How to competently fix "broken" / stopped working Python modules?

Question:

Increasingly, there are questions about "broken" / incompatible / stopped working modules in Python and the errors that go with it:

ImportError: DLL load failed: ...
ImportError: Could not import ...
UnsatisfiableError: The following specifications were found to be in conflict: ...

and many others…

The problems described above most often arise due to:

installing a new module
Python updates
module/module updates
updates/changes in VirtualEnv (Python virtual environment)
misuse of conda – for example, using the default virtual environment created by base (which is used by conda to manage other virtual environments), instead of an explicitly created new virtual environment

Question:

how to fix these problems and how to avoid them in the future?

Answer:

After a long "walking on the rake" (both under UNIX * and under Windows), I settled on the following approach, which has never let me down so far.

The basic idea is to install the Anaconda package and create independent virtual environments using the conda package manager.

Anaconda checks module version compatibility (including dependencies). This minimizes the chance of breaking Python by simply installing or updating some module(s).

The algorithm for installing Anaconda and creating a VirtualEnv (an independent Python virtual environment):

Installing Anaconda or Miniconda

Update the conda package manager ( NOTE: to avoid problems – always run conda from Anaconda Prompt ):
```
 conda update conda
```
Never "touch" the Python installed by default in the OS or installed by other software (for example, when installing Oracle Database, a separate Python is installed that will be used by Oracle). By "не трогайте" Python I mean making any changes that affect Python or its modules:

installing new modules
python update
module update

Do not install modules in the base virtual environment created by default. Install modules only in those virtual environments that you have explicitly created (see next point – 5 ). The base virtual environment is the technical environment created by conda to manage the rest of the virtual environments. If you don't want to break all virtual environments at once, don't touch base .
For each more or less independent Python project, create an independent virtual environment (VirtualEnv). You can additionally create one shared environment for common purposes. In this example, I will create a general environment called ml (Machine Learning) for Python 3.7 and a core set of modules for working on machine learning tasks (with Nvidia GPU support):
```
 conda create --name ml python=3.7 anaconda keras-gpu
```
In order to run Python / Jupyter / iPython / etc. From the created VirtualEnv, you can use one of the following options:

run Anaconda Prompt –> activate the necessary Wirth in it. environment ( conda activate <env_name> ) –> start ipython / Jupyter-Notebook

use cmd/shell script to run ipython from desired virt. environment:

 @echo off set conda_dir=%USERPROFILE%\Anaconda3 set env_name=%1 if "%env_name%"=="" set env_name=ml set env_dir=%conda_dir%\envs\%env_name% rem cd %env_dir% call %conda_dir%\Scripts\activate.bat %env_dir% %env_dir%\Scripts\ipython.exe Пример вызова: `c:\bin\ipy_env.cmd ml37`

use cmd/shell script to launch Jupyter-Notebook from desired virt. environment:

 @echo off set env_name=%1 if "%env_name%"=="" set env_name=ml set env_dir=%USERPROFILE%\Anaconda3\envs\%env_name% rem cd %env_dir% call %USERPROFILE%\Anaconda3\Scripts\activate.bat %env_dir% start cmd.exe /k "%USERPROFILE%\Anaconda3\envs\%env_name%\Scripts\jupyter-notebook.exe" Пример вызова: `C:\bin\jupyter_env.cmd torch`

for a project in PyCharm, you can specify an existing Conda Environment as Project Interpreter

To install a new module always try to do it in the following sequence:

always try to find the required module in the default Anaconda repository first
```
 conda search <module_name>
```
if the module is found, install it in our VirtualEnv ( ml in our example):
```
 conda install -n ml <module_name>
```
if the module is not found, then we try to find this module in the conda-forge repository (A community-led collection)
```
 conda search -c conda-forge <module_name>
```
if the module is found, install it in our VirtualEnv ( ml in our example):
```
 conda install -c conda-forge -n ml <module_name>
```
only if the required module is not found in either the original Anaconda repository or conda-forge – use pip install :
```
 conda activate ml pip install <module_name>
```

to update a module use the conda package manager:
```
 conda update -n ml <module_name>
```

Useful links:

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