Hdf5 library version mismatched error

Hello,
I am sorry if I am missing something simple. I am not an experienced programmer. When trying to import h5py I get the following error. I use anaconda python on Ubuntu 16. My LD_LIBRARY_PATH was set to a VTK folder so I changed it to «/home/davood/anaconda2/lib:» but I get the same error.
Any help is greatly appreciated.

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 1.8.17, library is 1.8.15
SUMMARY OF THE HDF5 CONFIGURATION
=================================

General Information:

	   HDF5 Version: 1.8.15-patch1
	  Configured on: Tue Aug 25 22:15:09 UTC 2015
	  Configured by: vagrant@localhost.localdomain
	 Configure mode: production
	    Host system: x86_64-unknown-linux-gnu
      Uname information: Linux localhost.localdomain 2.6.18-398.el5 #1 SMP Tue Sep 16 20:50:52 EDT 2014 x86_64 x86_64 x86_64 GNU/Linux
	       Byte sex: little-endian
	      Libraries: static, shared
     Installation point: /home/vagrant/miniconda/envs/_build

Compiling Options:

           Compilation Mode: production
                 C Compiler: /home/vagrant/miniconda/envs/_build/bin/gcc ( gcc (GCC) 4.8.5)
                     CFLAGS: 
                  H5_CFLAGS: -std=c99 -pedantic -Wall -Wextra -Wundef -Wshadow -Wpointer-arith -Wbad-function-cast -Wcast-qual -Wcast-align -Wwrite-strings -Wconversion -Waggregate-return -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations -Wredundant-decls -Wnested-externs -Winline -Wfloat-equal -Wmissing-format-attribute -Wmissing-noreturn -Wpacked -Wdisabled-optimization -Wformat=2 -Wunreachable-code -Wendif-labels -Wdeclaration-after-statement -Wold-style-definition -Winvalid-pch -Wvariadic-macros -Winit-self -Wmissing-include-dirs -Wswitch-default -Wswitch-enum -Wunused-macros -Wunsafe-loop-optimizations -Wc++-compat -Wstrict-overflow -Wlogical-op -Wlarger-than=2048 -Wvla -Wsync-nand -Wframe-larger-than=16384 -Wpacked-bitfield-compat -Wstrict-overflow=5 -Wjump-misses-init -Wunsuffixed-float-constants -Wdouble-promotion -Wsuggest-attribute=const -Wtrampolines -Wstack-usage=8192 -Wvector-operation-performance -Wsuggest-attribute=pure -Wsuggest-attribute=noreturn -Wsuggest-attribute=format -O3 -fomit-frame-pointer -finline-functions
                  AM_CFLAGS: 
                   CPPFLAGS: 
                H5_CPPFLAGS: -D_DEFAULT_SOURCE -D_BSD_SOURCE -D_GNU_SOURCE -D_POSIX_C_SOURCE=200112L   -DNDEBUG -UH5_DEBUG_API
                AM_CPPFLAGS: -D_LARGEFILE64_SOURCE -D_LARGEFILE_SOURCE  -I/home/vagrant/miniconda/envs/_build/include
           Shared C Library: yes
           Static C Library: yes

Statically Linked Executables: no
LDFLAGS:
H5_LDFLAGS:
AM_LDFLAGS: -L/home/vagrant/miniconda/envs/_build/lib
Extra libraries: -lrt -lz -ldl -lm
Archiver: ar
Ranlib: ranlib
Debugged Packages:
API Tracing: no

Languages:

                    Fortran: no

                        C++: yes
               C++ Compiler: /home/vagrant/miniconda/envs/_build/bin/g++ ( g++ (GCC) 4.8.5)
                  C++ Flags: 
               H5 C++ Flags:  
               AM C++ Flags: 
         Shared C++ Library: yes
         Static C++ Library: yes

Features:

              Parallel HDF5: no
         High Level library: yes
               Threadsafety: no
        Default API Mapping: v18

With Deprecated Public Symbols: yes
I/O filters (external): deflate(zlib)
MPE: no
Direct VFD: no
dmalloc: no
Clear file buffers before write: yes
Using memory checker: no
Function Stack Tracing: no
Strict File Format Checks: no
Optimization Instrumentation: no
Bye…

