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Python kernel continually crashes when executing a cell

I'm working on applying a clustering algorithm (sklearn.AgglomerativeClustering) on a dataset. I've tried running this same block of code in Spyder IDE and VScode, each time the cell runs for about 35-45s then returns a message that the kernel crashed unexpectedly and and new kernel is created. Im using Python 3.10 with Anaconda package manager.
Spyder had no information about the kernel crash but in VScode I was pointed to this github post Kernel-crashes. I figured somehow my numpy installation was affecting kernel performance or execution. I created a new virtual env using conda; re-installed numpy, pandas, sci-kit learn, etc. Same kernel crash occurring with the same message.
I am using a new Macbook with the m1 chip. Unsure if that's has an influence or is a hint at how to solve this issue. Did see the sci-kit learn installation docs had separate section sci-kit learn install docs about installing with m1 chip but to be honest wasn't exactly sure how to interpret and use that info.
this is the code Im trying to run that causes the kernel to crash. the pca_features is the numpy array return after running .fit_transform method on my raw data. PCA executed fine for what it's worth and showed no issues. Tried using subset of rows, first 5000, to see if that helped cell run at all but still no luck.
Error Message:
The Kernel crashed while executing code in the the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details.
Canceled future for execute_request message before replies were done
Im open to other clustering algorithms but fact that I can't get this to execute makes me less confident any others (e.g. KMeans) would successfully run. But I could be wrong.
My goal is get a unsupervised clustering algorithm fitted to the data so I can get labels for each observation and compare it against primary components from PCA.
```
aggclus = AgglomerativeClustering(n_clusters = 4, affinity="euclidean",linkage="ward")
subset_pcafeatures = pca_features[:5000,5]
cluster_labels = aggclus.fit_predict(pca_features)
```
Any tips/advice/help/assistance would be much appreciated. Thank you
Updated 29-01-23
output from $ conda list
# packages in environment at ../anaconda3/envs/work-env:
#
# Name Version Build Channel
appnope 0.1.3 pyhd8ed1ab_0 conda-forge
asttokens 2.2.1 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 pyhd8ed1ab_3 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
blas 2.116 openblas conda-forge
blas-devel 3.9.0 16_osxarm64_openblas conda-forge
bottleneck 1.3.5 py310h96f19d2_0
brotli 1.0.9 h1a28f6b_7
brotli-bin 1.0.9 h1a28f6b_7
bzip2 1.0.8 h620ffc9_4
ca-certificates 2023.01.10 hca03da5_0
certifi 2022.12.7 py310hca03da5_0
comm 0.1.2 pyhd8ed1ab_0 conda-forge
contourpy 1.0.5 py310h525c30c_0
cycler 0.11.0 pyhd3eb1b0_0
debugpy 1.5.1 py310hc377ac9_0
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
entrypoints 0.4 pyhd8ed1ab_0 conda-forge
executing 1.2.0 pyhd8ed1ab_0 conda-forge
fftw 3.3.9 h1a28f6b_1
fonttools 4.25.0 pyhd3eb1b0_0
freetype 2.12.1 h1192e45_0
giflib 5.2.1 h80987f9_1
ipykernel 6.20.2 pyh736e0ef_0 conda-forge
ipython 8.8.0 pyhd1c38e8_0 conda-forge
jedi 0.18.2 pyhd8ed1ab_0 conda-forge
joblib 1.1.1 py310hca03da5_0
jpeg 9e h1a28f6b_0
jupyter_client 7.4.9 pyhd8ed1ab_0 conda-forge
jupyter_core 5.1.1 py310hca03da5_0
kiwisolver 1.4.4 py310h313beb8_0
lcms2 2.12 hba8e193_0
lerc 3.0 hc377ac9_0
libblas 3.9.0 16_osxarm64_openblas conda-forge
libbrotlicommon 1.0.9 h1a28f6b_7
libbrotlidec 1.0.9 h1a28f6b_7
libbrotlienc 1.0.9 h1a28f6b_7
libcblas 3.9.0 16_osxarm64_openblas conda-forge
libcxx 14.0.6 h848a8c0_0
libdeflate 1.8 h1a28f6b_5
libffi 3.4.2 hca03da5_6
