Show HN: Wetlands – a lightweight Python library for managing Conda environments(arthursw.github.io)
arthursw.github.io
Show HN: Wetlands – a lightweight Python library for managing Conda environments
https://arthursw.github.io/wetlands/0.2.0/
44 comments
> Wetlands not only simplifies the creation of isolated Conda environments with specific dependencies, but also allows you to run
I've been using Conda for 10 years as my default package manager on my devices (not pipenv or poetry etc). I started because it was "the way" for data science but I kept with it because the syntax is really intuitive to me (conda create, conda activate).
I'm not sure what problem you are solving here -- the issues with conda IMO are that it is overkill for the rest of the python community, so conda-forge has gradually declined and I typically create a conda environment then use pip for the latest libraries. Managing the conda environments though is not my issue -- that part works so well that I keep with it.
If you could explain why you created this and what problems you are solving with an example, that would be helpful. All package managers are aimed at "avoiding dependency conflicts" so that doesn't really communicate to me what this is and what real problem it solves.
I've been using Conda for 10 years as my default package manager on my devices (not pipenv or poetry etc). I started because it was "the way" for data science but I kept with it because the syntax is really intuitive to me (conda create, conda activate).
I'm not sure what problem you are solving here -- the issues with conda IMO are that it is overkill for the rest of the python community, so conda-forge has gradually declined and I typically create a conda environment then use pip for the latest libraries. Managing the conda environments though is not my issue -- that part works so well that I keep with it.
If you could explain why you created this and what problems you are solving with an example, that would be helpful. All package managers are aimed at "avoiding dependency conflicts" so that doesn't really communicate to me what this is and what real problem it solves.
I used to use conda, but have switched entirely to uv now
As someone new to Python: what was ever the appeal of conda that uv doesn't satisfy?
conda doesn't just package python libraries, but also the C/Fortran/other bits that the scipy stack often depended on. With the rise of binary wheels that is less needed though
Fundamentally it is a fresh usr/bin per environment with all that can go into that. Not just python tooling. R packages. Binaries. All of that. Env can be exported as a yaml file and trivially shared without appending some header to all scripts you write.
I think it's more about tool X vs Y, but about ecosystems and packaging approaches; in other words Python packaging (which has tools like pip, uv or poetry) vs conda packaging (which has tools like conda itself, mamba or pixi). https://pypackaging-native.github.io/ is an excellent starting point to learn about the limitations on Python packaging for native dependencies and compiled extensions.
Distributing non Python packages via the same channel that Python packages may depend on. E.g, h5py depending on libhdf5.
As someone using Python from 2017, there was no uv then, and conda worked fine but was glacially slow.
A python dependency manager manager manager. Truly we live in an age of unprecedented code abstraction and complexity. And I love that you install and manage wetlands itself with pip. An ouroboros matrioska of code.
Why not just call it swamp? It would better describe the python ecosystem mess ;)
Jokes aside, this feels very meta: package manager for a package manager for a package manager. Reminds me of the old RFC1925: "you can always add another layer of abstraction". That RFC also says "perfection has been reached not when there is nothing left to add, but when there is nothing left to take away".
And as a hpc admin, I'm not offering my users any help with conda and let them suffer on their own. Instead I'm showing them the greener pastures with spack, easybuild and eessi whenever I can. And they're slowly migrating over.
Jokes aside, this feels very meta: package manager for a package manager for a package manager. Reminds me of the old RFC1925: "you can always add another layer of abstraction". That RFC also says "perfection has been reached not when there is nothing left to add, but when there is nothing left to take away".
And as a hpc admin, I'm not offering my users any help with conda and let them suffer on their own. Instead I'm showing them the greener pastures with spack, easybuild and eessi whenever I can. And they're slowly migrating over.
it was also my first thought on abstractions of abstractions, thanks for sharing :)
could you elaborate a bit more on why HPC world is special when it comes to configuring the environment?
I always feel it is a typical problem in software development, to separate operating system env from the application env.
do you use spack / easybuild on your personal computer, for example if you need to install a package that is not part of the distribution?
could you elaborate a bit more on why HPC world is special when it comes to configuring the environment?
I always feel it is a typical problem in software development, to separate operating system env from the application env.
do you use spack / easybuild on your personal computer, for example if you need to install a package that is not part of the distribution?
I do for macOS and Linux :). Windows support is also coming along.
There isn’t anything particularly special about the HPC world other than the need for many different configurations of the same software for many different CPU and GPU architectures. You might want to have several versions of the same application installed at once, with different options or optimizations enabled. Spack enables that. Environments are one way to keep the different software stacks separate (though, like nix, spack uses a store model to keep installs in separate directories, as well).
