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Getting DLL error when running tensorflow/keras program on python gpu 3.8

I am trying to run my simple AI program but I keep getting this DLL error:
ImportError: Could not find the DLL(s) 'msvcp140_1.dll'. TensorFlow requires that these DLLs be installed in a directory that is named in your %PATH% environment variable.
You may install these DLLs by downloading "Microsoft C++ Redistributable for Visual Studio 2015, 2017 and 2019" for your platform from this URL:
https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads
I went ahead and downloaded that support file and restarted my computer but that didn't work. I also found the specified dll file in my system 32 and made sure it was in my PATH environment for both system and user. Still doesn't work. I am using python3.8 with gpu enabled. Am
I missing something? Is this a version issue because it has never happened on my previous projects.
Here are my libraries:
_tflow_select 2.3.0
absl-py 0.12.0
aggdraw 1.3.12
aiofiles 0.7.0
aiohttp 3.7.4
alembic 1.6.4
argon2-cffi 20.1.0
astor 0.8.1
asttokens 2.0.5
astunparse 1.6.3
async-timeout 3.0.1
async_generator 1.10
attrs 20.3.0
autokeras 1.0.16
backcall 0.2.0
beautifulsoup4 4.9.3
blas 1.0
bleach 3.3.0
blinker 1.4
brotlipy 0.7.0
ca-certificates 2021.7.5
cachetools 4.2.2
certifi 2021.5.30
cffi 1.14.5
chardet 4.0.0
click 7.1.2
cliff 3.8.0
cmaes 0.8.2
cmd2 2.1.2
colorama 0.4.4
colorlog 5.0.1
coverage 5.5
cryptography 3.4.7
cycler 0.10.0
cython 0.29.23
decorator 5.0.9
defusedxml 0.7.1
docopt 0.6.2
entrypoints 0.3
executing 0.6.0
fastapi 0.65.1
flaml 0.6.9
flatbuffers 1.12
freetype 2.10.4
gast 0.4.0
gin-config 0.4.0
google-auth 1.30.0
google-auth-oauthlib 0.4.4
google-pasta 0.2.0
greenlet 1.1.0
grpcio 1.34.1
h11 0.12.0
h5py 3.1.0
hdf5 1.10.5
icc_rt 2019.0.0
icecream 2.1.0
icu 58.2
idna 2.10
importlib-metadata 3.10.0
importlib_metadata 3.10.0
intel-openmp 2021.2.0
ipykernel 5.3.4
ipython 7.25.0
ipython_genutils 0.2.0
ipywidgets 7.6.3
jedi 0.18.0
jinja2 3.0.1
joblib 1.0.1
jpeg 9b
jsonschema 3.2.0
jupyter_client 6.1.12
jupyter_core 4.7.1
jupyterlab_pygments 0.1.2
jupyterlab_widgets 1.0.0
keras 2.7.0
keras-applications 1.0.8
keras-nightly 2.5.0.dev2021032900
keras-preprocessing 1.1.2
keras-tuner 1.0.4
kiwisolver 1.3.1
kt-legacy 1.0.4
libclang 12.0.0
libpng 1.6.37
libprotobuf 3.14.0
libsodium 1.0.18
libtiff 4.1.0
lightgbm 3.3.1
llvmlite 0.37.0
lxml 4.6.3
lz4-c 1.9.3
m2w64-gcc-libgfortran 5.3.0
m2w64-gcc-libs 5.3.0
m2w64-gcc-libs-core 5.3.0
m2w64-gmp 6.1.0
m2w64-libwinpthread-git 5.0.0.4634.697f757
mako 1.1.4
markdown 3.3.4
markupsafe 2.0.1
matplotlib 3.3.4
matplotlib-base 3.3.4
matplotlib-inline 0.1.2
mistune 0.8.4
mkl 2021.2.0
mkl-service 2.3.0
mkl_fft 1.3.0
mkl_random 1.2.1
msys2-conda-epoch 20160418
multidict 5.1.0
nbclient 0.5.3
nbconvert 6.1.0
nbformat 5.1.3
nest-asyncio 1.5.1
notebook 6.4.0
numba 0.54.0
numexpr 2.7.3
numpy 1.19.5
oauthlib 3.1.0
olefile 0.46
opencv-contrib-python 4.5.3.56
opencv-python 4.5.3.56
openssl 1.1.1k
opt-einsum 3.3.0
opt_einsum 3.1.0
optuna 2.8.0
orca 1.6
packaging 20.9
pandas 1.2.4
pandocfilters 1.4.3
parso 0.8.2
pbr 5.6.0
pickleshare 0.7.5
pillow 8.2.0
pip 21.3.1
pipreqs 0.4.10
plotly 4.14.3
plotly-orca 1.3.1
prettytable 2.1.0
prometheus_client 0.11.0
prompt-toolkit 3.0.17
protobuf 3.16.0
psutil 5.8.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.20
pydantic 1.8.1
pydot 1.4.2
pygments 2.9.0
pyjwt 2.1.0
pyopenssl 20.0.1
pyparsing 2.4.7
pyperclip 1.8.2
pyqt 5.9.2
pyreadline 2.1
pyreadline3 3.3
pyrsistent 0.17.3
pysocks 1.7.1
python 3.8.0
python-dateutil 2.8.1
python-dotenv 0.17.1
python-editor 1.0.4
python-multipart 0.0.5
python_abi 3.8
pytz 2021.1
pywin32 227
pywinpty 0.5.7
pyyaml 5.4.1
pyzmq 20.0.0
qt 5.9.7
requests 2.25.1
requests-oauthlib 1.3.0
retrying 1.3.3
rsa 4.7.2
scikit-learn 0.24.2
scipy 1.6.2
seaborn 0.11.1
send2trash 1.5.0
setuptools 52.0.0
sip 4.19.13
six 1.15.0
soupsieve 2.2.1
sqlalchemy 1.4.22
sqlite 3.35.4
starlette 0.14.2
stevedore 3.3.0
tables 3.6.1
tensorboard 2.7.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.0
tensorflow 2.5.0
tensorflow-addons 0.13.0
tensorflow-base 2.3.0
tensorflow-estimator 2.5.0
tensorflow-gpu 2.4.1
tensorflow-io-gcs-filesystem 0.21.0
termcolor 1.1.0
terminado 0.9.4
testpath 0.5.0
threadpoolctl 2.1.0
tk 8.6.10
toolz 0.11.1
tornado 6.1
tqdm 4.61.2
traitlets 5.0.5
typeguard 2.12.1
typing-extensions 3.7.4.3
typing_extensions 3.7.4.3
urllib3 1.26.4
uvicorn 0.13.4
vc 14.2
visualkeras 0.0.2
vs2015_runtime 14.27.29016
watchgod 0.6
wcwidth 0.2.5
webencodings 0.5.1
websockets 8.1
werkzeug 1.0.1
wheel 0.36.2
widgetsnbextension 3.5.1
win_inet_pton 1.1.0
wincertstore 0.2
winpty 0.4.3
wrapt 1.12.1
xgboost 1.3.3
xz 5.2.5
yaml 0.2.5
yarg 0.1.9
yarl 1.6.3
zeromq 4.3.3
zipp 3.4.1
zlib 1.2.11
zstd 1.4.9

