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conda-forge-admin stuarteberg ocefpaf cyrush tylere pitrou jjhelmus tacaswell lexual qwhelan silverlabucl anacondarecipes jakirkham-feedstocks kain88-de carlodri regro-cf-autotick-bot tschoonj conda-forge-linter xylar minrk djhoese khornlund pkgw bgruening ax3l scopatz martinrenou duncanmmacleod mattip kmuehlbauer anthchirp isuruf takluyver xhochy satra melund chrisburr mardiehl h-vetinari ramonaoptics lavaux hmaarrfk krande apmorton marcpaternoh5py-feedstock's Issues
h4py Installing its own HDF5 Instead of Building Off Predefined HDF5
Issue:
Title says it all here. When installing h5py via conda, it installs its own version of HDF5 and builds off of that. In doing so, necessary dev-level header files are not compilable and cause installation failures of codes that rely on it. However, installing via pip will only install HDF5 if no dev-level path is found (the correct response).
Why is this?
Environment (
conda list):
$ conda list
# packages in environment at /home/user/anaconda2:
#
# Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py27_0
_libgcc_mutex 0.1 main
anaconda-client 1.7.2 py27_0
anaconda-navigator 1.9.7 py27_0
asn1crypto 0.24.0 py27_0
astropy 2.0.14 pypi_0 pypi
atomicwrites 1.3.0 pypi_0 pypi
attrs 19.1.0 py27_1
backports 1.0 py_2
backports.functools_lru_cache 1.5 py_2
backports.shutil_get_terminal_size 1.0.0 py27_2
backports.tempfile 1.0 py_1
backports.weakref 1.0.post1 py_1
backports_abc 0.5 py27_0
beautifulsoup4 4.7.1 py27_1
bleach 3.1.0 py27_0
bzip2 1.0.8 h7b6447c_0
ca-certificates 2019.5.15 1
certifi 2019.6.16 py27_1
cffi 1.12.3 py27h2e261b9_0
chardet 3.0.4 py27_1003
click 7.0 py27_0
clyent 1.2.2 py27_1
conda 4.7.11 py27_0
conda-build 3.18.9 py27_3
conda-env 2.6.0 1
conda-package-handling 1.3.11 py27_0
conda-verify 3.4.2 py_1
configparser 3.7.4 py27_0
contextlib2 0.5.5 py27_0
cryptography 2.7 py27h1ba5d50_0
cycler 0.10.0 pypi_0 pypi
dbus 1.13.6 h746ee38_0
decorator 4.4.0 py27_1
defusedxml 0.6.0 py_0
docutils 0.15.2 pypi_0 pypi
entrypoints 0.3 py27_0
enum34 1.1.6 py27_1
expat 2.2.6 he6710b0_0
filelock 3.0.12 py_0
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
funcsigs 1.0.2 pypi_0 pypi
functools32 3.2.3.2 py27_1
future 0.17.1 py27_0
futures 3.3.0 py27_0
glib 2.56.2 hd408876_0
glob2 0.7 py_0
gmp 6.1.2 h6c8ec71_1
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.9.0 pypi_0 pypi
icu 58.2 h9c2bf20_1
idna 2.8 py27_0
ipaddress 1.0.22 py27_0
ipykernel 4.10.0 py27_0
ipython 5.8.0 py27_0 ipython_genutils 0.2.0 py27_0
ipywidgets 7.5.1 py_0
jinja2 2.10.1 py27_0
jpeg 9b h024ee3a_2
jsonschema 3.0.1 py27_0
jupyter_client 5.3.1 py_0
jupyter_core 4.5.0 py_0
jupyterlab 0.33.11 py27_0
jupyterlab_launcher 0.11.2 py27h28b3542_0
kiwisolver 1.1.0 pypi_0 pypi
libarchive 3.3.3 h5d8350f_5
libedit 3.1.20181209 hc058e9b_0
libffi 3.2.1 hd88cf55_4
libgcc-ng 9.1.0 hdf63c60_0
liblief 0.9.0 h7725739_2
libpng 1.6.37 hbc83047_0
libsodium 1.0.16 h1bed415_0
libstdcxx-ng 9.1.0 hdf63c60_0
libtiff 4.0.10 h2733197_2
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.9 hea5a465_1
lz4-c 1.8.1.2 h14c3975_0
lzo 2.10 h49e0be7_2
markupsafe 1.1.1 py27h7b6447c_0
matplotlib 2.2.4 pypi_0 pypi
mistune 0.8.4 py27h7b6447c_0
more-itertools 5.0.0 pypi_0 pypi
mpi4py 3.0.2 pypi_0 pypi
mpmath 1.1.0 pypi_0 pypi
navigator-updater 0.2.1 py27_0
nbconvert 5.5.0 py_0
nbformat 4.4.0 py27_0
ncurses 6.1 he6710b0_1
nose 1.3.7 pypi_0 pypi
notebook 5.7.8 py27_0
numpy 1.16.4 pypi_0 pypi
olefile 0.46 py27_0
openssl 1.1.1c h7b6447c_1
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py27_1
patchelf 0.9 he6710b0_3
pathlib2 2.3.4 py27_0
pcre 8.43 he6710b0_0
pexpect 4.7.0 py27_0
pickleshare 0.7.5 py27_0
pillow 6.1.0 py27h34e0f95_0
pip 19.1.1 py27_0
pkginfo 1.5.0.1 py27_0
pluggy 0.7.1 pypi_0 pypi
prometheus_client 0.7.1 py_0
prompt_toolkit 1.0.15 py27_0
psutil 5.6.3 py27h7b6447c_0
ptyprocess 0.6.0 py27_0
py 1.8.0 pypi_0 pypi py-lief 0.9.0 py27h7725739_2 pycosat 0.6.3 py27h14c3975_0 pycparser 2.19 py27_0 pygments 2.4.2 py_0 pyopenssl 19.0.0 py27_0 pyparsing 2.4.2 pypi_0 pypi pyqt 5.9.2 py27h05f1152_2 pyrsistent 0.14.11 py27h7b6447c_0 pysocks 1.7.0 py27_0 pytest 3.6.4 pypi_0 pypi python 2.7.16 h8b3fad2_3 python-dateutil 2.8.0 py27_0 python-libarchive-c 2.8 py27_13 pytz 2019.2 py_0 pyyaml 5.1.1 py27h7b6447c_0 pyzmq 18.1.0 py27he6710b0_0 qt 5.9.7 h5867ecd_1 qtpy 1.8.0 py_0 readline 7.0 h7b6447c_5 requests 2.22.0 py27_0 ripgrep 0.10.0 hc07d326_0 ruamel_yaml 0.15.46 py27h14c3975_0 scandir 1.10.0 py27h7b6447c_0 scipy 1.2.2 pypi_0 pypi send2trash 1.5.0 py27_0 setuptools 41.0.1 py27_0 simplegeneric 0.8.1 py27_2 simpy 3.0.11 pypi_0 pypi singledispatch 3.4.0.3 py27_0 sip 4.19.8 py27hf484d3e_0 six 1.12.0 py27_0 soupsieve 1.9.2 py27_0 sqlite 3.29.0 h7b6447c_0 subprocess32 3.5.4 py27h7b6447c_0 sympy 1.4 pypi_0 pypi terminado 0.8.2 py27_0 testpath 0.4.2 py27_0 tk 8.6.8 hbc83047_0 tornado 5.1.1 py27h7b6447c_0 tqdm 4.32.1 py_0 traitlets 4.3.2 py27_0 urllib3 1.24.2 py27_0 wcwidth 0.1.7 py27_0 webencodings 0.5.1 py27_1 wheel 0.33.4 py27_0 widgetsnbextension 3.5.0 py27_0 xz 5.2.4 h14c3975_4 yaml 0.1.7 had09818_2 zeromq 4.3.1 he6710b0_3 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0
Details about
conda and system ( conda info ):
$ conda info
active environment : base
active env location : /home/user/anaconda2
shell level : 1
user config file : /home/user/.condarc
populated config files :
conda version : 4.7.11
conda-build version : 3.18.9
python version : 2.7.16.final.0
virtual packages : __cuda=9.1
base environment : /home/user/anaconda2 (writable)
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/user/anaconda2/pkgs
/home/user/.conda/pkgs
envs directories : /home/user/anaconda2/envs
/home/user/.conda/envs
platform : linux-64
user-agent : conda/4.7.11 requests/2.22.0 CPython/2.7.16 Linux/4.15.0-58-generic ubuntu/18.04.3 glibc/2.27
UID:GID : 1001:1001
netrc file : None
offline mode : False
h5py is running against HDF5 1.10.2 when it was built against 1.10.3, this may cause problems
I'm seeing a new warning message with both Anaconda2 and Anaconda3 that use this package from conda-forge. Here is the message:
Python 3.6.6 | packaged by conda-forge | (default, Jul 26 2018, 11:48:23) [MSC v.1900 64 bit (AMD64)]
Type 'copyright', 'credits' or 'license' for more information
IPython 7.0.1 -- An enhanced Interactive Python. Type '?' for help.
In [1]: import h5py
C:\ProgramData\Anaconda3\lib\site-packages\h5py\__init__.py:72: UserWarning: h5py is running against HDF5 1.10.2 when it was built against 1.10.3, this may cause problems
'{0}.{1}.{2}'.format(*version.hdf5_built_version_tuple)
Other info from anaconada2:
>conda list h*5
# packages in environment at C:\ProgramData\Anaconda2:
#
# Name Version Build Channel
h5py 2.8.0 py27hb295e13_3 conda-forge
hdf5 1.10.2 vc9_0 [vc9] conda-forge
and anaconda3
>conda list h*5
# packages in environment at C:\ProgramData\Anaconda3:
#
# Name Version Build Channel
h5py 2.8.0 py36h54c06b0_3 conda-forge
hdf5 1.10.2 vc14_0 [vc14] conda-forge
h5py import fails with error on undefined H5Pget_fapl_direct
Solution to issue cannot be found in the documentation.
- I checked the documentation.