libgfortran 5.0.0 11_3_0_hca03da5_28
libgfortran5 11.3.0 h009349e_28
liblapack 3.9.0 16_osxarm64_openblas conda-forge
liblapacke 3.9.0 16_osxarm64_openblas conda-forge
libopenblas 0.3.21 openmp_hc731615_3 conda-forge
libpng 1.6.37 hb8d0fd4_0
libsodium 1.0.18 h27ca646_1 conda-forge
libtiff 4.5.0 h2fd578a_0
libwebp 1.2.4 h68602c7_0
libwebp-base 1.2.4 h1a28f6b_0
llvm-openmp 14.0.6 hc6e5704_0
lz4-c 1.9.4 h313beb8_0
matplotlib 3.6.2 py310hca03da5_0
matplotlib-base 3.6.2 py310h8bbb115_0
matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge
missingno 0.4.2 pyhd3eb1b0_1
munkres 1.1.4 py_0
ncurses 6.4 h313beb8_0
nest-asyncio 1.5.6 pyhd8ed1ab_0 conda-forge
numexpr 2.8.4 py310hecc3335_0
numpy 1.23.5 py310hb93e574_0
numpy-base 1.23.5 py310haf87e8b_0
openblas 0.3.21 openmp_hf78f355_3 conda-forge
openssl 1.1.1s h1a28f6b_0
packaging 23.0 pyhd8ed1ab_0 conda-forge
pandas 1.5.2 py310h46d7db6_0
parso 0.8.3 pyhd8ed1ab_0 conda-forge
pexpect 4.8.0 pyh1a96a4e_2 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 9.3.0 py310hf4a492f_1
pip 22.3.1 py310hca03da5_0
platformdirs 2.6.2 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.36 pyha770c72_0 conda-forge
psutil 5.9.0 py310h1a28f6b_0
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge
pygments 2.14.0 pyhd8ed1ab_0 conda-forge
pyparsing 3.0.9 py310hca03da5_0
python 3.10.9 hc0d8a6c_0
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
pytz 2022.7 py310hca03da5_0
pyzmq 23.2.0 py310hc377ac9_0
readline 8.2 h1a28f6b_0
scikit-learn 1.2.0 py310h313beb8_0
scipy 1.9.3 py310h20cbe94_0
seaborn 0.12.2 py310hca03da5_0
setuptools 65.6.3 py310hca03da5_0
six 1.16.0 pyh6c4a22f_0 conda-forge
sqlite 3.40.1 h7a7dc30_0
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
threadpoolctl 2.2.0 pyh0d69192_0
tk 8.6.12 hb8d0fd4_0
tornado 6.2 py310h1a28f6b_0
traitlets 5.8.1 pyhd8ed1ab_0 conda-forge
typing-extensions 4.4.0 hd8ed1ab_0 conda-forge
typing_extensions 4.4.0 pyha770c72_0 conda-forge
tzdata 2022g h04d1e81_0
wcwidth 0.2.6 pyhd8ed1ab_0 conda-forge
wheel 0.37.1 pyhd3eb1b0_0
xz 5.2.10 h80987f9_1
zeromq 4.3.4 hbdafb3b_1 conda-forge
zlib 1.2.13 h5a0b063_0
zstd 1.5.2 h8574219_0

bash: cd: /home/user/.conda/pkgs: No such file or directory

I have had the loader problem with my conda similar to this post:
AttributeError: 'NoneType' object has no attribute 'loader'
So when can not solve it i have decides to reinstall conda by These command :
conda install anaconda-clean
anaconda-clean --yes
wget https://repo.anaconda.com/archive/Anaconda3-2020.11-Linux-x86_64.sh
sudo bash Anaconda3-2020.11-Linux-x86_64.sh
So based of this post my Conda information is :
active environment : base
active env location : /home/so/anaconda3
shell level : 1
user config file : /home/so/.condarc populated config files : /home/so/.condarc
conda version : 4.9.2
conda-build version : 3.20.5
python version : 3.8.5.final.0
virtual packages : __cuda=11.0=0
__glibc=2.27=0
__unix=0=0
__archspec=1=x86_64
base environment : /home/so/anaconda3 (read only)
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/so/anaconda3/pkgs
/home/so/.conda/pkgs
envs directories : /home/so/.conda/envs
/home/so/anaconda3/envs
platform : linux-64
user-agent : conda/4.9.2 requests/2.24.0 CPython/3.8.5 Linux/5.4.0-66-generic ubuntu/18.04.5 glibc/2.27
UID:GID : 1000:1000
netrc file : None
offline mode : False
``` and `conda config --show-sources` is:
``` conda config --show-sources
==> /home/so/.condarc <== ssl_verify: True channels:
- defaults
``` and `conda list --show-channel-urls` is:
``` conda list --show-channel-urls
# packages in environment at /home/so/anaconda3:
#
# Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py38_0 defaults
_libgcc_mutex 0.1 main defaults alabaster 0.7.12 py_0 defaults
anaconda 2020.11 py38_0 defaults
