Attempt to install `nvidia` packages with the new site-packages layout

[hartikainen]

🐞 bug report

Affected Rule

n/a

Is this a regression?

No. Indeed, there are several similar issues already open. See e.g. #2156, pytorch/pytorch#117350, pytorch/pytorch#101314. I also know that the venv/site-packages layout is still experimental and thus this is not really a bug. I still wanted to share the information in case someone finds it useful (and also to provide a log for myself to come back to this next time I have time to look into it).

Description

I tried my luck in installing the nvidia CUDA libraries (nvidia-cuda-runtime-cu12 and friends) to see if I could get them to work out of the box with the new venv/site-packages layout (#2156). The TLDR is: no, they didn't work out of the box. I've added some data from my initial investigation below.

🔬 Minimal Reproduction

e2d73e2

🔥 Exception or Error

Details

bazel run //:test
Starting local Bazel server (8.2.1) and connecting to it...
INFO: Analyzed target //:test (138 packages loaded, 9986 targets configured).
INFO: Found 1 target...
Target //:test up-to-date:
 bazel-bin/test
INFO: Elapsed time: 34.576s, Critical Path: 23.99s
INFO: 22 processes: 63 action cache hit, 22 internal.
INFO: Build completed successfully, 22 total actions
INFO: Running command line: external/bazel_tools/tools/test/test-setup.sh ./test
exec ${PAGER:-/usr/bin/less} "$0" || exit 1
Executing tests from //:test
-----------------------------------------------------------------------------
ERROR:2025-09-01 19:24:47,302:jax._src.xla_bridge:487: Jax plugin configuration error: Exception when calling jax_plugins.xla_cuda12.initialize()
Traceback (most recent call last):
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py", line 201, in _version_check
 version = get_version()
 ^^^^^^^^^^^^^
RuntimeError: jaxlib/cuda/versions_helpers.cc:81: operation cusparseGetProperty(MAJOR_VERSION, &major) failed: The cuSPARSE library was not found.

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/_src/xla_bridge.py", line 485, in discover_pjrt_plugins
 plugin_module.initialize()
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py", line 328, in initialize
 _check_cuda_versions(raise_on_first_error=True)
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py", line 266, in _check_cuda_versions
 _version_check("cuSPARSE", cuda_versions.cusparse_get_version,
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax_plugins/xla_cuda12/__init__.py", line 205, in _version_check
 raise RuntimeError(err_msg) from e
RuntimeError: Unable to load cuSPARSE. Is it installed?
WARNING:2025-09-01 19:24:47,311:jax._src.xla_bridge:864: An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.
Hello, nvidia=<module 'nvidia' (namespace) from ['/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/nvidia']>!
Hello, jax=<module 'jax' from '/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/__init__.py'>!
mujoco=<module 'mujoco' from '/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/mujoco/__init__.py'>!
Traceback (most recent call last):
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test_stage2_bootstrap.py", line 474, in <module>
 main()
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test_stage2_bootstrap.py", line 468, in main
 _run_py_path(main_filename, args=sys.argv[1:])
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test_stage2_bootstrap.py", line 284, in _run_py_path
 runpy.run_path(main_filename, run_name="__main__")
 File "<frozen runpy>", line 287, in run_path
 File "<frozen runpy>", line 98, in _run_module_code
 File "<frozen runpy>", line 88, in _run_code
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/test.py", line 20, in <module>
 main()
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/test.py", line 16, in main
 print(f"{jax.devices('gpu')=}")
 ^^^^^^^^^^^^^^^^^^
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/_src/xla_bridge.py", line 1010, in devices
 return get_backend(backend).devices()
 ^^^^^^^^^^^^^^^^^^^^
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/_src/xla_bridge.py", line 944, in get_backend
 return _get_backend_uncached(platform)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/_src/xla_bridge.py", line 925, in _get_backend_uncached
 platform = canonicalize_platform(platform)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/home/user/.cache/bazel/_bazel_user/78efd52eda40769e82a38d778950bc83/execroot/_main/bazel-out/k8-fastbuild/bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/jax/_src/xla_bridge.py", line 728, in canonicalize_platform
 raise RuntimeError(f"Unknown backend: '{platform}' requested, but no "
RuntimeError: Unknown backend: 'gpu' requested, but no platforms that are instances of gpu are present. Platforms present are: cpu

