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conda list -f --json spyder creates huge load

I do use anaconda on an ubuntu 20.04 and since a week or so I have a problem with processes like
/home/csi/anaconda3/bin/python /home/csi/anaconda3/bin/conda list -f --json spyder
creating a huge load between 40 und 160. The number of processes is about 110.
I have been using 4 environments created by ananconda-navigator all of them are Python 3-Projects, none of them is called spyder. One is called spyder5.
I am using the activation of conda python with the delivered .bashrc-snippet.
:~$ conda list conda
# packages in environment at /home/csi/anaconda3:
#
# Name Version Build Channel
_anaconda_depends 2020.07 py38_0
anaconda custom py38_1
anaconda-client 1.9.0 py38h06a4308_0
anaconda-navigator 2.1.2 py38h06a4308_0
anaconda-project 0.10.2 pyhd3eb1b0_0
conda 4.11.0 py38h06a4308_0
conda-build 3.20.5 py38_1
conda-content-trust 0.1.1 pyhd3eb1b0_0
conda-env 2.6.0 1
conda-pack 0.6.0 pyhd3eb1b0_0
conda-package-handling 1.7.3 py38h27cfd23_1
conda-repo-cli 1.0.4 pyhd3eb1b0_0
conda-token 0.3.0 pyhd3eb1b0_0
conda-verify 3.4.2 py_1
The process starts automatically again after a while. So to work without that huge load I created a bash script trying to kill all those processes every ten seconds.
I did not try to uninstall anaconda3 and recreate all Environments. I will do this when I am upgrading the Ubuntu-Version in May anyway.
But does anyone have had an issue alike? Or does anyone know howto deactivate the periodically start of conda list? This is really annoying.
If you need more information I would be happy to provide them for you!

So after a while an update of conda fixed this issue. However, this was strange behaviour.
I did reinstall spyder via pip3 command of the anaconda installation. After that I updated anaconda and conda via conda command.
:~$ pip3 install --force-reinstall spyder
:~$ conda update anaconda
:~$ conda update conda
This fixed the issue of "conda list" processes but not the issue of the one process creating a huge load. However now the system is usable again.

anaconda/spyder scikit learn update 0.21.3 to 0.22.2

anaconda/spyder scikit learn update 0.21.3 to 0.22.2
(base) mm#mm:~$ python --version
Python 3.7.4
(base) mm#mm:~$ anaconda --version
anaconda Command line client (version 1.7.2)
My scikit-learn version is 0.21.3 but I can't update to 0.22.2
I have tried multpile ways to update but I think this version is not included in my channels. I am using the default channels
How can I update sckit-learn using conda or any other possible way
my history (commands I have tried):
1509 conda update conda
1510 spyder
1511 conda list
1512 conda update scikit-learn
1513 anaconda
1514 navigator-updater
1515 conda list scikit-learn
1516 conda install scikit-learn=0.22.2
1517 conda install scikit-learn=0.22
1518 conda install scikit-learn==0.22.2
1519 conda install -c intel scikit-learn
1520 spyder
1521 history
And the error I get when trying to conda install:
(base) mm#mm:~$ conda install scikit-learn=0.22.2
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
PackagesNotFoundError: The following packages are not available from current channels:
- scikit-learn=0.22.2
Current channels:
- 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

Not every package and not always the most recent versions of packages are available from the default channels when using conda install.
There are two ways you can check what is available from your configured channels:
conda search scikit-learn gives you a list of all packages that match the name scikit-learn and the available versions. On linux x64, this gives me this:
# Name Version Build Channel
<shortened to last few lines of list>
scikit-learn 0.22.1 py36h22eb022_0 pkgs/main
scikit-learn 0.22.1 py36hd81dba3_0 pkgs/main
scikit-learn 0.22.1 py37h22eb022_0 pkgs/main
scikit-learn 0.22.1 py37hd81dba3_0 pkgs/main
scikit-learn 0.22.1 py38h22eb022_0 pkgs/main
scikit-learn 0.22.1 py38hd81dba3_0 pkgs/main
So only 0.22.1 is available from the default channels. You can also check using anaconda.org where you can use the saerch function to get this:
Note the small gray numbers that indicate that anaconda / scikit-learn (the mirror of the default channel) only has version 0.22.1, in accordance with conda search output.
Note also that conda-forge / scikit-learn has your desired version, so you can use this command to install:
conda install -c conda-forge scikit-learn=0.22.2

Anaconda python: Pinning channel and not package version

Every time I use conda install -c some_channel to install a Python package, conda tries to update other packages from some_channel. From example, here I'm trying to install hdbscan from conda-forge, and conda wants to update ca-certificates as well.
The following NEW packages will be INSTALLED:
hdbscan: 0.8.18-py36h7eb728f_0 conda-forge
The following packages will be UPDATED:
ca-certificates: 2018.03.07-0 anaconda --> 2018.8.24-ha4d7672_0 conda-forge
certifi: 2018.8.24-py36_1 anaconda --> 2018.8.24-py36_1001 conda-forge
openssl: 1.0.2p-h1de35cc_0 anaconda --> 1.0.2p-h470a237_1 conda-forge
Proceed ([y]/n)? n
Note that conda tries to do this even if the package versions from the two channels are identical. So this is not just about upgrading to newer version.
Can I pin the channel for packages without pinning the version numbers? I wouldn't mind updating ca-certificates if the update comes from anaconda channel.

