Comments (4)
I guess that would be important to have a conda-forge Cuda package first: conda-forge/staged-recipes#6240
But you could still create the recipe now for pytorch-cpu and enable the GPU build when this cuda PR is merged.
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As for the differents Cuda version build, the new build variants of conda 3.0 could be used: https://conda.io/docs/user-guide/tasks/build-packages/variants.html.
One recipe will be sufficient to build all the different packages.
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That would also allow building pytorch with a CentOS6 glibc version (often default on universities cluster unfortunately...).
Ping @pjh5
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we dont expect to be in conda-forge for some time for various reasons:
- one is staffing, we are quite low-staffed on the builds right now, and cannot maintain SLAs for conda-forge changes that might be needed
- the second is that our builds take several hours on 40-core powerful servers, they take 3x more time on Azure pipelines and Travis machines (we build the binaries for 7 CUDA architectures which is what makes them so long, and on top of that, we build for 3 to 4 CUDA versions)
- the third is that CuDNN and NCCL often have known bugs that are not fixed until the next release cycle (months away), so we are very particular about controlling our versions and statically linking them for these libraries, which is not in
conda-forge
spirit - the fourth is that we dont have stable builds yet, and we need to be in control of our destiny for a bit and keep tweaking things several times before we get to a working stable binary, so we prefer to control our build scripts tightly and push to them quickly
Also we cannot build on CentOS6
- because it's default shipped libstdc++ has a bug that we run into.
- various CUDA bits are almost not supported on CentOS6
We used to build (and I took various pains to make sure binaries until 0.3.0 are built on CentOS6), but at some point we had big blockers that we ran into, and I gave up and switched to CentOS7. Thankfully universities are now largely switching to CentOS7.
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Related Issues (20)
- Create POC for building wheels based on : https://github.com/pypa/cibuildwheel
- aarch64 linux: torch.compile performance is 2x slow with nightly torch wheel compared to the wheel built with 'build_aarch64_wheel.py' script HOT 18
- `numpy v2.0.0rc1` on pytorch/ conda channel problem HOT 6
- Deprecate LEGACY_DOCKER_IMAGE
- Docker Images Validate. Fix arm64 docker builds to not contain cuda versions HOT 1
- Build cuda:12.4.0-cudnn8-devel-ubuntu22.04 docker image and host it in pytorch AWS HOT 3
- OOM error in building ARM wheel
- Triton build is failing from time to time with error on python 3.12 with Exit code 137
- Move binary Docker manywheel and conda build workflows and scripts to pytorch/pytorch repo
- Unable to build docker image from Builder Repo HOT 4
- Are cuda aarch64 build available anywhere? HOT 3
- Move release and analytics workflows scripts to test-infra
- Move magma builds from builder to test-infra
- Move all validation workflows and scripts from builder to test-infra
- [BE] More code-reuse across tests HOT 1
- CentOS Docker failures: mirrorlist.centos.org no longer online HOT 1
- Unable to build Docker image with release/2.4 branch HOT 1
- Packaging for libcufile
- Extend validation framework with additional tests HOT 1
- Cannot install scikit-learn from conda-forge channel alongside pytorch from the pytorch channel
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