Warning! ***HDF5 library version mismatched error*** #1068

Comments

winash12 commented Jul 13, 2018

Ubuntu 18.04 LTS

Python version (e.g. 2.7, 3.5) 3.6

Where Python was acquired (e.g. system Python on MacOS or Linux, Anaconda on
Windows)
System Python on Linux

h5py version (e.g. 2.6)

• The full traceback/stack trace shown (if it appears)

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 1.10.1, library is 1.10.2
SUMMARY OF THE HDF5 CONFIGURATION
=================================

General Information:

Compiling Options:

Linking Options:

Statically Linked Executables:
LDFLAGS:
H5_LDFLAGS:
AM_LDFLAGS: -L/usr/local/lib
Extra libraries: -lz -ldl -lm
Archiver: ar
AR_FLAGS: cr
Ranlib: ranlib

Languages:

Features:

With deprecated public symbols: yes
I/O filters (external): deflate(zlib)
MPE: no
Direct VFD: no
dmalloc: no
Packages w/ extra debug output: none
API tracing: no
Using memory checker: no
Memory allocation sanity checks: no
Metadata trace file: no
Function stack tracing: no
Strict file format checks: no
Optimization instrumentation: no

h5py.version.info contains the needed versions, which can be displayed by

where python should be substituted for the path to python used to install
h5py with.
I am getting this problem after upgrading to Ubuntu 18.04 LTS.
I have this as LD_LIBRARY_PATH /usr/local/lib and I have my installation of libhd5 from scratch and also I installed h5py by typing python3.6 setup.py install.

When I type find . -name «libhdf5*» under root this is what I get —

./usr/local/lib/libhdf5_hl.so.100.1.0
./usr/local/lib/libhdf5_hl.so.100
./usr/local/lib/libhdf5.so
./usr/local/lib/libhdf5_hl.a
./usr/local/lib/libhdf5.la
./usr/local/lib/libhdf5.so.101
./usr/local/lib/libhdf5.settings
./usr/local/lib/libhdf5.so.101.1.0
./usr/local/lib/libhdf5.a
./usr/local/lib/libhdf5_hl.la
./usr/local/lib/libhdf5_hl.so

I believe Ubuntu 18.04 came with it’s own version of hd5 which I purged and yet I get the above problem. Should I take this to the hdf5 mailing list ?

The text was updated successfully, but these errors were encountered:

Источник

Warning! ***HDF5 library version mismatched error*** #853

Comments

engineeringmath commented Mar 13, 2017

Hello,
I am sorry if I am missing something simple. I am not an experienced programmer. When trying to import h5py I get the following error. I use anaconda python on Ubuntu 16. My LD_LIBRARY_PATH was set to a VTK folder so I changed it to «/home/davood/anaconda2/lib:» but I get the same error.
Any help is greatly appreciated.

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 1.8.17, library is 1.8.15
SUMMARY OF THE HDF5 CONFIGURATION
=================================

General Information:

Compiling Options:

Statically Linked Executables: no
LDFLAGS:
H5_LDFLAGS:
AM_LDFLAGS: -L/home/vagrant/miniconda/envs/_build/lib
Extra libraries: -lrt -lz -ldl -lm
Archiver: ar
Ranlib: ranlib
Debugged Packages:
API Tracing: no

Languages:

Features:

With Deprecated Public Symbols: yes
I/O filters (external): deflate(zlib)
MPE: no
Direct VFD: no
dmalloc: no
Clear file buffers before write: yes
Using memory checker: no
Function Stack Tracing: no
Strict File Format Checks: no
Optimization Instrumentation: no
Bye.

The text was updated successfully, but these errors were encountered:

aragilar commented Mar 14, 2017

This sounds like you’ve got two installs of HDF5, probably one from ubuntu and one from anaconda? How did you install h5py? I’d remove the LD_LIBRARY_PATH completely, unless you need it for the actual code you are running, as that’s likely causing the problem.

engineeringmath commented Mar 14, 2017

@aragilar Thank you so much for your comment. I actually solved it a minute ago by running:

conda install -c anaconda hdf5=1.8.17

kingaj12 commented Feb 6, 2018 •

Just a comment to reinforce @aragilar advice: I had the same problem with an anaconda install of hdf5, and the issue was that there was an old Ubuntu hdf5 package. I ran apt-get upgrade; apt autoremove and that cleaned out the old stuff.