Issue
When installing phono3py from conda-forge inside a github CI action, it ends up with conda-forge::h5py-3.7.0-nompi_py39h63b1161_100
This version provides to a broken h5py that cannot be imported. The trivial script python3 -c "import h5py" fails with the error:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/share/miniconda/lib/python3.9/site-packages/h5py/__init__.py", line 33, in <module>
from . import version
File "/usr/share/miniconda/lib/python3.9/site-packages/h5py/version.py", line 15, in <module>
from . import h5 as _h5
File "h5py/h5.pyx", line 1, in init h5py.h5
ImportError: /usr/share/miniconda/lib/python3.9/site-packages/h5py/defs.cpython-39-x86_64-linux-gnu.so: undefined symbol: H5Pget_fapl_direct
I've tried to use conda to install h5py directly (i.e. not as a dependency of phono3py), but rather than the nompi version that is installed as a phono3py dependency, I end up with h5py pkgs/main/linux-64::h5py-3.7.0-py39h737f45e_0, even though I run conda install -c conda-forge h5py=3.7.0, and I don't know enough about conda to be able to test the package without it being added as a phono3py dependency.
Installed packages
# packages in environment at /usr/share/miniconda:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 2_kmp_llvm conda-forge
brotlipy 0.7.0 py39h27cfd23_1003
c-ares 1.18.1 h7f98852_0 conda-forge
ca-certificates 2022.9.14 ha878542_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
certifi 2022.9.14 pyhd8ed1ab_0 conda-forge
cffi 1.15.0 py39hd667e15_1
charset-normalizer 2.0.4 pyhd3eb1b0_0
colorama 0.4.4 pyhd3eb1b0_0
conda 4.14.0 py39hf3d152e_0 conda-forge
conda-content-trust 0.1.1 pyhd3eb1b0_0
conda-package-handling 1.8.1 py39h7f8727e_0
cryptography 36.0.0 py39h9ce1e76_0
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
cytoolz 0.11.0 py39h27cfd23_0
freetype 2.12.1 hca18f0e_0 conda-forge
h5py 3.7.0 nompi_py39h63b1161_100 conda-forge
hdf5 1.12.1 nompi_h2750804_100 conda-forge
idna 3.3 pyhd3eb1b0_0
jpeg 9e h166bdaf_2 conda-forge
kiwisolver 1.4.2 py39h295c915_0
krb5 1.19.2 hac12032_0
lcms2 2.12 hddcbb42_0 conda-forge
ld_impl_linux-64 2.35.1 h7274673_9
libblas 3.9.0 16_linux64_openblas conda-forge
libcblas 3.9.0 16_linux64_openblas conda-forge
libcurl 7.84.0 h91b91d3_0
libedit 3.1.20210910 h7f8727e_0
libev 4.33 h516909a_1 conda-forge
libffi 3.3 he6710b0_2
libgcc-ng 12.1.0 h8d9b700_16 conda-forge
libgfortran-ng 12.1.0 h69a702a_16 conda-forge
libgfortran5 12.1.0 hdcd56e2_16 conda-forge
liblapack 3.9.0 16_linux64_openblas conda-forge
libnghttp2 1.46.0 hce63b2e_0
libopenblas 0.3.21 pthreads_h78a6416_3 conda-forge
libpng 1.6.37 h753d276_4 conda-forge
libssh2 1.10.0 haa6b8db_3 conda-forge
libstdcxx-ng 9.3.0 hd4cf53a_17
libtiff 4.2.0 h85742a9_0
libwebp-base 1.2.4 h166bdaf_0 conda-forge
libzlib 1.2.12 h166bdaf_1 conda-forge
llvm-openmp 14.0.4 he0ac6c6_0 conda-forge
lz4-c 1.9.3 h9c3ff4c_1 conda-forge
matplotlib-base 3.3.4 py39h2fa2bec_0 conda-forge
ncurses 6.3 h7f8727e_2
numpy 1.20.3 py39hd249d9e_2 conda-forge
olefile 0.46 pyh9f0ad1d_1 conda-forge
openblas 0.3.21 pthreads_h[320](https://github.com/libAtoms/workflow/actions/runs/3063889471/jobs/4946460969#step:7:321)a7e8_3 conda-forge
openssl 1.1.1q h166bdaf_0 conda-forge
phono3py 2.3.2 py39h05a0c83_1 conda-forge
phonopy 2.15.0 py39hd257fcd_0 conda-forge
pillow 7.2.0 py39h6f3857e_2 conda-forge
pip 22.1.2 py39h06a4308_0
pycosat 0.6.3 py39h27cfd23_0
pycparser 2.21 pyhd3eb1b0_0
pyopenssl 22.0.0 pyhd3eb1b0_0
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
pysocks 1.7.1 py39h06a4308_0
python 3.9.12 h12debd9_0
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python_abi 3.9 2_cp39 conda-forge
pyyaml 6.0 py39hb9d737c_4 conda-forge
readline 8.1.2 h7f8727e_1
requests 2.27.1 pyhd3eb1b0_0
ruamel_yaml 0.15.100 py39h27cfd23_0
scipy 1.5.3 py39hee8e79c_0 conda-forge
setuptools 61.2.0 py39h06a4308_0
six 1.16.0 pyhd3eb1b0_1
spglib 2.0.1 py39hd257fcd_0 conda-forge
sqlite 3.38.2 hc218d9a_0
tk 8.6.11 h1ccaba5_0
toolz 0.11.2 pyhd3eb1b0_0
tornado 6.2 py39hb9d737c_0 conda-forge
tqdm 4.63.0 pyhd3eb1b0_0
tzdata 2022a hda174b7_0
urllib3 1.26.8 pyhd3eb1b0_0
wheel 0.37.1 pyhd3eb1b0_0
xz 5.2.5 h7b6447c_0
yaml 0.2.5 h7b6447c_0
zlib 1.2.12 h7f8727e_1
zstd 1.4.9 ha95c52a_0 conda-forgeEnvironment info
active environment : None
user config file : /home/runner/.condarc
populated config files :
conda version : 4.14.0
conda-build version : not installed
python version : 3.9.12.final.0
virtual packages : __linux=5.15.0=0
__glibc=2.31=0
__unix=0=0
__archspec=1=x86_64
base environment : /usr/share/miniconda (writable)
conda av data dir : /usr/share/miniconda/etc/conda
conda av metadata url : None
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /usr/share/miniconda/pkgs
/home/runner/.conda/pkgs
envs directories : /usr/share/miniconda/envs
/home/runner/.conda/envs
platform : linux-64
user-agent : conda/4.14.0 requests/2.27.1 CPython/3.9.12 Linux/5.15.0-1019-azure ubuntu/20.04.4 glibc/2.31
UID:GID : 1001:121
netrc file : None
offline mode : False[edited since conda list output seemed wrong]
Rerender with HDF5 1.10.3
Is there a reason that h5py cannot be built with hdf5 1.10.3? If it can be, I would be happy to submit at PR for this but I want to be clear if I should change 1.10.2 to 1.10.3 or just remove the pinning of the hdf5 version.
Background: I'm finding it very hard to create environments with the latest version of several packages (netcdf4, xarray, etc.) because of inconsistent hdf5 versions, and h5py is one critical bottleneck.
h5py in Mac OS X Newly installed Python 3.6 cannot load libhdf5.10.dylib
A recent installed Python 3.6 Ananconda, after I called, I have the following error.
python -c "import h5py"
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/jchan/anaconda/lib/python3.6/site-packages/h5py/__init__.py", line 24, in <module>
from . import _errors
ImportError: dlopen(/Users/jchan/anaconda/lib/python3.6/site-packages/h5py/_errors.cpython-36m-darwin.so, 2): Library not loaded: @rpath/libhdf5.10.dylib
Referenced from: /Users/jchan/anaconda/lib/python3.6/site-packages/h5py/_errors.cpython-36m-darwin.so
Reason: Incompatible library version: _errors.cpython-36m-darwin.so requires version 13.0.0 or later, but libhdf5.10.dylib provides version 12.0.0
The following is my conda list
_license 1.1 py36_1
alabaster 0.7.10 py36_0
anaconda custom py36_0
anaconda-client 1.6.3 py36_0
anaconda-navigator 1.6.2 py36_0
anaconda-project 0.6.0 py36_0
appnope 0.1.0 py36_0
appscript 1.0.1 py36_0
asn1crypto 0.22.0 py36_0
astroid 1.4.9 py36_0
astropy 1.3.2 np112py36_0
babel 2.4.0 py36_0
backports 1.0 py36_0
beautifulsoup4 4.6.0 py36_0
bitarray 0.8.1 py36_0
blaze 0.10.1 py36_0
bleach 1.5.0 py36_0
bokeh 0.12.5 py36_1
boto 2.46.1 py36_0
bottleneck 1.2.1 np112py36_0
bzip2 1.0.6 1 conda-forge
cffi 1.10.0 py36_0
chardet 3.0.3 py36_0
click 6.7 py36_0
cloudpickle 0.2.2 py36_0
clyent 1.2.2 py36_0
colorama 0.3.9 py36_0
conda 4.3.22 py36_0 conda-forge
conda-env 2.6.0 0 conda-forge
contextlib2 0.5.5 py36_0
cryptography 1.8.1 py36_0
curl 7.52.1 0
cycler 0.10.0 py36_0
cython 0.25.2 py36_0
cytoolz 0.8.2 py36_0
dask 0.14.3 py36_1
datashape 0.5.4 py36_0
decorator 4.0.11 py36_0
distributed 1.16.3 py36_0
docutils 0.13.1 py36_0
entrypoints 0.2.2 py36_1
et_xmlfile 1.0.1 py36_0
fastcache 1.0.2 py36_1
flask 0.12.2 py36_0
flask-cors 3.0.2 py36_0
freetype 2.5.5 2
get_terminal_size 1.0.0 py36_0
gevent 1.2.1 py36_0
greenlet 0.4.12 py36_0
h5py 2.7.0 np112py36_1 conda-forge
hdf5 1.8.18 0 conda-forge
heapdict 1.0.0 py36_1
html5lib 0.999 py36_0
icu 54.1 0
idna 2.5 py36_0
imagesize 0.7.1 py36_0
ipykernel 4.6.1 py36_0
ipython 5.3.0 py36_0
ipython_genutils 0.2.0 py36_0
ipywidgets 6.0.0 py36_0
isort 4.2.5 py36_0
itsdangerous 0.24 py36_0
jbig 2.1 0
jdcal 1.3 py36_0
jedi 0.10.2 py36_2
jinja2 2.9.6 py36_0
jpeg 9b 0
jsonschema 2.6.0 py36_0
jupyter 1.0.0 py36_3
jupyter_client 5.0.1 py36_0
jupyter_console 5.1.0 py36_0
jupyter_core 4.3.0 py36_0
lazy-object-proxy 1.2.2 py36_0
libgfortran 3.0.0 0 conda-forge
libiconv 1.14 0
libpng 1.6.27 0
libtiff 4.0.6 3
libxml2 2.9.4 0
libxslt 1.1.29 0
llvmlite 0.18.0 py36_0
locket 0.2.0 py36_1
lxml 3.7.3 py36_0
markupsafe 0.23 py36_2
matplotlib 2.0.2 np112py36_0
mistune 0.7.4 py36_0
mkl 2017.0.1 0
mkl-service 1.1.2 py36_3
mpmath 0.19 py36_1
msgpack-python 0.4.8 py36_0
multipledispatch 0.4.9 py36_0
navigator-updater 0.1.0 py36_0
nbconvert 5.1.1 py36_0
nbformat 4.3.0 py36_0
networkx 1.11 py36_0
nltk 3.2.3 py36_0
nose 1.3.7 py36_1
notebook 5.0.0 py36_0
numba 0.33.0 np112py36_0
numexpr 2.6.2 np112py36_0
numpy 1.12.1 py36_0
