstein GAN example.


Visualization with CHIBOARD

Chiboard is a browser based dashboard for managing experiments. Start it with chiboard.

Clicking on an experiment card leads to a detail page about that experiment which automatically spins up and embeds a TensorBoard:

See chi/board for a full overview.


Installation

Requires Python 3.6

git clone [email protected]:rmst/chi.git
pip install -e chi

Acknowledgements

The foundations for this work have been developed during projects at the following institutes.

  • Reasoning and Learning Lab (RLLab) at McGill University in Canada
  • Montreal Institute for Learning Algorithms (MILA) in Canada
  • Intelligent Autonomous Systems Lab (IAS) at TU-Darmstadt in Germany

The structures of this repo and readme were inspired by Keras-RL and Keras respectively.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].