Gamify • Key Features • Install and Use • Support • Issues • License • Download
Gamify puts the researcher back in the driver's seat in modern deep learning workflow by unlocking a new era of man-machine symbiosis in artificial intelligence model development. As the mission matures, developing state-of-the-art deep learning models will feel more like playing a Real-Time Strategy Game.
TL;DR
Gamify radically changes the hyperparameter optimization workflow by giving the researcher powerful tools to analyze, control, and optimize the process during the experiment. Gamify is an add-on to leading hyperparameter scanning solution Talos. Talos exposes Keras functionality entirely and there is no new syntax or templates to learn.
Gamify adds a visual command center to Talos experiments.
- Analyze experiment log
- Visualize experiment results
- Monitor and analyze epoch-by-epoch training performance
- Make changes in real-time to the experiment (coming soon)
Gamify works on Linux, Mac OSX, and Windows.
Install:
pip install gamify
Or download the repo.
At this point you might want to add gamify
to your PATH
, after which you can:
python gamify /path/to/talos/experiment
Otherwise replace python gamify
with python /path/to/gamify
Here /path/to/talos/experiment
refers to the folder Talos creates based on Scan(...experiment_name...)
from v0.6.2 onwards.
Note, the live updating epoch-by-epoch monitor requires the use of ExperimentLogCallback
in Talos. Which means that your input model model.fit
would like this:
out = model.fit(...
callbacks=[talos.utils.ExperimentLogCallback('minimal_iris', params)],
...)
You can use the regular Talos support channels for Gamify.
I want to... | Go to... |
---|---|
...troubleshoot | Docs · Wiki · GitHub Issue Tracker |
...report a bug | GitHub Issue Tracker |
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...get support | Stack Overflow · Spectrum Chat |
...have a discussion | Spectrum Chat |
If you use Gamify for published work, please cite:
Autonomio Gamify [Computer software]. (2019). Retrieved from http://github.com/autonomio/gamify.