anaconda-client 1.7.2 py38_0 defaults
anaconda-navigator 1.10.0 py38_0 defaults
anaconda-project 0.8.4 py_0 defaults
argh 0.26.2 py38_0 defaults
argon2-cffi 20.1.0 py38h7b6447c_1 defaults
asn1crypto 1.4.0 py_0 defaults
astroid 2.4.2 py38_0 defaults
astropy 4.0.2 py38h7b6447c_0 defaults
async_generator 1.10 py_0 defaults
atomicwrites 1.4.0 py_0 defaults
attrs 20.3.0 pyhd3eb1b0_0 defaults
autopep8 1.5.4 py_0 defaults
babel 2.8.1 pyhd3eb1b0_0 defaults
backcall 0.2.0 py_0 defaults
backports 1.0 py_2 defaults
backports.functools_lru_cache 1.6.1 py_0
defaults backports.shutil_get_terminal_size 1.0.0
py38_2 defaults backports.tempfile 1.0
py_1 defaults backports.weakref 1.0.post1
py_1 defaults beautifulsoup4 4.9.3
pyhb0f4dca_0 defaults bitarray 1.6.1
py38h27cfd23_0 defaults bkcharts 0.2
py38_0 defaults blas 1.0
mkl defaults bleach 3.2.1
py_0 defaults blosc 1.20.1
hd408876_0 defaults bokeh 2.2.3
py38_0 defaults boto 2.49.0
py38_0 defaults bottleneck 1.3.2
py38heb32a55_1 defaults brotlipy 0.7.0
py38h7b6447c_1000 defaults bzip2 1.0.8
h7b6447c_0 defaults ca-certificates 2020.10.14
0 defaults cairo 1.14.12
h8948797_3 defaults certifi 2020.6.20
pyhd3eb1b0_3 defaults cffi 1.14.3
py38he30daa8_0 defaults chardet 3.0.4
py38_1003 defaults click 7.1.2
py_0 defaults cloudpickle 1.6.0
py_0 defaults clyent 1.2.2
py38_1 defaults colorama 0.4.4
py_0 defaults conda 4.9.2
py38h06a4308_0 defaults conda-build 3.20.5
py38_1 defaults conda-env 2.6.0
1 defaults conda-package-handling 1.7.2
py38h03888b9_0 defaults conda-verify 3.4.2
py_1 defaults contextlib2 0.6.0.post1
py_0 defaults cryptography 3.1.1
py38h1ba5d50_0 defaults curl 7.71.1
hbc83047_1 defaults cycler 0.10.0
py38_0 defaults cython 0.29.21
py38he6710b0_0 defaults cytoolz 0.11.0
py38h7b6447c_0 defaults dask 2.30.0
py_0 defaults dask-core 2.30.0
py_0 defaults dbus 1.13.18
hb2f20db_0 defaults decorator 4.4.2
py_0 defaults defusedxml 0.6.0
py_0 defaults diff-match-patch 20200713
py_0 defaults distributed 2.30.1
py38h06a4308_0 defaults docutils 0.16
py38_1 defaults entrypoints 0.3
py38_0 defaults et_xmlfile 1.0.1
py_1001 defaults expat 2.2.10
he6710b0_2 defaults fastcache 1.1.0
py38h7b6447c_0 defaults filelock 3.0.12
py_0 defaults flake8 3.8.4
py_0 defaults flask 1.1.2
py_0 defaults fontconfig 2.13.0
h9420a91_0 defaults freetype 2.10.4
h5ab3b9f_0 defaults fribidi 1.0.10
h7b6447c_0 defaults fsspec 0.8.3
py_0 defaults future 0.18.2
py38_1 defaults get_terminal_size 1.0.0
haa9412d_0 defaults gevent 20.9.0
py38h7b6447c_0 defaults glib 2.66.1
h92f7085_0 defaults glob2 0.7
py_0 defaults gmp 6.1.2
h6c8ec71_1 defaults gmpy2 2.0.8
py38hd5f6e3b_3 defaults graphite2 1.3.14
h23475e2_0 defaults greenlet 0.4.17
py38h7b6447c_0 defaults gst-plugins-base 1.14.0
hbbd80ab_1 defaults gstreamer 1.14.0
hb31296c_0 defaults h5py 2.10.0
py38h7918eee_0 defaults harfbuzz 2.4.0
hca77d97_1 defaults hdf5 1.10.4
hb1b8bf9_0 defaults heapdict 1.0.1
py_0 defaults html5lib 1.1
py_0 defaults icu 58.2
he6710b0_3 defaults idna 2.10
py_0 defaults imageio 2.9.0
py_0 defaults imagesize 1.2.0
py_0 defaults importlib-metadata 2.0.0
py_1 defaults importlib_metadata 2.0.0
1 defaults iniconfig 1.1.1
py_0 defaults intel-openmp 2020.2
254 defaults intervaltree 3.1.0
py_0 defaults ipykernel 5.3.4
py38h5ca1d4c_0 defaults ipython 7.19.0
py38hb070fc8_0 defaults ipython_genutils 0.2.0
py38_0 defaults ipywidgets 7.5.1
py_1 defaults isort 5.6.4
py_0 defaults itsdangerous 1.1.0
py_0 defaults jbig 2.1
hdba287a_0 defaults jdcal 1.4.1
py_0 defaults jedi 0.17.1
py38_0 defaults jeepney 0.5.0
pyhd3eb1b0_0 defaults jinja2 2.11.2
py_0 defaults joblib 0.17.0
py_0 defaults jpeg 9b
h024ee3a_2 defaults json5 0.9.5
py_0 defaults jsonschema 3.2.0
py_2 defaults jupyter 1.0.0
py38_7 defaults jupyter_client 6.1.7
