Kubeflow vs PyTorch: What are the differences?
Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. 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.
Kubeflow and PyTorch can be primarily classified as "Machine Learning" tools.
Kubeflow 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 Kubeflow with 7.04K GitHub stars and 1.03K GitHub forks.
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