7wik commented Sep 2, 2018 •

I have tried the below solution.
import os ; os.environ[‘HDF5_DISABLE_VERSION_CHECK’]=’2′
before importing tensorflow. It just suppressed the error, but I am not sure if it’s safe to do so. Is doing this advisable?

tacaswell commented Sep 4, 2018

@7wik no, this is not safe and you run the risk of data corruption and segfaults (as the warning says), but it might work.

Miladiouss commented Dec 12, 2018

I was able to solve this by doing the following (as suggested here):

oxhaowang commented May 7, 2019

To those of you whose python packages not managed by Conda experiencing the same issue

Had the same issue recently on a Mac machine, except that the header version is older than the library:

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

The hdf5 library on my Mac machine is managed by homebrew: brew upgrade hdf5

Python packages are managed by pip and h5py package version is 2.9.0

Checked a few online discussions, my guess was that the older headers was installed by other packages, in the end I was able to resolve this issue by upgrading pytables

Источник

HDF5 library version mismatched error in unit tests #623

Comments

ahwillia commented Apr 19, 2017

Apologies if this is duplicated elsewhere. I tried uninstalling/reinstalling pytables and h5py as suggested elsewhere, but to no avail.

Is there a set of instructions (for Ubuntu ideally) that explains how to find hdf5 headers and match them to what PyTables is using?

The text was updated successfully, but these errors were encountered:

tomkooij commented Apr 19, 2017

Your pytables was compiled against HDF5 1.8.11 and is now runtime linked against HDF5 1.8.17.

You seem to have installed pytables in a conda env. ( /home/alex/anaconda2/envs/tf/ ). The HDF5 config matches the conda hdf5 package.

Did you install pytables from a conda package? wheel? compiled from source?

It looks like it was compiled against HDF5 1.8.11 and now (runtime) linked against HDF5 1.8.17 from conda.

Please describe your env and the way you installed pytables, including:
pkg-config —libs hdf5
pip freeze
conda list

ahwillia commented Apr 19, 2017

Huh I had been installing using pip (pytables was a dependency for a different package). Uninstalling and reinstalling with conda seemed to do the trick.

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Источник

Ошибка версии библиотеки HDF5 — HDF5 вер. 1.10.4

Я пытаюсь импортировать некоторые пакеты с помощью spyder (OS x64), Anaconda и pyton 3.x. Ошибка довольно известна в Интернете. Предлагаемое решение — согласовать версию библиотеки 1.10.5 с HDF5 (моя — 1.10.4).

Вопрос в том, что я не могу найти HDF5 версии 1.10.5 и, с другой стороны, не могу понять, что я могу понизить.

По этой ссылке: https://anaconda.org/conda-forge/hdf5, похоже, существует версия 1.10 .5 но когда я набираю подсказку анаконды conda install -c conda-forge hdf5 версия остается 1.10.4.

Здесь установлены все пакеты:

Во всяком случае, я не понимаю, почему в приглашении HDF5 — версия 1.10.4, а в предупреждении — версия HDF5 1.10.5.

9 ответов

Возможно, я опоздал, но я решил эту проблему, обновив hdf5 до 1.10.5.

В Windows 10 с помощью anaconda вы можете сделать это:

Создайте виртуальную среду conda:

У меня была такая же проблема.

Предупреждение! Ошибка несоответствия версии библиотеки HDF5 Заголовки — 1.10.4, библиотека — 1.10.6.

Мое решение — создать другую среду conda и сделать все conda с conda-forge. Поскольку hdf5 1.10.4 был установлен с помощью следующей команды на моем ПК win10 без графического процессора. Python — это 3.7.10.

Конда установить тензор потока

По приведенной выше команде пришла версия 1.10.4. Итак, я должен был сделать следующее.

Conda установить conda-forge tensorflow

Затем была установлена ​​1.10.6. Настоятельно рекомендуется установка conda-forge с conda.

Черт, у меня была такая же ошибка, показанная в подсказке анаконды, и причина, поверьте мне, действительно глупая.

Я работал в многозадачном режиме и forgot to activate the environment получил две разные версии HDF5.

Обязательно conda activate environment_name перед запуском Jupyter Notebook.

У меня была такая же проблема, как и у вас. Это произошло потому, что tenorflow был установлен conda. И ошибка пропадает при использовании канала анаконда.

