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Tensorpack

Tensorpack is a training interface based on TensorFlow.

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Features:

It's Yet Another TF wrapper, but different in:

  1. Focus on training speed.

    • Speed comes for free with tensorpack -- it uses TensorFlow in the efficient way with no extra overhead. On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.

    • Data-parallel multi-GPU training is off-the-shelf to use. It runs as fast as Google's official benchmark.

    • See tensorpack/benchmarks for the benchmark scripts.

  2. Focus on large datasets.

    • It's painful to read/preprocess data through TF. Tensorpack helps you load large datasets (e.g. ImageNet) in pure Python with autoparallelization.
  3. It's not a model wrapper.

    • There are too many symbolic function wrappers. Tensorpack includes only a few common models. You can use any symbolic function library inside tensorpack, including tflayers/Keras/slim/tflearn/tensorlayer/....

See tutorials to know more about these features.

Instead of showing you 10 random networks with random accuracy, tensorpack examples faithfully replicate papers and care about performance. And everything runs on multiple GPUs. Some highlights:

Vision:

Reinforcement Learning:

Speech / NLP:

Install:

Dependencies:

  • Python 2.7 or 3
  • Python bindings for OpenCV (Optional, but required by a lot of features)
  • TensorFlow >= 1.3.0 (Optional if you only want to use tensorpack.dataflow alone as a data processing library)
# install git, then:
pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
# or add `--user` to avoid system-wide installation.

Citing Tensorpack:

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

@misc{wu2016tensorpack,
  title={Tensorpack},
  author={Wu, Yuxin and others},
  howpublished={\url{https://github.com/tensorpack/}},
  year={2016}
}

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A Neural Net Training Interface on TensorFlow

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