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)

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():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.

Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework.

In PyTorch with * or mul(). ` or mul()` can multiply 0D or more D tensors by element-wise multiplication:

When I was at Facebook, I wrote a Python script to extract shell scripts from GitHub Actions workflows, so we could run them all through ShellCheck: https://github.com/pytorch/pytorch/blob/69e0bda9996865e319db...

PyTorch: An open-source deep learning framework that facilitates dynamic computational graphs, making it flexible and efficient for research and production.