First, check to see if msvcp140.dll (not msvcp140_1.dll) is installed.
Download it here: https://www.microsoft.com/en-us/download/details.aspx?id=53587
Restart your computer and see if it works.
If that doesn't work try copying msvcp140_1.dll into: C:\Users\PCName\AppData\Local\Programs\Python\Python38
If nothing still works, it could be a version issue.
As far as I know, Python 3.8 support requires TensorFlow 2.2 or later.
Run this:
pip install tensorflow ==2.2.0
Look here for system requirements: https://www.tensorflow.org/install/pip#system-requirements
Tensorflow will work only on Windows 7 or later (64-bit) (According to the above link)
Package location: https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-2.7.0-cp38-cp38-win_amd64.whl
If you are using 32-bit python,
replace your 32-bit python with a 64-bit version. Tensorflow does not support 32-bit architecture.

How does one see all the packages that have been installed in developer mode with conda?

I did conda list but I can't see what I am looking for. Are conda packages installed with conda developed . not shown?
# packages in environment at /Users/pinocchio/anaconda3/envs/myenv:
#
# Name Version Build Channel
absl-py 0.9.0 py37_0
appnope 0.1.0 py37hc8dfbb8_1001 conda-forge
asn1crypto 1.3.0 py37_0
astroid 2.3.3 py37_0
attrs 19.3.0 py_0 conda-forge
backcall 0.1.0 py_0 conda-forge
beautifulsoup4 4.8.2 py37_0
blas 1.0 mkl
bleach 3.1.4 pyh9f0ad1d_0 conda-forge
bzip2 1.0.8 h1de35cc_0
c-ares 1.15.0 h1de35cc_1001
ca-certificates 2020.1.1 0
cairo 1.14.12 hc4e6be7_4
certifi 2020.4.5.1 py37_0
cffi 1.14.0 py37hb5b8e2f_0
chardet 3.0.4 py37_1003
conda 4.8.3 py37_0
conda-build 3.18.11 py37_0
conda-package-handling 1.6.0 py37h1de35cc_0
cryptography 2.8 py37ha12b0ac_0
cycler 0.10.0 py37_0
decorator 4.4.2 py_0 conda-forge
defusedxml 0.6.0 py_0 conda-forge
entrypoints 0.3 py37hc8dfbb8_1001 conda-forge
expat 2.2.6 h0a44026_0
filelock 3.0.12 py_0
fontconfig 2.13.0 h5d5b041_1
freetype 2.9.1 hb4e5f40_0
fribidi 1.0.5 h1de35cc_0
gettext 0.19.8.1 h15daf44_3
glib 2.63.1 hd977a24_0
glob2 0.7 py_0
graphite2 1.3.13 h2098e52_0
graphviz 2.40.1 hefbbd9a_2
grpcio 1.16.1 py37h044775b_1
harfbuzz 1.8.8 hb8d4a28_0
icu 58.2 h4b95b61_1
idna 2.9 py_1
importlib-metadata 1.6.0 py37hc8dfbb8_0 conda-forge
importlib_metadata 1.6.0 0 conda-forge
intel-openmp 2019.4 233
ipykernel 5.2.0 py37h43977f1_1 conda-forge
ipython 7.13.0 py37hc8dfbb8_2 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
isort 4.3.21 py37_0
jedi 0.16.0 py37hc8dfbb8_1 conda-forge
jinja2 2.11.1 py_0
jpeg 9b he5867d9_2
json5 0.9.0 py_0 conda-forge
jsonschema 3.2.0 py37hc8dfbb8_1 conda-forge
jupyter_client 6.1.2 py_0 conda-forge
jupyter_core 4.6.3 py37hc8dfbb8_1 conda-forge
jupyterlab 2.0.1 py_0 conda-forge
jupyterlab_server 1.0.7 py_0 conda-forge
kiwisolver 1.1.0 py37h0a44026_0
lazy-object-proxy 1.4.3 py37h1de35cc_0
libarchive 3.3.3 h786848e_5
libcxx 4.0.1 hcfea43d_1
libcxxabi 4.0.1 hcfea43d_1
libedit 3.1.20181209 hb402a30_0
libffi 3.2.1 h475c297_4
libgfortran 3.0.1 h93005f0_2
libiconv 1.15 hdd342a3_7
liblief 0.9.0 h2a1bed3_2
libpng 1.6.37 ha441bb4_0
libprotobuf 3.11.4 hd9629dc_0
libsodium 1.0.17 h01d97ff_0 conda-forge
libtiff 4.1.0 hcb84e12_0
libxml2 2.9.9 hf6e021a_1
lz4-c 1.8.1.2 h1de35cc_0
lzo 2.10 h362108e_2
markdown 3.1.1 py37_0
markupsafe 1.1.1 py37h1de35cc_0
matplotlib 3.1.3 py37_0
matplotlib-base 3.1.3 py37h9aa3819_0
mccabe 0.6.1 py37_1
mistune 0.8.4 py37h0b31af3_1000 conda-forge
mkl 2019.4 233
mkl-service 2.3.0 py37hfbe908c_0
mkl_fft 1.0.15 py37h5e564d8_0
mkl_random 1.1.0 py37ha771720_0
nbconvert 5.6.1 py37_0 conda-forge
nbformat 5.0.4 py_0 conda-forge
ncurses 6.2 h0a44026_0
ninja 1.9.0 py37h04f5b5a_0
notebook 6.0.3 py37_0 conda-forge
numpy 1.18.1 py37h7241aed_0
numpy-base 1.18.1 py37h6575580_1
olefile 0.46 py37_0
openssl 1.1.1g h1de35cc_0
pandoc 2.9.2 0 conda-forge
pandocfilters 1.4.2 py_1 conda-forge
pango 1.42.4 h060686c_0
parso 0.6.2 py_0 conda-forge
pcre 8.43 h0a44026_0
pexpect 4.8.0 py37hc8dfbb8_1 conda-forge
pickleshare 0.7.5 py37hc8dfbb8_1001 conda-forge
pillow 6.2.1 py37hb68e598_0
pip 20.0.2 py37_1
pixman 0.38.0 h1de35cc_0
pkginfo 1.5.0.1 py37_0
prometheus_client 0.7.1 py_0 conda-forge
prompt-toolkit 3.0.5 py_0 conda-forge
protobuf 3.11.4 py37h0a44026_0
psutil 5.7.0 py37h1de35cc_0
ptyprocess 0.6.0 py_1001 conda-forge
py-lief 0.9.0 py37h1413db1_2
pycosat 0.6.3 py37h1de35cc_0
pycparser 2.20 py_0
pygments 2.6.1 py_0 conda-forge
pylint 2.4.4 py37_0
pyopenssl 19.1.0 py37_0
pyparsing 2.4.6 py_0
pyrsistent 0.16.0 py37h9bfed18_0 conda-forge
pysocks 1.7.1 py37_0
python 3.7.7 hc70fcce_0_cpython
python-dateutil 2.8.1 py_0 conda-forge
python-graphviz 0.13.2 pypi_0 pypi
python-libarchive-c 2.8 py37_13
python_abi 3.7 1_cp37m conda-forge
pytorch 1.4.0 py3.7_0 pytorch
pytz 2019.3 py_0
pyyaml 5.3.1 py37h1de35cc_0
pyzmq 18.1.1 py37h0a44026_0
readline 8.0 h1de35cc_0
requests 2.23.0 py37_0
ripgrep 11.0.2 he32d670_0
ruamel_yaml 0.15.87 py37h1de35cc_0
send2trash 1.5.0 py_0 conda-forge
setuptools 46.1.1 py37_0
six 1.14.0 py37_0
soupsieve 2.0 py_0
sqlite 3.31.1 ha441bb4_0
tensorboard 2.0.0 pyhb38c66f_1
terminado 0.8.3 py37hc8dfbb8_1 conda-forge
testpath 0.4.4 py_0 conda-forge
tk 8.6.8 ha441bb4_0
torchvision 0.2.1 py_2 soumith
tornado 6.0.4 py37h9bfed18_1 conda-forge
tqdm 4.43.0 py_0
traitlets 4.3.3 py37hc8dfbb8_1 conda-forge
urllib3 1.25.8 py37_0
wcwidth 0.1.9 pyh9f0ad1d_0 conda-forge
webencodings 0.5.1 py_1 conda-forge
werkzeug 1.0.0 py_0
wheel 0.34.2 py37_0
wrapt 1.12.1 py37h1de35cc_1
xz 5.2.4 h1de35cc_4
yaml 0.1.7 hc338f04_2
zeromq 4.3.2 h6de7cb9_2 conda-forge
zipp 3.1.0 py_0 conda-forge
zlib 1.2.11 h1de35cc_3
zstd 1.3.7 h5bba6e5_0
utils or something like that should be there but its not.
Update:
I've tried what this suggests and it does not work. I conda develop . my package and then when I run the python interpreter and try to import it the import fails. Why?
>>> import my_pkg
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'my_pkg'
I also tried to install it with pip pip install -e and it doesn't find my package either after I go to the interpreter and try to import it. Why is that?

