sdk-python VS cupy

My problem with Temporal is that it doesn't support gevent https://github.com/temporalio/sdk-python/issues/59

> Unless Pydantic is downloading all OS binaries with the package and loading the right one at runtime, this would become a "problem" as well.

Nah, it's not that bad. I built a Rust-backed Python library used by many [0], and with setuptools-rust (maturin wasn't flexible enough at the time) and cibuildwheel and GH actions, the wheels are built/shipped with the shared libraries embedded and the end user never has to worry or even be aware of its presence.

Pydantic has already been shipping a binary mode with an option for pure Python, so maybe they'll keep the pure Python mode around.

0 - https://github.com/temporalio/sdk-python

Either way, let us know how it goes! Building something cool? We’d love to hear about it! Our forum has a new Show & Tell section. If you want to share, we’ll send you some sweet swag. Have feedback on how we can do better? We want to know that too. Raise an issue in the SDK or samples repos, or send us an email ([email protected]).

We built to logic backing the Temporal Python SDK[0] in Rust and leverage PyO3. Unfortunately Maturin didn't let us do some of the advanced things we needed to do for wheel creation (at the time, unsure now), so we use setuptools-rust with Poetry.

0 - https://github.com/temporalio/sdk-python

Going to shamelessly plug Temporal’s Python SDK which was designed for asyncio.

https://github.com/temporalio/sdk-python

Disclaimer: I work for Temporal

hey sorry for taking a while to respond, was busy with personal stuff and hope you see this.

1. yes. its on the order of months. start watching https://github.com/temporalio/sdk-python

2. temporal itself will not produce something like that, because we much rather have a lively community of third party maintainers/startups do that and be their supporters rather than competitors. interested?

I did not expect anything interesting, but this is actually cool.

> A full implementation of the NumPy API. Not something "NumPy-like" — just literally the NumPy API, with the same functions and the same arguments.

I suppose it's like https://cupy.dev/

What was the difference in runtime performance, and did you try CuPy?

https://github.com/cupy/cupy :

> CuPy is a NumPy/SciPy-compatible array library for GPU-accelerated computing with Python. CuPy acts as a drop-in replacement to run existing NumPy/SciPy code on NVIDIA CUDA or AMD ROCm platforms.

Projects using CuPy:

It's more effort though. You will likely have to format your data in specific ways for the GPU to efficiently process it. I've done this kind of thing with PyTorch tensors, but there are also math-specific libraries like CuPy. If you only have millions, Numpy should be fine.

I'd also recommend checking out CuPy which aims to fully re-implement the Numpy api for CUDA GPUs, while taking advantage of Nvidia's specialized libraries like cuBLAS, cuRAND, cuSOLVER etc. The tradeoff being that it only works with Nvidia GPUs.

u/Spataner's answer is great. If you WANT GPU-enabled numpy functions, I would check out CuPy: https://cupy.dev/

Not sure how VS Code is relevant here as it's just you IDE, shouldn't have any influence on this. Now, seeing as you're using numpy (which has no gpu support), you could try and use something like CuPy in place of numpy. I'm not sure about the interoperability because I've never used this myself, but if you're lucky it could be as simple as just replacing all numpy calls with the same CuPy calls (or replacing all import numpy as np with import cupy as np ).

Cupy replicates the numpy and scipy APIs but runs on the GPU.

For that, you can use cupy[0], PyTorch[1] or Tensorflow[2]. They all mimic the numpy's API with the possibility to use your GPU.

[0] https://cupy.dev/