Consider not capping major versions for many of our dependencies #648
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Firstly, just to clarify:
This issue was due to a deficiency in jazzband/pip-tools#1372 (as I just mentioned: jupyter-book/jupyter-book#1590 (comment)), not because it is actually impossible to install, e.g. using pip. Using upper-pinnings should never prohibit jupyter-book from being installed, if a new dependency version is released, in fact it should be quite the opposite. |
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So this is where I stand: Just looking at jupyter-book for now: it is almost an "end-user tool", whereby you could actually pin every dependency to an exact version, and know that it will never break. If you remove upper-pinnings, jupyter-book will always eventually break, Those users will then come to us and say "hey jupyter-book is broken": what do we then do? Is this really what you want to be telling every user of jupyter-book, i.e. that it's not enough to simply install jupyter-book and expect it to work? Now, its true that pinning major versions does not "guarantee" that a dependency update will never break jupyter-book; it is quite possible for dependencies to (inadvertently or otherwise) introduce breaking changes in minor versions. If you have upper-pinnings, it is indeed true, that eventually there will be (primarily power) users that encounter an issue with wanting to use the latest version of another python package, alongside jupyter-book. |
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Basically, the two extremes here are:
In principle, you could have these as two corresponding releases of jupyter-book, e.g. |
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I think that you raise a lot of good points - agreed we don't want Jupyter Book to become unstable for people because our upstream dependencies introduce breaking changes in unexpected ways. As you say, it's a balance between these extremes. Quick clarification on the dependencies our users likely bring with themOne clarification: maybe I am not understanding the way dependencies work in Jupyter Book, but I think that installing In my experience, this user persona does almost no environment management. They just use a single kitchen sink environment and So I would think that our users in particular are likely to be vulnerable to "kitchen sink environments causing dependency constraint conflicts". (that said, we have had a few reports of this but not a ton, so I don't know how much of an issue it is). Investigation into other projects in adjacent ecosystemsI was asking around other projects to see how they handle this. It doesn't seem like many people use upper limits for their dependencies (e.g. Dask doesn't, scikit learn doesn't, pandas doesn't, jupyterlab uses a hybrid approach. So what do these projects do to avoid this potential problem? A common pattern was including a CI/CD job that explicitly tests against unstable versions of upstream dependencies. That way they know before releases are made if it will break their test suites. For example:
Speaking to folks in those communities it sounds like the workflow is roughly:
Maybe this could be a nice balance between "make sure our users don't have instability" and "don't constrain our users' dependency chains too strongly". Our tech stack is a bit different so I'm not sure how this would work with, eg, our regression tests w html and such, but maybe worth a try? Curious what you/others think about it. |
Then you can't really use https://iscinumpy.dev/post/bound-version-constraints as a reference point, since the author specifically states in the comments:
i.e. a major point of the thesis, is that you put the responsibility of version management on the user |
Well, your kernel for code execution can be entirely separate from your execution environment: https://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments. |
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@choldgraf invited me to weigh in here from Twitter, thanks 😄 My perspective is that I am 99% against upper-level pins, and only not 100% because "never say never". I personally struggled with the pins in I'll also reply to a couple of comments from the thread that stood out to me.
I suspect this target persona does a fair amount of environment management, especially if they use conda. I think this is generally taught as "best practice" when you're learning conda. That said, they can still run into conflicts between dependencies of
Yes, I think this is what you should say. But instead of creating a requirements file, say "please do
So your position is that environment management/requirements.txt file is too complicated or not desirable, but multiple execution environments for different notebooks is feasible? That doesn't seem consistent to me...
I doubt this is true. I think if you're at a point where you're sharing enough content that you want to put it into As to the proposal by @choldgraf:
I think capping the EB stack in Capping dependencies further down the stack is much harder to overcome. Now I need to fork that package, bump its dependencies, and then install from my fork if I want to update anything. Since those (MyST-NB, MyST-parser, etc.) are (to me) meant more as libraries that folks can install, the chances of having dependency conflicts is much higher. By the way, feel free to pin docutils with |
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Heya,
That's incorrect, you can't "start to rely on a dependency" without releasing a new version, then jupyter book could still be installed with the old version of the dependency |
Can you give a dev version of what you were trying to install? |
Only if that old version is still available... It could get yanked due to a security vulnerability or any other reason, really. But this is a side point anyways.
