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Dec. 16, 2018
Dec. 25, 2015


Tensorpack is a neural network training interface based on TensorFlow.

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It's Yet Another TF high-level API, with speed, and flexibility built together.

  1. 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 training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack.

    • Data-parallel multi-GPU/distributed training strategy is off-the-shelf to use. It scales as well as Google's official benchmark.

    • See tensorpack/benchmarks for some benchmark scripts.

  2. Focus on large datasets.

    • You don't usually need Symbolic programming often makes data processing harder. Tensorpack helps you efficiently process large datasets (e.g. ImageNet) in pure Python with autoparallelization.
  3. It's not a model wrapper.

    • There are 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 tutorials and documentations to know more about these features.


We refuse toy examples. Instead of showing you 10 arbitrary networks trained on toy datasets, Tensorpack examples faithfully replicate papers and care about reproducing numbers, demonstrating its flexibility for actual research.


Reinforcement Learning:

Speech / NLP:



  • Python 2.7 or 3.3+. Python 2.7 is supported until it retires in 2020.
  • Python bindings for OpenCV (Optional, but required by a lot of features)
  • TensorFlow ≥ 1.3. (If you only want to use tensorpack.dataflow alone as a data processing library, TensorFlow is not needed)
pip install --upgrade git+
# or add `--user` to install to user's local directories

Citing Tensorpack:

If you use Tensorpack in your research or wish to refer to the examples, please cite with:

  author={Wu, Yuxin and others},

Latest Releases
 Nov. 9 2018
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