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lmcinnes / umap

Uniform Manifold Approximation and Projection
BSD 3-Clause "New" or "Revised" License
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Segfault in smooth_knn_dist #421

Open prihoda opened 4 years ago

prihoda commented 4 years ago

I am getting a Segmentation fault (core dumped) on fit with any input data on linux.

UMAP version: 0.4.2 (happens with 0.4.1 as well)

OS: centos-release-7-6.1810.2.el7.centos.x86_64

I also tried running using Binder on ubuntu but there it works all OK.

Traceback with python -q -X faulthandler:

Current thread 0x00007f43ce617740 (most recent call first):
  File "/SFS/user/wp/prihodad/git/usum-hpc/condaenv/lib/python3.7/site-packages/umap/umap_.py", line 607 in fuzzy_simplicial_set
  File "/SFS/user/wp/prihodad/git/usum-hpc/condaenv/lib/python3.7/site-packages/umap/umap_.py", line 1777 in fit
  File "/SFS/user/wp/prihodad/git/usum-hpc/condaenv/lib/python3.7/site-packages/umap/umap_.py", line 1973 in fit_transform

I added print statements before the smooth_knn_dist call and got:

local_connectivity 1.0
knn_dists [[0.         0.02700001 0.02700001 ... 0.03100002 0.03500003 0.03500003]
 [0.         0.         0.         ... 0.10399997 0.10399997 0.10399997]
 [0.         0.         0.         ... 0.10399997 0.10399997 0.10399997]
 ...
 [0.         0.03799999 0.03799999 ... 0.04699999 0.04699999 0.04699999]
 [0.         0.009      0.009      ... 0.07499999 0.08499998 0.09399998]
 [0.         0.04699999 0.05699998 ... 0.12300003 0.13200003 0.14200002]]
n_neighbors 15
Fatal Python error: Segmentation fault

So the error happens somewhere in the smooth_knn_dist function.

Output of pip freeze:

biopython==1.76
bokeh==2.0.2
certifi==2020.4.5.1
chardet==3.0.4
click==7.1.2
cloudpickle==1.4.1
colorcet==2.0.2
cycler==0.10.0
dask==2.15.0
datashader==0.10.0
datashape==0.5.2
decorator==4.4.2
distributed==2.15.2
fsspec==0.7.3
HeapDict==1.0.1
holoviews==1.13.2
idna==2.9
imageio==2.8.0
Jinja2==2.11.2
joblib==0.14.1
kiwisolver==1.2.0
llvmlite==0.32.0
locket==0.2.0
Markdown==3.2.1
MarkupSafe==1.1.1
matplotlib==3.2.1
msgpack==1.0.0
multipledispatch==0.6.0
networkx==2.4
numba==0.49.0
numpy==1.18.4
packaging==20.3
pandas==1.0.3
panel==0.9.5
param==1.9.3
partd==1.1.0
Pillow==7.1.2
psutil==5.7.0
pyct==0.4.6
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2020.1
pyviz-comms==0.7.4
PyWavelets==1.1.1
PyYAML==5.3.1
requests==2.23.0
scikit-image==0.16.2
scikit-learn==0.22.2.post1
scipy==1.4.1
six==1.14.0
sortedcontainers==2.1.0
tbb==2020.0.133
tblib==1.6.0
toolz==0.10.0
tornado==6.0.4
tqdm==4.46.0
typing-extensions==3.7.4.2
umap-learn==0.4.2
urllib3==1.25.9
usum==0.1.5
xarray==0.15.1

Output of conda env export:

