uv pip install inconsistent failure on Windows #1491

Wofiel · 2024-02-16T14:07:16Z

Windows 22H2
Using PowerShell env (in either Windows Terminal or powershell.exe, non-elevated)

When using uv pip install -r requirements.txt, I will frequently get the following error:

error: Failed to download distributions
  Caused by: Failed to fetch wheel: setproctitle==1.3.3
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\Temp\1\.tmp56ZHLz\.tmpMXOwQn to \\?\C:\Users\[____]\AppData\Local\Temp\1\.tmp56ZHLz\archive-v0\7RXhEx7TcTNKRTlP3vSED
  Caused by: Access is denied. (os error 5)

This appears to be independent of the package, though seemingly happens more often with some than others.

If run with --no-cache, it's almost impossible to install, as at least one thing is likely to fail in the process and progress restarted.

If run without --no-cache, retrying a number of times will eventually succeed (however, it will leave .tmp[_______] folders listed in the error messages in C:\Users\[____]\AppData\Local\uv\cache).

The text was updated successfully, but these errors were encountered:

AucaCoyan · 2024-02-16T14:48:38Z

I did a test on my windows pc and debian on a VM, and did experiment the same results as the issue.
On Windows 10, using nushell

"distributions=2.2.1" | save requirements.txt
uv venv
uv pip install -r requirements.txt

gives

error: Failed to download and build: distributions==2.2.1
  Caused by: Failed to build: distributions==2.2.1
  Caused by: Build backend failed to determine metadata through `prepare_metadata_for_build_wheel`:
--- stdout:

--- stderr:
<string>:177: SyntaxWarning: invalid escape sequence '\S'
Traceback (most recent call last):
  File "<string>", line 10, in <module>
  File "C:\Users\aucac\AppData\Local\Temp\.tmpn2KafH\.venv\Lib\site-packages\setuptools\build_meta.py", line 366, in prepare_metadata_for_build_wheel
    self.run_setup()
  File "C:\Users\aucac\AppData\Local\Temp\.tmpn2KafH\.venv\Lib\site-packages\setuptools\build_meta.py", line 480, in run_setup
    super().run_setup(setup_script=setup_script)
  File "C:\Users\aucac\AppData\Local\Temp\.tmpn2KafH\.venv\Lib\site-packages\setuptools\build_meta.py", line 311, in run_setup
    exec(code, locals())
  File "<string>", line 31, in <module>
ModuleNotFoundError: No module named 'numpy'
---

and in Debian is the same (give or take the paths and other stuff).

Then I compared with pip

Collecting distributions==2.2.1 (from -r requirements.txt (line 1))
  Downloading distributions-2.2.1.tar.gz (1.5 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 6.1 MB/s eta 0:00:00
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [21 lines of output]
      <string>:177: SyntaxWarning: invalid escape sequence '\S'
      Traceback (most recent call last):
        File "C:\Users\aucac\scoop\apps\python\current\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
          main()
        File "C:\Users\aucac\scoop\apps\python\current\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
          json_out['return_val'] = hook(**hook_input['kwargs'])
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "C:\Users\aucac\scoop\apps\python\current\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
          return hook(config_settings)
                 ^^^^^^^^^^^^^^^^^^^^^
        File "C:\Users\aucac\AppData\Local\Temp\pip-build-env-z4nk3ptj\overlay\Lib\site-packages\setuptools\build_meta.py", line 325, in get_requires_for_build_wheel
          return self._get_build_requires(config_settings, requirements=['wheel'])
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "C:\Users\aucac\AppData\Local\Temp\pip-build-env-z4nk3ptj\overlay\Lib\site-packages\setuptools\build_meta.py", line 295, in _get_build_requires
          self.run_setup()
        File "C:\Users\aucac\AppData\Local\Temp\pip-build-env-z4nk3ptj\overlay\Lib\site-packages\setuptools\build_meta.py", line 480, in run_setup
          super().run_setup(setup_script=setup_script)
        File "C:\Users\aucac\AppData\Local\Temp\pip-build-env-z4nk3ptj\overlay\Lib\site-packages\setuptools\build_meta.py", line 311, in run_setup
          exec(code, locals())
        File "<string>", line 31, in <module>
      ModuleNotFoundError: No module named 'numpy'
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.

[notice] A new release of pip is available: 23.2.1 -> 24.0
[notice] To update, run: python.exe -m pip install --upgrade pip

And have around the same result.
Also, distributions is not updated 2017
Can you copy and paste your requirements.txt dependencies?

Wofiel · 2024-02-16T15:37:33Z

That appears to be a different error. This isn't about the particular package distributions. This appears to be very specifically just an error somewhere in the renaming of folders.

Just tried using a requirements file off the shelf: https://github.com/google-research/kubric/blob/main/requirements.txt

I've added the full log including retries this time, showing how it's not always the same package, and retries will eventually succeed. (Also generating 300+MB of trash in the leftover .tmp[______] folders in C:\Users\[____]\AppData\Local\uv\cache)

Subsequent installs are fine after the first install (and fast!), as everything is already in the cache.

