Track, Visualize, and Manage TensorFlow Training Runs
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jjallaire add artifacts_dir argument to training_run/tuning_run
allows for control over where/whether run artifcats are scanned for (pass NULL) to not scan at all
Latest commit b134f2d Sep 5, 2018

tfruns: Track, Visualize, and Manage Training Runs


The tfruns package provides a suite of tools for managing TensorFlow training runs and experiments from R:

  • Track the hyperparameters, metrics, output, and source code of every training run.

  • Compare hyperparmaeters and metrics across runs to find the best performing model.

  • Automatically generate reports to visualize individual training runs or comparisons between runs.

  • No changes to source code required (run data is automatically captured for all Keras and TF Estimator models).

You can find documentation for the tfruns package at