PyInstaller VS Pytorch

Not sure if fixed in this patch, but pyinstaller had an issue in 3.12.0 https://github.com/pyinstaller/pyinstaller/issues/7992

You are right. I think I've misremembered the module name - it was uwsgi, not uvicorn.

This is a github issue where I discussed my original issue with PyInstaller devs - the dev explained the situation very well: https://github.com/pyinstaller/pyinstaller/issues/6362

PyInstaller seemed like the most maintained and developed tool to freeze python script into an executable, so I went with it. As expected, the freezed interface with the model was gigabytes large, so I had to figure out how to squeeze this. Fortunately, Onnx worked wonders and packaged the model into an inference only state, so I could throw away the Pytorch and Torchtext dependencies when freezing with Pyinstaller.Now the size of the executable with the model was 43MB instead of 4GB.

You might want to see Pyinstaller and auto_py_to_exe if you want a GUI interface.

It uses the pyinstaller command behind and please read their docs if you want to know more details.

I also found another link on github that may have some solutions to try: https://github.com/pyinstaller/pyinstaller/issues/3600

I made a Django react app. Now I want to make it a desktop application so that the user does not have type python manage.py runserver and also activate the environment every time. I used pyinstaller. I did all the steps mentioned for django

First, instead of creating a VM for windows, you may need to use a software called Wine mentioned in the pyinstaller FAQs

PyTorch (https://pytorch.org/)

torch is a package to manage tensors and dynamic neural networks in python (GitHub)

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():