If you look carefully after running conda develop . (where the setup.py file is) you will notice that the command outputs the following:
(automl) brandBrandoParetoopareto~/ultimate-utils/uutils $ conda develop .
added /Users/brandBrandoParetoopareto/ultimate-utils/uutils
completed operation for: /Users/brandBrandoParetoopareto/ultimate-utils/uutils
if you then check the sys.path python has then you can that new path has been added:
(automl) brandBrandoParetoopareto~/ultimate-utils/uutils $ python
Python 3.7.7 (default, Mar 26 2020, 10:32:53)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> for p in sys.path:
... print(p)
...
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python37.zip
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7/lib-dynload
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7/site-packages
/Users/brandBrandoParetoopareto/automl-meta-learning/automl
/Users/brandBrandoParetoopareto/higher
/Users/brandBrandoParetoopareto/ultimate-utils/uutils
you can confirm this by removing it and printing the contents of sys.path:
(automl) brandBrandoParetoopareto~/ultimate-utils/uutils $ conda develop -u .
uninstalled: /Users/brandBrandoParetoopareto/ultimate-utils/uutils
check sys path again:
(automl) brandBrandoParetoopareto~/ultimate-utils/uutils $ python
Python 3.7.7 (default, Mar 26 2020, 10:32:53)
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import sys
>>> for p in sys.path:
... print(p)
...
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python37.zip
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7/lib-dynload
/Users/brandBrandoParetoopareto/anaconda3/envs/automl/lib/python3.7/site-packages
/Users/brandBrandoParetoopareto/automl-meta-learning/automl
/Users/brandBrandoParetoopareto/higher
now it's gone!
Also, note that conda or python (not sure which) also adds the current path always. This is important to note because when I did import tests outside of the repo I was working it wasn't working but it was inside the repo which seemed very mysterious which I can only attribute to that.
Addendum
to check python sys.path from terminal run that command as a string with python cmd:
python -c "import sys; print(sys.path)"
even better:
python -c "import sys; [print(p) for p in sys.path]"
Original answer: https://stackoverflow.com/a/59903590/1601580

Pyinstaller is missing modulename._modulename

I'm using pyinstaller to build an .exe file on windows including a QT GUI and some image read and write functions. In my main python file, I do import tifffile for reading .tiff images. Pyinstaller compiles my project without errors. When I run it, I get a warning in my terminal saying:
path_to_my_project\tifffile\tiffffile.py:8211: UserWarning: No module named 'tifffile._tifffile' Functionality might be degraded or be slow.
However, I can use the parts of my app that use the corresponding functions from the tifffile module. My question now is, how I can fix this or if it's save to ignore, how to suppress the warning.
That's the conda environment I'm using if of any importance:
altgraph 0.16.1 pypi_0 pypi
blas 1.0 mkl
ca-certificates 2019.5.15 1
certifi 2019.6.16 py37_1
dc-imgproc 1.1.1 pypi_0 pypi
future 0.17.1 pypi_0 pypi
hdf5 1.8.20 hac2f561_1
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
intel-openmp 2019.5 281
jpeg 9b hb83a4c4_2
libopencv 3.4.2 h20b85fd_0
libpng 1.6.37 h2a8f88b_0
libtiff 4.0.10 hb898794_2
macholib 1.11 pypi_0 pypi
mkl 2019.5 281
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.14 py37h14836fe_0
mkl_random 1.0.2 py37h343c172_0
numpy 1.16.5 py37h19fb1c0_0
numpy-base 1.16.5 py37hc3f5095_0
opencv 3.4.2 py37h40b0b35_0
openssl 1.1.1d he774522_0
pefile 2019.4.18 pypi_0 pypi
pip 19.2.2 py37_0
py-opencv 3.4.2 py37hc319ecb_0
pyinstaller 3.5.dev0+gb54a15d7 pypi_0 pypi
pyqt 5.9.2 py37h6538335_2
pyqtgraph 0.10.0 py37h28b3542_3
python 3.7.4 h5263a28_0
pywin32-ctypes 0.2.0 pypi_0 pypi
qt 5.9.7 vc14h73c81de_0
qtpy 1.9.0 py_0
scipy 1.3.1 py37h29ff71c_0
setuptools 41.0.1 py37_0
sip 4.19.8 py37h6538335_0
six 1.12.0 py37_0
sqlite 3.29.0 he774522_0
tifffile 0.15.1 py37h452e1ab_1001 conda-forge
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_0
wheel 0.33.4 py37_0
wincertstore 0.2 py37_0
xz 5.2.4 h2fa13f4_4
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_0
Update I can reproduce the behaviour, if I put only import tifffile into an empty python file and run pyinstaller on it.

HDF5 library version error - HDF5 ver 1.10.4

I'm trying to import some packages with spyder (OS x64), Anaconda and pyton 3.x
The error is pretty famous in the internet. The solution proposed is to match the version of the library 1.10.5 with the HDF5 (mine is 1.10.4)
The question is that I can't find HDF5 version 1.10.5
and, the other hand, cannot understand what I could downgrade.
At this link: https://anaconda.org/conda-forge/hdf5 seems exist version 1.10.5 but when I type in the prompt of anaconda conda install -c conda-forge hdf5
the version remain 1.10.4.
Here the warning:
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...
Here all the packages installed:
# 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
Anyway, I don't understand why in the prompt HDF5 is version 1.10.4 and in the warning, version of HDF5 is 1.10.5

Maybe I am late, but I resolved this problem by upgrading hdf5 to 1.10.5.
On Windows 10, with anaconda you can do this:
conda install -c conda-forge hdf5=1.10.5

I'll leave this here, since it's a top stack thread for me without clear answer.
pip uninstall h5py
pip install h5py
If you are using conda to install tensorflow it installs h5py with 1.10.5 version and on top installs hdf5 1.10.4. Creating conflict that resolves after pip "juggling" since 1.10.4 satisfies the latest h5py.