  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=0_gnu
  - ca-certificates=2020.4.5.1=hecc5488_0
  - certifi=2020.4.5.1=py37hc8dfbb8_0
  - ld_impl_linux-64=2.34=h53a641e_0
  - libffi=3.2.1=he1b5a44_1007
  - libgcc-ng=9.2.0=h24d8f2e_2
  - libgomp=9.2.0=h24d8f2e_2
  - libstdcxx-ng=9.2.0=hdf63c60_2
  - ncurses=6.1=hf484d3e_1002
  - openssl=1.1.1g=h516909a_0
  - pip=20.1=pyh9f0ad1d_0
  - python=3.7.6=h8356626_5_cpython
  - python_abi=3.7=1_cp37m
  - readline=8.0=hf8c457e_0
  - setuptools=46.1.3=py37hc8dfbb8_0
  - sqlite=3.30.1=hcee41ef_0
  - tk=8.6.10=hed695b0_0
  - wheel=0.34.2=py_1
  - xz=5.2.5=h516909a_0
  - zlib=1.2.11=h516909a_1006
  - pip:
    - biopython==1.76
    - bokeh==2.0.2
    - chardet==3.0.4
    - click==7.1.2
    - cloudpickle==1.4.1
    - colorcet==2.0.2
    - cycler==0.10.0
    - dask==2.15.0
    - datashader==0.10.0
    - datashape==0.5.2
    - decorator==4.4.2
    - distributed==2.15.2
    - fsspec==0.7.3
    - heapdict==1.0.1
    - holoviews==1.13.2
    - idna==2.9
    - imageio==2.8.0
    - jinja2==2.11.2
    - joblib==0.14.1
    - kiwisolver==1.2.0
    - llvmlite==0.32.0
    - locket==0.2.0
    - markdown==3.2.1
    - markupsafe==1.1.1
    - matplotlib==3.2.1
    - msgpack==1.0.0
    - multipledispatch==0.6.0
    - networkx==2.4
    - numba==0.49.0
    - numpy==1.18.4
    - packaging==20.3
    - pandas==1.0.3
    - panel==0.9.5
    - param==1.9.3
    - partd==1.1.0
    - pillow==7.1.2
    - psutil==5.7.0
    - pyct==0.4.6
    - pyparsing==2.4.7
    - python-dateutil==2.8.1
    - pytz==2020.1
    - pyviz-comms==0.7.4
    - pywavelets==1.1.1
    - pyyaml==5.3.1
    - requests==2.23.0
    - scikit-image==0.16.2
    - scikit-learn==0.22.2.post1
    - scipy==1.4.1
    - six==1.14.0
    - sortedcontainers==2.1.0
    - tbb==2020.0.133
    - tblib==1.6.0
    - toolz==0.10.0
    - tornado==6.0.4
    - tqdm==4.46.0
    - typing-extensions==3.7.4.2
    - umap-learn==0.4.2
    - urllib3==1.25.9
    - usum==0.1.5
    - xarray==0.15.1
lmcinnes commented 4 years ago

I'll try to get to this when I can, but segfaults inside of numba can be very difficult to track down well.

prihoda commented 4 years ago

Thanks, let me know if I can help

tracek commented 4 years ago

I also get a segfault on 0.4.2. My conda env:

