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Dropping CUDA 11.8 as a default CUDA version

CUDA 11.8 is the last holdover from the old days before conda-forge switched to the new and shiny CUDA 12+ infrastructure, where the CUDA toolchain is provided as native conda-packages, rather than a blob in an image.

For CUDA-enabled feedstocks, we've been building both 11.8 and 12.6 by default for a while now, but many feedstocks (notably pytorch, tensorflow, onnx, jax etc.) have dropped CUDA 11.8 for many months already.

Due to various constraints (details below), we are dropping CUDA 11.8 as a default version in our global pinning on June 5th. It will still be possible to opt into building CUDA 11.8 on a per-feedstock basis where this is necessary or beneficial.

The above-mentioned contraints are mainly:

  • it complicates our pinning due to needing to switch images and compilers with 11.8.
  • it keeps us from migrating to newer CUDA 12.x versions necessary to support new architectures.
  • it's not compatible with VS2022, which is due to become the default toolchain on windows in conda-forge soon (the previous VS2019 has reached end-of-life more than a year ago).
  • it complicates our infrastructure in several places, due to the big differences between the before/after of the new CUDA architecture.

After we have removed CUDA 11.8 from the pinning, any feedstock still building that version will drop the respective CI jobs upon rerendering. For feedstocks wanting to keep building CUDA 11.8 a bit longer, we have provided a custom migrator.

The way to make use of this is to copy cuda118.yaml from the global pinning into .ci_support/migrations on your feedstock. If the migrations subfolder doesn't exist, please create it. Once that's committed (and there are no skips in the recipe for CUDA 11.8), rerendering the feedstock will reinstate the builds for CUDA 11.8. If you have trouble with that, please open a thread on https://conda-forge.zulipchat.com/.

Finally, please let us know in the issue if your feedstock still needs to support CUDA 11.8 and why (later down the line we'll want to drop support also in conda-forge-ci-setup, and knowing what feedstocks - if any - still need CUDA 11.8 will help guide the decision on timing).