py_0 defaults jupyter_console 6.2.0
py_0 defaults jupyter_core 4.6.3
py38_0 defaults jupyterlab 2.2.6
py_0 defaults jupyterlab_pygments 0.1.2
py_0 defaults jupyterlab_server 1.2.0
py_0 defaults keyring 21.4.0
py38_1 defaults kiwisolver 1.3.0
py38h2531618_0 defaults krb5 1.18.2
h173b8e3_0 defaults lazy-object-proxy 1.4.3
py38h7b6447c_0 defaults lcms2 2.11
h396b838_0 defaults ld_impl_linux-64 2.33.1
h53a641e_7 defaults libarchive 3.4.2
h62408e4_0 defaults libcurl 7.71.1
h20c2e04_1 defaults libedit 3.1.20191231
h14c3975_1 defaults libffi 3.3
he6710b0_2 defaults libgcc-ng 9.1.0
hdf63c60_0 defaults libgfortran-ng 7.3.0
hdf63c60_0 defaults liblief 0.10.1
he6710b0_0 defaults libllvm10 10.0.1
hbcb73fb_5 defaults libllvm9 9.0.1
h4a3c616_1 defaults libpng 1.6.37
hbc83047_0 defaults libsodium 1.0.18
h7b6447c_0 defaults libspatialindex 1.9.3
he6710b0_0 defaults libssh2 1.9.0
h1ba5d50_1 defaults libstdcxx-ng 9.1.0
hdf63c60_0 defaults libtiff 4.1.0
h2733197_1 defaults libtool 2.4.6
h7b6447c_1005 defaults libuuid 1.0.3
h1bed415_2 defaults libxcb 1.14
h7b6447c_0 defaults libxml2 2.9.10
hb55368b_3 defaults libxslt 1.1.34
hc22bd24_0 defaults llvmlite 0.34.0
py38h269e1b5_4 defaults locket 0.2.0
py38_1 defaults lxml 4.6.1
py38hefd8a0e_0 defaults lz4-c 1.9.2
heb0550a_3 defaults lzo 2.10
h7b6447c_2 defaults markupsafe 1.1.1
py38h7b6447c_0 defaults matplotlib 3.3.2
0 defaults matplotlib-base 3.3.2
py38h817c723_0 defaults mccabe 0.6.1
py38_1 defaults mistune 0.8.4
py38h7b6447c_1000 defaults mkl 2020.2
256 defaults mkl-service 2.3.0
py38he904b0f_0 defaults mkl_fft 1.2.0
py38h23d657b_0 defaults mkl_random 1.1.1
py38h0573a6f_0 defaults mock 4.0.2
py_0 defaults more-itertools 8.6.0
pyhd3eb1b0_0 defaults mpc 1.1.0
h10f8cd9_1 defaults mpfr 4.0.2
hb69a4c5_1 defaults mpmath 1.1.0
py38_0 defaults msgpack-python 1.0.0
py38hfd86e86_1 defaults multipledispatch 0.6.0
py38_0 defaults navigator-updater 0.2.1
py38_0 defaults nbclient 0.5.1
py_0 defaults nbconvert 6.0.7
py38_0 defaults nbformat 5.0.8
py_0 defaults ncurses 6.2
he6710b0_1 defaults nest-asyncio 1.4.2
pyhd3eb1b0_0 defaults networkx 2.5
py_0 defaults nltk 3.5
py_0 defaults nose 1.3.7
py38_2 defaults notebook 6.1.4
py38_0 defaults numba 0.51.2
py38h0573a6f_1 defaults numexpr 2.7.1
py38h423224d_0 defaults numpy 1.19.2
py38h54aff64_0 defaults numpy-base 1.19.2
py38hfa32c7d_0 defaults numpydoc 1.1.0
pyhd3eb1b0_1 defaults olefile 0.46
py_0 defaults openpyxl 3.0.5
py_0 defaults openssl 1.1.1h
h7b6447c_0 defaults packaging 20.4
py_0 defaults pandas 1.1.3
py38he6710b0_0 defaults pandoc 2.11
hb0f4dca_0 defaults pandocfilters 1.4.3
py38h06a4308_1 defaults pango 1.45.3
hd140c19_0 defaults parso 0.7.0
py_0 defaults partd 1.1.0
py_0 defaults patchelf 0.12
he6710b0_0 defaults path 15.0.0
py38_0 defaults path.py 12.5.0
0 defaults pathlib2 2.3.5
py38_0 defaults pathtools 0.1.2
py_1 defaults patsy 0.5.1
py38_0 defaults pcre 8.44
he6710b0_0 defaults pep8 1.7.1
py38_0 defaults pexpect 4.8.0
py38_0 defaults pickleshare 0.7.5
py38_1000 defaults pillow 8.0.1
py38he98fc37_0 defaults pip 20.2.4
py38h06a4308_0 defaults pixman 0.40.0
h7b6447c_0 defaults pkginfo 1.6.1
py38h06a4308_0 defaults pluggy 0.13.1
py38_0 defaults ply 3.11
py38_0 defaults prometheus_client 0.8.0
py_0 defaults prompt-toolkit 3.0.8
py_0 defaults prompt_toolkit 3.0.8
0 defaults psutil 5.7.2
py38h7b6447c_0 defaults ptyprocess 0.6.0
py38_0 defaults py 1.9.0
py_0 defaults py-lief 0.10.1
py38h403a769_0 defaults pycodestyle 2.6.0
py_0 defaults pycosat 0.6.3
py38h7b6447c_1 defaults pycparser 2.20
py_2 defaults pycurl 7.43.0.6
py38h1ba5d50_0 defaults pydocstyle 5.1.1
py_0 defaults pyflakes 2.2.0
py_0 defaults pygments 2.7.2
pyhd3eb1b0_0 defaults pylint 2.6.0
py38_0 defaults pyodbc 4.0.30
py38he6710b0_0 defaults pyopenssl 19.1.0
