
<p><code>Tensorflow</code>, <code>PyTorch</code> are currently the most popular deep learning packages.</p>
<p><code>Caffe2</code> is intended to be a framework for production edge deployment whereas <code>TensorFlow</code> is more suited towards server production and research. Essentially your target uses are very different. TensorFlow is intended for researchers and servers while <code>Caffe2</code> is aimed towards mobile phones and other relatively computationally constrained platforms. These development goals are reflected in the designs of each framework. <code>Caffe2</code> is now part of <code>PyTorch</code>.</p>
<ul>
  <li><code>TensorFlow</code>:
    <ul>
      <li>by Google, for both research and deployment.</li>
      <li>Github: <a href="https://github.com/tensorflow/tensorflow">https://github.com/tensorflow/tensorflow</a></li>
    </ul>
  </li>
  <li><code>pytorch</code>
    <ul>
      <li>by Facebook, more research-friendly.</li>
      <li>Github: <a href="https://github.com/pytorch/pytorch">https://github.com/pytorch/pytorch</a></li>
    </ul>
  </li>
  <li><code>Caffe2</code>
    <ul>
      <li>by Facebook, more developer-friendly. Used on their mobile apps</li>
      <li><code>Caffe2</code> is now merged with <code>PyTorch</code>(code now lives in <code>PyTorch</code>'s repo, maybe a full merge later on?)</li>
      <li>both <code>PyTorch</code> and <code>Caffe2</code> support <code>ONNX</code>(<a href="https://onnx.ai/">https://onnx.ai/</a>) for interchangeability</li>
    </ul>
  </li>
</ul>


Tensorflow vs PyTorch

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