HDF5库版本错误- HDF5 ver1.10.4
HDF5库版本错误- HDF5 ver1.10.4
提问于 2019-09-08 13:53:02
我试图用spyder (操作系统x64)、Anaconda和Pyton3.x导入一些软件包--这个错误在互联网上非常有名。所提出的解决方案是将库的版本1.10.5与HDF5 (我的1.10.4)相匹配。
问题是我找不到1.10.5版本的HDF5，另一方面，我也无法理解我可以降级的是什么。
在这个链接中：https://anaconda.org/conda-forge/hdf5似乎存在1.10.5版本，但是当我输入anaconda conda install -c conda-forge hdf5提示符时，版本仍然是1.10.4。
在此警告：
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
Configured by: Visual Studio 15 2017 Win64
Host system: Windows.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/2017/Community/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/cl.exe 19.16.27027.1
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/2017/Community/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/cl.exe 19.16.27027.1
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...在这里安装的所有软件包：
# packages in environment at C:\Users\Megaport\Anaconda3\envs\venv:
#
# Name Version Build Channel
_py-xgboost-mutex 2.0 cpu_0
_tflow_select 2.3.0 mkl
absl-py 0.8.0 pypi_0 pypi
alabaster 0.7.12 py37_0
asn1crypto 0.24.0 py37_0
astor 0.8.0 pypi_0 pypi
astroid 2.2.5 py37_0
atomicwrites 1.3.0 py37_1
attrs 19.1.0 py37_1
babel 2.7.0 py_0
backcall 0.1.0 py37_0
blas 1.0 mkl
bleach 3.1.0 py37_0
ca-certificates 2019.5.15 1
certifi 2019.6.16 py37_1
cffi 1.12.3 py37h7a1dbc1_0
chardet 3.0.4 py37_1003
cloudpickle 1.2.1 py_0
colorama 0.4.1 py37_0
cryptography 2.7 py37h7a1dbc1_0
cycler 0.10.0 py37_0
decorator 4.4.0 py37_1
defusedxml 0.6.0 py_0
docutils 0.15.2 py37_0
entrypoints 0.3 py37_0
fastcache 1.1.0 py37he774522_0
freetype 2.9.1 ha9979f8_1
gast 0.2.2 pypi_0 pypi
google-pasta 0.1.7 pypi_0 pypi
grpcio 1.23.0 pypi_0 pypi
h5py 2.10.0 pypi_0 pypi
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
idna 2.8 py37_0
imagesize 1.1.0 py37_0
importlib_metadata 0.19 py37_0
intel-openmp 2019.4 245
ipykernel 5.1.2 py37h39e3cac_0
ipython 7.8.0 py37h39e3cac_0
ipython_genutils 0.2.0 py37_0
isort 4.3.21 py37_0
jedi 0.15.1 py37_0
jinja2 2.10.1 py37_0
joblib 0.13.2 py37_0
jpeg 9b hb83a4c4_2
jsonschema 3.0.2 py37_0
jupyter_client 5.3.1 py_0
jupyter_core 4.5.0 py_0
keras 2.2.4 0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py37_0
keras-preprocessing 1.1.0 py_1
keyring 18.0.0 py37_0
kiwisolver 1.1.0 py37ha925a31_0
lazy-object-proxy 1.4.2 py37he774522_0
libmklml 2019.0.5 0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.8.0 h7bd577a_0
libsodium 1.0.16 h9d3ae62_0
libxgboost 0.90 0
m2w64-gcc-libgfortran 5.3.0 6
m2w64-gcc-libs 5.3.0 7
m2w64-gcc-libs-core 5.3.0 7
m2w64-gmp 6.1.0 2
m2w64-libwinpthread-git 5.0.0.4634.697f757 2
markdown 3.1.1 py37_0
markupsafe 1.1.1 py37he774522_0
matplotlib 3.1.1 py37hc8f65d3_0
mccabe 0.6.1 py37_1
mistune 0.8.4 py37he774522_0
mkl 2019.4 245
mkl-service 2.0.2 py37he774522_0
mkl_fft 1.0.14 py37h14836fe_0
mkl_random 1.0.2 py37h343c172_0
more-itertools 7.2.0 py37_0
mpmath 1.1.0 py37_0
msys2-conda-epoch 20160418 1
nbconvert 5.5.0 py_0
nbformat 4.4.0 py37_0
numpy 1.17.2 pypi_0 pypi
numpy-base 1.16.4 py37hc3f5095_0
numpydoc 0.9.1 py_0