numpydoc 0.6.0 py36_0
odo 0.5.0 py36_1
olefile 0.44 py36_0
openpyxl 2.4.7 py36_0
openssl 1.0.2l 0
packaging 16.8 py36_0
pandas 0.20.1 np112py36_0
pandocfilters 1.4.1 py36_0
partd 0.3.8 py36_0
path.py 10.3.1 py36_0
pathlib2 2.2.1 py36_0
patsy 0.4.1 py36_0
pep8 1.7.0 py36_0
pexpect 4.2.1 py36_0
pickleshare 0.7.4 py36_0
pillow 4.1.1 py36_0
pip 9.0.1 py36_1
ply 3.10 py36_0
prompt_toolkit 1.0.14 py36_0
psutil 5.2.2 py36_0
ptyprocess 0.5.1 py36_0
py 1.4.33 py36_0
pycosat 0.6.2 py36_0
pycparser 2.17 py36_0
pycrypto 2.6.1 py36_6
pycurl 7.43.0 py36_2
pyflakes 1.5.0 py36_0
pygments 2.2.0 py36_0
pylint 1.6.4 py36_1
pyodbc 4.0.16 py36_0
pyopenssl 17.0.0 py36_0
pyparsing 2.1.4 py36_0
pyqt 5.6.0 py36_1
pytables 3.4.2 np112py36_1 conda-forge
pytest 3.0.7 py36_0
python 3.6.1 2
python-dateutil 2.6.0 py36_0
python.app 1.2 py36_4
pytz 2017.2 py36_0
pywavelets 0.5.2 np112py36_0
pyyaml 3.12 py36_0
pyzmq 16.0.2 py36_0
qt 5.6.2 2
qtawesome 0.4.4 py36_0
qtconsole 4.3.0 py36_0
qtpy 1.2.1 py36_0
readline 6.2 2
requests 2.14.2 py36_0
rope 0.9.4 py36_1
ruamel_yaml 0.11.14 py36_1
scikit-image 0.13.0 np112py36_0
scikit-learn 0.18.1 np112py36_1
scipy 0.19.0 np112py36_0
seaborn 0.7.1 py36_0
setuptools 27.2.0 py36_0
simplegeneric 0.8.1 py36_1
singledispatch 3.4.0.3 py36_0
sip 4.18 py36_0
six 1.10.0 py36_0
snowballstemmer 1.2.1 py36_0
sortedcollections 0.5.3 py36_0
sortedcontainers 1.5.7 py36_0
sphinx 1.5.6 py36_0
spyder 3.1.4 py36_0
sqlalchemy 1.1.9 py36_0
sqlite 3.13.0 0
statsmodels 0.8.0 np112py36_0
sympy 1.0 py36_0
tblib 1.3.2 py36_0
terminado 0.6 py36_0
testpath 0.3 py36_0
tk 8.5.18 0
toolz 0.8.2 py36_0
tornado 4.5.1 py36_0
traitlets 4.3.2 py36_0
unicodecsv 0.14.1 py36_0
unixodbc 2.3.4 0
wcwidth 0.1.7 py36_0
werkzeug 0.12.2 py36_0
wheel 0.29.0 py36_0
widgetsnbextension 2.0.0 py36_0
wrapt 1.10.10 py36_0
xlrd 1.0.0 py36_0
xlsxwriter 0.9.6 py36_0
xlwings 0.10.4 py36_0
xlwt 1.2.0 py36_0
xz 5.2.2 1
yaml 0.1.6 0
zict 0.1.2 py36_0
zlib 1.2.8 3
h5py 3.7.0 ARM not building
Comment:
Currently the ARM builds still fail in CI, see #114.
Maintainers merged the above PR to at least move forward with the green builds (linux x64, osx, windows).
Adding a list with the failing architectures after processing of https://github.com/conda-forge/h5py-feedstock/runs/7074185121.
h5py allows hdf5 dependencies that do not work together
- I read the conda-forge documentation and could not find the solution for my problem there.
Issue:
The h5py dependency specification allows h5py-hdf5 combinations to be selected that do not work together. This causes problems when using mamba.
Mamba is a drop-in replacement for conda with a more powerful dependency solver. However, mamba (or any other solver) can only do its magic when packages conflict if they do not work together.
Mamba is a bit unpredictable, and I've not yet been able to reliably reproduce the problem. But it is technically possible to get the same problem with conda. For me these two packages were selected by mamba:
h5py conda-forge/linux-64::h5py-3.2.1-nompi_py39h98ba4bc_100
hdf5 conda-forge/linux-64::hdf5-1.10.6-nompi_h3c11f04_101
Conda also happily installs these if explicitly asked (so conda also thinks they aren't conflicting), but the combination doesn't work:
$ conda create -n h5pytest "h5py=3.2.1=nompi_py39h98ba4bc_100" "hdf5=1.10.6=nompi_h3c11f04_101"
$ conda activate h5pytest
$ python -c "import h5py"
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "[..]/miniconda3/envs/h5pytest/lib/python3.9/site-packages/h5py/__init__.py", line 33, in <module>
from . import version
File "[..]/miniconda3/envs/h5pytest/lib/python3.9/site-packages/h5py/version.py", line 15, in <module>
from . import h5 as _h5
File "h5py/h5.pyx", line 1, in init h5py.h5
ImportError: [..]/miniconda3/envs/h5pytest/lib/python3.9/site-packages/h5py/defs.cpython-39-x86_64-linux-gnu.so: undefined symbol: H5Pset_fapl_ros3
Is it possible to somehow make the dependencies of h5py more narrow such that it will conflict with any hdf5 package that it is not compatible with?
E.g. maybe it is possible to allow only exactly one version of hdf5 as a dependency for each h5py package? (A different one for each h5py package: one that works for that build.)
Or maybe the build string can be used to distinguish different feature sets? That is, expanding the mechanism that is currently used to distinguish mpi and nompi builds, to also prevent other conflicts.
(Note that this particular conflict will always be possible until one or both of the offending packages are actually removed from the repository.)
Vice-versa, is there perhaps a mechanism that solvers like mamba can use to distinguish compatible versions in another way? As in, is there maybe something mamba might do to prevent such collisions without relying on package specific workarounds? (I'm not involved with mamba; I like it because it is so fast.)
h5py is running against HDF5 1.12.1 when it was built against 1.12.2
Solution to issue cannot be found in the documentation.
- I checked the documentation.
Issue
We ran into the well-known "h5py is running against HDF5 x when it was built against y" issue.
We could circumvent it by pinning to an ealier h5py version: mamba install -c conda-forge "h5py=3.6.0".
Interestingly the error is conditional on the order of imports.
Doing just import h5py results in
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\osthege\AppData\Local\Continuum\miniconda3\envs\dibecs_6.2.2\lib\site-packages\h5py\__init__.py", line 33, in <module>
from . import version
File "C:\Users\osthege\AppData\Local\Continuum\miniconda3\envs\dibecs_6.2.2\lib\site-packages\h5py\version.py", line 15, in <module>
from . import h5 as _h5
File "h5py\h5.pyx", line 1, in init h5py.h5
ImportError: DLL load failed while importing defs: Die angegebene Prozedur wurde nicht gefunden.
But doing python -c "import arviz;import h5py" gives the error below.
I have a feeling that this might be an incompatibility with NetCDF4.
C:\Users\osthege\AppData\Local\Continuum\miniconda3\envs\dibecs_6.2.2\lib\site-packages\h5py\__init__.py:36: UserWarning: h5py is running against HDF5 1.12.1 when it was built against 1.12.2, this may cause problems
_warn(("h5py is running against HDF5 {0} when it was built against {1}, "
Warning! ***HDF5 library version mismatched error***
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.12.2, library is 1.12.1
SUMMARY OF THE HDF5 CONFIGURATION
=================================
General Information:
-------------------
HDF5 Version: 1.12.1
Configured on: 2022-03-04
Configured by: Ninja
Host system: Windows-10.0.17763
Uname information: Windows
Byte sex: little-endian
Installation point: D:/bld/hdf5_split_1646412547396/_h_env/Library
Compiling Options:
------------------
Build Mode: RELEASE
Debugging Symbols: OFF
Asserts: OFF
Profiling: OFF
Optimization Level: OFF
Linking Options:
----------------
Libraries:
Statically Linked Executables: OFF
LDFLAGS: /machine:x64
H5_LDFLAGS:
AM_LDFLAGS:
Extra libraries: D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libcurl.lib;D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libssl.lib;D:/bld/hdf5_split_1646412547396/_h_env/Library/lib/libcrypto.lib
Archiver: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/lib.exe
Ranlib: :
Languages:
----------
C: YES
C Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/cl.exe 19.16.27045.0
CPPFLAGS:
H5_CPPFLAGS:
AM_CPPFLAGS:
CFLAGS: /DWIN32 /D_WINDOWS
H5_CFLAGS: /W3;/wd4100;/wd4706;/wd4127
AM_CFLAGS:
Shared C Library: YES
Static C Library: YES
Fortran: OFF
Fortran Compiler:
Fortran Flags:
H5 Fortran Flags:
AM Fortran Flags:
Shared Fortran Library: YES
Static Fortran Library: YES
C++: ON
C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio/2019/Enterprise/VC/Tools/MSVC/14.16.27023/bin/HostX64/x64/cl.exe 19.16.27045.0
C++ Flags:
H5 C++ Flags: /W3;/wd4100;/wd4706;/wd4127
AM C++ Flags:
Shared C++ Library: YES
Static C++ Library: YES
JAVA: OFF
JAVA Compiler:
Features:
---------
Parallel HDF5: OFF
Parallel Filtered Dataset Writes:
Large Parallel I/O:
High-level library: ON
Build HDF5 Tests: ON
Build HDF5 Tools: ON
Threadsafety: ON (recursive RW locks: )
Default API mapping: v112
With deprecated public symbols: ON
I/O filters (external): DEFLATE
MPE:
Direct VFD:
Mirror VFD:
(Read-Only) S3 VFD: 1
(Read-Only) HDFS VFD:
dmalloc:
Packages w/ extra debug output:
API Tracing: OFF
Using memory checker: OFF
Memory allocation sanity checks: OFF
Function Stack Tracing: OFF
Use file locking: best-effort
Strict File Format Checks: OFF
Optimization Instrumentation:
Bye...
