Pipenv VS Pytorch

pipenv solves this by having both kinds of requirement files: Pipfile lists package names and known constraints on which versions can be used, while Pipfile.lock gives specific package versions with hashes. Theoretically the Pipfile (and its lockfile) format were supposed to be a standard that many different tools could use, but I haven't seen it get adopted much outside of pipenv itself, so I'm not sure if it's really going to catch on.

* Tons of longstanding bugs that fail to be resolved in any sort of timely manner like this one: https://github.com/pypa/pipenv/issues/2413

I am sick and tired of the python packaging space pushing this godawful tool for Python. Please just use pip-tools or poetry instead.

ℹ️ You may use a different virtual environment manager like Pipenv or poetry.

Hey lua would you mind looking at this question for me? https://github.com/pypa/pipenv/discussions/5411

Alternatively, you can look into Pipenv, which has a lot more tools to develop secure applications with.

https://github.com/pypa/pipenv/releases https://bodhi.fedoraproject.org/updates/?search=pipenv (pipenv-2021.5.29-7.fc35 for the current Fedora release)

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():