🌍 Your Environment

Operating System:

$ lsb_release -a
No LSB modules are available.
Distributor ID: Ubuntu
Description: Ubuntu 24.04.2 LTS
Release: 24.04
Codename: noble

Output of bazel version:

$ bazel --version
bazel 8.2.1

Rules_python version:

4a422b0

Additional Information

I first diffed uv venv-generated site-packages against a py_binary target built with venvs_site_packages=yes. The differences are minor, with just missing RECORD files and different INSTALLER files from the bazel environment. The INSTALLER file diffs are expected. The missing RECORD files I'm not sure of. They are missing from all the following packages:

Details

nvidia_cublas_cu12-12.9.1.4.dist-info.diff
nvidia_cuda_cupti_cu12-12.9.79.dist-info.diff
nvidia_cuda_nvcc_cu12-12.9.86.dist-info.diff
nvidia_cuda_nvrtc_cu12-12.9.86.dist-info.diff
nvidia_cuda_runtime_cu12-12.9.79.dist-info.diff
nvidia_cudnn_cu12-9.12.0.46.dist-info.diff
nvidia_cufft_cu12-11.4.1.4.dist-info.diff
nvidia_cusolver_cu12-11.7.5.82.dist-info.diff
nvidia_cusparse_cu12-12.5.10.65.dist-info.diff
nvidia_nccl_cu12-2.27.7.dist-info.diff
nvidia_nvjitlink_cu12-12.9.86.dist-info.diff
nvidia_nvshmem_cu12-3.3.24.dist-info.diff

Here's an example diff for one of them, nvidia_cublas_cu12-12.9.1.4.dist-info.diff:

Details

 diff -ruN ./.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/INSTALLER ./bazel-bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/INSTALLER
--- ./.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/INSTALLER	2025-09-01 18:19:24.134031041 +0000
+++ ./bazel-bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/INSTALLER	2025-08-28 20:28:52.097396422 +0000
@@ -1 +1 @@
-uv
\ No newline at end of file
+https://github.com/bazel-contrib/rules_python
\ No newline at end of file
diff -ruN ./.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/RECORD ./bazel-bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/RECORD
--- ./.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/RECORD	2025-09-01 18:19:24.141031054 +0000
+++ ./bazel-bin/test.runfiles/_main/_test.venv/lib/python3.12/site-packages/nvidia_cublas_cu12-12.9.1.4.dist-info/RECORD	1970-01-01 00:00:00.000000000 +0000
@@ -1,16 +0,0 @@
-nvidia/cublas/include/cublas.h,sha256=a0lLqy-k47NuwyDjuueC3W0Mpc908MTU7o5sMJqE-1w,41246
-nvidia/cublas/include/cublasLt.h,sha256=Zo_7r-ZRHWkohyAf1jLotjx0KBGWYh7vV2m6fLM7TFo,104268
-nvidia/cublas/include/cublasXt.h,sha256=CW9dyXYGSUW1wEXrVVyhU6OxBK1PUvMoYdVGlQT7L9A,37380
-nvidia/cublas/include/cublas_api.h,sha256=1dtyy6TIQB-ymoRH5k5rnBJaMN4sPjhSQt5lf-TpbL4,375691
-nvidia/cublas/include/cublas_v2.h,sha256=qxMdB5jb97luEfw61LEAB-Wlr8A9DLBvO4rRypDCNKw,15460
-nvidia/cublas/include/nvblas.h,sha256=dXCLR-2oUiJFzLsDtIAK09m42ct4G0HWdYzBUuDPXpc,23341
-nvidia/cublas/lib/libcublas.so.12,sha256=1jRW2U8bjiQIqKlTjIdA60dMnrpIqE9CCxXi4Q-80Eg,105140976
-nvidia/cublas/lib/libcublasLt.so.12,sha256=lt30iRyk9O0UdZZng6kYXfKr1kqBw8xxO5bQNmlvxUE,749205904
-nvidia/cublas/lib/libnvblas.so.12,sha256=9BKnB5ZDn2pEtI5RHlJ9Ht90GOcz7bpFGJJpqA6WIxE,753824
-nvidia_cublas_cu12-12.9.1.4.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2
-nvidia_cublas_cu12-12.9.1.4.dist-info/METADATA,sha256=9QYFRxRORca_jQAL9Q8kKFFOzH7d0swlXbn4mDUr0UY,1707
-nvidia_cublas_cu12-12.9.1.4.dist-info/RECORD,,
-nvidia_cublas_cu12-12.9.1.4.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
-nvidia_cublas_cu12-12.9.1.4.dist-info/WHEEL,sha256=pQkYcE_zO8O3IUE-PXGJ2qj02_9aLe-AqqPPB3Nn1Xg,109
-nvidia_cublas_cu12-12.9.1.4.dist-info/licenses/License.txt,sha256=rW9YU_ugyg0VnQ9Y1JrkmDDC-Mk_epJki5zpCttMbM0,59262
-nvidia_cublas_cu12-12.9.1.4.dist-info/top_level.txt,sha256=fTkAtiFuL16nUrB9ytDDtpytz2t0B4NvYTnRzwAhO14,7