Installed packages
aeppl 0.0.35 pypi_0 pypi
aesara 2.8.2 pypi_0 pypi
aiobotocore 2.4.0 pypi_0 pypi
aiohttp 3.8.1 pypi_0 pypi
aioitertools 0.10.0 pypi_0 pypi
aiosignal 1.2.0 pypi_0 pypi
appdirs 1.4.4 pypi_0 pypi
argon2-cffi 21.3.0 pypi_0 pypi
argon2-cffi-bindings 21.2.0 pypi_0 pypi
arviz 0.12.1 pypi_0 pypi
astroid 2.12.9 py38haa244fe_0 conda-forge
asttokens 2.0.8 pypi_0 pypi
async-timeout 4.0.2 pypi_0 pypi
attrs 22.1.0 pyh71513ae_1 conda-forge
backcall 0.2.0 pypi_0 pypi
beautifulsoup4 4.11.1 pypi_0 pypi
black 22.8.0 py38haa244fe_0 conda-forge
bleach 5.0.1 pypi_0 pypi
botocore 1.27.74 pypi_0 pypi
brotli 1.0.9 h8ffe710_7 conda-forge
brotli-bin 1.0.9 h8ffe710_7 conda-forge
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2022.9.14 h5b45459_0 conda-forge
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
cachetools 5.2.0 pypi_0 pypi
cairo 1.16.0 hd694305_1013 conda-forge
certifi 2022.9.14 pyhd8ed1ab_0 conda-forge
cffi 1.15.1 pypi_0 pypi
cfgv 3.3.1 pypi_0 pypi
cftime 1.6.1 pypi_0 pypi
charset-normalizer 2.1.1 pypi_0 pypi
click 8.1.3 py38haa244fe_0 conda-forge
cloudpickle 2.2.0 pypi_0 pypi
colorama 0.4.5 pyhd8ed1ab_0 conda-forge
cons 0.4.5 pypi_0 pypi
coverage 6.4.4 py38h294d835_0 conda-forge
csaps 1.1.0 pypi_0 pypi
cycler 0.11.0 pyhd8ed1ab_0 conda-forge
dask 2022.9.0 pypi_0 pypi
debugpy 1.6.3 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
defusedxml 0.7.1 pypi_0 pypi
dill 0.3.5.1 pyhd8ed1ab_0 conda-forge
distlib 0.3.6 pypi_0 pypi
distributed 2022.9.0 pypi_0 pypi
entrypoints 0.4 pypi_0 pypi
et-xmlfile 1.1.0 pypi_0 pypi
etuples 0.3.8 pypi_0 pypi
executing 1.0.0 pypi_0 pypi
expat 2.4.8 h39d44d4_0 conda-forge
fastjsonschema 2.16.1 pypi_0 pypi
fastprogress 1.0.3 pypi_0 pypi
filelock 3.8.0 pypi_0 pypi
font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge
font-ttf-inconsolata 3.000 h77eed37_0 conda-forge
font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge
font-ttf-ubuntu 0.83 hab24e00_0 conda-forge
fontconfig 2.14.0 hce3cb01_0 conda-forge
fonts-conda-ecosystem 1 0 conda-forge
fonts-conda-forge 1 0 conda-forge
fonttools 4.37.2 py38h91455d4_0 conda-forge
freetype 2.12.1 h546665d_0 conda-forge
fribidi 1.0.10 h8d14728_0 conda-forge
frozenlist 1.3.1 pypi_0 pypi
fsspec 2022.8.2 pypi_0 pypi
future 0.18.2 pypi_0 pypi
getopt-win32 0.1 h8ffe710_0 conda-forge
gettext 0.19.8.1 ha2e2712_1008 conda-forge
glib 2.72.1 h7755175_0 conda-forge
glib-tools 2.72.1 h7755175_0 conda-forge
graphite2 1.3.13 1000 conda-forge
graphviz 6.0.1 h8f5d4a1_0 conda-forge
gst-plugins-base 1.20.3 h001b923_1 conda-forge
gstreamer 1.20.3 h6b5321d_1 conda-forge
gts 0.7.6 h7c369d9_2 conda-forge
h5py 3.7.0 nompi_py38h35e7eba_101 conda-forge
hagelkorn 1.2.3 pypi_0 pypi
harfbuzz 5.1.0 h27de254_0 conda-forge
hdf5 1.12.2 nompi_h2a0e4a3_100 conda-forge
heapdict 1.0.1 pypi_0 pypi
icu 70.1 h0e60522_0 conda-forge
identify 2.5.5 pypi_0 pypi
idna 3.4 pypi_0 pypi
imageio 2.21.3 pypi_0 pypi
imageio-ffmpeg 0.4.7 pypi_0 pypi
importlib-metadata 4.12.0 pypi_0 pypi
importlib-resources 5.9.0 pypi_0 pypi
iniconfig 1.1.1 pyh9f0ad1d_0 conda-forge
intel-openmp 2022.1.0 h57928b3_3787 conda-forge
ipykernel 6.15.3 pypi_0 pypi
ipython 8.5.0 pypi_0 pypi
ipython-genutils 0.2.0 pypi_0 pypi
ipywidgets 8.0.2 pypi_0 pypi
isort 5.10.1 pyhd8ed1ab_0 conda-forge
jedi 0.18.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
jmespath 1.0.1 pypi_0 pypi
joblib 1.1.0 pypi_0 pypi
jpeg 9e h8ffe710_2 conda-forge
jsonpickle 2.2.0 pypi_0 pypi
jsonschema 4.16.0 pypi_0 pypi
jupyter 1.0.0 pypi_0 pypi
jupyter-client 7.3.4 pypi_0 pypi
jupyter-console 6.4.4 pypi_0 pypi
jupyter-core 4.11.1 pypi_0 pypi
jupyterlab-pygments 0.2.2 pypi_0 pypi
jupyterlab-widgets 3.0.3 pypi_0 pypi
kiwisolver 1.4.4 py38hbd9d945_0 conda-forge
krb5 1.19.3 h1176d77_0 conda-forge
lazy-object-proxy 1.7.1 py38h294d835_1 conda-forge
lcms2 2.12 h2a16943_0 conda-forge
lerc 4.0.0 h63175ca_0 conda-forge
libblas 3.9.0 16_win64_mkl conda-forge
libbrotlicommon 1.0.9 h8ffe710_7 conda-forge
libbrotlidec 1.0.9 h8ffe710_7 conda-forge
libbrotlienc 1.0.9 h8ffe710_7 conda-forge
libcblas 3.9.0 16_win64_mkl conda-forge
libclang 14.0.6 default_h77d9078_0 conda-forge
libclang13 14.0.6 default_h77d9078_0 conda-forge
libcurl 7.83.1 h789b8ee_0 conda-forge
libdeflate 1.14 hcfcfb64_0 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libgd 2.3.3 h891f43f_3 conda-forge
libglib 2.72.1 h3be07f2_0 conda-forge
libiconv 1.16 he774522_0 conda-forge
liblapack 3.9.0 16_win64_mkl conda-forge
libogg 1.3.4 h8ffe710_1 conda-forge
libpng 1.6.37 h1d00b33_4 conda-forge
libpython 2.2 py38haa244fe_1 conda-forge
libsqlite 3.39.3 hcfcfb64_0 conda-forge
libssh2 1.10.0 h680486a_3 conda-forge
libtiff 4.4.0 h8e97e67_4 conda-forge
libvorbis 1.3.7 h0e60522_0 conda-forge
libwebp 1.2.4 h8ffe710_0 conda-forge
libwebp-base 1.2.4 h8ffe710_0 conda-forge
libxcb 1.13 hcd874cb_1004 conda-forge
libzlib 1.2.12 hcfcfb64_3 conda-forge
llvmlite 0.38.1 py38h57a6900_0 conda-forge
locket 1.0.0 pypi_0 pypi
logical-unification 0.4.5 pypi_0 pypi
lxml 4.9.1 pypi_0 pypi
m2w64-binutils 2.25.1 5 conda-forge
m2w64-bzip2 1.0.6 6 conda-forge
m2w64-crt-git 5.0.0.4636.2595836 2 conda-forge
m2w64-gcc 5.3.0 6 conda-forge
m2w64-gcc-ada 5.3.0 6 conda-forge
m2w64-gcc-fortran 5.3.0 6 conda-forge
m2w64-gcc-libgfortran 5.3.0 6 conda-forge
m2w64-gcc-libs 5.3.0 7 conda-forge
m2w64-gcc-libs-core 5.3.0 7 conda-forge
m2w64-gcc-objc 5.3.0 6 conda-forge
m2w64-gmp 6.1.0 2 conda-forge
m2w64-headers-git 5.0.0.4636.c0ad18a 2 conda-forge
m2w64-isl 0.16.1 2 conda-forge
m2w64-libiconv 1.14 6 conda-forge
m2w64-libmangle-git 5.0.0.4509.2e5a9a2 2 conda-forge
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 conda-forge
m2w64-make 4.1.2351.a80a8b8 2 conda-forge
m2w64-mpc 1.0.3 3 conda-forge
m2w64-mpfr 3.1.4 4 conda-forge
m2w64-pkg-config 0.29.1 2 conda-forge
m2w64-toolchain 5.3.0 7 conda-forge
m2w64-tools-git 5.0.0.4592.90b8472 2 conda-forge
m2w64-windows-default-manifest 6.4 3 conda-forge
m2w64-winpthreads-git 5.0.0.4634.697f757 2 conda-forge
m2w64-zlib 1.2.8 10 conda-forge
markupsafe 2.1.1 pypi_0 pypi
matplotlib 3.5.3 py38haa244fe_2 conda-forge
matplotlib-base 3.5.3 py38h3268a40_2 conda-forge
matplotlib-inline 0.1.6 pypi_0 pypi
matrixprofile 1.1.10 pypi_0 pypi
mccabe 0.7.0 pyhd8ed1ab_0 conda-forge
minikanren 1.0.3 pypi_0 pypi
mistune 2.0.4 pypi_0 pypi
mkl 2022.1.0 h6a75c08_874 conda-forge
mkl-service 2.4.0 py38hb96a4b1_0 conda-forge
msgpack 1.0.4 pypi_0 pypi
msys2-conda-epoch 20160418 1 conda-forge
multidict 6.0.2 pypi_0 pypi
multipledispatch 0.6.0 pypi_0 pypi
munkres 1.1.4 pyh9f0ad1d_0 conda-forge
mypy 0.971 py38h294d835_0 conda-forge
mypy_extensions 0.4.3 py38haa244fe_5 conda-forge
nbclient 0.6.8 pypi_0 pypi
nbconvert 7.0.0 pypi_0 pypi
nbformat 5.5.0 pypi_0 pypi
