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Replace setup.py develop with pip install -e (#156710)

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#156027 already replaced most use of `python setup.py develop`. This PR only adds a few more occurrences.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/156710
Approved by: https://github.com/atalman
This commit is contained in:
Klaus Zimmermann
2025-09-02 15:49:19 +02:00
committed by PyTorch MergeBot
parent acece97c3a
commit 9c957723a0
3 changed files with 17 additions and 17 deletions
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24
CONTRIBUTING.md
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@ -88,13 +88,13 @@ source venv/bin/activate # or `& .\venv\Scripts\Activate.ps1` on Windows
* If you want to have no-op incremental rebuilds (which are fast), see [Make no-op build fast](#make-no-op-build-fast) below.
* When installing with `python -m pip install -e .` (in contrast to `python -m pip install .`) Python runtime will use
* When installing with `python -m pip install -e . -v --no-build-isolation` (in contrast to `python -m pip install . -v --no-build-isolation`) Python runtime will use
the current local source-tree when importing `torch` package. (This is done by creating [`.egg-link`](https://wiki.python.org/moin/PythonPackagingTerminology#egg-link) file in `site-packages` folder)
This way you do not need to repeatedly install after modifying Python files (`.py`).
However, you would need to reinstall if you modify Python interface (`.pyi`, `.pyi.in`) or non-Python files (`.cpp`, `.cc`, `.cu`, `.h`, ...).
One way to avoid running `python -m pip install -e .` every time one makes a change to C++/CUDA/ObjectiveC files on Linux/Mac,
One way to avoid running `python -m pip install -e . -v --no-build-isolation` every time one makes a change to C++/CUDA/ObjectiveC files on Linux/Mac,
is to create a symbolic link from `build` folder to `torch/lib`, for example, by issuing following:
```bash
pushd torch/lib; sh -c "ln -sf ../../build/lib/libtorch_cpu.* ."; popd
@ -116,7 +116,7 @@ source venv/bin/activate # or `& .\venv\Scripts\Activate.ps1` on Windows
Next run `python setup.py clean`. After that, you can install in editable mode again.
* If you run into errors when running `python -m pip install -e .`, here are some debugging steps:
* If you run into errors when running `python -m pip install -e . -v --no-build-isolation`, here are some debugging steps:
1. Run `printf '#include <stdio.h>\nint main() { printf("Hello World");}'|clang -x c -; ./a.out` to make sure
your CMake works and can compile this simple Hello World program without errors.
2. Nuke your `build` directory. The `setup.py` script compiles binaries into the `build` folder and caches many
@ -132,7 +132,7 @@ source venv/bin/activate # or `& .\venv\Scripts\Activate.ps1` on Windows
python -m pip install -r requirements.txt
python -m pip install --no-build-isolation -v -e .
```
4. The main step within `python -m pip install -e .` is running `cmake --build build` from the `build` directory. If you want to
4. The main step within `python -m pip install -e . -v --no-build-isolation` is running `make` from the `build` directory. If you want to
experiment with some environment variables, you can pass them into the command:
```bash
ENV_KEY1=ENV_VAL1[, ENV_KEY2=ENV_VAL2]* CMAKE_FRESH=1 python -m pip install --no-build-isolation -v -e .
@ -645,9 +645,9 @@ can be selected interactively with your mouse to zoom in on a particular part of
the program execution timeline. The `--native` command-line option tells
`py-spy` to record stack frame entries for PyTorch C++ code. To get line numbers
for C++ code it may be necessary to compile PyTorch in debug mode by prepending
your `python -m pip install -e .` call to compile PyTorch with `DEBUG=1`.
Depending on your operating system it may also be necessary to run `py-spy` with
root privileges.
your `python -m pip install -e . -v --no-build-isolation` call to compile
PyTorch with `DEBUG=1`. Depending on your operating system it may also be
necessary to run `py-spy` with root privileges.
`py-spy` can also work in an `htop`-like "live profiling" mode and can be
tweaked to adjust the stack sampling rate, see the `py-spy` readme for more
@ -655,10 +655,10 @@ details.
## Managing multiple build trees
One downside to using `python -m pip install -e .` is that your development
version of PyTorch will be installed globally on your account (e.g., if
you run `import torch` anywhere else, the development version will be
used).
One downside to using `python -m pip install -e . -v --no-build-isolation` is
that your development version of PyTorch will be installed globally on your
account (e.g., if you run `import torch` anywhere else, the development version
will be used).
If you want to manage multiple builds of PyTorch, you can make use of
[venv environments](https://docs.python.org/3/library/venv.html) to maintain
@ -719,7 +719,7 @@ options.
### Code completion and IDE support
When using `python -m pip install -e .`, PyTorch will generate
When using `python -m pip install -e . -v --no-build-isolation`, PyTorch will generate
a `compile_commands.json` file that can be used by many editors
to provide command completion and error highlighting for PyTorch's
C++ code. You need to `pip install ninja` to generate accurate