schedule VS taichi

Thank you for your insight!

I asked about it to the dev if you're interested, by the way. No replies yet though, since the lib isn't very active to begin with.

https://github.com/dbader/schedule/issues/614

similar to this this library in python3

schedule

https://github.com/dbader/schedule should do the trick with running periodically.

https://github.com/dbader/schedule might help as well

Python provides two different modules, sched and concurrent.futures and it is in the hands of the user to implement the ScheduledThreadPoolExecutor. Though there are packages that provide scheduling in Python, there is no implementation as close to the one provided by Java. So, I’ve implemented the same with the APIs to closely resemble the ones in Java.

You can use the Schedule package

You could look into using cron (a UNIX utility you can use to run commands on a schedule) or schedule, which I don't have a ton of experience with but which is pretty popular and well supported.

I need to run a certain task periodically on my Flask application. I decided to use a simple library - Schedule (https://github.com/dbader/schedule) for doing this. I am running the task scheduler on a separate thread from the main application thread. Here's the relevant code snippet.

taichi – Productive, portable, and performant GPU programming in Python

ETH Zurich is using it for their physics sim courses, University of Utah is using it for simulations (SIGGRAPH 2022), OPPO (they make smart devices running Android), Kuaishou uses it for liquid and gas simulation on GPUs. Lots of GPU accelerated sim stuff.

https://www.taichi-lang.org/

https://www.researchgate.net/publication/337118128_Taichi_a_...

https://github.com/taichi-dev/taichi

Check our the realease note (https://github.com/taichi-dev/taichi/releases) for more improvements.

I've recently started using Taichi (https://taichi-lang.org/) for numerical codes and the fact it doesn't try to trick you into thinking it's numpy is a nice "feature". ;)

Taichi v1.4.0 is released! See what's new: - Taichi AOT, along with a native Taichi Runtime library: Native applications can now load compiled AOT modules and launch Taichi kernels without a Python interpreter. - Taichi ndarray: An array object that holds contiguous multi-dimensional data to allow easy data exchange with external libraries. - Dynamic index: Use variable indices whenever necessary on all backends without affecting the performance of those matrices with only constant indices. See deprecation and more improvements in the release note.

Oh, then if you're not already tied to any particular VFX software, I might as well recommend Taichi again.

It's an upgrade of a basic version. See changes to the source code here: https://github.com/taichi-dev/taichi/pull/6851