Selenium WebDriver VS Pytorch

Selenium is an extensively used open-source automation framework for web applications. It allows for cross-browser testing by automating browser actions, making it a staple tool for end-to-end testing in diverse web development environments.

While similar to Puppeteer, Cypress, and Selenium, there are some differences. Let’s find out what they are.

browser.get('http://selenium.dev/')

Cypress is a next-generation front-end Automation testing tool built for modern web applications. It is a JavaScript-based end-to-end testing framework and is known for faster test execution as compared with other testing tools (like Selenium or Protractor).

Removing ExpectedConditions from documentation makes sense since it was removed from the codebase around 2019 already.

https://github.com/SeleniumHQ/selenium --> trunk

You won't be able to test the javascript function itself from within python, but you can exercise the front-end code using something like cypress (https://cypress.io) or the older but still respectable selenium (https://selenium.dev).

once you solve this you may run into: https://github.com/SeleniumHQ/selenium/issues/11750, so add that workaround.

Other libraries with similar functionality are Selenium, which is very popular outside the JavaScript world, and Playwright, a younger step-brother of Puppeteer.

🔗 https://selenium.dev

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():