I don't remember the versions precisely, but it was equivalent to |
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As a casual user I did run into too strict pinning by various jupyter-book components as well. In my case it was nothing dramatic—more of a minor nuisance, especially since forcing pip to ignore the pinning worked just fine™. I think another cost for the end user that isn't mentioned is the inability to get bugfixes in the dependencies if they land in the newer versions. While larger packages would backport bugfixes, in practice this doesn't happen too often in the ecosystem.
I agree that it is cleaner to separate book building from code execution, but it adds maintenance complexity, especially to CI. In practice I expect that hardly anybody does this. Is there an EBP repository that can serve as an example of this more systematic approach? |
Pinning to major versions means you get every bug fix, from minor and patch releases |
Of course. I expect, however, that some bugfixes land in major releases. |
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I just landed here and only read the first post in this thread. When packiagng the various executable book projects in conda forge, these upper bounds are often problematic for the conda solver. Taking them away would give the SAT solvers more flexibility in finding solutions that are likely to still result in consistent functioning environments for users. This would give less headaches for downstream packagers, environment managers, and users. |
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One option is that you don't add upper bounds on dependencies in general, but only add them to bugfix releases when you know a dependency update breaks that past released version. Example: Release version 4.1.0 of your package that depends on NumPy. You know 4.1.0 only works with So instead of trying to predict what future versions might break your packages in the present, just wait till a dependency does break one of your old versions and bugfix it. This would allow flexible package manager solves as time moves on, but also gives you the control to keep the latest bugfix releases working for as many past versions of your software as you want to maintain. This wouldn't work for anyone installing with exact version pins to your software, but it would work for anyone installing with |
Why would the solver install 4.1.1 and numpy 1.12, and not just 4.1.0 and numpy 1.13? |
Because the user would be upgrading your software and the package manager solver tries to give the newest possible versions of all dependencies. |
Which is exactly what the "Backsolving is usually wrong" section of https://iscinumpy.dev/post/bound-version-constraints/ explains, i.e. once you remove upper pinning, that's it, the only fix for breaking changes is to release a new version that supports the change, or tell users to pin |
Why is 4.1.0 + 1.13 not newer than 4.1.1 + 1.12, when considering all dependencies? |
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Just want to clarify here, I am not advocating for every package upper pinning, I see the arguments for libraries. |
It isn't, but we don't consdier all dependencies in Python (at least not for libraries and often not for apps either). My suggestion assumes the user is trying to keep some small set of packages at the top of their stack up-to-date and functioning, either one package or a collection of packages. If I'm using your package in my environment I have n packages I directly depend on. I only care that the API from those n packages works for what I'm doing (user or library viewpoints). Everything my n packages depend on can be any version as long as the API for the n packages is fixed and the dependency stack fits the constraints of all packages involved. There is the rare situation that I need API from your 4.1.* and API from numpy 1.18.* I'd be out of luck at that point and have to modify my code to work with a newer version of your package or older versions of numpy. No way around that. With NPM you could have a numpy 1.18 and numpy 1.12 installed in the same dependency tree, but you can't for pip or conda. The later tries to provide the latest versions of all packages in the entire tree given a desired state of a subset of packages you specify in your requirements.txt, setup.py, pyproject.toml, environment.yml, etc. I'm suggesting something similar to metadata patches, which is also mentioned in the blog post:
We can't patch the metadata but we can release a bugfix with the upper bounds. But yes, things could still break for a user because any dependency in the stack could have a incompatibility that conflicts with the new upper bound you add. But that is often rare and honestly, people just have to upgrade their software to be at versions that are compatible in present time. It's very hard to maintain compatibility for old software stacks without exact pins (and even impossible. For example, try installing Plone 2 from the early 2000s with buildout which does do exact pins). |
There may be a big difference if you think of them each as the only package you are trying to get working in a given "top of the stack" environment (environment for me = collection of consistent packages). A user may be using jupyter book to explain concepts that require a large number of software packages, so they also need to have all that software installed along with jupyter book in the same environment because jupyter book executes code cells in the book. So adding hard constraints on jupyter book's dependencies means it could then be impossible to setup the environment you need for your book. |
Jupyter book could have fixed pins if you execute the jupyter cells in a different environment. That should be possible through selecting the right kernel (right?). If that can be done, then you can have two environments: 1) an environment with only jupyter book and 2) an environment with all the software you need to execute cells in your book. |
I've already explained why this is not actually the case; a core concept of jupyter is having the kernel (where all the software is installed) separate from the client environment, i.e. where jupyter-book is installed. We should make this easier for users to achieve |
It is, but it's also a very confusing and unusual concept for most users. Setting up different virtual environments is a tough concept for average users too. Most expect |
Which is exactly why I said we should make it easier 😉 |
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Sorry, couldn't resist 😅, but over at https://github.com/pallets/jinja and https://github.com/pallets/markupsafe this kind of highlights my "fear"; within a few hours of a marksafe release that breaks a version of jinja , they are now inundated with people telling them their package is broken (many because readthedocs builds are suddenly failing):
Fair, they are telling people:
But, I doubt that's going to stop them getting many, many more "complaints" before this is over |
Yep, that's a fair concern, I don't think anyone disputes that this could happen. However, the EB projects are not nearly at the level of adoption that Jinja is at, and I think you're optimizing for the wrong problem at the current moment in the EB hype cycle. Besides, what's the worst case? You end up with a few dozen issues that you have to close? Even a few hundred could be handled in not too long a time, especially if it's a generic response like "Please upgrade". |
As long as one of you guys is willing to man the issue boards when this happens 😅 But it is not just the problem of closing issues with a generic response; |
Sure, happy to help! 😃
Yeah, perhaps not ideal, but given specific instructions for how to resolve it, I don't see it as too negative. And anyway, that particular issue is theoretical at the moment for EB users, whereas the issue of problems because of the capped pins is real and being felt by at least three power users in this thread. |
Yes, but it's theoretical now, largely because of the pinning, that's the point; I've seen very few issues for general users, with our current setup, but that won't be theoretical once pins are removed. Which again, (simplistically) comes back to: "do the wants of the few outweigh the need of the many"
You are obviously in the other camp. |
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I'm also happy to help triage issues and close them down if we start heading down that road. It sounds like there are reasonable arguments to make on both sides, and it's a question of which user groups we want to inconvenience, and to what degree. One thing we should do is think strategically about the different types of users, and their role in the project at large. For example, power users are smaller in number, but usually play a larger role evangelizing for a project, and are more likely to participate in the maintainer pipeline by making contributions. On the other hand the broader user community is much larger and often a "silent majority" as Chris suggests above, and you don't want to forget about those folks. Here's a quick thought - one way we could relieve some of this pressure is by keeping our version pinning as close as possible to
