On 29/11/21 6:01 am, Hao Wang wrote:
Dear pypy-devs,
The pip installation of pypy (pypy -m pip install scipy, for example)
is slower than python-pip clean. Is there a way to fix this problem ?
Bravo!
Hao Wang
Thanks for giving PyPy a try. When pre-compiled binary wheels are not
available, python must compile from source. This takes time. We have
been making an effort to convince project maintainers to provide binary
wheels for PyPy. SciPy has a complicated workflow and a skeleton support
team. That together with the fact that currently PyPy + SciPy is much
slower to run CI (small snippets of code run in tests are not PyPy's
strong point, and the c-api emulation layer makes things worse) mean
that SciPy is hesitant to add support for PyPy, see the PR where the CI
runs were removed [0] in 2019.
Our long-term proposal to solve this problem is to get pybind11 (for c++
code), cython (for c code), and f2py (for fortran code) to generate
wrappers using HPy [1] instead of the C-API, which will speed up testing
with PyPy. A second possibility is that the scientific python community
will move away from its heavy dependency on the CPython C-API and more
towards using higher-level protocols [4]. Both of these efforts will
take many years unless heavily sponsored.
A more specific short-term solution for your installation problem is to
use conda-forge's miniforge [3]. This will solve the installation
problem, but PyPy will still be slow on scipy-heavy workflows. The
conda-forge project supplies binary packages for pypy3.7, and is
discussing pypy3.8 support [2]:
conda create -n pypy3.7 pypy
conda activate pypy3.7
conda install scipy
Matti
[0] https://github.com/scipy/scipy/pull/10085
[1] https://hpyproject.org/
[2] https://github.com/conda-forge/conda-forge-pinning-feedstock/issues/2089
[3] https://github.com/conda-forge/miniforge
[4] https://labs.quansight.org/blog/2021/11/pydata-extensibility-vision/
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