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https://github.com/pytorch/pytorch.git
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4dce5b71a0
Modernize the development installation:
```bash
# python setup.py develop
python -m pip install --no-build-isolation -e .
# python setup.py install
python -m pip install --no-build-isolation .
```
Now, the `python setup.py develop` is a wrapper around `python -m pip install -e .` since `setuptools>=80.0`:
- pypa/setuptools#4955
`python setup.py install` is deprecated and will emit a warning during run. The warning will become an error on October 31, 2025.
- 9c4d383631/setuptools/command/install.py (L58-L67)
> ```python
> SetuptoolsDeprecationWarning.emit(
> "setup.py install is deprecated.",
> """
> Please avoid running ``setup.py`` directly.
> Instead, use pypa/build, pypa/installer or other
> standards-based tools.
> """,
> see_url="https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html",
> due_date=(2025, 10, 31),
> )
> ```
- pypa/setuptools#3849
Additional Resource:
- [Why you shouldn't invoke setup.py directly](https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156027
Approved by: https://github.com/ezyang
2.5 KiB
2.5 KiB
Benchmarking tool for the autograd API
This folder contain a set of self-contained scripts that allows you to benchmark autograd with different common models. It is designed to run the benchmark before and after your change and will generate a table to share on the PR.
To do so, you can use functional_autograd_benchmark.py to run the benchmarks before your change (using as output before.txt) and after your change (using as output after.txt).
You can then use compare.py to get a markdown table comparing the two runs.
The default arguments of functional_autograd_benchmark.py should be used in general. You can change them though to force a given device or force running even the (very) slow settings.
Sample usage
# Make sure you compile pytorch in release mode and with the same flags before/after
export DEBUG=0
# When running on CPU, it might be required to limit the number of cores to avoid oversubscription
export OMP_NUM_THREADS=10
# Compile pytorch with the base revision
git checkout main
python -m pip install --no-build-isolation -v -e .
# Install dependencies:
# Scipy is required by detr
pip install scipy
# Run the benchmark for the base
# This will use the GPU if available.
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output before.txt
# Compile pytorch with your change
popd
git checkout your_feature_branch
python -m pip install --no-build-isolation -v -e .
# Run the benchmark for the new version
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output after.txt
# Get the markdown table that you can paste in your github PR
python compare.py
popd
Files in this folder:
functional_autograd_benchmark.pyis the main entry point to run the benchmark.compare.pyis the entry point to run the comparison script that generates a markdown table.torchaudio_models.pyandtorchvision_models.pycontains code extracted from torchaudio and torchvision to be able to run the models without having a specific version of these libraries installed.ppl_models.py,vision_models.pyandaudio_text_models.pycontain all the getter functions used for the benchmark.
Benchmarking against functorch
# Install stable functorch:
pip install functorch
# or install from source:
pip install git+https://github.com/pytorch/functorch
# Run the benchmark for the base
# This will use the GPU if available.
pushd benchmarks/functional_autograd_benchmark
python functional_autograd_benchmark.py --output bench-with-functorch.txt