Path: csiph.com!fu-berlin.de!uni-berlin.de!individual.net!not-for-mail From: Martin =?UTF-8?Q?Sch=C3=B6=C3=B6n?= Newsgroups: comp.lang.python Subject: Re: Formatting (was: Best practise for managing Conda environments?) Date: 25 Sep 2026 08:53:53 GMT Organization: @ Home Lines: 203 Message-ID: References: Mime-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit X-Trace: individual.net PYRGCe0+76ZhU+DHFkwP7gmsyQyP7Pw3sDvyUFVYHNJb3MWZYy Cancel-Lock: sha1:mhIYXxgmqgFT4/U2s16x+olHs7Q= sha256:mep6bte6ff/CEYzaKDnsKQVikkCgn+5SXzm6IgPyW4g= User-Agent: slrn/1.0.3 (Linux) Xref: csiph.com comp.lang.python:198065 Den 2026-09-24 skrev Martin Schöön : > > > My problem is the unique 'mark up' I need to weed out... > > Also, some questionable tips are hiding among the good ones. I will keep > them but add comments. I think it illustrates nicely how careful you > need be when reading LLM produce. > OK, let's see how I managed. Org-mode kind of markup used: * Heading 1 ** Heading 2 ~code~ + list bullet *bold* /emphasis/ # Python style inline comment by me. ################################################################### Mistral Small 4 on bets practice for managing virtual environments using conda. Managing virtual environments with Conda (Anaconda/Miniconda) is essential for isolating project dependencies and avoiding conflicts. Here are the best practices. By following these practices, you ensure reproducible, conflict-free environments for your projects. * Create isolated environments Always create a dedicated environment for each project to avoid dependency conflicts. Create a new environment with a specific Python version: ~conda create --name my_project python=3.9~ Activate the environment: ~conda activate my_project~ *Best Practice:* + Use descriptive names (e.g., `my_project_dev', `data_science_env'). + Specify Python versions explicitly (`python=3.9' instead of just `python'). * Export and recreate environments To share or replicate an environment, export its dependencies to a YAML file. Export the environment to a YAML file: ~conda env export --name my_project > environment.yml~ Recreate the environment from the YAML file: ~conda env create --file environment.yml~ *Best Practice:* + Commit environment.yml to version control (e.g., Git) for reproducibility. + Use `conda env export --from-history' to export only explicitly installed packages (avoids unnecessary dependencies). * Update environments safely Avoid breaking dependencies by updating packages carefully. Update all packages in the environment: ~conda update --all~ # This is /not/ recommended by NSC! Update a specific package: ~conda update numpy~ *Best Practice:* + Test updates in a staging environment before applying them to production. + Use `conda list' to check installed versions before updating. * Remove unused environments Clean up old environments to save disk space. List all environments: ~conda env list~ Remove an environment: ~conda env remove --name old_env~ *Best Practice:* + Regularly prune unused environments (`conda clean --all' removes cached packages). * Use conda-forge for additional packages Prefer conda-forge for community-maintained packages. Add conda-forge as a channel: ~conda config --add channels conda-forge~ Install a package from conda-forge: ~conda install some_package~ *Best Practice:* + Prioritize conda-forge over the default Anaconda channel for broader package availability. * Avoid mixing conda and pip If you must use pip, install it first in the Conda environment. ~conda install pip~ ~pip install some_pip_package~ *Best Practice:* + Prefer Conda packages when possible to avoid conflicts. + If using pip, document it in environment.yml under `pip:'. # According to NSC you should /never/ install packages using conda # /after/ you have used pip. Do /all/ conda (mamba) installs before # using pip! * Use `conda clean' regularly Remove unused packages and caches to free up space. ~conda clean --all~ *Best Practice:* + Run this periodically to keep your Conda installation lean. * Use environment variables for paths Set environment variables to avoid hardcoding paths. Set a variable in the current shell: ~export MY_DATA_DIR=/path/to/data~ Or set it in the Conda environment: ~conda env config vars set MY_DATA_DIR=/path/to/data~ ~conda activate my_project~ **Best Practice:** + Use `.env` files for project-specific variables (load them with `python-dotenv`). * Use mamba for faster dependency resolution Mamba is a drop-in replacement for Conda that resolves dependencies much faster. Install mamba: ~conda install mamba -n base -c conda-forge~ Use mamba instead of conda: ~mamba install numpy pandas~ *Best Practice:* + Use mamba for large environments to speed up installation. * Document dependencies clearly Include a README.md in your project with: - Environment setup instructions. - Key dependencies. - How to activate the environment. * Example Workflow ** Create environment ~conda create --name my_project python=3.9~ ~conda activate my_project~ ** Install packages ~conda install numpy pandas scikit-learn~ ~pip install some_pip_package~ ** Export environment ~conda env export --from-history > environment.yml~ ** Share environment Share environment.yml and README.md with collaborators. #################################################################### I hope this is readable. Note that some advice above is /not/ good practice according to the instructions supplied by NSC and NCS provides plausible motivation for their advice. https://nsc.gitlab-pages.liu.se/berzelius-training/managing-conda-environments-best-practices/2.%20best-practices/ I have tested none of this so far. The weather has been too nice :-) /Martin