How to share conda environments across platforms

This answer is given with the assumption that you would like to make sure that the same versions of the packages that you generally care about are on different platforms and that you don't care about the exact same versions of all packages in the entire dependency tree. If you are trying to install the exact same version of all packages in your entire dependency tree that has a high likelihood of failure since some conda packages have different dependencies for osx/win/linux. For example, the recipe for otrobopt will install different packages on Win vs. osx/linux, so the environment list would be different.

Recommendation: manually create an environment.yaml file and specify or pin only the dependencies that you care about. Let the conda solver do the rest. Probably worth noting is that conda-env (the tool that you use to manage conda environments) explicitly recommends that you "Always create your environment.yml file by hand."

Then you would just do conda env create --file environment.yml

Have a look at the readme for conda-env.

They can be quite simple:

name: basic_analysis
dependencies:
  - numpy
  - pandas

Or more complex where you pin dependencies and specify anaconda.org channels to install from:

name: stats-web
channels:
  - javascript
dependencies:
  - python=3.4   # or 2.7 if you are feeling nostalgic
  - bokeh=0.9.2
  - numpy=1.9
  - nodejs=0.10
  - flask
  - pip:
    - Flask-Testing

In either case, you can create an environment with conda env create --file environment.yaml.

NOTE: You may need to use .* as a version suffix if you're using an older version of conda.

Whilst it is possible to create your environment.yml file by hand, you can ensure that your environment works across platforms by using the conda env export --from-history flag.

This will only include packages that you’ve explicitly asked for, as opposed to including every package in your environment.

For example, if you create an environment and install a package conda install python=3.8 numpy, it will install numerous other dependencies as well as python and numpy.

If you then run conda env export > environment.yml, your environment.yml file will include all the additional dependencies conda automatically installed for you.

On the other hand, running conda env export --from-history will just create environment.yml with python=3.8 and numpy and thus will work across platforms.

Answer adapted from the docs.