There isn’t anything particularly special about the HPC world other than the need for many different configurations of the same software for many different CPU and GPU architectures. You might want to have several versions of the same application installed at once, with different options or optimizations enabled. Spack enables that. Environments are one way to keep the different software stacks separate (though, like nix, spack uses a store model to keep installs in separate directories, as well).
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When I started the project, Spack was not available on Windows so it was not an option. Now I should reconsider this. Thanks!
Thanks for your comments! I fully agree that the Python ecosystem is (overly) complex, and Conda is generally not necessary for Python tools.
I made this library for a workflow management system, which can use any tool packaged with Conda, not just Python tools. The tools can be binaries made in C++, Java programs, or anything Conda can containerize. Note that Docker is not an option because it cannot be installed automatically on all platforms (and because of performances on non-Linux OS).
My users do not have to worry about command lines to install tools since Wetlands is installed in the workflow management system. Each tool is installed when the user executes a workflow using it.
In the bio-image analysis and medical imaging communities —as well as many others— scientists are often unfamiliar with the Python ecosystem and the concept of virtual environments. However, they rely heavily on a wide range of tools, each with numerous dependencies written in various languages. Applications with a built-in package management system like Wetlands greatly simplify their workflow by handling the complex task of setting up environments for these tools behind the scenes.
For example, Napari is an excellent viewer for multi-dimensional images written in Python which can be easily extended via plugins. There are hundreds of plugins, to do things like image denoising, registration, segmentation, particle tracking, etc. Plugins depend on tools (like Segment-Anything-Model, Cellpose, Stardist, etc.) which cannot be installed in the same environment. Wetlands can come to the rescue and isolate each plugin in its own environment.
I hope the purpose of Wetlands is clearer now :)
I made this library for a workflow management system, which can use any tool packaged with Conda, not just Python tools. The tools can be binaries made in C++, Java programs, or anything Conda can containerize. Note that Docker is not an option because it cannot be installed automatically on all platforms (and because of performances on non-Linux OS).
My users do not have to worry about command lines to install tools since Wetlands is installed in the workflow management system. Each tool is installed when the user executes a workflow using it.
In the bio-image analysis and medical imaging communities —as well as many others— scientists are often unfamiliar with the Python ecosystem and the concept of virtual environments. However, they rely heavily on a wide range of tools, each with numerous dependencies written in various languages. Applications with a built-in package management system like Wetlands greatly simplify their workflow by handling the complex task of setting up environments for these tools behind the scenes.
For example, Napari is an excellent viewer for multi-dimensional images written in Python which can be easily extended via plugins. There are hundreds of plugins, to do things like image denoising, registration, segmentation, particle tracking, etc. Plugins depend on tools (like Segment-Anything-Model, Cellpose, Stardist, etc.) which cannot be installed in the same environment. Wetlands can come to the rescue and isolate each plugin in its own environment.
I hope the purpose of Wetlands is clearer now :)
Why do people use Conda instead of uv?
Conda manages binaries and their native dependencies together, including shared libraries[0]. This offers significant advantages over uv and pip when distributing packages with C extensions, such as dependency resolution that accounts for shared library requirements, and better package isolation.
[0]: https://docs.conda.io/projects/conda-build/en/latest/resourc...
[0]: https://docs.conda.io/projects/conda-build/en/latest/resourc...
Use PDM with the UV backend - this accomplishes this in a much more lightweight and performant way.
Does PDM manage C/Fortran library dependencies? IIRC conda was the only solution for managing both native and python dependencies but I haven't really looked elsewhere.
With wheels and the manylinux specifications there's less of a usecase for that, but still could be useful
With wheels and the manylinux specifications there's less of a usecase for that, but still could be useful
Not sure about Fortran - but C for sure, yes.
Where does it fetch the C packages from? I always thought PDM was a _Python_ package manager, so the only source is PyPI or another index.
PDM has plugins, such as being able to invoke conda commands: https://github.com/pdm-project/awesome-pdm
Otherwise I don't know what they're talking about, it is indeed a Python package manager.
Otherwise I don't know what they're talking about, it is indeed a Python package manager.
The PyPI ecosystem can not, for the foreseeable future, replicate the scope of the conda ecosystem. From microarch builds to library deduplication, conda is a more general purpose solution. That doesn't mean that one "wins out" (and, for reference I predominantly use Python's PyPI), but they're not the same tools.
The conda ecosystem was a early adopter of standardized binary packages.
Now it's mostly behind us, but there used to be a time where pypi didn't have wheels (a 2012 thing), or manylinux wheels (a 2016 thing) for most libraries. pip install was a world of pain if you didn't have the "correct source packages" in your system.
And now several of those projects built back then, they're no longer projects but deployed systems, might as well stick to what is working.
Now it's mostly behind us, but there used to be a time where pypi didn't have wheels (a 2012 thing), or manylinux wheels (a 2016 thing) for most libraries. pip install was a world of pain if you didn't have the "correct source packages" in your system.