Contrasted with regular Python pip, which installs first try, with or without cache.

PS C:\dev\uv_test_uv> uv venv; .\.venv\Scripts\activate.ps1; uv pip install -r requirements.txt
Using Python 3.11.6 interpreter at C:\dev\uv_test_uv\.venv\Scripts\python.exe
Creating virtualenv at: .venv
Resolved 136 packages in 31.02s
error: Failed to download distributions
  Caused by: Failed to fetch wheel: regex==2023.12.25
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpXU7l5Y to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\IPIdPmnxfhPlAbb4aByib
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 216ms
   Built dill==0.3.1.1
error: Failed to download distributions
  Caused by: Failed to fetch wheel: fastavro==1.9.4
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpvibAxB to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\xESJmy5hSHMPWyOAki-mV
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 202ms
   Built crcmod==1.7
error: Failed to download distributions
  Caused by: Failed to unzip wheel: crcmod==1.7
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpuSxG40 to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\8seE9kMwlOJb0FO7kkUiO
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 168ms
   Built hdfs==2.7.3
   Built docopt==0.6.2
   Built promise==2.3
   Built pyjsparser==2.7.1
   Built cloudml-hypertune==0.1.0.dev6
error: Failed to download distributions
  Caused by: Failed to fetch wheel: rapidfuzz==3.6.1
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpnchaw9 to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\fslC-i4Ih5c8itkG3OCU1
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 199ms
   Built google-apitools==0.5.31
error: Failed to download distributions
  Caused by: Failed to fetch wheel: scikit-learn==1.4.1.post1
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpyPARfj to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\BTqIbBmeNiUJZ4iFLjvr5
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 187ms
error: Failed to download distributions
  Caused by: Failed to fetch wheel: scikit-learn==1.4.1.post1
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpQ9zLLc to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\N6_d31F0MmeWfay8zuoy1
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 198ms
error: Failed to download distributions
  Caused by: Failed to fetch wheel: pyarrow==11.0.0
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpoDjYqG to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\77wbDpLZdJXaCe7d8Bgl4
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 180ms
error: Failed to download distributions
  Caused by: Failed to fetch wheel: scipy==1.12.0
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\[____]\AppData\Local\uv\cache\.tmpJrQ1Eo to \\?\C:\Users\[____]\AppData\Local\uv\cache\archive-v0\oJCgnzumPreaL33Y2A_jb
  Caused by: Access is denied. (os error 5)
(.venv) PS C:\dev\uv_test_uv> uv pip install -r requirements.txt
Resolved 136 packages in 193ms
Downloaded 3 packages in 1m 30s
Installed 136 packages in 41.33s
 + absl-py==1.4.0
 + apache-beam==2.54.0
 + astunparse==1.6.3
 + attrs==23.2.0
 + bidict==0.23.0
 + cachetools==5.3.2
 + certifi==2024.2.2
 + charset-normalizer==3.3.2
 + click==8.1.7
 + cloudml-hypertune==0.1.0.dev6
 + cloudpickle==2.2.1
 + colorama==0.4.6
 + crcmod==1.7
 + dataclasses==0.6
 + deprecated==1.2.14
 + dill==0.3.1.1
 + dm-tree==0.1.8
 + dnspython==2.5.0
 + docopt==0.6.2
 + etils==1.7.0
 + fastavro==1.9.4
 + fasteners==0.19
 + flatbuffers==23.5.26
 + fsspec==2024.2.0
 + gast==0.5.4
 + google-api-core==2.17.1
 + google-apitools==0.5.31
 + google-auth==2.28.0
 + google-auth-httplib2==0.1.1
 + google-auth-oauthlib==1.2.0
 + google-cloud-aiplatform==1.42.0
 + google-cloud-bigquery==3.17.2
 + google-cloud-bigquery-storage==2.24.0
 + google-cloud-bigtable==2.23.0
 + google-cloud-core==2.4.1
 + google-cloud-datastore==2.19.0
 + google-cloud-dlp==3.15.1
 + google-cloud-language==2.13.1
 + google-cloud-pubsub==2.19.4
 + google-cloud-pubsublite==1.9.0
 + google-cloud-recommendations-ai==0.10.8
 + google-cloud-resource-manager==1.12.1
 + google-cloud-spanner==3.42.0
 + google-cloud-storage==2.14.0
 + google-cloud-videointelligence==2.13.1
 + google-cloud-vision==3.7.0
 + google-crc32c==1.5.0
 + google-pasta==0.2.0
 + google-resumable-media==2.7.0
 + googleapis-common-protos==1.62.0
 + grpc-google-iam-v1==0.13.0
 + grpc-interceptor==0.15.4
 + grpcio==1.60.1
 + grpcio-status==1.60.1
 + h5py==3.10.0
 + hdfs==2.7.3
 + httplib2==0.22.0
 + idna==3.6
 + imageio==2.34.0
 + importlib-resources==6.1.1
 + joblib==1.3.2
 + js2py==0.74
 + jsonpickle==3.0.2
 + jsonschema==4.21.1
 + jsonschema-specifications==2023.12.1
 + keras==2.15.0
 + levenshtein==0.25.0
 + libclang==16.0.6
 + markdown==3.5.2
 + markupsafe==2.1.5
 + ml-dtypes==0.2.0
 + munch==4.0.0
 + numpy==1.24.4
 + oauth2client==4.1.3
 + oauthlib==3.2.2
 + objsize==0.7.0
 + opt-einsum==3.3.0
 + orjson==3.9.14
 + overrides==7.7.0
 + packaging==23.2
 + pandas==2.2.0
 + pillow==10.2.0
 + promise==2.3
 + proto-plus==1.23.0
 + protobuf==4.25.3
 + psutil==5.9.8
 + pyarrow==11.0.0
 + pyarrow-hotfix==0.6
 + pyasn1==0.5.1
 + pyasn1-modules==0.3.0
 + pydot==1.4.2
 + pyjsparser==2.7.1
 + pymongo==4.6.1
 + pyparsing==3.1.1
 + pypng==0.20220715.0
 + pyquaternion==0.9.9
 + python-dateutil==2.8.2
 + python-levenshtein==0.25.0
 + pytz==2024.1
 + rapidfuzz==3.6.1
 + referencing==0.33.0
 + regex==2023.12.25
 + requests==2.31.0
 + requests-oauthlib==1.3.1
 + rpds-py==0.18.0
 + rsa==4.9
 + scikit-learn==1.4.1.post1
 + scipy==1.12.0
 + setuptools==69.1.0
 + shapely==2.0.3
 + singledispatchmethod==1.0
 + six==1.16.0
 + sqlparse==0.4.4
 + tensorboard==2.15.2
 + tensorboard-data-server==0.7.2
 + tensorflow==2.15.0
 + tensorflow-datasets==4.9.4
 + tensorflow-estimator==2.15.0
 + tensorflow-intel==2.15.0
 + tensorflow-io-gcs-filesystem==0.31.0
 + tensorflow-metadata==1.13.1
 + termcolor==2.4.0
 + threadpoolctl==3.3.0
 + toml==0.10.2
 + tqdm==4.66.2
 + traitlets==5.14.1
 + trimesh==4.1.3
 + typing-extensions==4.9.0
 + tzdata==2024.1
 + tzlocal==5.2
 + urllib3==2.2.0
(.venv) PS C:\dev\uv_test_uv>

