Fix Python – Resolving new pip backtracking runtime issue


Asked By – RunOrVeith

The new pip dependency resolver that was released with version 20.3 takes an inappropriately long time to install a package.
On our CI pipeline yesterday, a docker build that used to take ~10 minutes timed out after 1h of pip installation messages like this (almost for every library that is installed by any dependency there is a similar log output):

INFO: pip is looking at multiple versions of setuptools to determine which version is compatible with other requirements. This could take a while.
  Downloading setuptools-50.0.0-py3-none-any.whl (783 kB)
  Downloading setuptools-49.6.0-py3-none-any.whl (803 kB)
  Downloading setuptools-49.5.0-py3-none-any.whl (803 kB)
  Downloading setuptools-49.4.0-py3-none-any.whl (803 kB)
  Downloading setuptools-49.3.2-py3-none-any.whl (790 kB)
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run, you can press Ctrl + C to do so. To improve how pip performs, tell us what happened here:
  Downloading setuptools-49.3.1-py3-none-any.whl (790 kB)
  Downloading setuptools-49.3.0-py3-none-any.whl (790 kB)
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I am quite confused whether we are using the new pip resolver correctly, especially since

- Substantial improvements in new resolver for performance, output and error messages, avoiding infinite loops, and support for constraints files.

The behavior seen is described as backtracking in the release notes. I understand why it is there.
It specifies that I can use a constraint file (looks like a requirements.txt) that fixes the version of the dependencies to reduce the runtime using
pip install -c constraints.txt

What is the best way to produce this constraints file? Currently, the best way I can think of is running pip install locally in a new virtual environment, then using pip freeze > constraints.txt. However, this still takes a lot of time for the local install (it’s been stuck for about 10 minutes now).
The notes do mention that This means the “work” is done once during development process, and so will save users this work during deployment.

With the old dependency resolver, I was able to install this package in less than a minute locally.

What is the recommended process here?

Edit: I just found out that some of the dependencies are pointing directly to out internal gitlab server. If I instead install directly from our internal package registry, it works in a couple of minutes again.

Now we will see solution for issue: Resolving new pip backtracking runtime issue


Latest update (2022-02)

There seems to be major update in pip just few days old (version 22.0, release notes + relevant issue on github).

I haven’t tested it in more detail but it really seems to me that they optimized installation order calculation in complex case in such way that it resolves many issues we all encountered earlier. But I will need more time to check it.

Anyway, the rest of this answer is still valid and smart requirements pinning suitable for particular project is a good practice imo.

Since I encountered similar issue I agree this is quite annoying. Backtracking might be useful feature but you don’t want to wait hours to complete with uncertain success.

I found several option that might help:

  • Use the old resolver (--use-deprecated=legacy-resolver) proposed in the answer by @Daniel Davee, but this is more like temporary solution than a proper one.
  • Skip resolving dependencies with --no-deps option. I would not recommend this generally but in some cases you can have a working set of packages versions although there are some conflicts.
  • Reduce the number of versions pip will try to backtrack and be more strict on package dependencies. This means instead of putting e.g. numpy in my requirements.txt, I could try numpy >= 1.18.0 or be even more strict with numpy == 1.18.0. The strictness might help a lot.

Check the following sources:

I still do not have a proper answer that would always help but the best practice for requirements.txt seems to “pin” package versions. I found pip-tools that could help you manage this even with constrains.txt (but I am in an experimental phase so I can not tell you more).

Update (2021-04)

It seems author of the question was able to fix the issue (something with custom gitlab server) but I would like to extend this answer since it might be useful for others.

After reading and trying I ended up with pinning all my package versions to a specific one. This really should be the correct way. Although everything can still work without it, there might be cases where if you don’t pin your dependencies, your package manager will silently install a new version (when it’s released) with possible bugs or incompatibility (this happens to me with dask last this year).

There are several tools which might help you, I would recommend one of these approaches:

Easiest one with pipreqs

  • pipreqs is a library which generates pip requirements.txt file based on imports of any project
  • you can start by pip install pipreqs and runnning just pipreqs in your project root (or eventually with --force flag if your requirements already exists)
  • it will easily create requirements.txt with pinned versions based on imports in your project and versions taken from your environment
  • then you can at any time create new environment based on this requirements.txt

This is really simple tool (you even do not need to write your requirements.txt). It does not allow you to create something complex (might not be a good choice for bigger projects), last week I found one strange behavior (see this) but generally I’m happy with this tool as it usually works perfectly.

Using pip-tools

There are several other tools commonly used like pip-tools, Pipenv or Poetry. You can read more in Faster Docker builds with pipenv, poetry, or pip-tools or Python Application Dependency Management in 2018 (older but seems still valid to me). And it still seems to me that the best option (although it depends on your project/use case) is pip-tools.

You can (this is one option, see more in docs):

  • create (the same format as requirements.txt, it’s up to you whether you pin some package dependency or not)
  • then you can use it by pip install pip-tools and running pip-compile
  • this will generate new requirements.txt file where all versions are pinned, it’s clear, what is the origin
  • (Optionally) you can run it with --generate-hashes option
  • then you can (as with pipreqs) at any time create new environment based on this requirements.txt
  • pip-tools offer you --upgrade option to upgrade the final reqs
  • supports layered requirements (e.g. having dev and prod versions)
  • there is integration with pre-commit
  • offers pip-sync tool to update your environment based on requirements.txt

There are few more stuff you can do with it and I really love the integration with pre-commit. This allows you to use the same requirements as before (just with .in suffix) and add pre-commit hook that automatically updates requirements.txt (so you will never experience having different local environment from the generated requirements.txt which might easily happen when you run something manually).

This question is answered By – Nerxis

This answer is collected from stackoverflow and reviewed by FixPython community admins, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0