channels:
  - plotly
  - comet_ml
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=conda_forge
  - _openmp_mutex=4.5=1_llvm
  - argtable2=2.13=h14c3975_1001
  - attrs=19.3.0=py_0
  - aubio=0.4.9=py36h866369f_1
  - audioread=2.1.8=py36h9f0ad1d_2
  - backcall=0.1.0=py_0
  - backoff=1.8.0=py_0
  - bleach=3.1.3=pyh8c360ce_0
  - blosc=1.17.1=he1b5a44_0
  - bokeh=2.0.0=py36h9f0ad1d_0
  - boto3=1.12.14=py_0
  - botocore=1.15.26=pyh9f0ad1d_0
  - bzip2=1.0.8=h516909a_2
  - ca-certificates=2020.4.5.1=hecc5488_0
  - cachetools=3.1.1=py_0
  - certifi=2020.4.5.1=py36h9f0ad1d_0
  - cffi=1.14.0=py36hd463f26_0
  - chardet=3.0.4=py36h9f0ad1d_1006
  - chart-studio=1.1.0=py_0
  - click=7.0=py_0
  - cloudpickle=1.3.0=py_0
  - colorcet=2.0.1=py_0
  - cryptography=2.8=py36h45558ae_2
  - cycler=0.10.0=py_2
  - cytoolz=0.10.1=py36h516909a_0
  - dask=2.12.0=py_0
  - dask-core=2.12.0=py_0
  - datashader=0.10.0=py_0
  - datashape=0.5.4=py_1
  - dbus=1.13.6=he372182_0
  - decorator=4.4.2=py_0
  - defusedxml=0.6.0=py_0
  - distributed=2.12.0=py36_0
  - docutils=0.15.2=py36_0
  - eigen=3.3.7=hc9558a2_1001
  - entrypoints=0.3=py36h9f0ad1d_1001
  - expat=2.2.9=he1b5a44_2
  - ffmpeg=4.1.3=h167e202_0
  - fftw=3.3.8=nompi_h7f3a6c3_1110
  - fontconfig=2.13.1=h86ecdb6_1001
  - freetype=2.10.1=he06d7ca_0
  - fsspec=0.6.3=py_0
  - gettext=0.19.8.1=hc5be6a0_1002
  - glib=2.58.3=py36hd3ed26a_1003
  - gmp=6.2.0=he1b5a44_2
  - gnutls=3.6.5=hd3a4fd2_1002
  - gst-plugins-base=1.14.5=h0935bb2_2
  - gstreamer=1.14.5=h36ae1b5_2
  - hdf5=1.10.2=hc401514_3
  - heapdict=1.0.1=py_0
  - icu=64.2=he1b5a44_1
  - idna=2.9=py_1
  - imageio=2.8.0=py_0
  - importlib-metadata=1.5.0=py36h9f0ad1d_1
  - importlib_metadata=1.5.0=1
  - ipykernel=5.1.4=py36h5ca1d4c_0
  - ipython=7.13.0=py36h9f0ad1d_2
  - ipython_genutils=0.2.0=py_1
  - ipywidgets=7.5.1=py_0
  - jedi=0.16.0=py36h9f0ad1d_1
  - jinja2=2.11.1=py_0
  - jmespath=0.9.5=py_0
  - joblib=0.14.1=py_0
  - jpeg=9c=h14c3975_1001
  - json5=0.9.0=py_0
  - jsonschema=3.2.0=py36h9f0ad1d_1
  - jupyter_client=6.1.0=py_0
  - jupyter_core=4.6.3=py36h9f0ad1d_1
  - jupyterlab=2.0.1=py_0
  - jupyterlab_server=1.0.7=py_0
  - kiwisolver=1.1.0=py36hdb11119_1
  - lame=3.100=h14c3975_1001
  - lapack=3.6.1=ha44fe06_2
  - ld_impl_linux-64=2.34=h53a641e_0
  - libblas=3.8.0=16_openblas
  - libcblas=3.8.0=16_openblas
  - libclang=9.0.1=default_hde54327_0
  - libffi=3.2.1=he1b5a44_1007
  - libflac=1.3.1=he1b5a44_1002
  - libgcc-ng=9.2.0=h24d8f2e_2
  - libgfortran=3.0.0=1
  - libgfortran-ng=7.3.0=hdf63c60_5
  - libiconv=1.15=h516909a_1006
  - liblapack=3.8.0=16_openblas
  - libllvm8=8.0.1=hc9558a2_0
  - libllvm9=9.0.1=hc9558a2_0
  - libogg=1.3.2=h516909a_1002
  - libopenblas=0.3.9=h5ec1e0e_0
  - libpng=1.6.37=hed695b0_1
  - librosa=0.7.2=py_0
  - libsndfile=1.0.28=he1b5a44_1000
  - libsodium=1.0.17=h516909a_0
  - libstdcxx-ng=9.2.0=hdf63c60_2
  - libtiff=4.1.0=hc7e4089_6
  - libuuid=2.32.1=h14c3975_1000
  - libvorbis=1.3.5=h516909a_1002
  - libwebp-base=1.1.0=h516909a_3
  - libxcb=1.13=h14c3975_1002
  - libxkbcommon=0.10.0=he1b5a44_0
  - libxml2=2.9.10=hee79883_0
  - llvm-openmp=9.0.1=hc9558a2_2