AucaCoyan · 2024-02-16T15:49:49Z

I see, thanks for the log! Again, I have a different output sadly 😢. I think I am doing something wrong too.

In my case, it didn't found a compatible version of tensorflow. If you are interested, here I leave the details, but I think my case is unrelated to this issue.

(.venv) PS C:\Users\aucac\repos\exp-python\uv> uv pip install -r .\requirements.txt
  × No solution found when resolving dependencies:
  ╰─▶ Because only the following versions of tensorflow are available:
          tensorflow<=2.5.3
          tensorflow>=2.6.0,<=2.6.5
          tensorflow>=2.7.0,<=2.7.4
          tensorflow>=2.8.0,<=2.8.4
          tensorflow>=2.9.0,<=2.9.3
          tensorflow>=2.10.0,<=2.10.1
          tensorflow>=2.11.0,<=2.11.1
          tensorflow>=2.12.0,<=2.12.1
          tensorflow>=2.13.0,<=2.13.1
          tensorflow>=2.14.0,<=2.14.1
          tensorflow>=2.15.0
      and tensorflow==0.12.0 is unusable because no wheels are available with a matching Python ABI, we can conclude that any of:
          tensorflow<0.12.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==0.12.1 is unusable because no wheels are available with a matching Python ABI, we can conclude that any of:
          tensorflow<1.0.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.0.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.0.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.1.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.1.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.2.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.2.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.2.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.3.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.4.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.4.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.4.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.5.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.5.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.5.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.6.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.6.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.7.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.7.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.7.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.8.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.9.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.9.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.10.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.10.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.10.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.11.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.12.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.12.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.12.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.12.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.12.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.13.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.13.2
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.13.2 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.14.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.15.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.15.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.15.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.15.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.15.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==1.15.4 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<1.15.5
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==1.15.5 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.0.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.0.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.0.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.0.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.0.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.0.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.0.4 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.1.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.1.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.1.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.1.2
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.1.2 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.1.3 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.1.4
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.1.4 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.2.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.2.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.2.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.2.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.2.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.2.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.3.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.3.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.3.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.3.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.3.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.3.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.3.4 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.4.0
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.4.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.4.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.4.2
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.4.2 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.4.3 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.4.4
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.4.4 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.5.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.5.1
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.5.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.5.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.5.3
          tensorflow>2.5.3,<2.6.0
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.5.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.6.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.6.1
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.6.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.6.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.6.3
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.6.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.6.4 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.6.5
          tensorflow>2.6.5,<2.7.0
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.6.5 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.7.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.7.1
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.7.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.7.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.7.3
          tensorflow>2.7.4,<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.7.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.7.4 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.8.0
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.8.0 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.8.1 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.8.2
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.8.2 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.8.3 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.8.4
          tensorflow>2.8.4,<2.9.0
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.8.4 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.9.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.9.1
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.9.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.9.2 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.9.3
          tensorflow>2.9.3,<2.10.0
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.9.3 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.10.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.10.1
          tensorflow>2.10.1,<2.11.0
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.10.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.11.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.11.1
          tensorflow>2.11.1,<2.12.0
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.11.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.12.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.12.1
          tensorflow>2.12.1,<2.13.0
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.12.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.13.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.13.1
          tensorflow>2.13.1,<2.14.0
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.13.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.14.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that any of:
          tensorflow<2.14.1
          tensorflow>2.14.1,<2.15.0
       cannot be used.
      And because tensorflow==2.14.1 is unusable because no wheels are available with a matching Python ABI and tensorflow==2.15.0 is unusable because no wheels are available with a
      matching Python ABI, we can conclude that tensorflow<2.15.0.post1 cannot be used.
      And because tensorflow==2.15.0.post1 is unusable because no wheels are available with a matching Python ABI and you require tensorflow, we can conclude that the requirements are
      unsatisfiable.

