CUDA: Difference between revisions

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Or you can use binary-packaged versions of CUDA compatible software, such as [https://github.com/edolstra/nix-warez/tree/master/blender blender-bin] for Blender.

{{info|If you will be using <code>cudaSupport</code> in packages, it is recommended you utilize a [[#Setting up CUDA Binary Cache|CUDA ~~Binary Cache~~]].}}

{{info|If you will be using <code>cudaSupport</code> in packages, it is recommended you utilize a [[#Setting up CUDA Binary Cache|CUDA binary cache]].}}

Without a [[#Setting up CUDA Binary Cache|CUDA ~~Cache~~]], any CUDA compatible package installed with <code>cudaSupport</code> will be compiled from source. This is because NixOS Foundation does not build (and therefore [https://cache.nixos.org/ cache.nixos.org] does not cache) CUDA packages.

Without a [[#Setting up CUDA Binary Cache|CUDA cache]], any CUDA compatible package installed with <code>cudaSupport</code> will be compiled from source. This is because NixOS Foundation does not build (and therefore [https://cache.nixos.org/ cache.nixos.org] does not cache) CUDA packages.

For larger programs like Blender, that process can be very resource-intensive. If ~~you know~~ you are installing a large package ~~and~~ are not using a cache, on older or weaker hardware it is recommended to reduce the number of cores and/or jobs that the process will take, to prevent a ~~crash~~ from resource limits. This can be done with the <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> flags, for more details see the [https://github.com/NixOS/nix/blob/master/doc/manual/source/advanced-topics/cores-vs-jobs.md Tuning Cores & Jobs] page.

For larger programs like Blender, that process can be very resource-intensive. If you are installing large CUDA-enabled package(s) that either are not cached or you are not using a cache, then (especially on older or weaker hardware) it is recommended to reduce the number of cores and/or jobs that the process will take, to prevent a system freeze from resource limits. This can be done with the <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> flags, for more details see the [https://github.com/NixOS/nix/blob/master/doc/manual/source/advanced-topics/cores-vs-jobs.md Tuning Cores & Jobs] manual page.

If you don't want to deal with the increased time that compilation will take when <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> are set below maximum, you can also try simply closing other running processes to see if that frees up enough resources for compilation to be successful.

→ For specifics on setting up Blender with CUDA (and OptiX) see: [[Blender#CUDA & OptiX]].

@@ Line 204: / Line 204: @@
 Or you can use binary-packaged versions of CUDA compatible software, such as [https://github.com/edolstra/nix-warez/tree/master/blender blender-bin] for Blender.
-{{info|If you will be using <code>cudaSupport</code> in packages, it is recommended you utilize a [[#Setting up CUDA Binary Cache|CUDA Binary Cache]].}}
+{{info|If you will be using <code>cudaSupport</code> in packages, it is recommended you utilize a [[#Setting up CUDA Binary Cache|CUDA binary cache]].}}
-Without a [[#Setting up CUDA Binary Cache|CUDA Cache]], any CUDA compatible package installed with <code>cudaSupport</code> will be compiled from source. This is because NixOS Foundation does not build (and therefore [https://cache.nixos.org/ cache.nixos.org] does not cache) CUDA packages.
+Without a [[#Setting up CUDA Binary Cache|CUDA cache]], any CUDA compatible package installed with <code>cudaSupport</code> will be compiled from source. This is because NixOS Foundation does not build (and therefore [https://cache.nixos.org/ cache.nixos.org] does not cache) CUDA packages.
-For larger programs like Blender, that process can be very resource-intensive. If you know you are installing a large package and are not using a cache, on older or weaker hardware it is recommended to reduce the number of cores and/or jobs that the process will take, to prevent a crash from resource limits. This can be done with the <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> flags, for more details see the [https://github.com/NixOS/nix/blob/master/doc/manual/source/advanced-topics/cores-vs-jobs.md Tuning Cores & Jobs] page.
+For larger programs like Blender, that process can be very resource-intensive. If you are installing large CUDA-enabled package(s) that either are not cached or you are not using a cache, then (especially on older or weaker hardware) it is recommended to reduce the number of cores and/or jobs that the process will take, to prevent a system freeze from resource limits. This can be done with the <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> flags, for more details see the [https://github.com/NixOS/nix/blob/master/doc/manual/source/advanced-topics/cores-vs-jobs.md Tuning Cores & Jobs] manual page.
+If you don't want to deal with the increased time that compilation will take when <code>--max-jobs</code> / <code>-j</code> and <code>--cores</code> are set below maximum, you can also try simply closing other running processes to see if that frees up enough resources for compilation to be successful.
 &rarr; For specifics on setting up Blender with CUDA (and OptiX) see: [[Blender#CUDA & OptiX]].