If we used something like the GitHub dependabot, this could make it easier to quickly note when a new release has been made, merge it if tests pass, and cut a patch release assuming our APIs won't break. I think the only potential downside is if there are so many PRs that it creates noise, but we could set it on a schedule to run fairly infrequently. This might be a relatively simple step forward here without drastically changing our pinning approach. |
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FWIW there have been multiple times where setting up an environment with executablebooks projects has proven problematic, see e.g. executablebooks/MyST-NB#289, executablebooks/MyST-NB#333. I can't say specifically that this is due to the upper-bound pinning problem; all I can say is that anecdotally I've experienced more dependency problems within the executablebooks umbrella than other projects/ecosystems that I work with. As another example, I recently tried to set up a development environment to make a contribution to MyST-NB, following the instructions in the contributor guide: $ git clone https://github.com/executablebooks/MyST-NB
$ cd MyST-NB
$ git checkout master
# Create a clean virtual env
$ python -m venv mystnb-dev
$ source mystnb-dev/bin/activate
$ pip list # Verify environment is empty
Package Version
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pip 22.0.3
setuptools 60.7.1
wheel 0.37.1
$ pip install -e .[code_style,testing,rtd]The output of the last step is included in full here: Output of pip install -e .[code_style,testing,rtd]Obtaining file:///home/ross/repos/MyST-NB
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Building wheels for collected packages: coverage, matplotlib, cPyparsing, tornado
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Successfully built coverage matplotlib cPyparsing tornado
Installing collected packages: webencodings, wcwidth, snowballstemmer, Send2Trash, pytz, ptyprocess, pickleshare, nodeenv, mpmath, mistune, ipython-genutils, distlib, cPyparsing, certifi, backcall, alabaster, zipp, websocket-client, urllib3, typing-extensions, traitlets, tornado, toolz, toml, testpath, tenacity, sympy, sphinxcontrib-serializinghtml, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, soupsieve, sniffio, smmap, six, pyzmq, pyyaml, pyrsistent, pyparsing, pygments, pycparser, py, prompt-toolkit, prometheus-client, pluggy, platformdirs, pillow, pexpect, parso, pandocfilters, numpy, nest-asyncio, more-itertools, MarkupSafe, kiwisolver, jupyterlab-widgets, imagesize, idna, identify, greenlet, filelock, entrypoints, docutils, defusedxml, decorator, cycler, coverage, colorama, charset-normalizer, cfgv, babel, attrs, virtualenv, terminado, sqlalchemy, requests, python-dateutil, plotly, packaging, matplotlib-inline, markdown-it-py, latexcodec, jupyterlab-pygments, jupyter-core, jsonschema, jinja2, jedi, importlib-metadata, gitdb, coconut, cffi, beautifulsoup4, anyio, sphinx, pytest, pybtex, pre-commit, pandas, nbformat, mdit-py-plugins, matplotlib, jupyter-client, ipython, GitPython, bokeh, bleach, argon2-cffi-bindings, sphinx-togglebutton, sphinx-panels, sphinx-copybutton, pytest-datadir, pytest-cov, pydata-sphinx-theme, pybtex-docutils, nbclient, myst-parser, jupytext, ipykernel, argon2-cffi, altair, sphinxcontrib-bibtex, sphinx-book-theme, pytest-regressions, nbconvert, notebook, jupyter-server, widgetsnbextension, jupyter-server-mathjax, nbdime, ipywidgets, jupyter-sphinx, jupyter-cache, myst-nb
Running setup.py develop for myst-nb
Successfully installed GitPython-3.1.27 MarkupSafe-2.1.0 Send2Trash-1.8.0 alabaster-0.7.12 altair-4.2.0 anyio-3.5.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 attrs-21.4.0 babel-2.9.1 backcall-0.2.0 beautifulsoup4-4.10.0 bleach-4.1.0 bokeh-2.4.2 cPyparsing-2.4.5.0.1.1 certifi-2021.10.8 cffi-1.15.0 cfgv-3.3.1 charset-normalizer-2.0.12 coconut-1.4.3 colorama-0.4.4 coverage-4.5.4 cycler-0.11.0 decorator-5.1.1 defusedxml-0.7.1 distlib-0.3.4 docutils-0.16 entrypoints-0.4 filelock-3.6.0 gitdb-4.0.9 greenlet-1.1.2 identify-2.4.11 idna-3.3 imagesize-1.3.0 importlib-metadata-4.11.2 ipykernel-5.5.6 ipython-7.32.0 ipython-genutils-0.2.0 ipywidgets-7.6.5 jedi-0.18.1 jinja2-3.0.3 jsonschema-4.4.0 jupyter-cache-0.4.3 jupyter-client-7.1.2 jupyter-core-4.9.2 jupyter-server-1.13.5 jupyter-server-mathjax-0.2.5 jupyter-sphinx-0.3.2 jupyterlab-pygments-0.1.2 jupyterlab-widgets-1.0.2 jupytext-1.11.5 kiwisolver-1.3.2 latexcodec-2.0.1 markdown-it-py-1.1.0 matplotlib-3.3.4 matplotlib-inline-0.1.3 mdit-py-plugins-0.2.8 mistune-0.8.4 more-itertools-8.12.0 mpmath-1.2.1 myst-nb-0.13.2 myst-parser-0.15.2 nbclient-0.5.11 nbconvert-6.4.2 