      hint: Pre-releases are available for tensorflow in the requested range (e.g., 2.15.0rc1), but pre-releases weren't enabled (try: `--prerelease=allow`)

If somebody wants me to try something, just tell! 😄

omdaniel · 2024-02-20T14:55:17Z

Just to add some more context to this, I get the same error and it will fail on different packages on each attempt if running uv clean in between

(.venv) PS C:\Program Files\envs\naduv> uv pip install ipython

Resolved 16 packages in 1.68s

error: Failed to download distributions
  Caused by: Failed to fetch wheel: prompt-toolkit==3.0.43
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Program Files\envs\pipx\venvs\venvs\uv\uvcache\.tmpvKXHjN to \\?\C:\Program Files\envs\pipx\venvs\venvs\uv\uvcache\archive-v0\VF2P3BmAtyIqJo8V9e9Jb
  Caused by: Access is denied. (os error 5)

(.venv) PS C:\Program Files\envs\naduv> uv clean

Clearing cache at: C:\Program Files\envs\pipx\venvs\venvs\uv\uvcache
Removed 369 files (3.6MiB)

(.venv) PS C:\Program Files\envs\n\n\n\naduv> uv pip install ipython

Resolved 16 packages in 1.77s

error: Failed to download distributions
  Caused by: Failed to fetch wheel: pygments==2.17.2
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Program Files\envs\pipx\venvs\venvs\uv\uvcache\.tmpA8BLxj to \\?\C:\Program Files\envs\pipx\venvs\venvs\uv\uvcache\archive-v0\xoGgYMWlHY-hrr7FtBrhm
  Caused by: Access is denied. (os error 5)

I suspect something in the temp file naming is randomly causing issues and after I tried 5 times with "uv clean" in-between it finished but it is happening frequently enough that there is a real issue. I am using the latest uv release 0.15 at the time of posting this issue

omdaniel · 2024-02-20T15:44:13Z

For many packages I can just run uv pip install [package] again without using "uv clean" and it will work without the "os error 5" but with numpy 1.26.4 it triggers every time and I tried it 15 times in a row

charliermarsh · 2024-02-20T15:55:54Z

I think this is an error when attempting to persist the unzipped wheel to the wheel cache?

omdaniel · 2024-02-20T18:44:41Z

I think this is an error when attempting to persist the unzipped wheel to the wheel cache?

The contents of the temp folder in $UV_CACHE_DIR are unzipped after the install fails but not sure if the process is asking the os to move the contents before the temp folder is created

charliermarsh · 2024-02-20T18:46:31Z

The overall approach is: unzip into a temporary folder, then move that folder into the cache to persist it. (This avoids persisting incomplete downloads that may error partway through.)

omdaniel · 2024-02-22T16:22:49Z

I think this issue may crop up in corporate or more secure environments that have some layer of security running over all processes. I think there is a slight lag between when uv unzips the files where that process is ran in a sandbox and there is a slight delay before the os will allow access to the resulting folder while its scanned, I think simply catching the "os error" and retrying with some timeout would prevent this error on systems that have some security layer that temporarily effects permissions and also why a package like numpy (big and many small files) would consistently trip and medium packages would occasionally go through and occasionally trip.

i.e. you may not be able to re-produce this error in dev without setting up a test box that use one of these paid corporate security software to trigger it. You generally only create windows binaries on tagged releases, if you can create a dev build binary I can test it on my system

Wofiel · 2024-02-22T19:21:29Z

I think this issue may crop up in corporate or more secure environments that have some layer of security running over all processes.

Ah, this is consistent with the experience in the first post.

omdaniel · 2024-02-27T14:42:42Z

@Wofiel Our issue may be too niche to get traction (which I don't disagree with at this stage of uv) time to learn rust and attempt a PR

nathanjmcdougall · 2024-02-28T23:52:35Z

I think this issue may crop up in corporate or more secure environments that have some layer of security running over all processes.

Ah, this is consistent with the experience in the first post.

This is also consistent with my experience.