nest-asyncio 1.5.5 pypi_0 pypi
netcdf4 1.6.1 pypi_0 pypi
nodeenv 1.7.0 pypi_0 pypi
notebook 6.4.12 pypi_0 pypi
numba 0.55.2 py38h860efb6_0 conda-forge
numpy 1.22.4 py38h1d2777f_0 conda-forge
omero-py 5.12.0 pypi_0 pypi
openjpeg 2.5.0 hc9384bd_1 conda-forge
openpyxl 3.0.10 pypi_0 pypi
openssl 1.1.1q h8ffe710_0 conda-forge
packaging 21.3 pyhd8ed1ab_0 conda-forge
paho-mqtt 1.6.1 pypi_0 pypi
pandas 1.4.4 py38h0ae2778_0 conda-forge
pandocfilters 1.5.0 pypi_0 pypi
pango 1.50.9 h480d202_0 conda-forge
parso 0.8.3 pypi_0 pypi
partd 1.3.0 pypi_0 pypi
pathspec 0.10.1 pyhd8ed1ab_0 conda-forge
patsy 0.5.2 pypi_0 pypi
pcre 8.45 h0e60522_0 conda-forge
pickleshare 0.7.5 pypi_0 pypi
pillow 9.2.0 py38h37aa274_2 conda-forge
pip 22.2.2 pyhd8ed1ab_0 conda-forge
pixman 0.40.0 h8ffe710_0 conda-forge
pkgutil-resolve-name 1.3.10 pypi_0 pypi
platformdirs 2.5.2 pyhd8ed1ab_1 conda-forge
pluggy 1.0.0 py38haa244fe_3 conda-forge
ply 3.11 py_1 conda-forge
pre-commit 2.20.0 pypi_0 pypi
prometheus-client 0.14.1 pypi_0 pypi
prompt-toolkit 3.0.31 pypi_0 pypi
protobuf 3.11.2 pypi_0 pypi
psutil 5.9.2 py38h91455d4_0 conda-forge
pthread-stubs 0.4 hcd874cb_1001 conda-forge
pure-eval 0.2.2 pypi_0 pypi
py 1.11.0 pyh6c4a22f_0 conda-forge
pycparser 2.21 pypi_0 pypi
pygments 2.13.0 pypi_0 pypi
pylint 2.15.2 pyhd8ed1ab_0 conda-forge
pymc 4.1.7 pypi_0 pypi
pyparsing 3.0.9 pyhd8ed1ab_0 conda-forge
pyqt 5.15.7 py38h75e37d8_0 conda-forge
pyqt5-sip 12.11.0 py38h885f38d_0 conda-forge
pyrff 2.0.2 pypi_0 pypi
pyrsistent 0.18.1 pypi_0 pypi
pytest 7.1.3 py38haa244fe_0 conda-forge
pytest-cov 3.0.0 pyhd8ed1ab_0 conda-forge
python 3.8.13 h9a09f29_0_cpython conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-graphviz 0.20.1 pyh22cad53_0 conda-forge
python-logging-loki 0.3.1 pypi_0 pypi
python_abi 3.8 2_cp38 conda-forge
pythonnet 2.5.2 pypi_0 pypi
pytz 2022.2.1 pyhd8ed1ab_0 conda-forge
pywin32 304 pypi_0 pypi
pywinpty 2.0.8 pypi_0 pypi
pyyaml 6.0 pypi_0 pypi
pyzmq 24.0.0 pypi_0 pypi
qt-main 5.15.6 hf0cf448_0 conda-forge
qtconsole 5.3.2 pypi_0 pypi
qtpy 2.2.0 pypi_0 pypi
requests 2.28.1 pypi_0 pypi
rfc3339 6.2 pypi_0 pypi
robotools 1.3.0 pypi_0 pypi
s3fs 2022.8.2 pypi_0 pypi
s3transfer 0.6.0 pypi_0 pypi
scikit-learn 1.1.2 pypi_0 pypi
scipy 1.9.1 py38h91810f7_0 conda-forge
seaborn 0.12.0 pypi_0 pypi
send2trash 1.8.0 pypi_0 pypi
setuptools 65.3.0 pyhd8ed1ab_1 conda-forge
sip 6.6.2 py38h885f38d_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
slack-sdk 3.18.3 pypi_0 pypi
sortedcontainers 2.4.0 pypi_0 pypi
soupsieve 2.3.2.post1 pypi_0 pypi
sqlite 3.39.3 hcfcfb64_0 conda-forge
stack-data 0.5.0 pypi_0 pypi
statsmodels 0.13.2 pypi_0 pypi
stumpy 1.11.1 pypi_0 pypi
tbb 2021.5.0 h91493d7_2 conda-forge
tblib 1.7.0 pypi_0 pypi
terminado 0.15.0 pypi_0 pypi
threadpoolctl 3.1.0 pypi_0 pypi
tinycss2 1.1.1 pypi_0 pypi
tk 8.6.12 h8ffe710_0 conda-forge
toml 0.10.2 pyhd8ed1ab_0 conda-forge
tomli 2.0.1 pyhd8ed1ab_0 conda-forge
tomlkit 0.11.4 pyha770c72_0 conda-forge
toolz 0.12.0 pypi_0 pypi
tornado 6.1 pypi_0 pypi
tqdm 4.64.1 pypi_0 pypi
traitlets 5.4.0 pyhd8ed1ab_0 conda-forge
tsfresh 0.19.0 pypi_0 pypi
typing 3.7.4.3 pypi_0 pypi
typing-extensions 4.3.0 hd8ed1ab_0 conda-forge
typing_extensions 4.3.0 pyha770c72_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
unicodedata2 14.0.0 py38h294d835_1 conda-forge
urllib3 1.26.12 pypi_0 pypi
vc 14.2 hb210afc_7 conda-forge
virtualenv 20.16.5 pypi_0 pypi
vs2015_runtime 14.29.30139 h890b9b1_7 conda-forge
wcwidth 0.2.5 pypi_0 pypi
webencodings 0.5.1 pypi_0 pypi
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
widgetsnbextension 4.0.3 pypi_0 pypi
wrapt 1.14.1 py38h294d835_0 conda-forge
xarray 2022.6.0 pypi_0 pypi
xarray-einstats 0.3.0 pypi_0 pypi
xlrd 2.0.1 pypi_0 pypi
xorg-kbproto 1.0.7 hcd874cb_1002 conda-forge
xorg-libice 1.0.10 hcd874cb_0 conda-forge
xorg-libsm 1.2.3 hcd874cb_1000 conda-forge
xorg-libx11 1.7.2 hcd874cb_0 conda-forge
xorg-libxau 1.0.9 hcd874cb_0 conda-forge
xorg-libxdmcp 1.1.3 hcd874cb_0 conda-forge
xorg-libxext 1.3.4 hcd874cb_1 conda-forge
xorg-libxpm 3.5.13 hcd874cb_0 conda-forge
xorg-libxt 1.2.1 hcd874cb_2 conda-forge
xorg-xextproto 7.3.0 hcd874cb_1002 conda-forge
xorg-xproto 7.0.31 hcd874cb_1007 conda-forge
xz 5.2.6 h8d14728_0 conda-forge
yarl 1.8.1 pypi_0 pypi
zeroc-ice 3.6.5 py38hb9ab681_4 conda-forge
zict 2.2.0 pypi_0 pypi
zipp 3.8.1 pypi_0 pypi
zlib 1.2.12 hcfcfb64_3 conda-forge
zstd 1.5.2 h7755175_4 conda-forgeEnvironment info
active environment : dibecs_6.2.2
active env location : C:\Users\osthege\AppData\Local\Continuum\miniconda3\envs\dibecs_6.2.2
shell level : 1
user config file : C:\Users\osthege\.condarc
populated config files : C:\Users\osthege\.condarc
conda version : 4.12.0
conda-build version : 3.10.9
python version : 3.7.2.final.0
virtual packages : __cuda=11.0=0
__win=0=0
__archspec=1=x86_64
base environment : C:\Users\osthege\AppData\Local\Continuum\miniconda3 (writable)
conda av data dir : C:\Users\osthege\AppData\Local\Continuum\miniconda3\etc\conda
conda av metadata url : None
channel URLs : https://repo.anaconda.com/pkgs/main/win-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/win-64
https://repo.anaconda.com/pkgs/r/noarch
https://repo.anaconda.com/pkgs/msys2/win-64
https://repo.anaconda.com/pkgs/msys2/noarch
package cache : C:\Users\osthege\AppData\Local\Continuum\miniconda3\pkgs
C:\Users\osthege\.conda\pkgs
C:\Users\osthege\AppData\Local\conda\conda\pkgs
envs directories : C:\Users\osthege\AppData\Local\Continuum\miniconda3\envs
C:\Users\osthege\.conda\envs
C:\Users\osthege\AppData\Local\conda\conda\envs
platform : win-64
user-agent : conda/4.12.0 requests/2.27.1 CPython/3.7.2 Windows/10 Windows/10.0.19041
administrator : False
netrc file : None
offline mode : FalseUsing h5py from Matlab
This is a bit of a bizarre use case, I know!
I'm working with a neuroscience library (https://github.com/NeurodataWithoutBorders/api-python) that provides a Matlab interface via a bridge to their Python API, using Matlab's support for accessing Python modules. This library uses h5py to work with HDF5 files. The issue is that Matlab bundles its own version of HDF5 (1.8.12 as of Matlab 2016b) and Strange Things can happen if h5py is using a different HDF5 library.
Unfortunately 1.8.12 is one of the HDF5 versions that isn't available in the default conda channel, nor is it available here. Do you have any advice on the best way to provide non-techy users (some of them on Windows, some on Mac OS) a conda-installable HDF5 1.8.12 and h5py to match?
Issue reading szip compressed datasets with anaconda install vs pip install
Issue:
conda install h5py and reading a h5 file with szip compression causes error above. No issues reading tables in same file with no compression.