  - llvmlite=0.31.0=py36hfa65bc7_1
  - locket=0.2.0=py_2
  - lz4-c=1.8.3=he1b5a44_1001
  - lzo=2.10=h14c3975_1000
  - markupsafe=1.1.1=py36h8c4c3a4_1
  - matplotlib=3.2.0=1
  - matplotlib-base=3.2.0=py36h250f245_1
  - mistune=0.8.4=py36h516909a_1000
  - more-itertools=8.2.0=py_0
  - mpg123=1.25.8=hf484d3e_1000
  - msgpack-python=1.0.0=py36hdb11119_1
  - multipledispatch=0.6.0=py_0
  - nbconvert=5.6.1=py36_0
  - nbformat=5.0.4=py_0
  - ncurses=6.1=hf484d3e_1002
  - nettle=3.4.1=h1bed415_1002
  - networkx=2.4=py_1
  - notebook=6.0.3=py36_0
  - nspr=4.25=he1b5a44_0
  - nss=3.47=he751ad9_0
  - numba=0.48.0=py36hb3f55d8_0
  - numexpr=2.7.1=py36hb3f55d8_0
  - numpy=1.18.1=py36h95a1406_0
  - olefile=0.46=py_0
  - openh264=1.8.0=hdbcaa40_1000
  - openssl=1.1.1g=h516909a_0
  - packaging=20.1=py_0
  - pandas=1.0.1=py36hb3f55d8_0
  - pandoc=2.9.2=0
  - pandocfilters=1.4.2=py_1
  - param=1.9.3=py_0
  - parso=0.6.2=py_0
  - partd=1.1.0=py_0
  - pcre=8.44=he1b5a44_0
  - pexpect=4.8.0=py36h9f0ad1d_1
  - pickleshare=0.7.5=py36h9f0ad1d_1001
  - pillow=7.0.0=py36h8328e55_1
  - pip=20.0.2=py_2
  - plotly=4.7.0=py_0
  - pluggy=0.12.0=py_0
  - prometheus_client=0.7.1=py_0
  - prompt-toolkit=3.0.4=py_0
  - psutil=5.7.0=py36h8c4c3a4_1
  - pthread-stubs=0.4=h14c3975_1001
  - ptyprocess=0.6.0=py_1001
  - py=1.8.1=py_0
  - pycparser=2.20=py_0
  - pyct=0.4.6=py_0
  - pyct-core=0.4.6=py_0
  - pygments=2.6.1=py_0
  - pyopenssl=19.1.0=py_1
  - pyparsing=2.4.6=py_0
  - pyqt=5.12.3=py36hcca6a23_1
  - pyrsistent=0.15.7=py36h8c4c3a4_1
  - pysocks=1.7.1=py36h9f0ad1d_1
  - pysoundfile=0.10.2=py_1001
  - pytables=3.4.4=py36h4f72b40_1
  - pytest=5.4.1=py36h9f0ad1d_0
  - python=3.6.10=h9d8adfe_1009_cpython
  - python-dateutil=2.8.1=py_0
  - python-dotenv=0.12.0=py_0
  - python_abi=3.6=1_cp36m
  - pytz=2019.3=py_0
  - pywavelets=1.1.1=py36hc1659b7_0
  - pyyaml=5.3.1=py36h8c4c3a4_0
  - pyzmq=19.0.0=py36h9947dbf_1
  - qt=5.12.5=hd8c4c69_1
  - readline=8.0=hf8c457e_0
  - requests=2.23.0=pyh8c360ce_2
  - resampy=0.2.2=py_0
  - retrying=1.3.3=py_2
  - s3transfer=0.3.3=py36_0
  - scikit-image=0.16.2=py36hb3f55d8_0
  - scikit-learn=0.22.1=py36hcdab131_1
  - scipy=1.4.1=py36h921218d_0
  - send2trash=1.5.0=py_0
  - setuptools=46.0.0=py36h9f0ad1d_2
  - six=1.14.0=py_1
  - sortedcontainers=2.1.0=py_0
  - sqlite=3.30.1=hcee41ef_0
  - tbb=2020.1=hc9558a2_0
  - tblib=1.6.0=py_0
  - terminado=0.8.3=py36h9f0ad1d_1
  - testpath=0.3.1=py36_1
  - tk=8.6.10=hed695b0_0
  - toolz=0.10.0=py_0
  - tornado=6.0.4=py36h8c4c3a4_1
  - tqdm=4.43.0=py_0
  - traitlets=4.3.3=py36h9f0ad1d_1
  - typing_extensions=3.7.4.1=py36h9f0ad1d_1
  - urllib3=1.25.7=py36h9f0ad1d_1
  - wcwidth=0.1.8=py_0
  - webencodings=0.5.1=py_1
  - wheel=0.34.2=py_1
  - widgetsnbextension=3.5.1=py36_0
  - x264=1!152.20180806=h14c3975_0
  - xarray=0.15.0=py_0
  - xorg-libxau=1.0.9=h14c3975_0
  - xorg-libxdmcp=1.1.3=h516909a_0
  - xz=5.2.4=h516909a_1002
  - yaafe=0.70=py36h0bee7d0_1
  - yaml=0.2.2=h516909a_1
  - zeromq=4.3.2=he1b5a44_2
  - zict=2.0.0=py_0
  - zipp=3.1.0=py_0
  - zlib=1.2.11=h516909a_1006
  - zstd=1.4.4=h3b9ef0a_2
  - pip:
    - pyqt5-sip==4.19.18
    - pyqtwebengine==5.12.1