FWIW, the versions that it tripped up on were debugpy==1.8.1 and markdown-it-py==3.0.0.

mkleinbort-ic · 2024-02-29T13:13:16Z

I hit this same error today.

trying to run:

 python -m uv pip install --system -r pyproject.toml --prerelease=allow

on windows.

I keep hitting

error: Failed to download and build: {SOME_PACKAGE_MAME}=={VERSION}
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\{USERNAME}\AppData\Local\Temp\.tmplED7gi\.tmpxkU2s1\{SOME_PACKAGE_MAME}-{VERSION} to \\?\C:\Users\{USERNAME}\AppData\Local\Temp\.tmplED7gi\built-wheels-v0\pypi\{SOME_PACKAGE_MAME}\{VERSION}\wwYdTWRMDuqFPxs7feX1m\{SOME_PACKAGE_MAME}-{VERSION}.tar.gz
  Caused by: Access is denied. (os error 5)

The package that causes the error seems to be random if I re-run the command.

konstin · 2024-02-29T15:53:01Z

Is anyone of you able to share (here or in private to konsti@astral.sh) what kind of security software or special windows settings you are running, so we can reproduce the failures you are experiencing?

mkleinbort-ic · 2024-03-02T14:51:40Z

I don't think there is anything special on my setup.

Runing gitbash
On windows 11
running python -m pip install -r requirements.txt works
running python -m uv pip install --system -r requirements.txt fails

charliermarsh · 2024-03-02T14:53:43Z

Is it possible that the use of the UNC prefix is itself the problem?

If I created a branch that omitted them, would anyone here be willing to cargo build locally and test it?

mkleinbort-ic · 2024-03-02T14:56:15Z

I could try it in the week - but don't have the tooling set up at the moment.

mkleinbort-ic · 2024-03-02T14:57:29Z

Do you mean this: "\\?\C:\..."?

charliermarsh · 2024-03-02T15:00:24Z

Yeah, that prefix on the file paths.

mkleinbort-ic · 2024-03-02T15:08:50Z

I don't know enough to help - but I definitely can't cd into "\?\C:" using powershell or bash

