Tensorpack is a training interface based on TensorFlow, with a focus on speed + flexibility. TensorFlow is powerful, but has its own drawbacks: Its low-level APIs are too hard and complicated for many users, and its existing high-level APIs sacrifice a lot in either speed or flexibility. The Tensorpack API brings speed and flexibility together.
Tensorpack is Yet Another TF high-level API, but different in:
- Focus on training speed.
- Speed comes for free with tensorpack -- it uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs 1.2~5x faster than the equivalent Keras code.
- Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use. It scales as well as Google's official benchmark. You cannot beat its speed unless you're a TensorFlow expert.
- See tensorpack/benchmarks for some benchmark scripts.
- Focus on large datasets.
- You don't usually need tf.data. Symbolic programming often makes data processing harder. Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in pure Python with autoparallelization.
- It's not a model wrapper.
- There are already too many symbolic function wrappers in the world. Tensorpack includes only a few common models, but you can use any symbolic function library inside tensorpack, including tf.layers/Keras/slim/tflearn/tensorlayer/...
See :doc:`tutorial/index` to know more about these features:
.. toctree:: :maxdepth: 3 tutorial/index modules/index