After switching to 0.3.10 (and not changing anything else) the problem is gone.

rhysnewell commented 3 years ago

Sorry to revive a potentially dead thread, but this issue seems to be rearing its head again. I'm getting a segfault in this exact same spot as well. It started when I began trying to play around with the numba threading layers (setting it tbb) in order to use UMAP with ProcessPoolExecutor. It happened very suddenly, and now consistently happens whenever I try to run UMAP inside a script, regardless of threading layer or if it is running inside a process pool.

The weird thing is that the seg fault does not occur if I just run UMAP inside of a python terminal, it only occurs when I run it via command line through a script.

The error looks like this:

08/07/2021 08:38:28 AM INFO: Finding disconnections...
Fatal Python error: Segmentation fault

Current thread 0x00007fd5f4381700 (most recent call first):
  File "/home/n10853499/.conda/envs/rosella-dev/lib/python3.8/site-packages/umap/umap_.py", line 313 in nearest_neighbors
  File "/home/n10853499/.conda/envs/rosella-dev/lib/python3.8/site-packages/umap/umap_.py", line 557 in fuzzy_simplicial_set
  File "/home/n10853499/.conda/envs/rosella-dev/lib/python3.8/site-packa^CSegmentation fault (core dumped)

and my conda environment looks like this:

# packages in environment at /home/n10853499/.conda/envs/rosella-dev:
#
# Name                    Version                   Build  Channel
_libgcc_mutex             0.1                 conda_forge    conda-forge
_openmp_mutex             4.5                       1_gnu    conda-forge
attrs                     21.2.0             pyhd8ed1ab_0    conda-forge
backcall                  0.2.0              pyh9f0ad1d_0    conda-forge
backports                 1.0                        py_2    conda-forge
backports.functools_lru_cache 1.6.4              pyhd8ed1ab_0    conda-forge
biopython                 1.79             py38h497a2fe_0    conda-forge
blis                      0.8.1                h7f98852_1    conda-forge
brotlipy                  0.7.0           py38h497a2fe_1001    conda-forge
bwa                       0.7.17               h5bf99c6_8    bioconda
bzip2                     1.0.8                h7f98852_4    conda-forge
ca-certificates           2021.5.30            ha878542_0    conda-forge
cachecontrol              0.12.6                     py_0    conda-forge
certifi                   2021.5.30        py38h578d9bd_0    conda-forge
cffi                      1.14.4           py38ha312104_0    conda-forge
chardet                   4.0.0            py38h578d9bd_1    conda-forge
charset-normalizer        2.0.0              pyhd8ed1ab_0    conda-forge
cryptography              3.4.7            py38ha5dfef3_0    conda-forge
curl                      7.71.1               he644dc0_3    conda-forge
cycler                    0.10.0                     py_2    conda-forge
cython                    0.29.24          py38h709712a_0    conda-forge
decorator                 5.0.9              pyhd8ed1ab_0    conda-forge
flight-genome             1.2.1              pyh5e36f6f_0    bioconda
freetype                  2.10.4               h0708190_1    conda-forge
gsl                       2.6                  he838d99_2    conda-forge
hdbscan                   0.8.27           py38h5c078b8_0    conda-forge
hdmedians                 0.14.2           py38hb5d20a5_0    conda-forge
htslib                    1.9                  h4da6232_3    bioconda
idna                      3.1                pyhd3deb0d_0    conda-forge
imageio                   2.9.0                      py_0    conda-forge
iniconfig                 1.1.1              pyh9f0ad1d_0    conda-forge
ipython                   7.26.0           py38he5a9106_0    conda-forge
ipython_genutils          0.2.0                      py_1    conda-forge
jedi                      0.18.0           py38h578d9bd_2    conda-forge
joblib                    0.17.0                     py_0    conda-forge
jpeg                      9d                   h36c2ea0_0    conda-forge
k8                        0.2.5                h9a82719_1    bioconda
kiwisolver                1.3.1            py38h1fd1430_1    conda-forge
krb5                      1.17.2               h926e7f8_0    conda-forge
lcms2                     2.12                 hddcbb42_0    conda-forge
ld_impl_linux-64          2.36.1               hea4e1c9_2    conda-forge
libcblas                  3.9.0               10_openblas    conda-forge                                                                                  [38/1790]
libcurl                   7.71.1               hcdd3856_3    conda-forge
libdeflate                1.6                  h516909a_0    conda-forge
libedit                   3.1.20191231         h46ee950_2    conda-forge
libffi                    3.2.1             he1b5a44_1007    conda-forge
libgcc-ng                 11.1.0               hc902ee8_8    conda-forge
libgfortran-ng            11.1.0               h69a702a_8    conda-forge
libgfortran5              11.1.0               h6c583b3_8    conda-forge
libgomp                   11.1.0               hc902ee8_8    conda-forge
liblapack                 3.9.0               10_openblas    conda-forge
libllvm10                 10.0.1               he513fc3_3    conda-forge
libopenblas               0.3.17          pthreads_h8fe5266_1    conda-forge
libpng                    1.6.37               h21135ba_2    conda-forge