The nvidia package, which is holding the actual implementation files, is identical between uv venv and rules_python venv, modulo the files being symlinked in the latter.

Despite the near-identical site-packages structure, the py_binary target is unable to load the shared nvidia libraries out of the box. After poking around a bit with strace, I see that libcusparse.so.12 exists but loading it fails because its transitive dependency of libnvJitLink.so.12 cannot be found¹. My guess is that this failure is caused by the dynamic linker resolving paths from the real file locations, which are different from the symlinked directory structure in Bazel-generated site-packages, breaking the run path lookups inside the C libraries.

As pointed out by @aignas (https://bazelbuild.slack.com/archives/CA306CEV6/p1756127831137709?thread_ts=1756088512.734139&cid=CA306CEV6), it would be interesting to debug this by copying the files instead of symlinking them to verify if it's really the symlinking that breaks things.

Questions and Things to Do

• Is this something that can/could be fixed from rules_python side? Or should the nvidia packages do something different to make the linker lookups work with symlinks?
• Is my diagnosis that the files exist but the symlinks make the linker upset even correct? Can there be something else here? Should try raw copies instead of symlinks.

Workaround for Jax

For what it's worth, I'm using Jax, not Pytorch as most others seem to do. I couldn't find any existing examples of patching Jax to preload the packages. Here's how I got around this issue:

Details

modified MODULE.bazel
@@ -67,6 +67,15 @@ pip.parse(
 "requirements.macos_arm64.txt": "osx_*",
 },
 )
+pip.override(
+ # file = "jax_cuda12_pjrt-0.7.1-py3-none-manylinux_2_27_x86_64.whl",
+ file = "jax_cuda12_pjrt-0.7.0-py3-none-manylinux2014_x86_64.whl",
+ patch_strip = 0,
+ patches = [
+ "//:jax_cuda12_pjrt.patch",
+ "//:jax_cuda12_pjrt_record.patch",
+ ],
+)
 use_repo(pip, "pypi")
 
 pip_onshape = use_extension("@rules_python//python/extensions:pip.bzl", "pip")

