I’ve spent the day building a conda recipe, the process wasn’t super-smooth, hopefully these notes will help others and/or maybe you can leave me a comment to improve my flow. The goal was to learn how to use conda to distribute a package that ordinarily I’d put on PyPI.
I’m using Linux 64bit (Mint 18 on an XPS 9550), conda 4.1 and conda build 1.21.14 (up to date as of today). My goal was to build a recipe (python_template_with_config_recipe) to install my python_template_with_config (a bit of boilerplate code I use sometimes when making a new project). That template has two modules, a test, a setup.py and it depends on numpy.
The short story:
- git clone https://github.com/ianozsvald/python_template_with_config_recipe.git
- cd inside, run “conda build –debug .”
- # note the period means “current directory” and that’s two dashes debug
- a local bzip will be built and you’ll see that the 1 test ran ok
On my machine the built code ends up in “~/anaconda3/pkgs/python_template_with_config-0.1-py35_0/lib/python3.5/site-packages/python_template_with_config” and the building takes place in “~/anaconda3/conda-bld/linux-64”.
In a new conda environment I can use “conda install –use-local python_template_with_config” and it’ll install the built recipe into the new environment.
To get started with this I first made a fresh empty conda environment (note that anaconda isn’t my default Python, hence the long-form access to `conda`):
$ ~/anaconda3/bin/conda create -n new_recipe_env python=3.5
$ . ~/anaconda3/bin/activate new_recipe_env
To check that my existing “setup.py” runs I use pip to install from git, we’ll need the setup.py in the conda recipe later so we want to confirm that it works:
$ pip install git+https://github.com/ianozsvald/python_template_with_config.git # runs setup.py
# $ pip uninstall python_template_with_config # use this if you need to uninstall whilst developing
I can check that this has installed as a module using:
In : from python_template_with_config import another_module In : another_module.a_math_function() # silly function just to check that numpy is installed Out: -2.4492935982947064e-16
Now I’ll make a second conda environment to develop the recipe:
$ ~/anaconda3/bin/conda create -n new_recipe_env2 python=3.5 # vanilla environment, no numpy
$ . ~/anaconda3/bin/activate new_recipe_env2
git clone https://github.com/ianozsvald/python_template_with_config_recipe.git
cd inside, run "conda build --debug ."
The recipe (meta.yaml) will look at the git repo for python_template_with_config, pull down a copy, build using build.sh and the store a bzip2 archive locally. The build step also notes that I can upload this to Anaconda using `$ anaconda upload /home/ian/anaconda3/conda-bld/linux-64/python_template_with_config-0.1-py35_0.tar.bz2`.
A few caveats occurred whilst creating the recipe:
- You need a bld.bat, build.sh, meta.yaml, at first I created bld.sh and meta.yml (both typos) and there were no complaints…just frustration on my part – the first clue was seeing “source tree in: /home/ian/anaconda3/conda-bld/work \n number of files: 0” in the build output
- When running conda build it seems to not overwrite the version in ~/anaconda3/pkgs/ – I ended up deleting “python_template_with_config-0.1-py35_0/” and “python_template_with_config-0.1-py35_0.tar.bz2” by hand just to make sure on each build iteration – I must be missing something here, please enlighten me (see note from Marco below)
- Having deleted the cached versions and fixed the typos I’d later see “number of files: 14”
- Later I added “run_tests.py” rather than “run_test.py”, I knew it wasn’t running as I’d added a “1/0” line inside run_tests.py that obviously wasn’t running (it should raise a ZeroDivisionError even if the tests did run ok). Again this was a typo on my part
- The above is tested on Linux, it ought to work on Windows but I’ve not tested it
- This meta.yaml installs from github, there’s a commented out line in there showing how to access the local source files instead
Marco Bonzanini has noted that “conda clean“, “conda clean -t” (tarballs) and “conda clean -p” (packages) can help with the caching issue mentioned above. He also notes “conda skeleton <pypi package url>” takes care of the boilerplate if you have a published version on PyPI, so that avoids the silly mistakes I made by hand. Cheers!
I didn’t get as far as uploading this to Anaconda to make it ‘public’ (as I don’t think that’s so useful) but I believe that final step is easy enough.
- conda recipe tutorial
- meta.yaml specification
- Notes on laying out a Python package (as a precursor to making a setup.py file)
- In my recipe in github I’ve added a set of useful links in the meta.yaml and run_tests.py
- conda-forge and conda-recipes have lots more examples
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.