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This repository was archived by the owner on Apr 14, 2022. It is now read-only.
Copy file name to clipboardExpand all lines: TROUBLESHOOTING.md
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There are a few known issues in the current version of the language server:
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- Import statement handling may be too strict, leading to "unresolved import" messages and the lack of analysis.
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- The language server considers the workspace root to be the root of user code imports. For the most part, this can be modified by adding additional folders to the `python.autoComplete.extraPaths` setting, for example, `"python.autoComplete.extraPaths": ["./src"]`, if `src` contains the user code. A `.env` file with `PYTHONPATH` set may also help.
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- Editable installs (`pip install -e` or `setup.py develop`) are known not to work with `extraPaths` when the package is installed. (#1139, #1013, #1137, #989, others).
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- Not all `__all__` statements can be handled.
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- Some modules may have an incorrect list of exported names.
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See [#620](https://github.com/Microsoft/python-language-server/issues/620),
- Inspection of some builds of some compiled libraries (some modules of numpy or pandas) may fail.
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- This will prevent the analysis from being completely accurate, but will not otherwise impact the
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language server, as the error occurs in another process. A popup may appear in Windows or macOS
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when the crash is detected by the operating system.
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See [#740](https://github.com/Microsoft/python-language-server/issues/740).
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- Analyzing large Conda environments leads to spikes in CPU usage.
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- In some cases, CPU usage is as high as 100%. See [#875](https://github.com/Microsoft/python-language-server/issues/875).
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- Persistent issues with high memory consumption for users.
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- In some contexts, users are experiencing higher than average amounts of memory being consumed. See [#832](https://github.com/Microsoft/python-language-server/issues/832).
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