I have the same problem with Windows 10. Here is what I did
Install some requirements for TensorFlow > 2.0
https://www.tensorflow.org/install/pip?lang
Create conda virtual environment:
conda create -n ai python==3.7.6
conda activate ai
conda install pandas matplotlib scikit-learn scrapy seaborn
conda install -c anaconda tensorflow

I had following same issue.
Warning! HDF5 library version mismatched error
Headers are 1.10.4, library is 1.10.6
My solution is making another conda environment and do every conda w/ 'conda-forge'.
Since hdf5 1.10.4 was installed with following command on my win10 PC w/ no GPU. Python is 3.7.10.
conda install tensorflow
By above command, 1.10.4 came with.
So, I should have done following.
conda install conda-forge tensorflow
Then, 1.10.6 was installed.
'conda-forge' w/ conda install is highly recommended.

I had the same problem as you. It came about because the tensorflow was installed by conda. And the error disappears when using channel anaconda.
conda install -c anaconda tensorflow

I actually solved this problem when I realized (on Mac OSX Mojave) that I had used Homebrew to install Octave, which was built to work with HDF5 1.10.5. I first ran in to this issue trying to install and run TensorFlow from iPython. I'm not actively using Octave, so I uninstalled Octave as well as HDF5 with
brew uninstall --force octave
brew uninstall hdf5
Then upon re-running
conda install h5py
and subsequently importing TensorFlow from iPython, everything seems to be working.

I have simple tensorflow code sum.py:
import tensorflow as tf
a = tf.Variable(1, name="a")
b = tf.Variable(2, name="b")
f = a + b
tf.print("The sum of a and b is", f)
===
I am window 10 user with Anaconda 3, tensorflow 2.0, jupyter, and pyhton 3.
I have similiar issues and i resovle the following iusses:
Error:
UserWarning: h5py is running against HDF5 1.10.5 when it was built against 1.10.4, this may cause problems
My environment was messad up with lots of pip install.
The following video resolve my problem.
https://www.youtube.com/watch?v=RgO8BBNGB8w&t=376s
It uses the tensorflow.yml with a list of clean environment:
https://github.com/jeffheaton/t81_558_deep_learning
Under the window prompt:
conda env create -v -f tensorflow
Then open anaconda prompt
conda acticate tensorflow
python sum.py
or in jupyter notebook. run with OK.

This happened to me when I installed tensorflow via
conda install -c conda-forge tensorflow
I resolved it as follows:
I uninstalled h5py and tensorflow by:
pip uninstall h5py
conda uninstall h5py
conda uninstall tensorflow
and reinstalled tensorflow by:
conda install -c anaconda tensorflow

Damn I had the same error shown in the anaconda prompt and the reason believe me is really silly.
I was multi-tasking and I forgot to activate the environment which resulted in two different versions of HDF5.
Please make sure to conda activate environment_name before launching the jupyter notebook.

How is it that torch is not installed by torchvision?