in powershel:
this works: cd C:\
this does not cd \\?\C:\

PS C:\> cd \\?\C:\
cd : Cannot process argument because the value of argument "path" is not valid. Change the value of the "path"
argument and run the operation again.
At line:1 char:1
+ cd \\?\C:\
+ ~~~~~~~~~~
    + CategoryInfo          : InvalidArgument: (:) [Set-Location], PSArgumentException
    + FullyQualifiedErrorId : Argument,Microsoft.PowerShell.Commands.SetLocationCommand

charliermarsh · 2024-03-02T15:17:01Z

Just curious -- what if you take the output of pwd, and then ls that prefixed with \\?\?

charliermarsh · 2024-03-02T15:17:28Z

Either way I'll put together a branch to remove these.

mkleinbort-ic · 2024-03-02T15:19:54Z

gregbedwell · 2024-03-02T15:32:43Z

The \\?\ prefix is just a way around the MAX_PATH (256 char) limitation in Windows API calls. I'd personally be surprised if it had an impact although I couldn't say for sure.

Having dealt with similar issues in other projects where Windows antivirus software is holding file locks after any file system operations, the best solution is usually just to retry the operation on failure a handful of times with a slightly increasing sleep in between attempts unfortunately.

mkleinbort · 2024-03-07T10:17:38Z

That's interesting - though retrying multiple times sort of defeats the purpose of UV being so fast.

I wonder why pip success but UV fails.

(Note we do use an antivirus)

omdaniel · 2024-03-07T14:24:56Z

That's interesting - though retrying multiple times sort of defeats the purpose of UV being so fast.

I wonder why pip success but UV fails.

(Note we do use an antivirus)

I think the effect on speed would be imperceptible to the end user. The uv speed up is more to do with dependency resolution and retrying is to deal with likely milliseconds of time that a security layer on Windows locks access to the extracted zip contents before uv can read and copy the data from the tmp folder to the archive

mkleinbort-ic · 2024-03-07T14:36:21Z

Oh, yes, happy if this is handled by uv

I just don't want to write a bash script on my end to retry the installation till it works

charliermarsh · 2024-03-07T14:41:51Z

Definitely want to fix this.

charliermarsh · 2024-03-14T01:07:50Z

I'll optimistically close, but can re-open if anything comes up.

robmcd · 2024-03-14T04:24:17Z

Upgrading to uv==0.1.20 fixed this for me.

charliermarsh · 2024-03-14T04:45:53Z

Great, thank you!

mattibo · 2024-03-14T07:10:27Z

for me most of the packages synced fine with version uv==0.1.20! But i still have issues with one package of i think bigger size (https://squidfunk.github.io/mkdocs-material/).

This leads to following error:

Resolved 1 package in 4ms
DEBUG No cache entry for: https://files.pythonhosted.org/packages/64/dc/9817689e3500b8e3967302201341dda7878f33d4ad089f9deb1126a091d7/mkdocs_material-9.5.13-py3-none-any.whl
DEBUG No credentials found for: https://files.pythonhosted.org/packages/64/dc/9817689e3500b8e3967302201341dda7878f33d4ad089f9deb1126a091d7/mkdocs_material-9.5.13-py3-none-any.whl
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
WARN Retrying rename from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0 due to transient error: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
error: Failed to download distributions
  Caused by: Failed to fetch wheel: mkdocs-material==9.5.13
  Caused by: Failed to read from the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\x\AppData\Local\uv\cache\.tmpXVEqoW to \\?\C:\Users\x\AppData\Local\uv\cache\archive-v0\ivw1_SgSoJofW7-c2dlZ0
  Caused by: Zugriff verweigert (os error 5)

ofek · 2024-03-14T13:21:42Z

Did this happen yet? #1491 (comment)

charliermarsh · 2024-03-14T13:23:26Z

Nope.

ofek · 2024-03-14T13:24:51Z

Ah okay, is there a tracking issue for that?

charliermarsh · 2024-03-14T13:39:31Z

I can do it, I just haven’t seen evidence that it’s a problem.

charliermarsh · 2024-03-14T13:45:22Z

To clarify: I understand that device paths can lead to confusing and incorrect behaviors. But we strip the device path prefix everywhere that matters (in my experience). So I'd like to understand why it would matter in the case above, for example.

ofek · 2024-03-14T13:50:34Z

One compelling reason, to me at least, is confusion for users. Until Rust happened I had quite literally never seen a UNC path in the wild; it was just some abstract concept I read about in the Windows APIs.

cc @mitsuhiko I'd be curious to get your opinion on this

mkleinbort-ic · 2024-03-14T14:04:49Z

I hit the issue again (on uv 0.1.20)

Running:

python -m uv pip install --python 3.11 --reinstall pylance==0.10.3

Resulted in

error: failed to remove file `C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy.libs/libopenblas64__v0.3.23-293-gc2f4bdbb-gcc_10_3_0-2bde3a66a51006b2b53eb373ff767a3f.dll`
  Caused by: Access is denied. (os error 5)

and re-running it resulted in

error: Cannot uninstall package; RECORD file not found at: C:\Users\x\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy-1.26.4.dist-info\RECORD

gregbedwell · 2024-03-14T14:29:50Z

One compelling reason, to me at least, is confusion for users. Until Rust happened I had quite literally never seen a UNC path in the wild; it was just some abstract concept I read about in the Windows APIs.

Note that by removing the UNC prefix you're potentially introducing a regression if the path is longer than 256 characters. In practice I don't know whether this is likely here, but I'd be interested to know the motivating reason for adding them in the first place. Was it in response to a long path issue?

See this page for more info:
https://learn.microsoft.com/en-gb/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces

ofek · 2024-03-14T14:34:54Z

Was it in response to a long path issue?