py_1 defaults pyparsing 2.4.7
py_0 defaults pyqt 5.9.2
py38h05f1152_4 defaults pyrsistent 0.17.3
py38h7b6447c_0 defaults pysocks 1.7.1
py38_0 defaults pytables 3.6.1
py38h9fd0a39_0 defaults pytest 6.1.1
py38_0 defaults python 3.8.5
h7579374_1 defaults python-dateutil 2.8.1
py_0 defaults python-jsonrpc-server 0.4.0
py_0 defaults python-language-server 0.35.1
py_0 defaults python-libarchive-c 2.9
py_0 defaults pytz 2020.1
py_0 defaults pywavelets 1.1.1
py38h7b6447c_2 defaults pyxdg 0.27
pyhd3eb1b0_0 defaults pyyaml 5.3.1
py38h7b6447c_1 defaults pyzmq 19.0.2
py38he6710b0_1 defaults qdarkstyle 2.8.1
py_0 defaults qt 5.9.7
h5867ecd_1 defaults qtawesome 1.0.1
py_0 defaults qtconsole 4.7.7
py_0 defaults qtpy 1.9.0
py_0 defaults readline 8.0
h7b6447c_0 defaults regex 2020.10.15
py38h7b6447c_0 defaults requests 2.24.0
py_0 defaults ripgrep 12.1.1
0 defaults rope 0.18.0
py_0 defaults rtree 0.9.4
py38_1 defaults ruamel_yaml 0.15.87
py38h7b6447c_1 defaults scikit-image 0.17.2
py38hdf5156a_0 defaults scikit-learn 0.23.2
py38h0573a6f_0 defaults scipy 1.5.2
py38h0b6359f_0 defaults seaborn 0.11.0
py_0 defaults secretstorage 3.1.2
py38_0 defaults send2trash 1.5.0
py38_0 defaults setuptools 50.3.1
py38h06a4308_1 defaults simplegeneric 0.8.1
py38_2 defaults singledispatch 3.4.0.3
py_1001 defaults sip 4.19.13
py38he6710b0_0 defaults six 1.15.0
py38h06a4308_0 defaults snappy 1.1.8
he6710b0_0 defaults snowballstemmer 2.0.0
py_0 defaults sortedcollections 1.2.1
py_0 defaults sortedcontainers 2.2.2
py_0 defaults soupsieve 2.0.1
py_0 defaults sphinx 3.2.1
py_0 defaults sphinxcontrib 1.0
py38_1 defaults sphinxcontrib-applehelp 1.0.2
py_0 defaults sphinxcontrib-devhelp 1.0.2
py_0 defaults sphinxcontrib-htmlhelp 1.0.3
py_0 defaults sphinxcontrib-jsmath 1.0.1
py_0 defaults sphinxcontrib-qthelp 1.0.3
py_0 defaults sphinxcontrib-serializinghtml 1.1.4
py_0 defaults sphinxcontrib-websupport 1.2.4
py_0 defaults spyder 4.1.5
py38_0 defaults spyder-kernels 1.9.4
py38_0 defaults sqlalchemy 1.3.20
py38h7b6447c_0 defaults sqlite 3.33.0
h62c20be_0 defaults statsmodels 0.12.0
py38h7b6447c_0 defaults sympy 1.6.2
py38h06a4308_1 defaults tbb 2020.3
hfd86e86_0 defaults tblib 1.7.0
py_0 defaults terminado 0.9.1
py38_0 defaults testpath 0.4.4
py_0 defaults threadpoolctl 2.1.0
pyh5ca1d4c_0 defaults tifffile 2020.10.1
py38hdd07704_2 defaults tk 8.6.10
hbc83047_0 defaults toml 0.10.1
py_0 defaults toolz 0.11.1
py_0 defaults tornado 6.0.4
py38h7b6447c_1 defaults tqdm 4.50.2
py_0 defaults traitlets 5.0.5
py_0 defaults typing_extensions 3.7.4.3
py_0 defaults ujson 4.0.1
py38he6710b0_0 defaults unicodecsv 0.14.1
py38_0 defaults unixodbc 2.3.9
h7b6447c_0 defaults urllib3 1.25.11
py_0 defaults watchdog 0.10.3
py38_0 defaults wcwidth 0.2.5
py_0 defaults webencodings 0.5.1
py38_1 defaults werkzeug 1.0.1
py_0 defaults wheel 0.35.1
py_0 defaults widgetsnbextension 3.5.1
py38_0 defaults wrapt 1.11.2
py38h7b6447c_0 defaults wurlitzer 2.0.1
py38_0 defaults xlrd 1.2.0
py_0 defaults xlsxwriter 1.3.7
py_0 defaults xlwt 1.3.0
py38_0 defaults xmltodict 0.12.0
py_0 defaults xz 5.2.5
h7b6447c_0 defaults yaml 0.2.5
h7b6447c_0 defaults yapf 0.30.0
py_0 defaults zeromq 4.3.3
he6710b0_3 defaults zict 2.0.0
py_0 defaults zipp 3.4.0
pyhd3eb1b0_0 defaults zlib 1.2.11
h7b6447c_3 defaults zope 1.0
py38_1 defaults zope.event 4.5.0
py38_0 defaults zope.interface 5.1.2
py38h7b6447c_0 defaults zstd 1.4.5
h9ceee32_0 defaults
but when i tried to install the Jupyter by conda install -c conda-forge jupyterlab command i get this error:
(base) so#so-Lenovo-ideapad-310-15IKB:~/Downloads$ conda install -c conda-forge jupyterlab
Collecting package metadata (current_repodata.json): failed
NotWritableError: The current user does not have write permissions to a required path.
