Help us testing 0.52.0 RC

We are inviting the boarder community and downstream projects to help us test Numba 0.52.0 and llvmlite 0.35.0 release candidates.

We would appreciate downstream projects to test our release candidates in their respective CI infrastructure and report regressions to our issue tracker.

We are also experimenting with the idea of getting sign-offs from the following opensource projects that have, in previous releases, reported regression issues:

  • Awkward
  • UMAP
  • librosa
  • RAPIDS
  • SDC
  • pydata sparse
  • clifford

Please comment in this thread if you can help with testing our packages and mention the project that you are associated with (if you can).

We are currently at RC2. Conda packages are in the numba channel. Wheels are available on PyPI.

Thanks in advance for everyone helping us test Numba.

I ran the Awkward Array integration tests, which includes all of our tests of Numba’s extension mechanism (extending it to recognize and correctly navigate Awkward Arrays). There were no errors.

Here’s the version information:

Python 3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 19:08:05) 
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numba
>>> numba.__version__
'0.52.0rc2'

and

% conda list
# packages in environment at /home/jpivarski/miniconda3/envs/numba-py38:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       1_gnu    conda-forge
attrs                     20.2.0             pyh9f0ad1d_0    conda-forge
ca-certificates           2020.6.20            hecda079_0    conda-forge
certifi                   2020.6.20        py38h924ce5b_2    conda-forge
iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
ld_impl_linux-64          2.35                 h769bd43_9    conda-forge
libblas                   3.9.0                2_openblas    conda-forge
libcblas                  3.9.0                2_openblas    conda-forge
libffi                    3.2.1             he1b5a44_1007    conda-forge
libgcc-ng                 9.3.0               h5dbcf3e_17    conda-forge
libgfortran-ng            9.3.0               he4bcb1c_17    conda-forge
libgfortran5              9.3.0               he4bcb1c_17    conda-forge
libgomp                   9.3.0               h5dbcf3e_17    conda-forge
liblapack                 3.9.0                2_openblas    conda-forge
libopenblas               0.3.12          pthreads_h4812303_1    conda-forge
libstdcxx-ng              9.3.0               h2ae2ef3_17    conda-forge
llvmlite                  0.35.0rc2        py38hf484d3e_0    numba
more-itertools            8.6.0              pyhd8ed1ab_0    conda-forge
ncurses                   6.2                  he1b5a44_2    conda-forge
numba                     0.52.0rc2       np1.11py3.8h04863e7_gd5984fad1_0    numba
numpy                     1.19.2           py38hf89b668_1    conda-forge
openssl                   1.1.1h               h516909a_0    conda-forge
packaging                 20.4               pyh9f0ad1d_0    conda-forge
pip                       20.2.4                     py_0    conda-forge
pluggy                    0.13.1           py38h924ce5b_3    conda-forge
py                        1.9.0              pyh9f0ad1d_0    conda-forge
pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge
pytest                    6.1.2            py38h578d9bd_0    conda-forge
python                    3.8.6           h852b56e_0_cpython    conda-forge
python_abi                3.8                      1_cp38    conda-forge
readline                  8.0                  he28a2e2_2    conda-forge
setuptools                49.6.0           py38h924ce5b_2    conda-forge
six                       1.15.0             pyh9f0ad1d_0    conda-forge
sqlite                    3.33.0               h4cf870e_1    conda-forge
tk                        8.6.10               hed695b0_1    conda-forge
toml                      0.10.2             pyhd8ed1ab_0    conda-forge
wheel                     0.35.1             pyh9f0ad1d_0    conda-forge
xz                        5.2.5                h516909a_1    conda-forge
zlib                      1.2.11            h516909a_1010    conda-forge

hi!

I tested 0.52.0rc2 at work and it passed CI. No changes in performance

Update: it turns out that mamba is using numba 0.51.2 instead of numba 0.52.0rc2. So we haven’t really tested 0.52.0rc2 yet.

Update2: after switching to conda for the environment creation, numba 0.52 is correctly installed. The execution time was reduced by 10%.

Hi! (@esc brought me here)

I ran the poliastro test suite with numba 0.52.0rc2 and all tests passed without errors. I unfortunately don’t have the capacity to run proper time benchmarks today, but from a couple of quick runs with both versions I don’t see much difference in performance.

Hi Siu

I’ve tested RBC with Numba 0.52 RC2 and there were no errors

~/git/rbc:rbc-38 (rbc) guilhermel $ python
Python 3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 19:08:05)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numba
nu>>> numba.__version__
'0.52.0rc2'

The clifford azure CI run against numba master appears to be passing still

Many thanks to all those who have replied so far, this is very encouraging.

0.52 RC2 seems to work fine with PyData/Sparse as well on macOS. Thanks for the awesome work!

I didn’t run any test suite, but I’ve used it casually for a bit and haven’t observed any issues

RC3 is now available.

conda-packages: numba and llvmlite
wheels and source tarballs: numba and llvmlite

Please see the RC3 milestone for a list of changes: https://github.com/numba/numba/milestone/49?closed=1. The changes in RC3 are minor or opaque to user-facing API—adjustment of optimization passes, changes of internal C code.

I’ve tested locally with the RAPIDS libraries branch-0.17 (cuDF, cuSpatial, cuML, cuGraph) with single- and multi-GPU with 0.52.0RC3 and not encountered any issues.

I re-ran my tests with RC3 and it passed CI (and kept the 10% performance improvements from RC2).