nbdime-3.1.1 nbformat-5.1.3 nest-asyncio-1.5.4 nodeenv-1.6.0 notebook-6.4.8 numpy-1.22.2 packaging-21.3 pandas-1.3.5 pandocfilters-1.5.0 parso-0.8.3 pexpect-4.8.0 pickleshare-0.7.5 pillow-9.0.1 platformdirs-2.5.1 plotly-5.6.0 pluggy-0.13.1 pre-commit-2.17.0 prometheus-client-0.13.1 prompt-toolkit-3.0.28 ptyprocess-0.7.0 py-1.11.0 pybtex-0.24.0 pybtex-docutils-1.0.1 pycparser-2.21 pydata-sphinx-theme-0.7.2 pygments-2.11.2 pyparsing-3.0.7 pyrsistent-0.18.1 pytest-5.4.3 pytest-cov-2.10.1 pytest-datadir-1.3.1 pytest-regressions-2.3.1 python-dateutil-2.8.2 pytz-2021.3 pyyaml-6.0 pyzmq-22.3.0 requests-2.27.1 six-1.16.0 smmap-5.0.0 sniffio-1.2.0 snowballstemmer-2.2.0 soupsieve-2.3.1 sphinx-4.4.0 sphinx-book-theme-0.1.10 sphinx-copybutton-0.5.0 sphinx-panels-0.4.1 sphinx-togglebutton-0.3.0 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-bibtex-2.4.1 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 sphinxcontrib-serializinghtml-1.1.5 sqlalchemy-1.4.31 sympy-1.9 tenacity-8.0.1 terminado-0.13.2 testpath-0.6.0 toml-0.10.2 toolz-0.11.2 tornado-6.1 traitlets-5.1.1 typing-extensions-4.1.1 urllib3-1.26.8 virtualenv-20.13.2 wcwidth-0.2.5 webencodings-0.5.1 websocket-client-1.3.1 widgetsnbextension-3.5.2 zipp-3.7.0
AFAICT the dependency resolver is working exactly as intended, but as a consequence of the strict pinning, I end up getting versions of some libraries (e.g. Output of `$ pytest`Traceback (most recent call last):
File "/home/ross/repos/MyST-NB/mystnb-dev/bin/pytest", line 8, in
sys.exit(main())
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/config/__init__.py", line 105, in main
config = _prepareconfig(args, plugins)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/config/__init__.py", line 257, in _prepareconfig
return pluginmanager.hook.pytest_cmdline_parse(
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/hooks.py", line 286, in __call__
return self._hookexec(self, self.get_hookimpls(), kwargs)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/manager.py", line 93, in _hookexec
return self._inner_hookexec(hook, methods, kwargs)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/manager.py", line 84, in
self._inner_hookexec = lambda hook, methods, kwargs: hook.multicall(
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/callers.py", line 203, in _multicall
gen.send(outcome)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/helpconfig.py", line 90, in pytest_cmdline_parse
config = outcome.get_result()
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/callers.py", line 80, in get_result
raise ex[1].with_traceback(ex[2])
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/callers.py", line 187, in _multicall
res = hook_impl.function(*args)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/config/__init__.py", line 836, in pytest_cmdline_parse
self.parse(args)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/config/__init__.py", line 1044, in parse
self._preparse(args, addopts=addopts)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/config/__init__.py", line 992, in _preparse
self.pluginmanager.load_setuptools_entrypoints("pytest11")
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/pluggy/manager.py", line 299, in load_setuptools_entrypoints
plugin = ep.load()
File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 162, in load
module = import_module(match.group('module'))
File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "", line 1050, in _gcd_import
File "", line 1027, in _find_and_load
File "", line 992, in _find_and_load_unlocked
File "", line 241, in _call_with_frames_removed
File "", line 1050, in _gcd_import
File "", line 1027, in _find_and_load
File "", line 1006, in _find_and_load_unlocked
File "", line 688, in _load_unlocked
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/assertion/rewrite.py", line 143, in exec_module
source_stat, co = _rewrite_test(fn, self.config)
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/_pytest/assertion/rewrite.py", line 330, in _rewrite_test
co = compile(tree, fn, "exec", dont_inherit=True)
TypeError: required field "lineno" missing from alias
output of `$ cd docs && make html`$ cd docs
$ make html
Running Sphinx v4.4.0
making output directory... done
Traceback (most recent call last):
File "/home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/coconut/_pyparsing.py", line 45, in
import cPyparsing as _pyparsing
ImportError: /home/ross/repos/MyST-NB/mystnb-dev/lib/python3.10/site-packages/cPyparsing.cpython-310-x86_64-linux-gnu.so: undefined symbol: _PyGen_Send