path: /home/so/.conda/pkgs/urls.txt
uid: 1000
gid: 1000
If you feel that permissions on this path are set incorrectly, you can manually
change them by executing
$ sudo chown 1000:1000 /home/so/.conda/pkgs/urls.txt
In general, it's not advisable to use 'sudo conda'.
And i have understand the /home/so/.conda/pkgs folder is not exist as you can see below:
(base) so#so-Lenovo-ideapad-310-15IKB:~$ cd /home/so/.conda/pkgs
bash: cd: /home/so/.conda/pkgs: No such file or directory
So what do you suggest to solve this problem?
Update:
I have found solution and write it as answer to this question for other usage, But I don't know why by removing the 'username:` part of the above command , like said here and shown below, it was not working?
(base) so#so-Lenovo-ideapad-310-15IKB:~$ sudo chmod -R +x /home/so/anaconda3
(base) so#so-Lenovo-ideapad-310-15IKB:~$ sudo chmod -R +x /home/so/.conda
(base) so#so-Lenovo-ideapad-310-15IKB:~$ conda update --all
Collecting package metadata (current_repodata.json): failed
NotWritableError: The current user does not have write permissions to a required path.
path: /home/so/.conda/pkgs/urls.txt
uid: 1000
gid: 1000
If you feel that permissions on this path are set incorrectly, you can manually
change them by executing
$ sudo chown 1000:1000 /home/so/.conda/pkgs/urls.txt
In general, it's not advisable to use 'sudo conda'.
Thanks.

It was the conda permission problem and based of this post and its answers, I tried these commands:
so#so-Lenovo-ideapad-310-15IKB:~$ sudo chown -R so: /home/so/anaconda3
so#so-Lenovo-ideapad-310-15IKB:~$ sudo chown -R so: /home/so/.conda
And my problem as you can see below solved:
Thanks.

Minimal example opencv, pyinstaller, conda

I'm trying to get a minimal example working using pyinstaller and opencv inside a conda environment.
So far, what I'm doing is:
conda create -n minimal_example python=3 pyinstaller opencv
which leaves me with the following packages:
# Name Version Build Channel
altgraph 0.17 py_0
blas 1.0 mkl
ca-certificates 2020.1.1 0
certifi 2020.6.20 py38_0
future 0.18.2 py38_1
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
intel-openmp 2020.1 216
jpeg 9b hb83a4c4_2
libopencv 4.0.1 hbb9e17c_0
libpng 1.6.37 h2a8f88b_0
libtiff 4.1.0 h56a325e_1
lz4-c 1.9.2 h62dcd97_0
macholib 1.11 py_0
mkl 2020.1 216
mkl-service 2.3.0 py38hb782905_0
mkl_fft 1.1.0 py38h45dec08_0
mkl_random 1.1.1 py38h47e9c7a_0
numpy 1.18.5 py38h6530119_0
numpy-base 1.18.5 py38hc3f5095_0
opencv 4.0.1 py38h2a7c758_0
openssl 1.1.1g he774522_0
pefile 2019.4.18 py_0
pip 20.1.1 py38_1
py-opencv 4.0.1 py38he44ac1e_0
pycryptodome 3.8.2 py38he774522_0
pyinstaller 3.6 py38h62dcd97_5
python 3.8.3 he1778fa_0
pywin32 227 py38he774522_1
pywin32-ctypes 0.2.0 py38_1000
setuptools 47.3.1 py38_0
six 1.15.0 py_0
sqlite 3.32.3 h2a8f88b_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_2
wheel 0.34.2 py38_0
wincertstore 0.2 py38_0
xz 5.2.5 h62dcd97_0
zlib 1.2.11 h62dcd97_4
zstd 1.4.4 ha9fde0e_3
I have a script called minimal_build.py with the following content:
print('Importing numpy')
import numpy as np
print(np.__version__)
print(np.__file__)
print('Importing OpenCV')
import cv2
I'm trying to package that script into an executable running
pyinstaller minimal_build.py
When running the resulting exe, I get the following output:
Importing numpy
C:\Users\me\Anaconda3\envs\minimal_example\lib\site-packages\PyInstaller\loader\pyimod03_importers.py:623: UserWarning: mkl-service package failed to import, therefore Intel(R) MKL initialization ensuring its correct out-of-the box operation under condition when Gnu OpenMP had already been loaded by Python process is not assured. Please install mkl-service package, see http://github.com/IntelPython/mkl-service
exec(bytecode, module.__dict__)
Traceback (most recent call last):
File "minimal_build.py", line 10, in <module>
File "C:\Users\me\Anaconda3\envs\minimal_example\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 623, in exec_module
exec(bytecode, module.__dict__)
File "site-packages\numpy\__init__.py", line 235, in <module>
File "C:\Users\me\Anaconda3\envs\minimal_example\lib\site-packages\PyInstaller\loader\pyimod03_importers.py", line 623, in exec_module
exec(bytecode, module.__dict__)
File "site-packages\mkl\__init__.py", line 54, in <module>
File "mkl\_mkl_service.pyx", line 27, in init mkl._py_mkl_service
ModuleNotFoundError: No module named 'six'
[9644] Failed to execute script minimal_build
So I'm guessing, that it tries to import numpy from my user folder instead of the package. What am I doing wrong?