Somehow when I do the install it installs torchvision but not torch. Command I am running as dictated from the main website:
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
then I do conda list but look:
$ conda list
# packages in environment at /home/ubuntu/anaconda3/envs/pytorch_p36:
#
# Name Version Build Channel
alabaster 0.7.10 py36h306e16b_0
anaconda-client 1.6.14 py36_0
anaconda-project 0.8.2 py36h44fb852_0
argparse 1.4.0 <pip>
asn1crypto 0.24.0 py36_0
astroid 1.6.3 py36_0
astropy 3.0.2 py36h3010b51_1
attrs 18.1.0 py36_0
autovizwidget 0.12.7 <pip>
babel 2.5.3 py36_0
backcall 0.1.0 py36_0
backports 1.0 py36hfa02d7e_1
backports.shutil_get_terminal_size 1.0.0 py36hfea85ff_2
bcrypt 3.1.6 <pip>
beautifulsoup4 4.6.0 py36h49b8c8c_1
bitarray 0.8.1 py36h14c3975_1
bkcharts 0.2 py36h735825a_0
blas 1.0 mkl
blaze 0.11.3 py36h4e06776_0
bleach 2.1.3 py36_0
blosc 1.14.3 hdbcaa40_0
bokeh 1.0.4 py36_0
boto 2.48.0 py36h6e4cd66_1
boto3 1.9.146 <pip>
boto3 1.9.134 py_0
botocore 1.12.146 <pip>
botocore 1.12.134 py_0
bottleneck 1.2.1 py36haac1ea0_0
bzip2 1.0.6 h14c3975_5
ca-certificates 2019.1.23 0
cached-property 1.5.1 <pip>
cairo 1.14.12 h8948797_3
certifi 2019.3.9 py36_0
cffi 1.11.5 py36h9745a5d_0
chardet 3.0.4 py36h0f667ec_1
click 6.7 py36h5253387_0
cloudpickle 0.5.3 py36_0
clyent 1.2.2 py36h7e57e65_1
colorama 0.3.9 py36h489cec4_0
contextlib2 0.5.5 py36h6c84a62_0
cryptography 2.3.1 py36hc365091_0
cudatoolkit 10.0.130 0
curl 7.60.0 h84994c4_0
cycler 0.10.0 py36h93f1223_0
cymem 2.0.2 py36hfd86e86_0
cython 0.28.2 py36h14c3975_0
cytoolz 0.9.0.1 py36h14c3975_0
dask 0.17.5 py36_0
dask-core 0.17.5 py36_0
dataclasses 0.6 py_0 fastai
datashape 0.5.4 py36h3ad6b5c_0
dbus 1.13.2 h714fa37_1
decorator 4.3.0 py36_0
defusedxml 0.6.0 py_0
dill 0.2.9 py36_0
distributed 1.21.8 py36_0
docker 3.7.2 <pip>
docker-compose 1.24.0 <pip>
docker-pycreds 0.4.0 <pip>
dockerpty 0.4.1 <pip>
docopt 0.6.2 <pip>
docutils 0.14 py36hb0f60f5_0
entrypoints 0.2.3 py36h1aec115_2
environment-kernels 1.1.1 <pip>
et_xmlfile 1.0.1 py36hd6bccc3_0
expat 2.2.5 he0dffb1_0
fastai 1.0.52 1 fastai
fastcache 1.0.2 py36h14c3975_2
fastprogress 0.1.21 py_0 fastai
filelock 3.0.4 py36_0
flask 1.0.2 py36_1
flask-cors 3.0.4 py36_0
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
fribidi 1.0.5 h7b6447c_0
get_terminal_size 1.0.0 haa9412d_0
gevent 1.3.0 py36h14c3975_0
glib 2.56.1 h000015b_0
glob2 0.6 py36he249c77_0
gmp 6.1.2 h6c8ec71_1
gmpy2 2.0.8 py36hc8893dd_2
graphite2 1.3.11 h16798f4_2
graphviz 2.40.1 h21bd128_2
greenlet 0.4.13 py36h14c3975_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.8.0 py36h989c5e5_3
harfbuzz 1.8.4 hec2c2bc_0
hdf5 1.10.2 hba1933b_1
hdijupyterutils 0.12.7 <pip>
heapdict 1.0.0 py36_2
html5lib 1.0.1 py36h2f9c1c0_0
icu 58.2 h9c2bf20_1
idna 2.6 py36h82fb2a8_1
imageio 2.3.0 py36_0
imagesize 1.0.0 py36_0
intel-openmp 2018.0.0 8
ipykernel 4.8.2 py36_0
ipyparallel 6.2.2 <pip>
ipython 6.4.0 py36_0
ipython_genutils 0.2.0 py36hb52b0d5_0
ipywidgets 7.2.1 py36_0
ipywidgets 7.4.0 <pip>
isort 4.3.4 py36_0
itsdangerous 0.24 py36h93cc618_1
jbig 2.1 hdba287a_0
jdcal 1.4 py36_0
jedi 0.12.0 py36_1
jinja2 2.10 py36ha16c418_0
jmespath 0.9.4 py_0
jpeg 9b h024ee3a_2
jsonschema 2.6.0 py36h006f8b5_0
jupyter 1.0.0 py36_4
jupyter_client 5.2.3 py36_0
jupyter_console 5.2.0 py36he59e554_1
jupyter_core 4.4.0 py36h7c827e3_0
jupyterlab 0.32.1 py36_0
jupyterlab_launcher 0.10.5 py36_0