Note that despite the project pinning, the doc build fails with a dependency error at import time. The specifics of the above are better suited for a I realize the "setting-up-a-dev-environment" case is not relevant for the primary use-case discussed here, i.e. users installing EBP tools for publishing their own work. However, users are not likely to be using EBP in a vacuum - they will be installing it into environments that have existing dependency constraints, where some of these observations are likely to be relevant. Anyway, I just wanted to share a bit more of my experience working with EBP tools, as I've definitely experienced more dependency issues under the EBP umbrella than e.g. other scientific Python libraries. It's definitely a complex issue and it could be that there are major flaws in my own workflow. |
Heya, @rossbar myst-nb does not currently support python 3.10, not in the setup.cfg classifiers, nor in the CI testing. should the python version be pinned to matplotlib is also specifically pinned because the tests were failing with 3.4.0: https://github.com/executablebooks/MyST-NB/blob/52f7fb7f4b36ad5ac12a6625a78403b8b2dfd36b/setup.cfg#L94-L95
So yes, I would say setting up a dev environment is a very different discussion: basically we only guarantee it works for the environments run on GH Actions: https://github.com/executablebooks/MyST-NB/actions/runs/1826013747 |
FYI, jupytext is no longer a dependency of jupyter-book (or any of the EBP stack): jupyter-book/jupyter-book#1645 (comment) |
That's fine, I think the general point still stands...
No, it should not be, unless there is a specific known issue with Python 3.10. Because in all likelihood, it will "just work". The problem here is that other package versions are pinned. If the other versions were unpinned, then this wouldn't be a problem, and you wouldn't have to also add the Python 3.10 pin. As far as I can tell, everyone who's commented on this issue is against upper version pinning (with the possible exception of @choldgraf who didn't express a strong opinion one way or the other). I also recognize that most of the people who've commented are essentially power users, so may not represent the "average" user; however, I would submit that at this point in the EBP hype-cycle, the average user is probably a power user. At this point, I've said my piece... I'll be unsubscribing from this issue. If you want any help with implementing or replying to issues, please open a new issue and tag me (or hit me on Twitter, |
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I'll just note that I'm trying not to express strong opinions because I want to listen to what others are saying here, and don't want to lead the discussion too much. I do agree that right now the overwhelming signal we seem to be getting is that version pinning is causing problems for people (with the caveat that the users here are the "power users" of this stack, though I agree that this is a particularly important class of users to keep happy). @chrisjsewell has a point that we aren't seeing complaints about upstream updates breaking things precisely because we are pinning upper versions. But I also don't think that this should just trump any and all feedback that people are giving about version pinning problematic. Compromise?Can we try to strike a compromise here between "pin everything" and "pin nothing"? E.g., going back to the original proposal, what if we added another step which was adding "canary" CI/CD jobs for our dependencies. So:
To me, this feels like it would:
To me the main downside with it is that it would add an extra layer of complexity to our CI/CD jobs, but looking across the Python ecosystem it seems that these canary deployments are pretty common practice, so I can't imagine it's causing too much extra pain for those communities (somebody correct me if I'm wrong). |
I would note we don't actually pin everything already. I think it would be helpful to have some specific here. jupyter-book (develop) dependencies = [
"click>=7.1,<9",
"docutils>=0.15,<0.18",
"jsonschema<4",
"linkify-it-py~=1.0.1",
"myst-nb~=0.13.1",
"pyyaml",
"sphinx>=3,<5",
"sphinx-comments",
"sphinx-copybutton",
"sphinx-external-toc~=0.2.3",
"sphinx-jupyterbook-latex~=0.4.6",
"sphinx-panels~=0.6.0",
"sphinx-thebe~=0.1.1",
"sphinx_book_theme~=0.1.4",
"sphinx_togglebutton",
"sphinxcontrib-bibtex>=2.2.0,<=2.5.0",
"sphinx-multitoc-numbering~=0.1.3",
]myst-nb (after executablebooks/MyST-NB#380) install_requires =
docutils>=0.15,<0.18
importlib_metadata
ipython
jupyter-cache~=0.5.0
myst-parser~=0.17.0
nbformat~=5.0
pyyaml
sphinx>=3.5,<5
sphinx-togglebutton~=0.3.0
typing-extensionsjupyter-cache (v0.5.0) install_requires =
attrs
click
