Framework Determinism Versions Save

Providing reproducibility in deep learning frameworks

v0.6.0

9 months ago

This is a re-release of version 0.5.0, which was accidentally released with stale files.

v0.5.0

10 months ago

In Seeder, add the ability to reseed generators with either

  1. a different seed for each worker (the existing functionality) or
  2. the same seed for each worker (added functionality).

v0.4.0

1 year ago

Enhanced Functionality

  • Add the Seeder tool for variance reduction. This is an experimental feature.
  • Rename the distribution from tensorflow-determinism to framework-reproducibility and rename the package from tfdeterminism to fwr13y.
  • Add fwr13y.d9m.tensorflow.enable_determinism, which makes a best-effort to enable determinism in whichever version of TensorFlow is being used.
  • Add a script to find commits in the TensorFlow repo related to determinism.
  • fwr13y.d9m.tensorflow.patch throws more specific exceptions.

Enhanced Testing / Higher Quality

  • Test patched determinism over a wider range of stock TensorFlow and NGC TensorFlow versions.

v0.3.0

4 years ago

Add patch availability for stock TensorFlow version 2.0, and test in eager mode.

Developed by Duncan Riach with thanks to Nathan Luehr for review.

v0.1.0

4 years ago

This release includes a patch for standard TF 1.14.0 that enables most deep learning TF models to train deterministically on GPUs. GPU-determinism support in the NVIDIA NGC TF containers is also described.

Developed by Duncan Riach with thanks to Nathan Luehr for review.

v0.2.0

4 years ago

New Functionality

  • Add patch availability on TensorFlow version 1.15
  • Print the version of tensorflow-determinism when patch is applied

Enhanced Testing / Higher Quality

  • Test that patch will throw exception on non-supported versions of TF
  • Test that patch will throw exception in NGC containers
  • Test that patch works in Python 3
  • Test that package will install when TensorFlow is not yet installed

Developed by Duncan Riach with thanks to Nathan Luehr for review.