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A high-level framework for advanced deep learning with TensorFlow
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.
rmst