DEBUG #299
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DEBUG #299
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Upstream keeps all magma-related routines in a separate libtorch_cuda_linalg library that is loaded dynamically whenever linalg functions are used. Given the library is relatively small, splitting it makes it possible to provide "magma" and "nomagma" variants that can be alternated between. Fixes conda-forge#275 Co-authored-by: Isuru Fernando <[email protected]>
…nda-forge-pinning 2024.12.04.13.54.14
Try to speed up magma/nomagma builds a bit. Rather than rebuilding the package 3 times (possibly switching magma → nomagma → magma again), build it twice at the very beginning and store the built files for later reuse in subpackage builds. While at it, replace the `pip wheel` calls with `setup.py build` to avoid unnecessarily zipping up and then unpacking the whole thing. In the end, we are only grabbing a handful of files for `libtorch*` packages and they are in predictable location in the build directory. `pip install` remains being used in the final builds for `pytorch`.
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Hi! This is the friendly automated conda-forge-linting service. I just wanted to let you know that I linted all conda-recipes in your PR ( I do have some suggestions for making it better though... For recipe/meta.yaml:
This message was generated by GitHub Actions workflow run https://github.com/conda-forge/conda-forge-webservices/actions/runs/12387955132. Examine the logs at this URL for more detail. |
…onda-forge-pinning 2024.12.06.16.06.14
The test currently refused to even start, since not all dependencies were satisfied.
5 tasks
Put all the rules in a single file. In the end, build_common.sh has pytorch-conditional code at the very end anyway, and keeping the code split like this only makes it harder to notice mistakes.
While technically upstream uses 2024.2.0, this is causing some of
the calls to fail with an error:
RuntimeError: MKL FFT error: Intel oneMKL DFTI ERROR: Inconsistent configuration parameters
Force <2024 that seems to work better.
Fixes conda-forge#301
Enable actually running a fixed random subset (1/5) of core tests to check for packaging-related regressions. We are not running the complete test suite because it takes too long to complete.
Per RuntimeError: Ninja is required to load C++ extensions
While there still doesn't seem to be a clear agreement which builds should be preferred, let's prefer "magma" to keep the current behavior unchanged for end users.
Replace the build number hacks with `track_features` to deprioritize generic BLAS over mkl, and CPU over CUDA. This is mostly intended to simplify stuff before trying to port to rattler-build.
…onda-forge-pinning 2024.12.12.16.25.16
Remove a leftover `skip` that prevented CUDA + generic BLAS build from providing all packages, notably `pytorch`. While at it, remove redundant [win] skip.
This reverts commit 4ad7437.
…onda-forge-pinning 2024.12.13.14.31.10
5 tasks
Tobias-Fischer
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Tobias-Fischer
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h-vetinari
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This was referenced Dec 18, 2024
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Can we close here @h-vetinari? |
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Demonstrate that fix in conda-forge/libcufile-feedstock#24 was not sufficient, and that conda-forge/libcufile-feedstock@733742d needs to be reverted.Debugging run for #298 (which contains the commits for the original goal of this PR) + #304