Deepo vs PyTorch: What are the differences?
Developers describe Deepo as "A Docker image containing almost all popular deep learning frameworks". Deepo is a Docker image with a full reproducible deep learning research environment. It contains most popular deep learning frameworks: theano, tensorflow, sonnet, pytorch, keras, lasagne, mxnet, cntk, chainer, caffe, torch. On the other hand, PyTorch is detailed as "A deep learning framework that puts Python first". PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Deepo and PyTorch can be categorized as "Machine Learning" tools.
Deepo and PyTorch are both open source tools. It seems that PyTorch with 29.6K GitHub stars and 7.18K forks on GitHub has more adoption than Deepo with 4.92K GitHub stars and 578 GitHub forks.
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What is Deepo?
What is PyTorch?
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