openssl 1.1.1c he774522_1
opt-einsum 3.0.1 pypi_0 pypi
packaging 19.1 py37_0
pandas 0.25.1 py37ha925a31_0
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py37_1
parso 0.5.1 py_0
pickleshare 0.7.5 py37_0
pip 19.2.2 py37_0
pluggy 0.12.0 py_0
prompt_toolkit 2.0.9 py37_0
protobuf 3.9.1 pypi_0 pypi
psutil 5.6.3 py37he774522_0
py 1.8.0 py37_0
py-xgboost 0.90 py37_0
py-xgboost-cpu 0.90 py37_0
pycodestyle 2.5.0 py37_0
pycparser 2.19 py37_0
pyflakes 2.1.1 py37_0
pygments 2.4.2 py_0
pylint 2.3.1 py37_0
pyopenssl 19.0.0 py37_0
pyparsing 2.4.2 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pyrsistent 0.14.11 py37he774522_0
pysocks 1.7.0 py37_0
pytest 5.0.1 py37_0
python 3.7.4 h5263a28_0
python-dateutil 2.8.0 py37_0
pytz 2019.2 py_0
pywin32 223 py37hfa6e2cd_1
pyyaml 5.1.2 py37he774522_0
pyzmq 18.1.0 py37ha925a31_0
qt 5.9.7 vc14h73c81de_0
qtawesome 0.5.7 py37_1
qtconsole 4.5.4 py_0
qtpy 1.9.0 py_0
requests 2.22.0 py37_0
rope 0.14.0 py_0
scikit-learn 0.21.2 py37h6288b17_0
scipy 1.3.1 py37h29ff71c_0
setuptools 41.2.0 pypi_0 pypi
sip 4.19.8 py37h6538335_0
six 1.12.0 pypi_0 pypi
snowballstemmer 1.9.0 py_0
sphinx 2.1.2 py_0
sphinxcontrib-applehelp 1.0.1 py_0
sphinxcontrib-devhelp 1.0.1 py_0
sphinxcontrib-htmlhelp 1.0.2 py_0
sphinxcontrib-jsmath 1.0.1 py_0
sphinxcontrib-qthelp 1.0.2 py_0
sphinxcontrib-serializinghtml 1.1.3 py_0
spyder 3.3.6 py37_0
spyder-kernels 0.5.1 py37_0
sqlite 3.29.0 he774522_0
sympy 1.4 py37_0
tb-nightly 1.15.0a20190806 pypi_0 pypi
tensorboard 1.14.0 py37he3c9ec2_0
tensorflow 1.14.0 mkl_py37h7908ca0_0
tensorflow-base 1.14.0 mkl_py37ha978198_0
tensorflow-estimator 1.14.0 py_0
termcolor 1.1.0 pypi_0 pypi
testpath 0.4.2 py37_0
tornado 6.0.3 py37he774522_0
traitlets 4.3.2 py37_0
urllib3 1.24.2 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_0
wcwidth 0.1.7 py37_0
webencodings 0.5.1 py37_1
werkzeug 0.15.6 pypi_0 pypi
wheel 0.33.6 pypi_0 pypi
win_inet_pton 1.1.0 py37_0
wincertstore 0.2 py37_0
wrapt 1.11.2 py37he774522_0
yaml 0.1.7 hc54c509_2
zeromq 4.3.1 h33f27b4_3
zipp 0.5.2 py_0
zlib 1.2.11 h62dcd97_3无论如何，我不明白为什么提示HDF5中的版本是1.10.4，而在警告中，HDF5的版本是1.10.5
回答 9
Stack Overflow用户
回答已采纳
发布于 2020-05-23 19:00:05
也许我迟到了，但我通过将hdf5升级到1.10.5来解决了这个问题。
在Windows 10上，使用anaconda可以这样做：
conda install -c conda-forge hdf5=1.10.5Stack Overflow用户
发布于 2020-02-01 08:22:58
我将把这个留在这里，因为它是我的顶级堆栈线程，没有明确的答案。
pip uninstall h5py
pip install h5py如果您使用conda来安装tensorflow，它会安装1.10.5版本的h5py，并在顶部安装hdf5 1.10.4。从1.10.4开始创建冲突，在pip“杂耍”之后解决，这满足了最新的h5py。
Stack Overflow用户
发布于 2020-03-03 11:34:43
我在Windows 10上也有同样的问题。
- 安装TensorFlow > 2.0 https://www.tensorflow.org/install/pip?lang的一些要求
- 创建conda虚拟环境：
conda create -n ai python==3.7.6
conda activate ai
conda install pandas matplotlib scikit-learn scrapy seaborn
conda install -c anaconda tensorflow页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/57842565
复制相关文章
相似问题
腾讯云开发者
Copyright © 2013 - 2026 Tencent Cloud. All Rights Reserved. 腾讯云 版权所有
深圳市腾讯计算机系统有限公司 ICP备案/许可证号：粤B2-20090059 
粤公网安备44030502008569号
腾讯云计算（北京）有限责任公司 京ICP证150476号 | 京ICP备11018762号