https://jakevdp.github.io/blog/2016/08/25/conda-myths-and-mi...
> Myth #5: conda doesn't work with virtualenv, so it's useless for my workflow
> Reality: You actually can install (some) conda packages within a virtualenv, but better is to use Conda's own environment manager: it is fully-compatible with pip and has several advantages over virtualenv.
> [...] the result seems to be fairly brittle – for example, trying to conda update python within the virtualenv fails in a very ungraceful and unrecoverable manner, seemingly related to the symlinks that underly virtualenv's architecture.
Doesn't sound like much of a myth then, if Conda's take on virtualenv is "you can technically do this, but everything will break ungracefully and unrecoverably, so please don't".
At the end, one of his conclusions is: "If you want to install Python packages within an Isolated environment, pip+virtualenv and conda+conda-env are mostly interchangeable". So don't change if you don't have to.
But he does give reasons why conda may be superior to virtualenv -- managing different version of Python, tracking non-Python dependencies, true isolation of environments, etc.
Edit: correction, there are two examples that take longer, one at 3.5 minutes, one around 8 minutes. I don’t think it changes any takeaways though.
It’s funny to me that you would expect operations involving “conda install anaconda” to be fast though. 11 minutes seems perfectly fast for that and would be comparable to pip or anything else if doing that huge of a set of package installs.
That is not at all a “trivial environment” like you said previously.
With its ability to install Python and non-python packages (including binaries), conda is my go-to for managing project environments and dependencies. Between the bioconda[2] and conda-forge[3] channels, it meets the needs of many on the computational side of the biological sciences. Being able to describe a full execution environment with a yaml file is huge win for replicable science.
1. https://conda.io/miniconda.html https://conda.io/docs/user-guide/install/index.html
Also, a conda package is not a replacement for a distutils/setuptools package. When building a conda package, one still calls setup.py. So every python conda package has to be a distutils/setuptools package anyway.
The fact that it's not a community-driven project might be one of the reasons.
Meanwhile, in a galaxy far away, people are also using buildout.
Being able to define and install external dependencies (e.g. ImageMagick, libsodium, etc.) from a configuration file local to a project is something I missed the most, especially when I'm working on several projects at once.
Any examples of how Nix itself doesn't do what you need? One example I can think of: Nix doesn't support Windows.
- there is Miniconda that doesn't force you to install all PyData packages. - virtual envs and needed packages are all defined in simple yaml file. - it works well with pip. So if a package isn't in Conda repository, you can install from pip. The annoyance here is that you must try conda, fail, and then try pip. - you can easily clone envs. So you can have some base envs with your usual packages (or one for Python 2 and another fo Python 3), and just clone them to start a new project.
I "feel" like it is overkill to use anaconda for things unrelated to 'data science' and the likes, but I'm not sure why I feel that way.
It kind of makes sense to use for other projects as well since you don't need to import all the things conda offers.
Do you have scipy/numpy/keras or cython somewhere in the deps? pipenv lock is slow, but not 20-30 mins slow unless there's a very very large download and/or a long compilation somewhere in there.
This wasn't for complex pipenv operations either. A simple command: pipenv run python main.py took progressively longer to execute.
If you configure the cache properly you might solve it, but yeah it's kinda dumb it has to do that just for locking.
This can make a difference of tens of minutes for some packages which have a 1 gigabyte (!!!) download.
I've never seen anything like that on a number of fairly large apps – a minute or two, at most. Are some of those dependencies extremely large or self-hosted somewhere other than PyPI?
I rely on it for pretty much everything and I didn't run into game breaking problems.
Anyway, for me the most annoying thing about the Python projects architecture (and about the whole Python perhaps) is that you can't split a module into multiple files so you have to either import everything manually all over the project trying to avoid circular imports or just put everything in a single huge source file - I usually choose the latter and I hate it. The way namespaces and scopes work in C# feels just so much better.
Oh, so you weren't around when Requests went 2.0 backward-incompatible (because they changed .json() with .json, or the other way around, can't remember) and half of PyPI, with its happy-go-lucky ">=1.0", broke...?
Since then, most people have learnt that you pin first and ask questions later.
There's no concept of explicit versus implicit dependencies. You install one package, and end up with five dependencies locked at exact versions when you do `pip freeze`. Which of those was the one you installed, and which ones are just dependencies-of-dependencies?
If you're consistent and ALWAYS update your requirements.txt first with explicit versions and NEVER use `pip freeze` you might be okay, but it's more painful than most of the alternatives that let you separate those concepts.
Recently I switched to pipenv because zappa insists on having virtualenv (as app dev I never had any need for it - but it seems my case is an exception, as I almost never work on multiple apps in parallel). Pipenv does make version management a bit easier, but it wasn't difficult (for me) to begin with.
From talking with other developers I know my view is somewhat unorthodox, but I haven't encountered the problems they describe, or the pain hasn't been that big for me to embrace all the issues that come with virtualenvs.
Interesting, this has never been a problem for me. I've built some large tools and while it isn't fast, it's always completed in a few minutes.
pipenv lock 5.65s user 0.29s system 77% cpu 7.639 total
it would probably take ages at work though. just opening a WSL terminal takes several seconds there, which is predictably instantaneous (<100ms) on fedora linux at home