error: tensorflow-gpu-2.13.0 not supported for interpreter python3.12

   error: tensorflow-gpu-2.13.0 not supported for interpreter python3.12

error: tensorflow-gpu-2.13.0 not supported for interpreter python3.12

 wyoming-faster-whisper[4505]:   File "/nix/store/dfp38l0dy3n97wvrgz5i62mwvsmshd3n-python3.12-faster-whisper-unstable-2024-07-26/lib/python3.12/site-packages/faster_whisper/transcribe.py", line 145, in __init__
 wyoming-faster-whisper[4505]:     self.model = ctranslate2.models.Whisper(
 wyoming-faster-whisper[4505]:                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^
 wyoming-faster-whisper[4505]: RuntimeError: CUDA failed with error unknown error
 systemd[1]: wyoming-faster-whisper-medium-en.service: Main process exited, code=exited, status=1/FAILURE

wyoming-faster-whisper[4745]:   File "/nix/store/dfp38l0dy3n97wvrgz5i62mwvsmshd3n-python3.12-faster-whisper-unstable-2024-07-26/lib/python3.12/site-packages/faster_whisper/transcribe.py", line 145, in __init__
wyoming-faster-whisper[4745]:     self.model = ctranslate2.models.Whisper(
wyoming-faster-whisper[4745]:                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^
wyoming-faster-whisper[4745]: RuntimeError: CUDA failed with error no CUDA-capable device is detected

Anyway in the interest of splitting my attention ever more thinly I decided to start trying to work on some approach toward evaluation of derivations and building them
The idea being to have

1. a service which is given a flake ref and an attribute path and efficiently produces a list of attribute paths to derivations exiting under the given attribute path and stores the eval time somewhere
2. a service which is given a flake ref and an attribute path to a derivation and produces the JSON representation of the closure of derivations required to realize the derivation, again storing eval time somewhere
3. a service which functions as a job scheduler, using historical data about costs (space, time, memory, CPU usage, etc.) and information about locality (existing store paths on different builders) to realize a derivation, which is updated upon realization of a derivation

https://github.com/ConnorBaker/nix-eval-graph

And I’ve decided to write it in Rust, which I am self teaching.

And I’ll probably use a graph database, because why not.

And I’ll use NixOS tests for integration testing, because also why not.

In reply to @connorbaker:matrix.org

Anyway in the interest of splitting my attention ever more thinly I decided to start trying to work on some approach toward evaluation of derivations and building them
The idea being to have

1. a service which is given a flake ref and an attribute path and efficiently produces a list of attribute paths to derivations exiting under the given attribute path and stores the eval time somewhere
2. a service which is given a flake ref and an attribute path to a derivation and produces the JSON representation of the closure of derivations required to realize the derivation, again storing eval time somewhere
3. a service which functions as a job scheduler, using historical data about costs (space, time, memory, CPU usage, etc.) and information about locality (existing store paths on different builders) to realize a derivation, which is updated upon realization of a derivation

In reply to @connorbaker:matrix.org
Unrelated to closure woes, I tried to package https://github.com/NVIDIA/MatX and https://github.com/NVIDIA/nvbench and nearly pulled my hair out. If anyone has suggestions for doing so without creating a patched and vendored copy of https://github.com/rapidsai/rapids-cmake or writing my own CMake for everything, I’d love to hear!

In reply to @ss:someonex.net
Awesome! I've been bracing myself to look into that too. What's your current idea regarding costs and locality?

In reply to @ss:someonex.net
we'd need to do that if were to package rapids itself too, wouldn't we?

In reply to @connorbaker:matrix.org
Currently I don't know how I'd even model it... but I've been told that job scheduling is a well-researched problem in HPC communities ;)
I started to write something about how I think of high-level tradeoffs between choosing where to build to build moar fast, reduce the number of rebuilds (if they are at all permitted), reduce network traffic, etc. and then thought "well what if the machines aren't homogenous" and I've decided it's time for bed.

In reply to @connorbaker:matrix.org
Unrelated -- if anyone has experience with NixOS VM tests and getting multiple nodes to talk to each other, I'd appreciate pointers. ping can resolve hostnames but curl can't for some reason (https://github.com/ConnorBaker/nix-eval-graph/commit/c5a1e2268ead6ff6ffaab672762c1eedee53f403).

In reply to @ss:someonex.net
True. I'm still yet to read up on how SLURM and friends do this. Shameless plug: https://github.com/sinanmohd/evanix (slides)

In reply to @ss:someonex.net
Should just work, what is the error?