HHVM VS Pytorch

Learned of this in https://news.ycombinator.com/item?id=37186869 it was posted 2 years ago to no discussion https://news.ycombinator.com/item?id=30291459 seems far more interesting than that.

https://github.com/facebook/hhvm/blob/master/hphp/hack/doc/H... appears to be where it was proposed, and has more background than the current docs.

also fredemmott is not working on it any more https://github.com/facebook/hhvm/issues/9376

Hack and Flow are also written in OCaml.

Here is a commit generated by clippy fix without Cargo involved: https://github.com/facebook/hhvm/commit/efea2c4dcc12fdbf3a2e3ce383bbdca2de5f93a9

Compared to PHP, with its visible community, Hack/HHVM does seem like a dead-end technology. But it’s very actively maintained and it powers the largest website on the planet. It definitely doesn’t make sense for the average PHP developer to switch (and the article steers clear of evangelising) but it also definitely doesn’t make sense for us to switch back.

PHP is nearly as old as the web. Change the conversation and say: I use Hack on the Hip Hop Virtual Machine

> 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.

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...