I happened to have the same problem as you
Here is the solution I tried:
conda create -n minimal_example python=3.7 pyinstaller
conda activate minimal_example
pip install opencv-python
not conda install opencv
my conda list:
# Name Version Build Channel
altgraph 0.17 py_0 defaults
ca-certificates 2020.1.1 0 defaults
certifi 2020.6.20 py37_0 defaults
future 0.18.2 py37_1 defaults
macholib 1.11 py_0 defaults
numpy 1.19.0 pypi_0 pypi
opencv-python 4.2.0.34 pypi_0 pypi
openssl 1.1.1g he774522_0 defaults
pefile 2019.4.18 py_0 defaults
pip 20.1.1 py37_1 defaults
pycryptodome 3.8.2 py37he774522_0 defaults
pyinstaller 3.6 py37h62dcd97_5 defaults
python 3.7.7 h81c818b_4 defaults
pywin32 227 py37he774522_1 defaults
pywin32-ctypes 0.2.0 py37_1000 defaults
setuptools 47.3.1 py37_0 defaults
sqlite 3.32.3 h2a8f88b_0 defaults
tqdm 4.46.1 py_0 defaults
vc 14.1 h0510ff6_4 defaults
vs2015_runtime 14.16.27012 hf0eaf9b_2 defaults
wheel 0.34.2 py37_0 defaults
wincertstore 0.2 py37_0 defaults
zlib 1.2.11 h62dcd97_4 defaults
then
pyinstaller -F path/to/your/python-file -p path/to/your/venv/python.exe

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 to get a completely clean python environment with miniconda?

I have tried:
conda create --name Leaf python==3.6.5
And if I do conda list
I can find:
# packages in environment at /home/roychang/miniconda3/envs/Leaf:
#
# Name Version Build Channel
ca-certificates 2018.03.07 0
certifi 2018.4.16 py36_0
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 7.2.0 hdf63c60_3
libstdcxx-ng 7.2.0 hdf63c60_3
ncurses 6.1 hf484d3e_0
openssl 1.0.2o h14c3975_1
pip 10.0.1 py36_0
python 3.6.5 hc3d631a_2
readline 7.0 ha6073c6_4
setuptools 40.0.0 py36_0
sqlite 3.24.0 h84994c4_0
tk 8.6.7 hc745277_3
wheel 0.31.1 py36_0
xz 5.2.4 h14c3975_4
zlib 1.2.11 ha838bed_2
But if I try to run some script which use some package didn't in this list (like kivy).
I think it shouldn't work, but it did!
So does MiniConda auto fetch the package from the origin python root?
How could I get a completely clean python environment with MiniConda?
UPDATE:
Seems that I didn't explain well, I had activated that environment and what I got after doing conda list was the result showing above.
I could run this script when I activated the environment, although I didn't install kivy under this environment. I only installed kivy to the origin python before I installed MiniConda.
from kivy.app import App
from kivy.core.window import Window
from kivy.uix.widget import Widget
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.button import Button
from kivy.uix.spinner import Spinner
from kivy.uix.label import Label
from kivy.uix.image import Image
from kivy.uix.textinput import TextInput
from kivy.graphics import Color, Ellipse, Line, Rectangle
from kivy.lang import Builder
And what else I found is if I try which python, it showed:
(Leaf) roychang#ThinkPad-T480:~$ which python
/home/roychang/miniconda3/bin/python
After I deactivated the environment and did which python again I got the same result.