Я оставлю это здесь, поскольку для меня это главный поток без четкого ответа.

Если вы используете conda для установки tensorflow, он устанавливает h5py с версией 1.10.5, а поверх устанавливает hdf5 1.10.4. Создание конфликта, который разрешается после «жонглирования» pip, начиная с 1.10.4, удовлетворяет последней версии h5py.

Я действительно решил эту проблему, когда понял (в Mac OSX Mojave), что использовал Homebrew для установки Octave, который был создан для работы с HDF5 1.10.5. Я впервые столкнулся с этой проблемой, пытаясь установить и запустить TensorFlow из iPython. Я не использую Octave активно, поэтому я удалил Octave, а также HDF5 с помощью

Затем при повторном запуске

А затем импортировав TensorFlow из iPython, все вроде работает.

У меня есть простой код тензорного потока sum.py:

=== Я являюсь пользователем окна 10 с Anaconda 3, tensorflow 2.0, jupyter и pyhton 3. У меня есть похожие проблемы, и я восстанавливаю следующие ошибки: Ошибка: UserWarning: h5py работает против HDF5 1.10.5, когда он был построен против 1.10. .4, это может вызвать проблемы

Моя среда была испорчена из-за большого количества установок pip. Следующее видео решает мою проблему. https://www.youtube.com/watch?v=RgO8BBNGB8w&t=376s

Он использует tensorflow.yml со списком чистой среды:

Под подсказкой окна:

conda env create -v -f tenorflow

Затем откройте подсказку анаконды

conda acticate tensorflow python sum.py или в блокноте jupyter. запустить с ОК.

Это случилось со мной, когда я установил тензорный поток через

conda install -c conda-forge тензор потока

Я решил это следующим образом:

Я удалил h5py и tensorflow:

conda удалить h5py

conda удалить tenorflow

И переустановил тензорный поток:

conda install -c anaconda tensorflow

Источник

h5py is running against HDF5 1.10.5 when it was built against 1.10.4 #54

Comments

jan-janssen commented Jun 7, 2019

Is it possible to pin HDF5 ? Otherwise I get the following error message when I upgrade.

The text was updated successfully, but these errors were encountered:

ocefpaf commented Jun 7, 2019

It is pinned. It may be that something is not OK with the HDF5 new build strings for the mpi variants.

Marietto2008 commented Feb 23, 2020

C:Usersmarietto2020DesktopMMDOpenMMDOpenMMD 1.03d-pose-baseline-vmd
(tensorflow) λ openposeto3d

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un programma eseguibile o un file batch.
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

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the detailed information[yes/no/warn]: yes
C:Usersmarietto2020.condaenvstensorflowlibsite-packagesh5py_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:

Compiling Options:

Linking Options:

Statically Linked Executables: OFF
LDFLAGS: /machine:x64
H5_LDFLAGS:
AM_LDFLAGS:
Extra libraries:
Archiver:
Ranlib:

Languages:

Features:

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.

tatianapg commented Apr 17, 2020

(April 16 2020)
Hi, I am working with python 3.7.
I had the same problem «UserWa ing: h5py is running against HDF5 1.10.5 when it was built against 1.10.4»
In my case I tried: conda uninstall/install hdf5, then conda uninstall/install h5py. They did nor work.
Finally I followed this suggestion (#39):
«The error disappears when using channel anaconda.
.
conda install -c anaconda keras
»
I had my environment created, so I only executed «conda install -c anaconda keras»
I got my environment with tensorflow. However I had a warning, when I typed: import tensorflow as tf
»
FutureWarning: Passing (type, 1) or ‘1type’ as a synonym of type is deprecate
in a future version of numpy, it will be understood as (type, (1,)) / ‘(1,)ty
»
The second time I typed: import tensorflow as tf, I got the version 1.14.0.
I have not done any tasks with tensorflow yet to verify if the warning brings problems.

dhruvsakalley commented May 11, 2020

Same problem with python 3.6
Trying to get together a conda environment with transfomers/tensorflow-gpu 2.1/pytorch/ allennlp and it’s not happening because of this error.
here is the conda env export -f tx36.yml

`
name: tx36
channels:

• pytorch
• conda-forge
• defaults
  dependencies:
• _tflow_select=2.1.0=gpu
• absl-py=0.9.0=py36_0
• arrow-cpp=0.15.1=py36h47fa567_6
• astor=0.8.0=py36_0
• awswrangler=0.3.1=py_0
• blas=1.0=mkl
• blinker=1.4=py36_0
• boost-cpp=1.69.0=h6a4c333_1000
• boto=2.49.0=py36_0
• boto3=1.13.6=pyh9f0ad1d_0
• botocore=1.16.6=pyh9f0ad1d_0
• brotli=1.0.7=he025d50_1001
• bzip2=1.0.8=hfa6e2cd_2
• c-ares=1.15.0=h2fa13f4_1001
• 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=py36h7a1dbc1_0
• chardet=3.0.4=py36_1003
• click=7.1.2=py_0
• cryptography=2.9.2=py36h7a1dbc1_0
• cudatoolkit=10.1.243=h74a9793_0
• cudnn=7.6.5=cuda10.1_0
• docutils=0.15.2=py36_0
• double-conversion=3.1.5=h6538335_2
• freetype=2.9.1=ha9979f8_1
• fsspec=0.7.3=py_0
• gast=0.2.2=py36_0
• gensim=3.8.0=py36hf9181ef_0
• gflags=2.2.2=he025d50_1002
• glog=0.4.0=h0174b99_3
• google-api-core=1.17.0=py36h21ff451_0
• google-auth=1.14.1=py_0
• google-auth-oauthlib=0.4.1=py_2
• google-cloud-core=1.3.0=py_0
• google-cloud-storage=1.28.0=py_0
• google-pasta=0.2.0=py_0
• google-resumable-media=0.5.0=py_1
• googleapis-common-protos=1.51.0=py36h21ff451_2
• grpc-cpp=1.26.0=h9f84659_0
• grpcio=1.27.2=py36h351948d_0
• h5py=2.10.0=py36h5e291fa_0
• hdf5=1.10.4=h7ebc959_0
• icc_rt=2019.0.0=h0cc432a_1
• idna=2.9=py_1
• intel-openmp=2020.0=166
• jmespath=0.9.5=py_0
• jpeg=9b=hb83a4c4_2
• keras-applications=1.0.8=py_0
• keras-preprocessing=1.1.0=py_1
• libboost=1.67.0=hd9e427e_4
• libpng=1.6.37=h2a8f88b_0
• libprotobuf=3.11.2=h1a1b453_0
• libtiff=4.1.0=h56a325e_0
• lz4-c=1.8.3=he025d50_1001
• markdown=3.1.1=py36_0
• mkl=2020.0=166
• mkl-service=2.3.0=py36hb782905_0
• mkl_fft=1.0.15=py36h14836fe_0
• mkl_random=1.1.0=py36h675688f_0
• ninja=1.9.0=py36h74a9793_0
• numpy=1.18.1=py36h93ca92e_0
• numpy-base=1.18.1=py36hc3f5095_1
• oauthlib=3.1.0=py_0
• olefile=0.46=py36_0
• openssl=1.1.1g=he774522_0
• opt_einsum=3.1.0=py_0
• pandas=1.0.3=py36hcc50265_1
• pg8000=1.15.2=py_0
• pillow=7.1.2=py36hcc1f983_0
• pip=20.0.2=py36_3
• pyarrow=0.15.1=py36h47e9c7a_0
• pyasn1=0.4.8=py_0
• pyasn1-modules=0.2.7=py_0
• pycparser=2.20=py_0
• pyjwt=1.7.1=py36_0
• pymysql=0.9.3=py_0
• pyopenssl=19.1.0=py36_0
• pyreadline=2.1=py36_1
• pysocks=1.7.1=py36_0
• python=3.6.10=h9f7ef89_2
• python-dateutil=2.8.1=py_0
• python_abi=3.6=1_cp36m
• pytorch=1.5.0=py3.6_cuda101_cudnn7_0
• pytz=2020.1=pyh9f0ad1d_0
• re2=2020.05.01=vc14ha925a31_0
• requests=2.23.0=py36_0
• requests-oauthlib=1.3.0=py_0
• rsa=4.0=py_0
• s3fs=0.4.2=py_0
• s3transfer=0.3.3=py36h9f0ad1d_1
• scipy=1.4.1=py36h9439919_0
• scramp=1.1.1=py_0
• setuptools=46.1.3=py36_0
• six=1.14.0=py36_0
• smart_open=2.0.0=py_0
• snappy=1.1.8=he025d50_1
• sqlite=3.31.1=h2a8f88b_1
• tenacity=6.2.0=py36h9f0ad1d_0
