Pylint VS Pytorch

A little introduction about pylint. Pylint is a static code analyzer, it analyses your code without actually running it. Pylint looks for potential errors, gives suggestions on coding standards that your code is not adhering to, potential places where refactoring might help, and also warnings about smelly code.

Pylint, on the other hand, focuses on code analysis and style checking. It offers extensive customization options and supports various coding standards. Pylint is known for its comprehensive reports and ability to detect a wide range of code issues.

In my opinion, the best way to expose configuration options is to read and parse them from the project's pyproject.toml file. Here's how Pylint handles it.

I even contributed to Pylint by submitting a new rule a few years ago : implicit-str-concat.

Implemented code formatting with Black and linting with Pylint in my CI pipeline. Here is my updated GitHub Actions Workflow file: ci.yml

One last thing to do before running the hooks is to create a config file, just like we did with flake8. For this you are going to create a pylintrc file at the roor of your project and copy the contents of the pylintrc file from the pylint repo (here is the link to it).

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():