Pylint VS tensorflow

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).

To get up to speed with TensorFlow, check their quickstart Support TensorFlow on GitHub ⭐

Explore here.

Part of the major userbase pull in GitHub revolves around hosting a considerable number of popular projects including Angular, React, Kubernetes, cpython, Ruby, tensorflow, and well even the software that powers this site Forem.

Right but that's not an inherent GPU determinism issue. It's a software issue.

https://github.com/tensorflow/tensorflow/issues/3103#issueco... is correct that it's not necessary, it's a choice.

Your line of reasoning appears to be "GPUs are inherently non-deterministic don't be quick to judge someone's code" which as far as I can tell is dead wrong.

Admittedly there are some cases and instructions that may result in non-determinism but they are inherently necessary. The author should thinking carefully before introducing non-determinism. There are many scenarios where it is irrelevant, but ultimately the issue we are discussing here isn't the GPU's fault.

and things like y = layers.ELU()(y) work as expected. I wanted to see a list of the available layers so I went to the Tensorflow GitHub repository and to the keras directory. There's a warning in that directory that says:

Exactly what language do you think TensorFlow is written in? :)

You can use Tensorflow's deep learning API for this.

Since you've specifically mentioned ML, here's Tenserflow's GitHub. I'm sure a quick glance through that will change your mind.