importlib-metadata
nbclient>=0.2,<0.6
nbformat
pyyaml
sqlalchemy>=1.3.12,<1.5
tabulatemyst-parser (v0.17.0) install_requires =
docutils>=0.15,<0.18
jinja2
markdown-it-py>=1.0.0,<3.0.0
mdit-py-plugins~=0.3.0
pyyaml
sphinx>=3.1,<5
typing-extensions |
|
Good point - I guess the answer for the above proposal would be: Unpin everything except for:
And in all projects (or at least, the "core stack" ones), start running a CI/CD job that tested all of these things against "latest". So: jupyter-book or if we wanted to also not pin our own core tools (or rather, only pin them if we know that the next major release will break things): |
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Also just a note that I think we have done a nice job of reducing our dependency exposure in general, so things like removing a dependency entirely on |
|
this appears to be going against the original proposal?
|
|
Sorry - I was restricting the EBP capping to just the "core" projects that we know would break things if they introduced a breaking change. I don't feel super strongly about this though. An example of this was the recent sphinx-togglebutton release. It overhauled the look and feel of the toggle button in a big way, and that's why I bumped the minor version, but it wasn't strictly a breaking change, so you could make a case for that being a Compare that with |
Description
Currently, we cap major versions of most dependencies. This helps ensure that we have stable behavior when dependencies release new major versions. When a new major version is released, we manually bump our cap and make sure that behavior still works as-expected in the PR.
There are obvious benefits to this approach, but I think there are also significant costs, and I wonder if we can relax this constraint a bit and take a more nuanced approach.
The problem we're introducing for users
By capping major versions, we may introduce headaches for our users. These are particularly problematic when they have complex environments with many versions installed. Specifically:
We run the risk that our dependency chain will be un-resolvable due to a conflict with some other package's dependencies.
If this happens, there's nothing that our users can do, other than wait for us to make a new release. Here's an extreme example of this with
click. tl;dr: Click released a new major version, one of our dependencies required that version, and it was then impossible to install Jupyter Book in a fresh environment until we make a new release.There are a lot of blog posts arguing back and forth about this, but here's a nice recent one that our own @chrisjsewell has chimed in on as well: Should You Use Upper Bound Version Constraints? from Henry Schreiner.
Why we shouldn't just un-cap everything
There are definitely benefits to having major version caps for the dependencies that we know will introduce breaking changes with version releases (looking at you
docutils) or those that have lots of complex interconnections with our codebase that might lead to unexpected outcomes. When we cap the versions for these dependencies, we help save our users unexpected headaches.So, I wonder if we can find a compromise and adopt a policy like the following:
Proposal: cap our own stack and unstable dependencies, and do not cap other dependencies
sphinxanddocutils(because they introduce many changes, are very complex, and our stack has many connection points with them)For the second group, we can selectively add dependency caps if we know there's a breaking change that we can't quickly resolve, and we can make a decision whether to keep that cap if we expect subsequent releases to also introduce breaking changes.
I think that this could be a way to balance "dependability of our environment" against "constraints that cause headaches for our users". For example, in the case of
click, we likely would not have used a cap because we use it in a targeted fashion, and wouldn't expect it to break our usage in the ways we're utilizing it. This would have saved us the time of then manually bumpingclickacross our repositories, and cutting new releases.EDIT: After some discussion below, I also propose that we add a CI/CD job for unstable versions to our most heavily-used repositories. These could be
canarydeployments that run againstmain/masterand we can use this to know when a breaking change is coming down the pipeline.Thoughts?
What do others think? Would this be a net-benefit for our time + the time of users? In particular I am curious what @chrisjsewell thinks since I know you care about UX stability for our users!
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