Updated version of tensorflow is not listed in "conda search"

Installed tensorflow 1.5.0 with the command "conda install -c conda-forge tensorflow". Installation of the version is confirmed when I import it in Jupyter.
But when I did the 'conda search tensorflow' in anaconda prompt, following is the result:
Conda search tensorflow
(C:\Users\User\Anaconda3) C:\Users\User>conda search tensorflow
Fetching package metadata .............
r-tensorflow 0.8.2 r3.4.1_0 defaults
1.4 r342h0bf44f9_0 defaults
1.4.3 r343h889e2dd_0 defaults
1.4.3 mro343h889e2dd_0 defaults
tensorflow 1.1.0 np112py35_0 defaults
1.1.0 np112py36_0 defaults
1.2.1 py35_0 defaults
1.2.1 py36_0 defaults
tensorflow-gpu 1.1.0 np112py35_0 defaults
1.1.0 np112py36_0 defaults
Can someone please throw light on the following:
Why isn't tensorflow 1.5.0 not listed above when it is being imported by default in Jupyter console?
Values for all versions displayed above are set as "defaults".Why?
How can I import different versions of tensorflow in different kernels?
How could I verify if an imported version of tensorflow invokes GPU?

tensorflow 1.5.0 isn't listed in the output from conda search tensorflow because it isn't available on any of your default channels. Just as you needed to specify the conda-forge channel to the conda install command, you also need to tell conda search to look on the conda-forge channel:
conda search -c conda-forge tensorflow
In the output from that command you'll see that some packages are marked conda-forge instead of defaults, which answers your next question: defaults means that the listed package is available from one of your default search channels. You can change conda's default channel settings by editing the .condarc file, normally found in your home directory.
From your other questions, I think you may be confusing conda search with conda list.
conda search shows the package versions available to install
conda list shows the package versions actually installed in the
current environment.
To use a specific version of a package in Jupyter you create a conda environment with that version, activate the environment, then start Jupyter:
activate myEnvironment
jupyter notebook
You may also be able to do this via the Anaconda Navigator or (on Windows) a start menu shortcut.

Install TensorFlow with specific version on Anaconda

TensorFlow has multiple versions, if I want to install a specific version in Anaconda, which command should I use?

I find the existing answers unsatisfying, as the OP asked specifically about Anaconda but the answers are just pip installs.
You can list the available versions for install doing
conda search tensorflow-gpu
which should give you some output that looks like
Loading channels: done
# Name Version Build Channel
tensorflow-gpu 1.4.1 0 pkgs/main
tensorflow-gpu 1.5.0 0 pkgs/main
tensorflow-gpu 1.6.0 0 pkgs/main
tensorflow-gpu 1.7.0 0 pkgs/main
tensorflow-gpu 1.8.0 h7b35bdc_0 pkgs/main
tensorflow-gpu 1.9.0 hf154084_0 pkgs/main
tensorflow-gpu 1.10.0 hf154084_0 pkgs/main
tensorflow-gpu 1.11.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 1.12.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 1.13.1 h0d30ee6_0 pkgs/main
tensorflow-gpu 1.14.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 1.15.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 2.0.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 2.1.0 h0d30ee6_0 pkgs/main
tensorflow-gpu 2.2.0 h0d30ee6_0 pkgs/main
If you need to specify a particular channel, the -c/--channel option is your friend, for example:
conda search -c conda-forge tensorflow-gpu
Then you can select your version by passing it to the install command, for example:
conda install tensorflow-gpu==2.0.0
If you needed the channel option in your search, you should add the same option to the conda install command. Note this will work the same for tensorflow (i.e. not the GPU version), just change the package name accordingly.
YAML Configuration
If you use YAML environment configuration files, you can do the same thing:
# environment.yaml
name: my_conda_env
channels:
- conda-forge
dependencies:
- tensorflow-gpu=2.0.0
Create your environment with command:
conda env create -f environment.yaml
or if you change the version of an already created environment:
conda env update -f environment.yaml

This is probably the simplest way to do it:
pip install --ignore-installed --upgrade tensorflow==1.4
If you want to see all available versions, you can check out https://pypi.python.org/pypi/tensorflow/json
I would highly recommend you use virtualenv or conda to isolate your tensorflow installation, especially if you want to play-test different versions and the CPU/GPU versions.

I am assuming that you are using Windows, python3.5, and CPU version of tensorflow.
let's first create conda environment.
C:> conda create -n tensorflow python=3.5
C:> activate tensorflow
(tensorflow)C:> # Your prompt should change
After creating the conda environment successfully, issue the correct command to install the specific version. I will guide you through installing three different versions.
To install version r1.0
(tensorflow)C:> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.1-cp35-cp35m-win_amd64.whl
To install version r1.3
(tensorflow)C:> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.3.0rc1-cp35-cp35m-win_amd64.whl
To install master version
(tensorflow)C:> pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl
let me know if this is what you are looking for

To install specific version of python, tensorflow while creating conda environment:
conda create -n python=3.6 tensorflow=1.12.0

I have a older project which has dependency to run on:
Python: 3.7
Tensorflow: 1.13.1
For this I create a virutal environment using anancoda like:
conda create -n tf python=3.7 tensorflow=1.13.1
// here more modules with specific version can be added
After that we to activate env:
conda activate tf
after this ouput was:
(tf) D:\ff\testM>
Environment changed from (base) --> (tf)
(base) D:\ff\testM>

I have a older project which has dependency to run on:
Python: 3.7
Tensorflow: 1.13.1
To create i used ananconda:
conda create -n tf python=3.7 tensorflow=1.13.1
// here more modules with specific version can be added
conda activate tf //Activate environment
(base) D:\ff\testM> --> (tf) D:\ff\testM>
// environment changes from base to tf