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Neural Network Toolbox on TensorFlow

Project Description

Tensorpack is a zero-overhead training interface based on TensorFlow.

See some examples to learn about the framework. Everything runs on multiple GPUs, because why not?

Speech / NLP:

Examples are not only for demonstration of the framework – you can train them and reproduce the results in papers.

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 correct way with no extra overhead. On various CNNs, it runs 1.5~1.7x faster than the equivalent Keras code.
    • Data-parallel multi-GPU/distributed training is off-the-shelf to use. It is as fast as Google’s official benchmark.
    • See tensorpack/benchmarks for some 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 already too many symbolic function wrappers. Tensorpack includes only a few common models, but you can use any other wrappers within tensorpack, including sonnet/Keras/slim/tflearn/tensorlayer/….

See tutorials to know more about these features.

Install:

Dependencies:

  • Python 2.7 or 3

  • TensorFlow >= 1.0.0 (>=1.1.0 for Multi-GPU)

  • Python bindings for OpenCV (Optional, but required by a lot of features)

    pip install -U git+https://github.com/ppwwyyxx/tensorpack.git
    # or add `--user` to avoid system-wide installation.
    

    If you only want to use tensorpack.dataflow alone as a data processing library, TensorFlow is also optional.

Release History

Release History

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0.8.0

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