Is this expected? I think it should use different python in different environment.
conda list in default environment (I found Kivy isn't here):
# Name Version Build Channel
asn1crypto 0.24.0 py36_0
ca-certificates 2018.03.07 0
certifi 2018.4.16 py36_0
cffi 1.11.5 py36h9745a5d_0
chardet 3.0.4 py36h0f667ec_1
conda 4.5.4 py36_0
conda-env 2.6.0 h36134e3_1
cryptography 2.2.2 py36h14c3975_0
idna 2.6 py36h82fb2a8_1
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 7.2.0 hdf63c60_3
libstdcxx-ng 7.2.0 hdf63c60_3
ncurses 6.1 hf484d3e_0
openssl 1.0.2o h20670df_0
pip 10.0.1 py36_0
pycosat 0.6.3 py36h0a5515d_0
pycparser 2.18 py36hf9f622e_1
pyopenssl 18.0.0 py36_0
pysocks 1.6.8 py36_0
python 3.6.5 hc3d631a_2
readline 7.0 ha6073c6_4
requests 2.18.4 py36he2e5f8d_1
ruamel_yaml 0.15.37 py36h14c3975_2
setuptools 39.2.0 py36_0
six 1.11.0 py36h372c433_1
sqlite 3.23.1 he433501_0
tk 8.6.7 hc745277_3
urllib3 1.22 py36hbe7ace6_0
wheel 0.31.1 py36_0
xz 5.2.4 h14c3975_4
yaml 0.1.7 had09818_2
zlib 1.2.11 ha838bed_2
pip list in default environment (kivy is found here):
Package Version
---------------------- ---------
asn1crypto 0.24.0
bcrypt 3.1.4
bitarray 0.8.3
boto 2.49.0
boto3 1.7.74
botocore 1.10.74
certifi 2018.4.16
cffi 1.11.5
chardet 3.0.4
click 6.7
coloredlogs 10.0
conda 4.5.4
cryptography 2.3
Cython 0.28.2
docutils 0.14
ffmpeg-python 0.1.15
ffmpy 0.2.2
future 0.16.0
humanfriendly 4.16.1
idna 2.7
jmespath 0.9.3
Kivy 1.10.1
Kivy-Garden 0.1.4
mysql-connector-python 8.0.11
numpy 1.15.0
paramiko 2.4.1
Pillow 5.2.0
pip 10.0.1
protobuf 3.6.0
pyasn1 0.4.4
pycosat 0.6.3
pycparser 2.18
Pygments 2.2.0
pymongo 3.7.1
PyNaCl 1.2.1
pyOpenSSL 18.0.0
pyperclip 1.6.2
PySocks 1.6.8
python-dateutil 2.7.3
pytz 2018.5
pyzmq 17.1.0
requests 2.19.1
ruamel-yaml 0.15.37
s3transfer 0.1.13
scipy 1.1.0
setuptools 40.0.0
six 1.11.0
torchfile 0.1.0
tornado 5.1
urllib3 1.23
visdom 0.1.8.4
websocket-client 0.48.0
wheel 0.31.1
zmq 0.0.0
pip list in Leaf (kivy is found here too):
Package Version
---------------------- ---------
asn1crypto 0.24.0
bcrypt 3.1.4
bitarray 0.8.3
boto 2.49.0
boto3 1.7.74
botocore 1.10.74
certifi 2018.4.16
cffi 1.11.5
chardet 3.0.4
click 6.7
coloredlogs 10.0
cryptography 2.3
Cython 0.28.2
docutils 0.14
ffmpeg-python 0.1.15
ffmpy 0.2.2
future 0.16.0
humanfriendly 4.16.1
idna 2.7
jmespath 0.9.3
Kivy 1.10.1
Kivy-Garden 0.1.4
mysql-connector-python 8.0.11
numpy 1.15.0
paramiko 2.4.1
Pillow 5.2.0
pip 10.0.1
protobuf 3.6.0
pyasn1 0.4.4
pycparser 2.18
Pygments 2.2.0
pymongo 3.7.1
PyNaCl 1.2.1
pyOpenSSL 18.0.0
pyperclip 1.6.2
python-dateutil 2.7.3
pytz 2018.5
pyzmq 17.1.0
requests 2.19.1
s3transfer 0.1.13
scipy 1.1.0
setuptools 40.0.0
six 1.11.0
torchfile 0.1.0
tornado 5.1
umbopython 0.3
urllib3 1.23
visdom 0.1.8.4
websocket-client 0.48.0
wheel 0.31.1
zmq 0.0.0
And:
roychang#ThinkPad-T480:~$ which pip
/home/roychang/miniconda3/bin/pip
roychang#ThinkPad-T480:~$ act Leaf
(Leaf) roychang#ThinkPad-T480:~$ which pip
/home/roychang/miniconda3/envs/Leaf/bin/pip
LAST UPDATE (PROBABLY):
Still not solved.
But don't know why python is separated now.
Tried to uninstall Kivy under Leaf, then Kivy disappeared from both pip list.
I then re-install it under Leaf, and only Leaf got Kivy now.

You have created a conda environment yes, but you need to activate. Use
jalazbe#DESKTOP:~$ conda activate Leaf
You may only use it if the name of the environment is on the left side of your prompt and
(Leaf) jalazbe#DESKTOP:~$
If you now execute
(Leaf) jalazbe#DESKTOP:~$ conda list
Then you will see the minimal libraries that conda need to run python.
To install new libraries use
(Leaf) jalazbe#DESKTOP:~$ conda install name-of-library
# example
(Leaf) jalazbe#DESKTOP:~$ conda install pandas
.
I recommend you read some more info at conda