Update to hdf5 1.10.5
The bot just went around trying to get everything on hdf5 1.10.5, but if a project has a build dependency on hdf5 and a run dependency on h5py, then hdf5 must be pinned to 1.10.4 because h5py is pinned to 1.10.4.
Require at least HDF5-1.10.7?
- I read the conda-forge documentation and could not find the solution for my problem there.
Would it be possible to upgrade HDF5 library requirement to at least v1.10.7? This version contains an important fix for h5py.h5ds.is_scale() per the release notes:
The High Level H5DSis_scale function was fixed to correctly handle CLASS and DIMENSION_SCALE attributes that were not written according to the Dimension Scales Specification.
There is also #91 that would fix this issue.
Problem with python version using parallel h5py in linux
Issue:
h5py with parallel support seems to be only available for python >= 3.7 in linux? In a newly updated conda (linux) I get
conda create --name h5py python=2.7 h5py=2.9.0=mpi_mpich_*
Collecting package metadata: done
Solving environment: failed
UnsatisfiableError: The following specifications were found to be in conflict:
- h5py==2.9.0[build=mpi_mpich_*] -> python[version='>=3.7,<3.8.0a0']
- python=2.7
On my mac (osx64) there is no error though, so it's only linux.
Same for both openmpi and mpich. Some additional insight can be obtained using conda search:
conda search h5py=2.9.0=mpi_openmpi_* --info
Loading channels: done
h5py 2.9.0 mpi_openmpi_hc1ffa35_1001
------------------------------------
file name : h5py-2.9.0-mpi_openmpi_hc1ffa35_1001.tar.bz2
name : h5py
version : 2.9.0
build : mpi_openmpi_hc1ffa35_1001
build number: 1001
size : 1.2 MB
license : BSD-3-Clause
subdir : linux-64
url : https://conda.anaconda.org/conda-forge/linux-64/h5py-2.9.0-mpi_openmpi_hc1ffa35_1001.tar.bz2
md5 : 1bb956e1d0ba6c8c1e161f4ed5cea2ca
timestamp : 2019-01-08 22:52:32 UTC
dependencies:
- hdf5 >=1.10.4,<1.10.5.0a0 mpi_openmpi_*
- libgcc-ng >=7.3.0
- mpi4py
- numpy >=1.9.3,<2.0a0
- openmpi >=3.1,<3.2.0a0
- python >=3.7,<3.8.0a0
- six
h5py 2.9.0 mpi_openmpi_hff00661_1001
------------------------------------
file name : h5py-2.9.0-mpi_openmpi_hff00661_1001.tar.bz2
name : h5py
version : 2.9.0
build : mpi_openmpi_hff00661_1001
build number: 1
size : 4.1 MB
license : BSD-3-Clause
subdir : linux-64
url : https://conda.anaconda.org/conda-forge/linux-64/h5py-2.9.0-mpi_openmpi_hff00661_1001.tar.bz2
md5 : af1b6313f7bd65b41acc59f435fd6b6a
timestamp : 2019-01-08 22:41:17 UTC
dependencies:
- hdf5 >=1.10.4,<1.10.5.0a0 mpi_openmpi_*
- libgcc-ng >=4.9
- mpi4py
- numpy >=1.9.3,<2.0a0
- openmpi >=3.1,<3.2.0a0
- python >=3.6,<3.7.0a0
- six
If we look at the last package: h5py-2.9.0-mpi_openmpi_hff00661_1001.tar.bz2, this is actually built for python 2.7. I do not understand how it can end up with a python 3.7 dependency? Perhaps you know what is happening @minrk?
Environment (
conda list):
conda list
# packages in environment at /home/mikael/anaconda3:
#
# Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py37_0
alabaster 0.7.12 py37_0
anaconda 2018.12 py37_0
anaconda-client 1.7.2 py37_0
anaconda-navigator 1.9.6 py37_0
anaconda-project 0.8.2 py37_0
asn1crypto 0.24.0 py37_0
astroid 2.1.0 py37_0
astropy 3.1 py37h7b6447c_0
atomicwrites 1.2.1 py37_0
attrs 18.2.0 py37h28b3542_0
babel 2.6.0 py37_0
backcall 0.1.0 py37_0
backports 1.0 py37_1
backports.os 0.1.1 py37_0
backports.shutil_get_terminal_size 1.0.0 py37_2
beautifulsoup4 4.6.3 py37_0
bitarray 0.8.3 py37h14c3975_0
bkcharts 0.2 py37_0
blas 1.0 mkl
blaze 0.11.3 py37_0
blosc 1.14.4 hdbcaa40_0
bokeh 1.0.2 py37_0
boto 2.49.0 py37_0
bottleneck 1.2.1 py37h035aef0_1
bzip2 1.0.6 h14c3975_5
ca-certificates 2018.03.07 0
cairo 1.14.12 h8948797_3
certifi 2018.11.29 py37_0
cffi 1.11.5 py37he75722e_1
chardet 3.0.4 py37_1
click 7.0 py37_0
cloudpickle 0.6.1 py37_0
clyent 1.2.2 py37_1
colorama 0.4.1 py37_0
conda 4.6.1 py37_0 conda-forge
conda-build 3.17.7 py37_0 conda-forge
conda-env 2.6.0 1
conda-verify 3.1.1 py37_0
contextlib2 0.5.5 py37_0
cryptography 2.4.2 py37h1ba5d50_0
curl 7.63.0 hbc83047_1000
cycler 0.10.0 py37_0
cython 0.29.2 py37he6710b0_0
cytoolz 0.9.0.1 py37h14c3975_1
dask 1.0.0 py37_0
dask-core 1.0.0 py37_0
datashape 0.5.4 py37_1
dbus 1.13.2 h714fa37_1
decorator 4.3.0 py37_0
defusedxml 0.5.0 py37_1
distributed 1.25.1 py37_0
docutils 0.14 py37_0
entrypoints 0.2.3 py37_2
et_xmlfile 1.0.1 py37_0
expat 2.2.6 he6710b0_0
fastcache 1.0.2 py37h14c3975_2
filelock 3.0.10 py37_0
flask 1.0.2 py37_1
flask-cors 3.0.7 py37_0
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
fribidi 1.0.5 h7b6447c_0
future 0.17.1 py37_0
get_terminal_size 1.0.0 haa9412d_0
gevent 1.3.7 py37h7b6447c_1
glib 2.56.2 hd408876_0
glob2 0.6 py37_1
gmp 6.1.2 h6c8ec71_1
gmpy2 2.0.8 py37h10f8cd9_2
graphite2 1.3.12 h23475e2_2
greenlet 0.4.15 py37h7b6447c_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.8.0 py37h989c5e5_3
harfbuzz 1.8.8 hffaf4a1_0
hdf5 1.10.2 hba1933b_1
heapdict 1.0.0 py37_2
html5lib 1.0.1 py37_0
icu 58.2 h9c2bf20_1
idna 2.8 py37_0
imageio 2.4.1 py37_0
imagesize 1.1.0 py37_0
importlib_metadata 0.6 py37_0
intel-openmp 2019.1 144
ipykernel 5.1.0 py37h39e3cac_0
ipython 7.2.0 py37h39e3cac_0
ipython_genutils 0.2.0 py37_0
ipywidgets 7.4.2 py37_0
isort 4.3.4 py37_0
itsdangerous 1.1.0 py37_0
jbig 2.1 hdba287a_0
jdcal 1.4 py37_0
jedi 0.13.2 py37_0
jeepney 0.4 py37_0
jinja2 2.10 py37_0
jpeg 9b h024ee3a_2
jsonschema 2.6.0 py37_0
jupyter 1.0.0 py37_7
jupyter_client 5.2.4 py37_0
jupyter_console 6.0.0 py37_0
jupyter_core 4.4.0 py37_0
jupyterlab 0.35.3 py37_0
jupyterlab_server 0.2.0 py37_0
keyring 17.0.0 py37_0
kiwisolver 1.0.1 py37hf484d3e_0
krb5 1.16.1 h173b8e3_7
lazy-object-proxy 1.3.1 py37h14c3975_2
libarchive 3.3.3 h5d8350f_5
libcurl 7.63.0 h20c2e04_1000
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libgfortran-ng 7.3.0 hdf63c60_0
liblief 0.9.0 h7725739_1
libpng 1.6.35 hbc83047_0
libsodium 1.0.16 h1bed415_0
libssh2 1.8.0 h1ba5d50_4
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.9 he85c1e1_2
libtool 2.4.6 h7b6447c_5
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.8 h26e45fe_1
libxslt 1.1.32 h1312cb7_0
llvmlite 0.26.0 py37hd408876_0
locket 0.2.0 py37_1
lxml 4.2.5 py37hefd8a0e_0
lz4-c 1.8.1.2 h14c3975_0
lzo 2.10 h49e0be7_2
markupsafe 1.1.0 py37h7b6447c_0
matplotlib 3.0.2 py37h5429711_0
mccabe 0.6.1 py37_1
mistune 0.8.4 py37h7b6447c_0
mkl 2019.1 144
mkl-service 1.1.2 py37he904b0f_5
mkl_fft 1.0.6 py37hd81dba3_0
mkl_random 1.0.2 py37hd81dba3_0
more-itertools 4.3.0 py37_0
mpc 1.1.0 h10f8cd9_1
mpfr 4.0.1 hdf1c602_3
mpmath 1.1.0 py37_0
msgpack-python 0.5.6 py37h6bb024c_1
multipledispatch 0.6.0 py37_0
navigator-updater 0.2.1 py37_0
nbconvert 5.4.0 py37_1
nbformat 4.4.0 py37_0
ncurses 6.1 he6710b0_1
networkx 2.2 py37_1
nltk 3.4 py37_1
nose 1.3.7 py37_2
notebook 5.7.4 py37_0
