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PyTorch vs Paperspace: What are the differences?
Developers describe PyTorch 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. On the other hand, Paperspace is detailed as "The way to access and manage limitless computing power in the cloud". It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
PyTorch and Paperspace can be primarily classified as "Machine Learning" tools.
PyTorch is an open source tool with 31.6K GitHub stars and 7.77K GitHub forks. Here's a link to PyTorch's open source repository on GitHub.
Pros of Paperspace
Pros of PyTorch
- Easy to use15
- Developer Friendly11
- Easy to debug10
- Sometimes faster than TensorFlow7
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Cons of Paperspace
Cons of PyTorch
- Lots of code3
- It eats poop1