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MXNet vs PyTorch: What are the differences?
MXNet: A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly; PyTorch: 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.
MXNet and PyTorch belong to "Machine Learning Tools" category of the tech stack.
MXNet and PyTorch are both open source tools. It seems that PyTorch with 30.5K GitHub stars and 7.46K forks on GitHub has more adoption than MXNet with 17.5K GitHub stars and 6.21K GitHub forks.
Pros of MXNet
- User friendly2
Pros of PyTorch
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
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Cons of MXNet
Cons of PyTorch
- Lots of code3
- It eats poop1
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What is MXNet?
What is PyTorch?
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What companies use MXNet?
What companies use PyTorch?
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What tools integrate with PyTorch?
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