kiwisolver 1.0.1 py36h764f252_0
krb5 1.14.2 hcdc1b81_6
lazy-object-proxy 1.3.1 py36h10fcdad_0
libcurl 7.60.0 h1ad7b7a_0
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libgfortran 3.0.0 1 conda-forge
libgfortran-ng 7.2.0 hdf63c60_3
libpng 1.6.37 hbc83047_0
libprotobuf 3.5.2 hd28b015_1 conda-forge
libsodium 1.0.16 h1bed415_0
libssh2 1.8.0 h9cfc8f7_4
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.9 he85c1e1_1
libtool 2.4.6 h544aabb_3
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.8 h26e45fe_1
libxslt 1.1.32 h1312cb7_0
llvmlite 0.23.1 py36hdbcaa40_0
locket 0.2.0 py36h787c0ad_1
lxml 4.2.1 py36h23eabaa_0
lzo 2.10 h49e0be7_2
markupsafe 1.0 py36hd9260cd_1
matplotlib 2.2.2 <pip>
matplotlib 3.0.3 py36h5429711_0
mccabe 0.6.1 py36h5ad9710_1
mistune 0.8.3 py36h14c3975_1
mkl 2018.0.3 1
mkl-service 1.1.2 py36h17a0993_4
mkl_fft 1.0.6 py36h7dd41cf_0
mkl_random 1.0.1 py36h629b387_0
mock 3.0.5 <pip>
more-itertools 4.1.0 py36_0
mpc 1.0.3 hec55b23_5
mpfr 3.1.5 h11a74b3_2
mpi 1.0 openmpi conda-forge
mpmath 1.0.0 py36hfeacd6b_2
msgpack 0.6.0 <pip>
msgpack-numpy 0.4.3.2 py36_0
msgpack-python 0.5.6 py36h6bb024c_0
multipledispatch 0.5.0 py36_0
murmurhash 1.0.2 py36he6710b0_0
nb_conda 2.2.1 py36_2 conda-forge
nb_conda_kernels 2.2.1 py36_0 conda-forge
nbconvert 5.4.1 py36_3
nbformat 4.4.0 py36h31c9010_0
ncurses 6.1 hf484d3e_0
networkx 2.1 py36_0
ninja 1.8.2 py36h6bb024c_1
nltk 3.3.0 py36_0
nose 1.3.7 py36hcdf7029_2
notebook 5.5.0 py36_0
numba 0.38.0 py36h637b7d7_0
numexpr 2.6.5 py36h7bf3b9c_0
numpy 1.15.4 py36h1d66e8a_0
numpy 1.15.4 <pip>
numpy-base 1.15.4 py36h81de0dd_0
numpydoc 0.8.0 py36_0
nvidia-ml-py3 7.352.0 py_0 fastai
odo 0.5.1 py36h90ed295_0
olefile 0.45.1 py36_0
onnx 1.4.1 <pip>
openmpi 3.1.0 h26a2512_3 conda-forge
openpyxl 2.5.3 py36_0
openssl 1.0.2r h7b6447c_0
packaging 17.1 py36_0
pandas 0.24.2 <pip>
pandas 0.23.0 py36h637b7d7_0
pandoc 1.19.2.1 hea2e7c5_1
pandocfilters 1.4.2 py36ha6701b7_1
pango 1.42.3 h8589676_0
paramiko 2.4.2 <pip>
parso 0.2.0 py36_0
partd 0.3.8 py36h36fd896_0
patchelf 0.9 hf79760b_2
path.py 11.0.1 py36_0
pathlib2 2.3.2 py36_0
patsy 0.5.0 py36_0
pcre 8.42 h439df22_0
pep8 1.7.1 py36_0
pexpect 4.5.0 py36_0
pickleshare 0.7.4 py36h63277f8_0
pillow 5.2.0 py36heded4f4_0
pip 10.0.1 py36_0
pixman 0.34.0 hceecf20_3
pkginfo 1.4.2 py36_1
plac 0.9.6 py36_0
plotly 2.7.0 <pip>
pluggy 0.6.0 py36hb689045_0
ply 3.11 py36_0
preshed 2.0.1 py36he6710b0_0
prompt_toolkit 1.0.15 py36h17d85b1_0
protobuf 3.5.2 py36hd28b015_0 conda-forge
protobuf3-to-dict 0.1.5 <pip>
psutil 5.4.5 py36h14c3975_0
psycopg2 2.7.5 <pip>
ptyprocess 0.5.2 py36h69acd42_0
py 1.5.3 py36_0
py4j 0.10.7 <pip>
pyasn1 0.4.5 <pip>
pycodestyle 2.4.0 py36_0
pycosat 0.6.3 py36h0a5515d_0
pycparser 2.18 py36hf9f622e_1
pycrypto 2.6.1 py36h14c3975_8
pycurl 7.43.0.1 py36hb7f436b_0
pyflakes 1.6.0 py36h7bd6a15_0
pygal 2.4.0 <pip>
pygments 2.2.0 py36h0d3125c_0
pykerberos 1.2.1 py36h14c3975_0
pylint 1.8.4 py36_0
PyNaCl 1.3.0 <pip>
pyodbc 4.0.23 py36hf484d3e_0
pyopenssl 18.0.0 py36_0
pyparsing 2.2.0 py36hee85983_1
pyqt 5.9.2 py36h751905a_0
pysocks 1.6.8 py36_0
pyspark 2.3.2 <pip>
pytables 3.4.3 py36h02b9ad4_2
pytest 3.5.1 py36_0
pytest-arraydiff 0.2 py36_0
pytest-astropy 0.3.0 py36_0
pytest-doctestplus 0.1.3 py36_0
pytest-openfiles 0.3.0 py36_0
pytest-remotedata 0.2.1 py36_0
python 3.6.5 hc3d631a_2
python-dateutil 2.7.3 py36_0
