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Tefla is a deep learning mini-framework that sits on top of Tensorflow. Tefla's primary goal is to enable simple, stable, end-to-end deep learning. This means that Tefla supports:

  • Data setup
  • Batch preprocessing and data layout.
  • Training
  • A model definition DSL.
  • A training config DSL.
  • Data loading with data-augmentation and rebalancing.
  • Training with support for visualization, logging, custom metrics, and most importantly - resumption of training from an earlier epoch with a new learning rate.
  • Pluggable learning rate decay policies.
  • Stability and solidity - which translates to days and weeks of training without memory blowup and epoch time degradations.
  • Tensorboard visualization of epoch metrics, augmented images, model graphs, and layer activations, weights and gradients.
  • Prediction (with ensembling via mean score or voting).
  • Metrics on prediction outputs.
  • First class support for transfer learning and fine-tuning based on vgg16, resnet50, resnet101, and resnet152.
  • Serving of models via a REST API (coming soon).

Tefla contains command line scripts to do batch preprocessing, training, prediction, and metrics, thus supporting a simple yet powerful deep learning workflow.

Documentation is coming soon. For now, the mnist example(s) can help you to get started.

Tefla is very much a work in progress. Contributions are welcome!

An interesting fork of tefla is available here: Both projects are evolving independently, with a cross-pollination of ideas.