I'm not a maintainer but I know the answer: that is the behavior of the standard library function to resolve a path. From my understanding it was basically a mistake but nobody can change it now. Instead, it's common to use the dunce crate or the normpath crate when you don't want to resolve symlinks.

edit: there is now also https://doc.rust-lang.org/std/path/fn.absolute.html on nightly

charliermarsh · 2024-03-14T14:50:34Z

We do use dunce and strip the prefix anywhere that it interfaces outside of standard library filesystem operations.

charliermarsh · 2024-03-14T14:51:17Z

@mkleinbort - Did you install that package prior to the latest uv? It could be leftover from your virtual environment being in a bad state.

charliermarsh · 2024-03-14T14:56:41Z

I'm open to changing to use the dunce-canonicalized version everywhere, but note that it doesn't, like, fully solve the problem (if it is a problem). dunce will still return a UNC prefix if the path is longer than 260 characters.

omdaniel · 2024-03-14T19:29:58Z

The merged PR #2419 has thus far eliminated failures on the handful pip installs I've done since upgrading. Thanks @charliermarsh

mkleinbort-ic · 2024-03-15T10:43:59Z

@charliermarsh

Not sure - I hit this error just now trying to upgrade to uv==0.1.21 (I'm on 0.1.20)

(Running on gitbash on a windows machine)

Running pythom -m pip install uv==0.1.21 succeeded

FredStober · 2024-05-02T18:05:43Z

Hi, I observed exactly this issue "Caused by: Failed to fetch wheel..."
when testing my dev environment on windows with uv==0.1.39.

The problem randomly appears on two different machines. One is running windows directly - and the other is using WSL with a windows mount. So far, the issue disappared after trying it another time.

jbcpollak · 2024-06-14T18:21:13Z

Ran into this issue with uv == 0.2.11, Python 3.12 and Windows 11 Pro. No specific enterprise extensions installed to Windows but we do have McAfee Antivirus installed.

axel-kah · 2024-06-27T15:01:51Z

Hi,

the latest version (uv 0.2.15 (bfc342da9 2024-06-24)) still has these issues. We see this on a certain percentage every on our Windows CI runners. They do have CrowdStrike messing with our workloads unfortunately - corporate policy 😢

The common denominator seems to be source distributions which uv tries to build wheels from. I've never seen this error for packages that are available as wheel.

Hypothesis: snakeoil scanners need more time to scan compressed archives (because of the decompression step) and uvs backoff algorithm just needs a little tweaking to accommodate for that.

A few source distribution only packages: fire, odfpy, mapdata, pywinusb

A bunch of error messages for these packages

$ .\hatch.exe run all.py$INTERPRETER_VERSION`:cov
Creating environment: all.py3.11
Installing project
error: Failed to download and build `pywinusb==0.4.2`
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmp3V6aqK\built-wheels-v3\.tmpsxHt5d\pywinusb-0.4.2 to \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmp3V6aqK\built-wheels-v3\index\70e6dbc41e17fe7c\pywinusb\0.4.2\w4S16F7Zw7fjYe2QmhqM_\pywinusb-0.4.2.zip
  Caused by: Access is denied. (os error 5)
  
$ .\hatch.exe run all.py$INTERPRETER_VERSION`:cov
Creating environment: all.py3.9
Installing Python distribution: 3.9
Installing project
Checking dependencies
Syncing dependencies
error: Failed to download and build `odfpy==1.4.1`
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmp30woUu\built-wheels-v3\.tmp7d4zhJ\odfpy-1.4.1 to \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmp30woUu\built-wheels-v3\index\70e6dbc41e17fe7c\odfpy\1.4.1\J27cgvxiwwWDBSM2Tnauu\odfpy-1.4.1.tar.gz
  Caused by: Access is denied. (os error 5)

$ hatch run uv --version
Creating environment: default
Installing Python distribution: 3.11
Installing project in development mode
Checking dependencies
Syncing dependencies
error: Failed to download and build `mapdata==3.11.1`
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmptBDtnM\built-wheels-v3\.tmpqFOiKl\mapdata-3.11.1 to \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmptBDtnM\built-wheels-v3\index\70e6dbc41e17fe7c\mapdata\3.11.1\T7WG4FPsti03XVV0KCULn\mapdata-3.11.1.tar.gz
  Caused by: Access is denied. (os error 5)

$ hatch run uv --version
Creating environment: default
Installing Python distribution: 3.11
Installing project in development mode
Checking dependencies
Syncing dependencies
error: Failed to download and build `fire==0.6.0`
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpVXpz23\built-wheels-v3\.tmpnCevMt\fire-0.6.0 to \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpVXpz23\built-wheels-v3\index\70e6dbc41e17fe7c\fire\0.6.0\pD3XrlBXA8_x3S3TYSW2o\fire-0.6.0.tar.gz
  Caused by: Access is denied. (os error 5)

johannesloibl · 2024-06-27T15:11:16Z

Never saw this kind of responsiveness in a project, respect! 🥇

zanieb · 2024-06-27T20:52:13Z

@axel-kah With --verbose logs do you see logs indicating that we are retrying? e.g. "Retrying rename from...."

We have a maximum backoff of 10s which is pretty long. Do you think we should be waiting longer than that?

axel-kah · 2024-06-27T22:37:50Z

@zanieb I modified the hatch script to install source-only packages with the latest uv and it seems it does not use any retry at all. Maybe that's the root cause.

verbose output

$ hatch run provoke-issue
Creating environment: default
Installing project in development mode
Checking dependencies
Syncing dependencies
cmd [1] | D:\glb\GwsU8_zK\0\uv.exe --version
uv 0.2.15 (bfc342da9 2024-06-24)
cmd [2] | D:\glb\GwsU8_zK\0\uv.exe --verbose pip install fire pywinusb nptdms odfpy ipyvuetable
DEBUG uv 0.2.15
DEBUG Searching for Python interpreter in system toolchains
DEBUG Found cpython 3.11.9 at `d:\glb\GwsU8_zK\0\.hatch_data\env\virtual\foo\Scripts\python.exe` (active virtual environment)