pytorch 1.1.0 py3.6_cuda10.0.130_cudnn7.5.1_0 pytorch
pytz 2018.4 py36_0
pywavelets 0.5.2 py36he602eb0_0
pyyaml 3.12 py36hafb9ca4_1
pyzmq 17.0.0 py36h14c3975_0
qt 5.9.6 h52aff34_0
qtawesome 0.4.4 py36h609ed8c_0
qtconsole 4.3.1 py36h8f73b5b_0
qtpy 1.4.1 py36_0
readline 7.0 ha6073c6_4
regex 2018.01.10 py36h14c3975_1000 fastai
requests 2.20.0 py36_1000 conda-forge
requests-kerberos 0.12.0 <pip>
rope 0.10.7 py36h147e2ec_0
ruamel_yaml 0.15.35 py36h14c3975_1
s3fs 0.1.5 py36_0
s3transfer 0.2.0 <pip>
s3transfer 0.2.0 py36_0
sagemaker 1.20.1 <pip>
sagemaker-pyspark 1.2.4 <pip>
scikit-image 0.13.1 py36h14c3975_1
scikit-learn 0.19.1 py36h7aa7ec6_0
scikit-learn 0.20.3 <pip>
scipy 1.1.0 py36hfc37229_0
seaborn 0.8.1 py36hfad7ec4_0
send2trash 1.5.0 py36_0
setuptools 39.1.0 py36_0
simplegeneric 0.8.1 py36_2
singledispatch 3.4.0.3 py36h7a266c3_0
sip 4.19.8 py36hf484d3e_0
six 1.11.0 py36h372c433_1
snappy 1.1.7 hbae5bb6_3
snowballstemmer 1.2.1 py36h6febd40_0
sortedcollections 0.6.1 py36_0
sortedcontainers 1.5.10 py36_0
spacy 2.0.18 py36hf484d3e_1000 fastai
sparkmagic 0.12.5 <pip>
sphinx 1.7.4 py36_0
sphinxcontrib 1.0 py36h6d0f590_1
sphinxcontrib-websupport 1.0.1 py36hb5cb234_1
spyder 3.2.8 py36_0
SQLAlchemy 1.2.11 <pip>
sqlalchemy 1.2.7 py36h6b74fdf_0
sqlite 3.23.1 he433501_0
statsmodels 0.9.0 py36h3010b51_0
sympy 1.1.1 py36hc6d1c1c_0
tblib 1.3.2 py36h34cf8b6_0
terminado 0.8.1 py36_1
testpath 0.3.1 py36h8cadb63_0
texttable 0.9.1 <pip>
thinc 6.12.1 py36h637b7d7_1000 fastai
tk 8.6.8 hbc83047_0
toolz 0.9.0 py36_0
torchvision 0.2.2 py_3 pytorch
tornado 5.0.2 py36_0
tqdm 4.31.1 py36_1
traitlets 4.3.2 py36h674d592_0
typing 3.6.4 py36_0
typing-extensions 3.7.2 <pip>
ujson 1.35 py36h14c3975_0
unicodecsv 0.14.1 py36ha668878_0
unixodbc 2.3.6 h1bed415_0
urllib3 1.23 py36_0
wcwidth 0.1.7 py36hdf4376a_0
webencodings 0.5.1 py36h800622e_1
websocket-client 0.56.0 <pip>
werkzeug 0.14.1 py36_0
wheel 0.31.1 py36_0
widgetsnbextension 3.2.1 py36_0
widgetsnbextension 3.4.2 <pip>
wrapt 1.10.11 py36h28b7045_0
xlrd 1.1.0 py36h1db9f0c_1
xlsxwriter 1.0.4 py36_0
xlwt 1.3.0 py36h7b00a1f_0
xz 5.2.4 h14c3975_4
yaml 0.1.7 had09818_2
zeromq 4.2.5 h439df22_0
zict 0.1.3 py36h3a3bf81_0
zlib 1.2.11 ha838bed_2
I was told I do have pytorch installed but my script keeps giving me this error:
$ cat nohup.out
Traceback (most recent call last):
File "high_performing_data_point_models_cifar10.py", line 5, in <module>
import torch
ModuleNotFoundError: No module named 'torch'
does that mean that I need to install it as pytroch and not torch? Is this not weird?
Note I am running this on an AWS instance p3.2xlarge. This keeps happening when I log out and then go back in that my torch package gets missing...?!?! :/
original post: https://discuss.pytorch.org/t/torchvision-installed-but-not-torch/51758
The issue persists even if I open just a python interactive and try to import it:
(pytorch_p36) ubuntu#ip-123-12-21-123:~$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'torch'
>>>
It also happens running the script directly:
(pytorch_p36) ubuntu#ip-123-12-21-123:~/project/folder$ python high_performing_data_point_models_cifar10.py
Traceback (most recent call last):
File "high_performing_data_point_models_cifar10.py", line 5, in <module>
import torch
ModuleNotFoundError: No module named 'torch'
I can't import torchvision either!
$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'torchvision'
>>>

