PythonNet VS swift

I looked into using .NET as a "backend" and the pythonnet library to bridge the gap, but unfortunately that library does not support async. To properly support it would require some kind of integration with the asyncio event loop APIs or implement the AbstractEventLoop interface and some how make it thread-safe - not even sure that's possible with the API design of asyncio itself.

If you're running the python within C# using Python.NET, then you're somewhat within-process and there are ways of sending the data across. (Return objects, channel/queues, whatever fits your use case.)

I was going to make a joke about Python .NET core .. but .. apparently it's a real thing.

I am not set on .NET, but just curious, so thanks for the suggestions. Interesting that it's billed as cross-plaform, but doesn't do it that well. I just searched 'python wrapper for .net' and found PythonNET. Also, it seems yes IronPython is active.

I created a Django based python project that interfaces to TIA Openness to create some Remote IO. To do this I use the pythonnet library. so that I can load the Openness DLL.

Any .NET language with https://github.com/pythonnet/pythonnet

I remember several years ago when differentiable programming was an object of interest to the programming community and Lattner was trying to make Swift for Tensorflow happen[1].

I'm of the opinion that it was ahead of its time: Swift hadn't (and still hasn't) made enough progress on Linux support for it to be taken seriously as a language for writing anything that isn't associated with Apple. However, as a result, Swift now has language-level differentiability in its compiler. I'd love to see Swift get used for projects like this, but I suppose the reality of the matter is that there are so many performant runtimes for 2D/3D physics that there just isn't much of a need for automatic differentiation (and its overhead) to solve these problems. The tooling nerd in me thinks this stuff is fascinating.

https://github.com/tensorflow/swift

there was a time when google attempted to integrate swift with tensorflow, but the project was abandoned, and the repo is archived now. I believe the swift community picked up some of the features, and they are still working on it.

Apple really is focusing on CoreML rather than differentiable swift, that was more of the vision of Swift4TF, which really was driven mostly by Google, until it was cancelled (I assume because of Chris Latner leaving google for SiFive): https://github.com/tensorflow/swift

to be fair, Swift for Tensorflow was dropped (Feb 21) way after this article was written (Aug 20) https://github.com/tensorflow/swift

Neat! This may have not been well known when they kicked off the project and wrote their reasoning. Here is what they had to say about Scala at the time of the document linked up-thread[0]:

"Java / C# / Scala (and other OOP languages with pervasive dynamic dispatch): These languages share most of the static analysis problems as Python: their primary abstraction features (classes and interfaces) are built on highly dynamic constructs, which means that static analysis of Tensor operations depends on "best effort" techniques like alias analysis and class hierarchy analysis. Further, because they are pervasively reference-based, it is difficult to reliably disambiguate pointer aliases."

If they were wrong about that, or if the state of the art has progressed in the meantime, that's great! You may well be right that Scala would be a good / the best choice if they started the project today.

[0]: https://github.com/tensorflow/swift/blob/main/docs/WhySwiftF...

It was not in the README