new file jax_cuda12_pjrt.patch
@@ -0,0 +1,10 @@
+--- jax_plugins/xla_cuda12/__init__.py
++++ jax_plugins/xla_cuda12/__init__.py
+@@ -123,6 +123,7 @@
+ We prefer the Python packages, if present. If not, we fall back to loading
+ them from LD_LIBRARY_PATH. By loading the libraries here, later lookups will
+ find these copies."""
++ _load("nvjitlink", ["libnvJitLink.so.12"])
+ _load("cuda_runtime", ["libcudart.so.12"])
+ _load("cu13", ["libcudart.so.13"])
+ # cuda_nvrtc isn't directly a dependency of JAX, but CUDNN appears to need it

new file jax_cuda12_pjrt_record.patch
@@ -0,0 +1,8 @@
+--- jax_cuda12_pjrt-0.7.0.dist-info/RECORD
++++ jax_cuda12_pjrt-0.7.0.dist-info/RECORD
+@@ -1,4 +1,4 @@
+-jax_plugins/xla_cuda12/__init__.py,sha256=5MWu6cM-YrC38VX6Qkay5IdWRn8Ekp7PuzW2LJoikaY,13182
++jax_plugins/xla_cuda12/__init__.py,sha256=cPm09fPeZlIf0ypdDr5g_1-rV3Ke9097eVW__lKDl48,13227
+ jax_plugins/xla_cuda12/version.py,sha256=dXnNpN9dnyGzJlW3C2fWP189bYNDvP0dHRXriwNP0bY,6733
+ jax_plugins/xla_cuda12/xla_cuda_plugin.so,sha256=VdGXGhi7-IMH7fAIIPIrRKLrl0AZwC2UIs5jQURXOyE,329428392
+ jax_cuda12_pjrt-0.7.0.dist-info/METADATA,sha256=KL0Di3q1g9gILfBxZDrpz9IcW5PSldd1Htwp8ddN1_w,579

Footnotes

1. This seems like a common issue and can be worked around by preloading the packages. See e.g. https://gist.github.com/qxcv/183c2d6cd81f7028b802b232d6a9dd62, https://github.com/pytorch/pytorch/pull/137059. What I'm trying to understand here is how to get these work out-of-the-box without having to patch the packages. ↩

[aignas]

A few extra things that we could do:

• In

  rules_python/python/private/py_executable.bzl

  Line 665 in 4a422b0

  venv_link = ctx.actions.declare_symlink(paths.join(base, venv_path))

  or related code paths we could try handling .so files differently from Python files - i.e. symlink the .so files individually instead of symlinking the directories that the files exist in. This could in theory give us more data about the situation.
• Copy the .so files, but that may take too long time for large .so files.

That said, this is a bit speculative on my side and it would be great to have a toy test for this in our integration test folder for the venv layout.

[rickeylev]

$EXEC_ORIGIN is something to look into, too. The docs on this are scant, but it's basically $ORIGIN but doesn't resolve the symlinks. (I'm pretty sure the Google patch for it was accepted years ago, so it's probably available with certain toolchains by now)

[groodt]

Haven't tried to run the repro, but it's unlikely to be the RECORD or INSTALLER files causing any issues here.

From my memory, uv is using hard-links on linux and windows, and clone (copy-on-write) on macos for creating their site-packages from the global download cache. Im not sure if that makes a difference here.

We could also try ldd the problematic .so inside the runfiles and see what it is looking for?

[aignas]

Googled and found an interesting related thing: bazelbuild/bazel#10809

[shayanhoshyari]

With some versions of nvidia packages I see even bigger problems, but that is NVIDIA issue, just mentioning here. seems they added an extra __init__.py that is breaking namespace nvidia/

I see nvidia_cublas_cu12==12.6.4.1 for example has this issue.

The original 12.9.1.4 from this issue does not.