Your conda list command shows that it was run from the environment called automl:
# packages in environment at /home/ubuntu/anaconda3/envs/automl:
However, when you show the commands that you are trying to run, you are doing so from the (pytorch_p36) environment.
You should run your conda install command while inside this pytorch_p36 environment.

Go to the official website and try installing it as said over there. As I just followed the official documentation and it is working fine.
Here is the step though.
conda install pytorch-cpu torchvision-cpu -c pytorch
You can follow the official documentation.
link

It is really easy to miss smth while working with several machines and several conda/python environments. I think it is a good idea to start from the very beginning. Here is installation process that works fine for me.
0. Connect to your aws-instance using SSH or PuTTY.
1. Create conda env named pytorch_p36 with python 3.6 on your aws machine:
user#aws-instance:~$ conda create -n pytorch_p36 python=3.6
2. Activate it:
user#aws-instance:~$ conda activate pytorch_p36
Or with (for older conda versions):
user#aws-instance:~$ source activate pytorch_p36
Now you should see (pytorch_p36) before your shell prompt:
(pytorch_p36) user#aws-instance:~$
3. Go to PyTorch website and choose appropriate installation command via conda. Run it in your shell:
(pytorch_p36) user#aws-instance:~$ conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
4. Verify the installation:
(pytorch_p36) user#aws-instance:~$ conda list
# packages in environment at /home/user/anaconda3/envs/pytorch_p36:
#
# Name Version Build Channel
...
cudatoolkit 10.0.130 0
...
python 3.6.9 h265db76_0
pytorch 1.1.0 py3.6_cuda10.0.130_cudnn7.5.1_0 pytorch
...
torchvision 0.3.0 py36_cu10.0.130_1 pytorch
...
5. Verify the running with nohup:
(pytorch_p36) user#aws-instance:~$ nohup python -c "import torch; import torchvision; print('PyTorch is fine!')" && cat nohup.out
nohup: ignoring input and appending output to 'nohup.out'
PyTorch is fine!

AWS Machine Learning AMI already has pytorch v1.1.0 and torchvision v0.2.2 installed in the predefined pytorch_p36 virtual environment. So you are not required to create new venv and install pytorch each time you log in. All you need is to run :
$ source activate pytorch_p36
(without dollar sign, obviously). Then, if you run pip show torch you will see that it is already there.