libssh2                   1.9.0                ha56f1ee_6    conda-forge
libstdcxx-ng              11.1.0               h56837e0_8    conda-forge
libtiff                   4.3.0                hf544144_0    conda-forge
libwebp-base              1.2.0                h7f98852_2    conda-forge
llvmlite                  0.36.0           py38h4630a5e_0    conda-forge
lockfile                  0.12.2                     py_1    conda-forge
lz4-c                     1.9.3                h9c3ff4c_1    conda-forge
matplotlib-base           3.4.2            py38hcc49a3a_0    conda-forge
matplotlib-inline         0.1.2              pyhd8ed1ab_2    conda-forge
minimap2                  2.21                 h5bf99c6_0    bioconda
more-itertools            8.8.0              pyhd8ed1ab_0    conda-forge
msgpack-python            1.0.2            py38h1fd1430_1    conda-forge
natsort                   7.1.1              pyhd8ed1ab_0    conda-forge
ncurses                   6.1               hf484d3e_1002    conda-forge
numba                     0.53.1           py38h8b71fd7_1    conda-forge
numpy                     1.21.1           py38h9894fe3_0    conda-forge
olefile                   0.46               pyh9f0ad1d_1    conda-forge
openblas                  0.3.17          pthreads_h4748800_1    conda-forge
openjpeg                  2.4.0                hb52868f_1    conda-forge
openssl                   1.1.1k               h7f98852_0    conda-forge
packaging                 21.0               pyhd8ed1ab_0    conda-forge
pandas                    1.3.1            py38h1abd341_0    conda-forge
parallel                  20160622                      1    bioconda
parso                     0.8.2              pyhd8ed1ab_0    conda-forge
patsy                     0.5.1                      py_0    conda-forge
perl                      5.32.1          0_h7f98852_perl5    conda-forge
perl-threaded             5.26.0                        0    bioconda
pexpect                   4.8.0              pyh9f0ad1d_2    conda-forge
pickleshare               0.7.5                   py_1003    conda-forge
pillow                    8.3.1            py38h8e6f84c_0    conda-forge
pip                       21.2.2             pyhd8ed1ab_0    conda-forge
pkg-config                0.29.2            h36c2ea0_1008    conda-forge
pluggy                    0.13.1           py38h578d9bd_4    conda-forge
prompt-toolkit            3.0.19             pyha770c72_0    conda-forge
ptyprocess                0.7.0              pyhd3deb0d_0    conda-forge
py                        1.10.0             pyhd3deb0d_0    conda-forge
pycparser                 2.20               pyh9f0ad1d_2    conda-forge
pygments                  2.9.0              pyhd8ed1ab_0    conda-forge
pynndescent               0.5.4              pyh6c4a22f_0    conda-forge
pyopenssl                 20.0.1             pyhd8ed1ab_0    conda-forge
pyparsing                 2.4.7              pyh9f0ad1d_0    conda-forge
pysam                     0.16.0.1         py38hbdc2ae9_1    bioconda
pysocks                   1.7.1            py38h578d9bd_3    conda-forge
pytest                    6.2.4            py38h578d9bd_0    conda-forge
python                    3.8.5           h4d41432_2_cpython    conda-forge
python-dateutil           2.8.2              pyhd8ed1ab_0    conda-forge
python_abi                3.8                      2_cp38    conda-forge
pytz                      2021.1             pyhd8ed1ab_0    conda-forge
readline                  8.0                  h46ee950_1    conda-forge
requests                  2.26.0             pyhd8ed1ab_0    conda-forge
rosella                   0.3.3                h443a992_0    bioconda
samtools                  1.9                 h10a08f8_12    bioconda
scikit-bio                0.5.6            py38h0b5ebd8_4    conda-forge
scikit-learn              0.24.2           py38hdc147b9_0    conda-forge
scipy                     1.7.1            py38h56a6a73_0    conda-forge
seaborn                   0.11.1               hd8ed1ab_1    conda-forge
seaborn-base              0.11.1             pyhd8ed1ab_1    conda-forge
setuptools                49.6.0           py38h578d9bd_3    conda-forge
six                       1.16.0             pyh6c4a22f_0    conda-forge
sqlite                    3.32.3               hcee41ef_1    conda-forge
starcode                  1.4                  h779adbc_1    bioconda
statsmodels               0.12.2           py38h5c078b8_0    conda-forge
tbb                       2020.2               h4bd325d_4    conda-forge
threadpoolctl             2.2.0              pyh8a188c0_0    conda-forge
tk                        8.6.10               h21135ba_1    conda-forge
toml                      0.10.2             pyhd8ed1ab_0    conda-forge
tornado                   6.1              py38h497a2fe_1    conda-forge
traitlets                 5.0.5                      py_0    conda-forge
umap-learn                0.5.1            py38h578d9bd_1    conda-forge
urllib3                   1.26.6             pyhd8ed1ab_0    conda-forge
vt                        2015.11.10           he941832_3    bioconda
wcwidth                   0.2.5              pyh9f0ad1d_2    conda-forge
wheel                     0.36.2             pyhd3deb0d_0    conda-forge
xz                        5.2.5                h516909a_1    conda-forge
zlib                      1.2.11            h516909a_1010    conda-forge
zstd                      1.5.0                ha95c52a_0    conda-forge
rhysnewell commented 3 years ago

