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Comes down to production issues and the efficiency of using something just because it's cool. It's not a 100% fool proof solution like Poetry but it works well enough where I haven't had a single issue since I started using this pattern and it mostly solves the problem. There's example repos at and that demonstrate how it's done.

Flock macintosh lagacy download install#

I've solved it in a slightly different way using pip directly by keeping top level deps in requirements.txt, freezing out a requirements-lock.txt file and referencing it with pip3 install using the -c flag. Things worked in the past but didn't work months in the future even when none of my top level dependencies changed.Ī lock file fixes this issue and it's still necessary with Docker. This has bitten me in the past a few times with Werkzeug and also the Vine library with Celery where it pulled in newer versions that had backwards incompatible changes that broke my app. This becomes a problem because now it could potentially mean running different versions in dev, CI and production. Suddenly you have a Docker image that's much different than what it was in June and the only thing that changed is time. You'll get Flask 2.0.1 but you might get a drastically newer version of Werkzeug, Jinja2, itsdangerous and Click because all of those dependencies are defined by Flask with the >= operator. Now let's fast forward to October 19th and you clone the project and run a docker-compose build. Let's say it's June 19th and your project exists on GitHub and you have a requirements.txt file with only `Flask=2.0.1` in it. Here's an example on why a lock file is still very important when using Docker: It is nice to pack all your 'empty' models (ready to train) as python packages and maybe even your 'trained models' (some data files to supplement the empty files) but it will take some iteration to make it all go smoothly. (Say you turn the words to vectors then put the vectors through a CNN or RNN.) The Python packaging system might be a logically correct place to store this data (a necessary part of the the model) but it's a big file that will cause hassles if you do everything right, big hassles if you do things wrong (like compress anaconda packages with slow bzip2, use whatever algorithm that Docker uses to superamplify I/O. The other one is that ML projects themselves contain data, often large amounts of data.įor instance a 'word2vec' style model might be 1 gigabyte and it might be a part of another model. The risk that it won't find a solution between a number of libraries that are only compatible with certain versions is high. One is that the dependencies are a beast.

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This includes stuff that has not been recently updated.Ī good guide on how to do this is here (see table comparison against poetry at the bottom of the article). This even works well with difficult installs of python packages that need to be compiled into executable software.

Personally, I think this combination gives the best amount of versatility. Personally, I use pipenv (manages package dependencies and sub-dependencies), pyenv (python version management), and pyenv-virtualenv (creates an isolated version of Python such as 3.8) together, in tandem, over poetry and others.

It is astonishing how much better it works compared to pip. Pipenv installs the right versions for the dependencies needed for the package you are installing, very well. It works /so much better/ than regular pip. Always use pipenv (although somebody else suggested pip-tools as better than pipenv) instead of pip for package installs.