Robot Framework VS Pytorch

Well, I work with software quality and despite not having a strong foundation in automation, one fine day I decided to make a change. I have been working with Robot Framework for a few months - and that's when I got a taste of the power of python. Some time later, I dabbled a little with Cypress and Playwright, always using javascript.

Robot Framework

I've used Lua/Busted in a data-heavy environment (telemetry from hospital ventilators). I've also used robot: https://robotframework.org/

I can't say whether any of these will work, but maybe one of: PyAutoGui pytest-qt Robot Framework + plugins

I'm looking for tools, strategies, libraries, etc. that would be useful for automating arbitrary desktop applications. Ideally something free and open source. Robot Framework (https://robotframework.org/) looks promising, although the docs seem deliberately unclear about how useable the open source libraries are without the cloud SaaS being sold on top.

Does anyone have experience in this area? What's your secret sauce for robust desktop automations?

In the industry I've seen the framework "Robot framework" https://robotframework.org/ used a lot for test automation.

Check out Robot Framework @ https://robotframework.org/

Greetings! They say all good things must come to an end, and with this post, so it is with my series of posts covering Robot Framework.

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.