The following changes seem to have partially fixed my issues. Numba parallelism seems to break compute_membership_strengths and fast_knn_indices for whatever reason:

diff --git a/umap/umap_.py b/umap/umap_.py
index 0ebb8f3..824a97d 100644
--- a/umap/umap_.py
+++ b/umap/umap_.py
@@ -352,7 +352,7 @@ def nearest_neighbors(
         "rhos": numba.types.float32[::1],
         "val": numba.types.float32,
     },
-    parallel=True,
+    parallel=False,
     fastmath=True,
 )
 def compute_membership_strengths(
diff --git a/umap/utils.py b/umap/utils.py
index 5eb7ddd..d6d3601 100644
--- a/umap/utils.py
+++ b/umap/utils.py
@@ -11,7 +11,7 @@ from sklearn.utils.validation import check_is_fitted
 import scipy.sparse

-@numba.njit(parallel=True)
+@numba.njit(parallel=False)
 def fast_knn_indices(X, n_neighbors):
     """A fast computation of knn indices.

The problem is that this seems to be significantly slower than it was previously which makes sense. Additionally, a second segfault begins to occur on a different set of data at a rather random point in the pynndescent module:

Python error: Segmentation fault
Thread 0x00007f3d61e58700 (most recent call first):
File "/home/n10853499/.conda/envs/rosella-dev/lib/python3.8/site-packages/pynndescent/pynndescent_.py", line 876 in __init__

Which is a call to this function:

            self._neighbor_graph = nn_descent(
                self._raw_data,
                self.n_neighbors,
                self.rng_state,
                effective_max_candidates,
                self._distance_func,
                self.n_iters,
                self.delta,
                low_memory=self.low_memory,
                rp_tree_init=True,
                init_graph=_init_graph,
                leaf_array=leaf_array,
                verbose=verbose,
            )

mor specifically, line 876 is where self.n_neighbors is used. I'm really not sure what is going on here, these errors are occuring in a fresh conda environment so I'm kind of at a loss.

Downgrading numba does not fix this issue. Downgrading pynndescent doesn't fix this issue either.