When comparing Pytorch vs Infer.NET, the Slant community recommends Infer.NET for most people. In the question“What are the best artificial intelligence frameworks?” Infer.NET is ranked 6th while Pytorch is ranked 9th. The most important reason people chose Infer.NET is:
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
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Pros
Pro Easy to install
Pro It has a rich ecosystem
Pro Supports both CPU and GPU
Even CPU Performance is good compared to other frameworks.
Pro Interops excellent with Python
Pro Easy to debug
Pro It is easy to write extension packages
Pro Supports multiple inference algorithms
Infer.NET supports expectation propagation (including belief propagation as a special case), variational message passing (also known as variational Bayes), max product (for discrete models), and block Gibbs sampling.
Pro Versatile
You can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.
Pro Cross platform
Can be used on Windows (.NET), OSX or Linux (using mono)
Cons
Con No commercial license
Infer.NET is free for academic use. However, at this time, commercial use of Infer.NET is limited to Microsoft. No other commercial licenses are available.