• tensorboard=2.1.0=py3_0
• tensorflow=2.1.0=gpu_py36h3346743_0
• tensorflow-base=2.1.0=gpu_py36h55f5790_0
• tensorflow-estimator=2.1.0=pyhd54b08b_0
• tensorflow-gpu=2.1.0=h0d30ee6_0
• termcolor=1.1.0=py36_1
• thrift-cpp=0.11.0=h1ebf3fd_3
• tk=8.6.8=hfa6e2cd_0
• torchvision=0.6.0=py36_cu101
• uriparser=0.9.3=he025d50_1
• vc=14.1=h0510ff6_4
• vs2015_runtime=14.16.27012=hf0eaf9b_1
• wheel=0.34.2=py36_0
• win_inet_pton=1.1.0=py36_0
• wincertstore=0.2=py36h7fe50ca_0
• wrapt=1.12.1=py36he774522_1
• xz=5.2.5=h62dcd97_0
• zlib=1.2.11=h62dcd97_4
• zstd=1.3.7=h508b16e_0
• pip:
  • alabaster==0.7.12
  • allennlp==0.9.0
  • atomicwrites==1.4.0
  • attrs==19.3.0
  • babel==2.8.0
  • blis==0.2.4
  • colorama==0.4.3
  • conllu==1.3.1
  • cycler==0.10.0
  • cymem==2.0.3
  • dataclasses==0.7
  • editdistance==0.5.3
  • filelock==3.0.12
  • flaky==3.6.1
  • flask==1.1.2
  • flask-cors==3.0.8
  • ftfy==5.7
  • future==0.18.2
  • gevent==20.5.0
  • greenlet==0.4.15
  • imagesize==1.2.0
  • importlib-metadata==1.6.0
  • itsdangerous==1.1.0
  • jinja2==2.11.2
  • joblib==0.14.1
  • jsonpickle==1.4.1
  • keras==2.3.1
  • kiwisolver==1.2.0
  • markupsafe==1.1.1
  • matplotlib==3.2.1
  • more-itertools==8.2.0
  • murmurhash==1.0.2
  • nltk==3.5
  • numpydoc==0.9.2
  • overrides==2.8.0
  • packaging==20.3
  • parsimonious==0.8.1
  • plac==0.9.6
  • pluggy==0.13.1
  • preshed==2.0.1
  • protobuf==3.11.3
  • py==1.8.1
  • pygments==2.6.1
  • pyparsing==2.4.7
  • pytest==5.4.2
  • pytorch-pretrained-bert==0.6.2
  • pytorch-transformers==1.1.0
  • pyyaml==5.3.1
  • regex==2020.5.7
  • responses==0.10.14
  • sacremoses==0.0.43
  • scikit-learn==0.22.2.post1
  • sentencepiece==0.1.86
  • seqeval==0.0.12
  • simpletransformers==0.28.0
  • snowballstemmer==2.0.0
  • spacy==2.1.9
  • sphinx==3.0.3
  • sphinxcontrib-applehelp==1.0.2
  • sphinxcontrib-devhelp==1.0.2
  • sphinxcontrib-htmlhelp==1.0.3
  • sphinxcontrib-jsmath==1.0.1
  • sphinxcontrib-qthelp==1.0.3
  • sphinxcontrib-serializinghtml==1.1.4
  • sqlparse==0.3.1
  • srsly==1.0.2
  • tensorboardx==2.0
  • thinc==7.0.8
  • tokenizers==0.7.0
  • tqdm==4.46.0
  • transformers==2.9.0
  • unidecode==1.1.1
  • urllib3==1.25.9
  • wasabi==0.6.0
  • wcwidth==0.1.9
  • werkzeug==1.0.1
  • word2number==1.1
  • zipp==3.1.0
    prefix: D:Anaconda3envstx36
    `

Here’s the error I get after python >>import transformers

D:Anaconda3envstx36libsite-packagesh5py_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:

Compiling Options:

Linking Options:

Statically Linked Executables: OFF
LDFLAGS: /machine:x64
H5_LDFLAGS:
AM_LDFLAGS:
Extra libraries:
Archiver:
Ranlib:

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