DEBUG Using Python 3.11.9 environment at .hatch_data\env\virtual\foo\Scripts\python.exe
DEBUG Acquired lock for `.hatch_data\env\virtual\foo`
DEBUG At least one requirement is not satisfied: ipyvuetable
DEBUG Using request timeout of 30s
DEBUG Solving with installed Python version: 3.11.9
DEBUG Adding direct dependency: fire*
DEBUG Adding direct dependency: pywinusb*
DEBUG Adding direct dependency: nptdms*
DEBUG Adding direct dependency: odfpy*
DEBUG Adding direct dependency: ipyvuetable*
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/fire/
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/pywinusb/
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/nptdms/
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/odfpy/
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/ipyvuetable/
DEBUG Acquired lock for `\\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\pywinusb\0.4.2`
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/packages/packages/38/b4/ecce4a3a0dac3b1bf5776943530cf4f36406fc9b3f4f3c31c8dcab2249eb/pywinusb-0.4.2.zip#sha256=e2f5e89f7b74239ca4843721a9bda0fc99014750630c189a176ec0e1b35e86df
DEBUG Acquired lock for `\\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\odfpy\1.4.1`
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/packages/packages/97/73/8ade73f6749177003f7ce3304f524774adda96e6aaab30ea79fd8fda7934/odfpy-1.4.1.tar.gz#sha256=db766a6e59c5103212f3cc92ec8dd50a0f3a02790233ed0b52148b70d3c438ec
DEBUG Searching for a compatible version of fire (*)
DEBUG Selecting: fire==0.6.0 (fire-0.6.0.tar.gz)
DEBUG Acquired lock for `\\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\fire\0.6.0`
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/packages/packages/1b/1b/84c63f592ecdfbb3d77d22a8d93c9b92791e4fa35677ad71a7d6449100f8/fire-0.6.0.tar.gz#sha256=54ec5b996ecdd3c0309c800324a0703d6da512241bc73b553db959d98de0aa66
DEBUG Acquired lock for `\\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\nptdms\1.9.0`
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/packages/packages/ff/05/8f560020155c1843d664248fb114e33eac0c1b3ad44fce6bfc2b5dd143c2/npTDMS-1.9.0.tar.gz#sha256=0e65c237e9d50b9b8e162b9c34171353a5ea05f4019c99c3e8ebc00722361cbc
DEBUG Downloading source distribution: odfpy==1.4.1
DEBUG Downloading source distribution: fire==0.6.0
DEBUG Downloading source distribution: nptdms==1.9.0
DEBUG Acquired lock for `\\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\ipyvuetable\0.4.0`
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/packages/packages/7f/dc/aeb142c5ab3563a8600c8d2409212d9ff7f869ff87adea1e1de252032f08/ipyvuetable-0.4.0.tar.gz#sha256=d88e4d87be451b3813e18a999bd3bb5800db38ba59c612e943029fd0804482e2
DEBUG Downloading source distribution: ipyvuetable==0.4.0
DEBUG Downloading source distribution: pywinusb==0.4.2
DEBUG Preparing metadata for: ipyvuetable==0.4.0
DEBUG No static `PKG-INFO` available for: ipyvuetable==0.4.0 (PkgInfo(UnsupportedMetadataVersion("2.1")))
DEBUG No static `pyproject.toml` available for: ipyvuetable==0.4.0 (MissingPyprojectToml)
INFO Ignoring empty directory
DEBUG Solving with installed Python version: 3.11.9
DEBUG Adding direct dependency: setuptools>=40.8.0
DEBUG No cache entry for: https://artifactory.acme.com/artifactory/api/pypi/pypi-acme-vir/simple/setuptools/
DEBUG Preparing metadata for: fire==0.6.0
DEBUG No static `PKG-INFO` available for: fire==0.6.0 (PkgInfo(UnsupportedMetadataVersion("2.1")))
DEBUG No static `pyproject.toml` available for: fire==0.6.0 (MissingPyprojectToml)
INFO Ignoring empty directory
DEBUG Preparing metadata for: nptdms==1.9.0
DEBUG No static `PKG-INFO` available for: nptdms==1.9.0 (PkgInfo(UnsupportedMetadataVersion("2.1")))
DEBUG No static `pyproject.toml` available for: nptdms==1.9.0 (PyprojectToml(FieldNotFound("project")))
INFO Ignoring empty directory
DEBUG Solving with installed Python version: 3.11.9
DEBUG Adding direct dependency: setuptools*
error: Failed to download and build `pywinusb==0.4.2`
  Caused by: Failed to write to the distribution cache
  Caused by: failed to rename file from \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\.tmpc3E2Z7\pywinusb-0.4.2 to \\?\C:\Users\gitlabwinrunner\AppData\Local\Temp\.tmpanLS7H\built-wheels-v3\index\70e6dbc41e17fe7c\pywinusb\0.4.2\or1SrhePMDCdKnzdyzMki\pywinusb-0.4.2.zip
  Caused by: Access is denied. (os error 5)

I also set RUST_LOG=trace and found only 1 or 2 instances of "Retrying rename from...." - but only during the initialization of the venv by hatch and not when uv installs the source-only packages.

EDIT: same is true for uv 0.2.17 (2eb1e6693 2024-06-26) (you guys are moving fast 😉 )

zanieb · 2024-06-27T22:54:45Z

Thanks for the details! I'll investigate.

zanieb · 2024-06-27T23:05:02Z

And I believe I've found a suspicious line :) if you want to give it a try #4605

edit: and another suspicious call addressed in #4606

#2419 appears to have only applied this retry to wheels that were already downloaded (though I would have to look more carefully to be certain). In #1491, we've gotten continued reports of spurious failures on Windows and tracing reveals that we are not applying our retry logic during the rename. I believe we're in this code path — switching to our backoff retry should resolve the failures.

MichaReiser added the windows Specific to the Windows platform label Feb 20, 2024

konstin mentioned this issue Feb 29, 2024

Add support for pip download command or fix "Failed to download distributions" error message in offline mode #2078

Closed

charliermarsh closed this as completed Mar 14, 2024

zanieb reopened this Jun 27, 2024

zanieb mentioned this issue Jun 27, 2024

Retry on spurious failures when caching built wheels #4605

Merged

uv pip install inconsistent failure on Windows #1491

uv pip install inconsistent failure on Windows #1491

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