numba 0.41.0 py37h962f231_0
numexpr 2.6.8 py37h9e4a6bb_0
numpy 1.15.4 py37h7e9f1db_0
numpy-base 1.15.4 py37hde5b4d6_0
numpydoc 0.8.0 py37_0
odo 0.5.1 py37_0
olefile 0.46 py37_0
openpyxl 2.5.12 py37_0
openssl 1.1.1a h7b6447c_0
packaging 18.0 py37_0
pandas 0.23.4 py37h04863e7_0
pandoc 1.19.2.1 hea2e7c5_1
pandocfilters 1.4.2 py37_1
pango 1.42.4 h049681c_0
parso 0.3.1 py37_0
partd 0.3.9 py37_0
patchelf 0.9 he6710b0_3
path.py 11.5.0 py37_0
pathlib2 2.3.3 py37_0
patsy 0.5.1 py37_0
pcre 8.42 h439df22_0
pep8 1.7.1 py37_0
pexpect 4.6.0 py37_0
pickleshare 0.7.5 py37_0
pillow 5.3.0 py37h34e0f95_0
pip 18.1 py37_0
pixman 0.34.0 hceecf20_3
pkginfo 1.4.2 py37_1
pluggy 0.8.0 py37_0
ply 3.11 py37_0
prometheus_client 0.5.0 py37_0
prompt_toolkit 2.0.7 py37_0
psutil 5.4.8 py37h7b6447c_0
ptyprocess 0.6.0 py37_0
py 1.7.0 py37_0
py-lief 0.9.0 py37h7725739_1
pycodestyle 2.4.0 py37_0
pycosat 0.6.3 py37h14c3975_0
pycparser 2.19 py37_0
pycrypto 2.6.1 py37h14c3975_9
pycurl 7.43.0.2 py37h1ba5d50_0
pyflakes 2.0.0 py37_0
pygments 2.3.1 py37_0
pylint 2.2.2 py37_0
pyodbc 4.0.25 py37he6710b0_0
pyopenssl 18.0.0 py37_0
pyparsing 2.3.0 py37_0
pyqt 5.9.2 py37h05f1152_2
pysocks 1.6.8 py37_0
pytables 3.4.4 py37ha205bf6_0
pytest 4.0.2 py37_0
pytest-arraydiff 0.3 py37h39e3cac_0
pytest-astropy 0.5.0 py37_0
pytest-doctestplus 0.2.0 py37_0
pytest-openfiles 0.3.1 py37_0
pytest-remotedata 0.3.1 py37_0
python 3.7.1 h0371630_7
python-dateutil 2.7.5 py37_0
python-libarchive-c 2.8 py37_6
pytz 2018.7 py37_0
pywavelets 1.0.1 py37hdd07704_0
pyyaml 3.13 py37h14c3975_0
pyzmq 17.1.2 py37h14c3975_0
qt 5.9.7 h5867ecd_1
qtawesome 0.5.3 py37_0
qtconsole 4.4.3 py37_0
qtpy 1.5.2 py37_0
readline 7.0 h7b6447c_5
requests 2.21.0 py37_0
rope 0.11.0 py37_0
ruamel_yaml 0.15.46 py37h14c3975_0
scikit-image 0.14.1 py37he6710b0_0
scikit-learn 0.20.1 py37hd81dba3_0
scipy 1.1.0 py37h7c811a0_2
seaborn 0.9.0 py37_0
secretstorage 3.1.0 py37_0
send2trash 1.5.0 py37_0
setuptools 40.6.3 py37_0
simplegeneric 0.8.1 py37_2
singledispatch 3.4.0.3 py37_0
sip 4.19.8 py37hf484d3e_0
six 1.12.0 py37_0
snappy 1.1.7 hbae5bb6_3
snowballstemmer 1.2.1 py37_0
sortedcollections 1.0.1 py37_0
sortedcontainers 2.1.0 py37_0
sphinx 1.8.2 py37_0
sphinxcontrib 1.0 py37_1
sphinxcontrib-websupport 1.1.0 py37_1
spyder 3.3.2 py37_0
spyder-kernels 0.3.0 py37_0
sqlalchemy 1.2.15 py37h7b6447c_0
sqlite 3.26.0 h7b6447c_0
statsmodels 0.9.0 py37h035aef0_0
sympy 1.3 py37_0
tblib 1.3.2 py37_0
terminado 0.8.1 py37_1
testpath 0.4.2 py37_0
tk 8.6.8 hbc83047_0
toolz 0.9.0 py37_0
tornado 5.1.1 py37h7b6447c_0
tqdm 4.28.1 py37h28b3542_0
traitlets 4.3.2 py37_0
unicodecsv 0.14.1 py37_0
unixodbc 2.3.7 h14c3975_0
urllib3 1.24.1 py37_0
wcwidth 0.1.7 py37_0
webencodings 0.5.1 py37_1
werkzeug 0.14.1 py37_0
wheel 0.32.3 py37_0
widgetsnbextension 3.4.2 py37_0
wrapt 1.10.11 py37h14c3975_2
wurlitzer 1.0.2 py37_0
xlrd 1.2.0 py37_0
xlsxwriter 1.1.2 py37_0
xlwt 1.3.0 py37_0
xz 5.2.4 h14c3975_4
yaml 0.1.7 had09818_2
zeromq 4.2.5 hf484d3e_1
zict 0.1.3 py37_0
zlib 1.2.11 h7b6447c_3
zstd 1.3.7 h0b5b093_0
Details about
conda and system ( conda info ):
conda info
active environment : base
active env location : /home/mikael/anaconda3
shell level : 1
user config file : /home/mikael/.condarc
populated config files : /home/mikael/.condarc
conda version : 4.6.1
conda-build version : 3.17.7
python version : 3.7.1.final.0
base environment : /home/mikael/anaconda3 (writable)
channel URLs : https://conda.anaconda.org/conda-forge/linux-64
https://conda.anaconda.org/conda-forge/noarch
https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/free/linux-64
https://repo.anaconda.com/pkgs/free/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/mikael/anaconda3/pkgs
/home/mikael/.conda/pkgs
envs directories : /home/mikael/anaconda3/envs
/home/mikael/.conda/envs
platform : linux-64
user-agent : conda/4.6.1 requests/2.21.0 CPython/3.7.1 Linux/4.15.0-43-generic ubuntu/18.04.1 glibc/2.27
UID:GID : 1000:1000
netrc file : None
offline mode : False
Add myself as maintainer
@conda-forge-admin, please add user @jan-janssen
The error disappears when using channel anaconda...
trouble installing h5py from conda forge, ros3 driver
- I read the conda-forge documentation and could not find the solution for my problem there.
Issue:
Hy devs. Thanks for your work on this. I'm having trouble installing h5py from conda forge. Here are the steps I am using:
$ conda create -n test_h5py python=3.8 -y
$ conda activate test_h5py
$ conda install -c conda-forge h5py -y
Collecting package metadata (current_repodata.json): done
Solving environment: done
## Package Plan ##
environment location: /Users/bendichter/opt/miniconda3/envs/ajile
added / updated specs:
- h5py
The following NEW packages will be INSTALLED:
cached-property conda-forge/noarch::cached-property-1.5.2-hd8ed1ab_1
cached_property conda-forge/noarch::cached_property-1.5.2-pyha770c72_1
h5py conda-forge/osx-64::h5py-3.3.0-nompi_py39h1bb8402_100
hdf5 conda-forge/osx-64::hdf5-1.10.6-nompi_h3e39495_100
libblas conda-forge/osx-64::libblas-3.9.0-8_openblas
libcblas conda-forge/osx-64::libcblas-3.9.0-8_openblas
libgfortran conda-forge/osx-64::libgfortran-4.0.0-7_5_0_h1a10cd1_23
libgfortran4 conda-forge/osx-64::libgfortran4-7.5.0-h1a10cd1_23
liblapack conda-forge/osx-64::liblapack-3.9.0-8_openblas
libopenblas conda-forge/osx-64::libopenblas-0.3.12-openmp_h63d9170_1
llvm-openmp conda-forge/osx-64::llvm-openmp-12.0.1-hda6cdc1_1
numpy conda-forge/osx-64::numpy-1.21.1-py39h7eed0ac_0
python_abi conda-forge/osx-64::python_abi-3.9-2_cp39
The following packages will be SUPERSEDED by a higher-priority channel:
certifi pkgs/main::certifi-2021.5.30-py39hecd~ --> conda-forge::certifi-2021.5.30-py39h6e9494a_0
openssl pkgs/main::openssl-1.1.1k-h9ed2024_0 --> conda-forge::openssl-1.1.1k-h0d85af4_0
$ python
>>> import h5py
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/bendichter/opt/anaconda3/envs/test_h5py/lib/python3.7/site-packages/h5py/__init__.py", line 33, in <module>
from . import version
File "/Users/bendichter/opt/anaconda3/envs/test_h5py/lib/python3.7/site-packages/h5py/version.py", line 15, in <module>
from . import h5 as _h5
File "h5py/h5.pyx", line 1, in init h5py.h5
ImportError: dlopen(/Users/bendichter/opt/anaconda3/envs/test_h5py/lib/python3.7/site-packages/h5py/defs.cpython-37m-darwin.so, 2): Symbol not found: _H5Pget_fapl_ros3
Referenced from: /Users/bendichter/opt/anaconda3/envs/test_h5py/lib/python3.7/site-packages/h5py/defs.cpython-37m-darwin.so
Expected in: /Users/bendichter/opt/anaconda3/envs/test_h5py/lib/libhdf5.103.dylib
in /Users/bendichter/opt/anaconda3/envs/test_h5py/lib/python3.7/site-packages/h5py/defs.cpython-37m-darwin.soI want to use the ros3 driver, so I can't simply downgrade h5py to an earlier version that does not support ros3.
