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Barrage is an opinionated supervised deep learning tool built on top of TensorFlow 2.x designed to standardize and orchestrate the training and scoring of complicated models. Barrage is built around a JSON config and the TensorFlow 2.x library using the Tensorflow.Keras API.

Official documentation can be found at:

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Guiding Principles

  1. Minimal Code: build well-tested, configurable, and reliable config recipes. Use custom code only when it is absolutely necessary.
  2. Component Reusability: decompose deep learning dataset processing into fundamental components (e.g. dataset loaders, data transformations, augmentation functions) to maximize reuse between models.
  3. Process Automation: best practices and artifacting are automatically configured (e.g. saving best checkpoint, creating TensorBoard, etc...) with defaults that can be adjusted in the config.
  4. Standardize API: takes an opinionated view and selects the production hardened variant of the many TensorFlow.Keras API choices (e.g. data type choices in
  5. Cross Domain: handles single/multi input/output networks seamlessly across domains (e.g. Computer Vision, Natural Language Processing, Time Series, etc...).

Select Feature Highlights

  1. Single/multi input/output: flexible across many types of networks.
  2. Loading: dataset in memory, on disk, in cloud storage, etc ...
  3. Transforms: fit transforms on a first-pass of the training dataset with the ability to:
    1. pass transform params to network builder (e.g. compute vocabulary size -> embedding layer).
    2. apply transform at batch time (e.g. mean variance normalization to input).
    3. undo transform after scoring (e.g. undo mean variance normalization to output).
  4. Augmentation: chain augmentation functions.
  5. Sampling: change the number of times a sample is selected in an epoch.

As well as standard TensorFlow.Keras features such as metrics, sample weights, etc...



pip install barrage

GitHub source:

git clone
cd barrage
python install