Taskflow VS Pytorch

For parallel programming nowadays, personally I reach for C++ Taskflow when I really care about performance, or a mix of core.async and running multiple load balanced instances when I’m doing more traditional web backend stuff in Clojure.

Also, you can take a look to good libraries. The problem is that very often libraries are heavily templated, so It could be hard. For example, I like the style of the Taskflow library, I think is very clear, is relatively small, while makes use of more advanced techniques: https://github.com/taskflow/taskflow

Cool. Thanks! How does it compare to taskflow?

I've not seen yet, but it's been a bit since I looked last, any evidence of being able to build a computation graph and "save" it to re-run on new inputs. Something like https://github.com/taskflow/taskflow

It seems you're describing something a generic parallel task framework. Check taskflow for a production ready example https://github.com/taskflow/taskflow/blob/master/

- https://github.com/taskflow/taskflow (I recommend to learn it first since its API and documentation are excellent)

If you want some sort of "job" system, where you submit items to a some sort of queue to be processed in parallel, try searching for a thread pool - there isn't one in the standard library, but there's about a million implementations online. There are more complicated versions of that idea, that describe computation as a directed acyclic graph, such as taskflow.

The tooling decisions affect engine design though. For example if you want to have visual representation of job graph as it happened in specific frame of interest you need to pass the information around about job relationships and output it to a tool of choice. For example see https://github.com/taskflow/taskflow

I am aware of DAGs of task based threading library like Taskflow and HPX however the benefit they have is not obvious to me, as the following sequential section depends on the parallel part being completed fully. If you want to suggest elaboration on the benefits of this approach would be welcome.

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():

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.