Environment (
conda list):
$ conda list
ca-certificates 2021.7.5 hecd8cb5_1
cached-property 1.5.2 hd8ed1ab_1 conda-forge
cached_property 1.5.2 pyha770c72_1 conda-forge
certifi 2021.5.30 py38h50d1736_0 conda-forge
h5py 3.3.0 nompi_py38h9a16e60_100 conda-forge
hdf5 1.10.6 nompi_h3e39495_100 conda-forge
libblas 3.9.0 8_openblas conda-forge
libcblas 3.9.0 8_openblas conda-forge
libcxx 10.0.0 1
libffi 3.3 hb1e8313_2
libgfortran 4.0.0 7_5_0_h1a10cd1_23 conda-forge
libgfortran4 7.5.0 h1a10cd1_23 conda-forge
liblapack 3.9.0 8_openblas conda-forge
libopenblas 0.3.12 openmp_h63d9170_1 conda-forge
llvm-openmp 12.0.1 hda6cdc1_1 conda-forge
ncurses 6.2 h0a44026_1
numpy 1.21.1 py38had91d27_0 conda-forge
openssl 1.1.1k h0d85af4_0 conda-forge
pip 21.2.2 py38hecd8cb5_0
python 3.8.11 h88f2d9e_0_cpython
python_abi 3.8 2_cp38 conda-forge
readline 8.1 h9ed2024_0
setuptools 52.0.0 py38hecd8cb5_0
sqlite 3.36.0 hce871da_0
tk 8.6.10 hb0a8c7a_0
wheel 0.36.2 pyhd3eb1b0_0
xz 5.2.5 h1de35cc_0
zlib 1.2.11 h1de35cc_3
Details about
conda and system ( conda info ):
$ conda info
active environment : ajile
active env location : /Users/bendichter/opt/miniconda3/envs/ajile
shell level : 2
user config file : /Users/bendichter/.condarc
populated config files : /Users/bendichter/.condarc
conda version : 4.10.3
conda-build version : not installed
python version : 3.9.5.final.0
virtual packages : __osx=10.16=0
__unix=0=0
__archspec=1=x86_64
base environment : /Users/bendichter/opt/miniconda3 (writable)
conda av data dir : /Users/bendichter/opt/miniconda3/etc/conda
conda av metadata url : None
channel URLs : https://repo.anaconda.com/pkgs/main/osx-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/osx-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /Users/bendichter/opt/miniconda3/pkgs
/Users/bendichter/.conda/pkgs
envs directories : /Users/bendichter/opt/miniconda3/envs
/Users/bendichter/.conda/envs
platform : osx-64
user-agent : conda/4.10.3 requests/2.25.1 CPython/3.9.5 Darwin/20.6.0 OSX/10.16
UID:GID : 502:20
netrc file : None
offline mode : False
Please, re-render
In order to build numpy 1.12.
h5py parallel build
The conda-forge hdf5 now offers a parallel build (conda-forge/hdf5-feedstock#90). I'd really like to see this channel offer a parallel h5py build as well. What can I do to help get this going?
Cannot install certain packages after h5py without breaking h5py
An example script is below. Running it gives me this error:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/Users/mhearne/anaconda/envs/shake/lib/python3.5/site-packages/h5py/__init__.py", line 24, in <module>
from . import _errors
ImportError: dlopen(/Users/mhearne/anaconda/envs/shake/lib/python3.5/site-packages/h5py/_errors.cpython-35m-darwin.so, 2): Library not loaded: @rpath/libhdf5.10.dylib
Referenced from: /Users/mhearne/anaconda/envs/shake/lib/python3.5/site-packages/h5py/_errors.cpython-35m-darwin.so
Reason: Incompatible library version: _errors.cpython-35m-darwin.so requires version 12.0.0 or later, but libhdf5.10.dylib provides version 11.0.0#!/bin/sh
source activate root
conda remove --name shake --all -y
conda create --name shake --channel conda-forge python=3 -y
source activate shake
conda install -y numpy
conda install -y scipy
conda install -y matplotlib
conda install -y jupyter
conda install -y h5py
python -c "import h5py"
conda install -y rasterio
python -c "import h5py"@conda-forge-admin, please rerender
@conda-forge-admin, please rerender
(Based on my previous experience, it will be necessary to comment an extra time for the rerender to actually get triggered.)
Yet another “h5py is running against HDF5 1.10.5 when it was built against 1.10.4” error while trying to run OpenMMD
Hello.
I'm trying to directly convert real-person videos to the motion of animation models (i.e. Miku, Anmicius) following the instructions located here :
https://github.com/peterljq/OpenMMD
and here :
https://www.youtube.com/watch?v=hKx6jl9a5-I
after having issued all the commands proposed,this is what happens :
C:\Users\marietto2020\Desktop\MMD\OpenMMD\OpenMMD 1.0\3d-pose-baseline-vmd
(tensorflow) λ openposeto3d
´╗┐es (40 sloc) 1.62 KB
"´╗┐es" is not recognized as an internal or external command or as a batch file.
Please input the path of result from OpenPose Execution: JSON folder
Input is limited to English characters and numbers.
Ôûáthe path of result from OpenPose Execution (JSON folder): json
The max number of people in your video.
If no input and press Enter, the number of be set to default: 1 person.
The max number of people in your video: 1
If you want the detailed information of GIF, input yes.
If no input and press Enter, the generation setting of GIF will be set to default.
warn If you input warn, then no GIF will be generated.
the detailed information[yes/no/warn]: yes
C:\Users\marietto2020.conda\envs\tensorflow\lib\site-packages\h5py_init_.py:40: UserWarning: h5py is running against HDF5 1.10.5 when it was built against 1.10.4, this may cause problems
'{0}.{1}.{2}'.format(*version.hdf5_built_version_tuple)
Warning! HDF5 library version mismatched error
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.10.4, library is 1.10.5
SUMMARY OF THE HDF5 CONFIGURATION
=================================
General Information:
HDF5 Version: 1.10.5
Configured on: 2019-03-04
Configured by: Visual Studio 14 2015 Win64
Host system: Windows-10.0.17763
Uname information: Windows
Byte sex: little-endian
Installation point: C:/Program Files/HDF5
Compiling Options:
Build Mode:
Debugging Symbols:
Asserts:
Profiling:
Optimization Level:
Linking Options:
Libraries:
Statically Linked Executables: OFF
LDFLAGS: /machine:x64
H5_LDFLAGS:
AM_LDFLAGS:
Extra libraries:
Archiver:
Ranlib:
Languages:
C: yes
C Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe 19.0.24218.1
CPPFLAGS:
H5_CPPFLAGS:
AM_CPPFLAGS:
CFLAGS: /DWIN32 /D_WINDOWS /W3
H5_CFLAGS:
AM_CFLAGS:
Shared C Library: YES
Static C Library: YES
Fortran: OFF
Fortran Compiler:
Fortran Flags:
H5 Fortran Flags:
AM Fortran Flags:
Shared Fortran Library: YES
Static Fortran Library: YES
C++: ON
C++ Compiler: C:/Program Files (x86)/Microsoft Visual Studio 14.0/VC/bin/x86_amd64/cl.exe 19.0.24218.1
C++ Flags: /DWIN32 /D_WINDOWS /W3 /GR /EHsc
H5 C++ Flags:
AM C++ Flags:
Shared C++ Library: YES
Static C++ Library: YES
JAVA: OFF
JAVA Compiler:
Features:
Parallel HDF5: OFF
Parallel Filtered Dataset Writes:
Large Parallel I/O:
High-level library: ON
Threadsafety: OFF
Default API mapping: v110
With deprecated public symbols: ON
I/O filters (external): DEFLATE DECODE ENCODE
MPE:
Direct VFD:
dmalloc:
Packages w/ extra debug output:
API Tracing: OFF
Using memory checker: OFF
Memory allocation sanity checks: OFF
Function Stack Tracing: OFF
Strict File Format Checks: OFF
Optimization Instrumentation:
Bye...
and the process stops there. How to fix the warning / error ? thanks.
@conda-forge-admin, please re-render
@conda-forge-admin, please re-render.
h5py for hdf5=1.12.0
- I read the conda-forge documentation and could not find the solution for my problem there.
Issue:
trying to install h5py with hdf5=1.12.0 and it complains about a conflict. i don't exactly know how the build-mechanism works, but would it be possible to enable builds for both 1.10.6 (currently seems to build against this) and 1.12.0 ?
NumPy matrix was not present in release 0
See 8899587#diff-e178b687b10a71a3348107ae3154e44cR35
I am removing those binaries from the channel as they conflict with the default channel and they are broken.
pyreadline dependency will break on Python 3.10
Just a heads-up on an approaching packaging problem:
Since #29 you have pyreadline as a dependency (btw this is undocumented in setup.py which delayed identifying this issue).
pyreadline is unmaintained, last commit 2015, and has since been abandoned by IPython.
Using pyreadline currently (e.g. py3.9) emits a deprecation warning. This warning will turn into a hard error in Python 3.10, and it does not look like this will be fixed in pyreadline.
pyreadline\py3k_compat.py:8: DeprecationWarning: Using or importing the ABCs from 'collections' instead of
from 'collections.abc' is deprecated since Python 3.3, and in 3.10 it will stop working
Pyreadline was added here for some functionality related to IPython completion (h5py/h5py#971). Considering Ipython has moved away from this some time ago, probably this code section should be reviewed/updated within h5py proper.
h5py is running against HDF5 1.10.5 when it was built against 1.10.4
Is it possible to pin HDF5 ? Otherwise I get the following error message when I upgrade.
UserWarning: h5py is running against HDF5 1.10.5 when it was built against 1.10.4, this may cause problems
'{0}.{1}.{2}'.format(*version.hdf5_built_version_tuple)
Python 3.7
Issue:
Python 3.7 not in build matrix. This is the last dependency I need to get a working py3.7 stack. Is anything blocking python3.7 builds?
Environment (
conda list):
$ conda list
Details about
conda and system ( conda info ):
$ conda info
Import h5py fails due to undefined symbol
- I read the conda-forge documentation and could not find the solution for my problem there because this error happens when building a package.
Issue:
Rebuild of a Python package fails in the test stage because h5py cannot be imported.
The relevant part of the log file on windows is
File "D:\bld\damask-pkgs_1635640207683\_test_env\lib\site-packages\damask\util.py", line 27, in <module>
import h5py
File "D:\bld\damask-pkgs_1635640207683\_test_env\lib\site-packages\h5py\__init__.py", line 33, in <module>
from . import version
File "D:\bld\damask-pkgs_1635640207683\_test_env\lib\site-packages\h5py\version.py", line 15, in <module>
from . import h5 as _h5
File "h5py\h5.pyx", line 1, in init h5py.h5
ImportError: DLL load failed while importing defs: The specified procedure could not be found.
and on Linux
File "/home/conda/feedstock_root/build_artifacts/damask-pkgs_1635640089558/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/lib/python3.9/site-packages/damask/util.py", line 27, in <module>
import h5py
File "/home/conda/feedstock_root/build_artifacts/damask-pkgs_1635640089558/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/lib/python3.9/site-packages/h5py/__init__.py", line 33, in <module>
from . import version
File "/home/conda/feedstock_root/build_artifacts/damask-pkgs_1635640089558/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/lib/python3.9/site-packages/h5py/version.py", line 15, in <module>
from . import h5 as _h5
File "h5py/h5.pyx", line 1, in init h5py.h5
ImportError: /home/conda/feedstock_root/build_artifacts/damask-pkgs_1635640089558/_test_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/lib/python3.9/site-packages/h5py/defs.cpython-39-x86_64-linux-gnu.so: undefined symbol: H5Pset_fapl_ros3
The full logs are available on https://dev.azure.com/conda-forge/84710dde-1620-425b-80d0-4cf5baca359d/_apis/build/builds/400217/logs/38 and https://dev.azure.com/conda-forge/84710dde-1620-425b-80d0-4cf5baca359d/_apis/build/builds/400217/logs/41
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