Details

nvidia/
 nvtx/
 __init__.py

But a usual pip install has

nvidia/
 __init__.py cuda_cupti cuda_runtime cufft curand cusparse nvjitlink
 cublas cuda_nvrtc cudnn cufile cusolver nccl nvtx

And I think it is due to the fact that the nvidia-* packages all include a __init__.py and makenvidia invalid namespace package.

Even when pip installing with uv I am getting things like

warning: The module `nvidia` is provided by more than one package, which causes an install race condition and can result in a broken module. Consider removing your dependency on either `nvidia-nvtx-cu12` (v12.6.77) or `nvidia-cuda-nvrtc-cu12` (v12.6.77).

[rickeylev]

Try using rules_python at head. Logic was added to look for overlapping paths and merge them. It can't solve the incorrect namespace packages nvidia has created, but it should result in a site-packages that looks like what a non-bazel package manager install looks like.

[shayanhoshyari]

Try using rules_python at head. Logic was added to look for overlapping paths and merge them. It can't solve the incorrect namespace packages nvidia has created, but it should result in a site-packages that looks like what a non-bazel package manager install looks like.

Thanks. I see it is #3268 . Giving a try. 🫡

[aignas]

And please let us know how it goes!

[shayanhoshyari]

Ok, I tried the head (a commit after #3268).

I properly get the following the venv in the runfiles

__init__.py cuda_cupti cuda_runtime cufft curand cusparse nvjitlink
cublas cuda_nvrtc cudnn cufile cusolver nccl nvtx

But I am still not able to import torch. I think I am hitting what was mentioned earlier in the thread. torch .so has rpath=$ORIGIN/../../nvidia/cusparselt/lib, but it seems the .so loader looks into ../ of the original folder that contains the .so and not the folder that contians the symlink.

I put full detail with my investigation with readelf and LD_DEBUG=libs (basically what suggested here (#3228 (comment)) in this link: https://github.com/shayanhoshyari/issue-reports/tree/main/rules_python/import_torch

---

In the meantime, let me see what uv does in its venvs, because I know it has a symlink mode and it works well.

[shayanhoshyari]

Ok UV works, and this seems to be the reason

if you do

uv venv .venv
uv pip install torch --link-mode=symlink

it also uses links, but torch can be imported with no problem. The difference is that

✅ Venv that UV makes: resolves torch/lib/../../<some cuda path>

nvidia/
 __init__.py # symlink
 cublas/
 __init__.py # symlink
 lib/
 libcublas.so.12 # symlink
 ...
torch/
 _C.so... # symlink
 libs/
 lib_torch_python.so.... # symlink
 ...

❌ Venv that Bazel makes: cannot resolve torch/lib/../../ <some cuda lib> (torch/lib/../.. goes to resolves to original torch folder)

nvidia/ # expanded due to file collision and https://github.com/bazel-contrib/rules_python/pull/3268
 __init__.py # symlink
 cublas/
 __init__.py # symlink
 lib/
 libcublas.so.12 # symlink
 ...
torch/ # symlink

I can verify that if I manually modify the UV one too look like bazel (delete torch and link parent), I fail to import and dependency .so files cannot be found.

---

Always expanding is one option. If to slow, another option is in addition to file conflict, if the wheel has any .so file, then just expand it.

---

For now I resorted to this. #2156 (comment)

[aignas]

So I guess one way for us to move ahead would be to ensure that we symlink the shared libraries one by one just like uv. That should still be fast enough because most of the files in wheels are Python files.

@rickeylev, what are your thoughts on this?

[rickeylev]

Thanks for the all the detail @shayanhoshyari !

symlink shared libs one by one

Yeah, I had the same thought. It should be fairly easy to modify the logic that creates the venv_symlinks values to special case .so files and add them as separate entries.

I am confused why that would work, but symlinking the directory wouldn't. IIUC, it shouldn't matter where the symlink occurs, since realpath() (which is what $ORIGIN does, iiuc) is going to end up with the same result regardless.