| 5 Jul 2024 |
 Jonas Chevalier | A very specific example is: one customer is using nvcr.io/nvidia/pytorch:23.08-py3 ( CUDA 12.21, cuDNN 8.9.4, Python 3.10, PyTorch 2.1.0 ) and looking to try out Nix to fix their reproducibility issues | 07:29:17 |
 SomeoneSerge (matrix works sometimes) | And in this case you'd suggest we provide cudaPackages'.cuda_12_21_cudnn_8_9_4? | 07:44:11 |
| SomeoneSerge (matrix works sometimes) | ...instead of referring to the manual and cudaPackages.overrideScope' (...)? | 07:44:49 |
| SomeoneSerge (matrix works sometimes) | * ...instead of referring to the manual and cudaPackages.overrideScope' (...) | 07:44:51 |
| Jonas Chevalier | I haven't thought about this deeply. One potentiality is to maintain a packageset like cudaPackages.pytorch_23_08 | 07:57:44 |
| SomeoneSerge (matrix works sometimes) | I think an out-of-tree collection of buildLayeredImage expressions reproducing nvcr images would make sense | 08:08:38 |
| SomeoneSerge (matrix works sometimes) | In-tree, maybe not so much because these sound like finalized compositions of packages | 08:09:10 |