And now several of those projects built back then, they're no longer projects but deployed systems, might as well stick to what is working.
Conda is still the recommended way to work with Intel's Python distribution, so there's a reason for it to live on my work computer.
The fastest graph library I know, graph-tool, needs to be installed with Conda too.
The fastest graph library I know, graph-tool, needs to be installed with Conda too.
that appears to be because the maintainers have chosen not to release any files
https://pypi.org/project/graph-tool/
that's not a limitation of pip, that's a limitation of the maintainers of graph-tool
https://pypi.org/project/graph-tool/
that's not a limitation of pip, that's a limitation of the maintainers of graph-tool
I agree, and yet here we are. If it wasn't built with automake I would even consider getting wheels built on the projects behalf, but I can't make heads or tails of M4.
EDIT: But let's not pretend getting cibuildwheel going for every supported variant of PythonMajVer/OS/(32/64) bit is an easy lift for small open source projects. Conda is still more ergonomic than this, although it's slowly dying because big projects have the manpower to stand up and maintain the CI pipeline for PyPI, so Conda is less and less necessary.
EDIT: But let's not pretend getting cibuildwheel going for every supported variant of PythonMajVer/OS/(32/64) bit is an easy lift for small open source projects. Conda is still more ergonomic than this, although it's slowly dying because big projects have the manpower to stand up and maintain the CI pipeline for PyPI, so Conda is less and less necessary.
there was still yet a time when "open source maintainer has to build binaries for x86, ARM, OSX, Windows in order to not get complaints but owns no windows licenses, ARM machines, OSX servers", and github now gives you all of that for free / automatic / declaratively with actions + cibuildwheel. so the value add from "conda" is way down from where it started.
“Pixi instead of uv” would be a more fair comparison, as Pixi is a more modern tool which still uses the conda package format and ecosystem, much like uv is a modernised pip which still uses the PyPI package format.
One thing an conda package can do which an PyPI package cannot is have binary dependencies: a conda package is linked upon installation, and packages can declare dependencies on shared libraries. As common example is numeric libraries depending on a BLAS implementation: in a conda/pixi environment you will get exactly one BLAS shared library linked into your process, used by numpy, scipy, optimisers, etc. For some foundational libraries like BLAS which have multiple implementations, the user even has the power to consistently switch the implementation within the environment, eg from OpenBLAS to Intel’s MKL.
The PyPI package format does not allow binary dependencies: wheels must be self-contained when it comes to binary code (not when it comes to Python code - which hopefully makes it clear that something here is inconsistent). Take any numerical python environment and enumerate the copies of BLAS you have, it is probably 3-5. All running their own threadpools.
Another very simple example is with inbuilt modules depending on native code, like the sqlite3 module. In a conda/pixi installation you are guaranteed that the python binary links against the same sqlite3 code as the command-line sqlite3 cli tool in the same environment. Stuff like this removes many cross-language or cross-tool hassles.
I prefer uv or poetry if I’m doing anything simple or pure python (or perhaps with a small binary dependency like an event loop). But pixi is the way to go for large environments with lots of extra tools and numerical libraries.
One thing an conda package can do which an PyPI package cannot is have binary dependencies: a conda package is linked upon installation, and packages can declare dependencies on shared libraries. As common example is numeric libraries depending on a BLAS implementation: in a conda/pixi environment you will get exactly one BLAS shared library linked into your process, used by numpy, scipy, optimisers, etc. For some foundational libraries like BLAS which have multiple implementations, the user even has the power to consistently switch the implementation within the environment, eg from OpenBLAS to Intel’s MKL.
The PyPI package format does not allow binary dependencies: wheels must be self-contained when it comes to binary code (not when it comes to Python code - which hopefully makes it clear that something here is inconsistent). Take any numerical python environment and enumerate the copies of BLAS you have, it is probably 3-5. All running their own threadpools.
Another very simple example is with inbuilt modules depending on native code, like the sqlite3 module. In a conda/pixi installation you are guaranteed that the python binary links against the same sqlite3 code as the command-line sqlite3 cli tool in the same environment. Stuff like this removes many cross-language or cross-tool hassles.
I prefer uv or poetry if I’m doing anything simple or pure python (or perhaps with a small binary dependency like an event loop). But pixi is the way to go for large environments with lots of extra tools and numerical libraries.
I use it because I do not need to create packages, and I often do a lot of interactive coding within a conda environment, not just running full Python scripts. At any given time I have a primary conda env I'm using with my set of daily use packages, eventually creating a new one for testing when there are major version upgrades to Python or a package I use frequently.
When I read the uv docs and see other people's examples, I have a hard time understanding how it works for my workflow. It seems I could continue using conda for environment management and only use uv for package installation and it would be much faster, but that also feels a little shaky and potential for error combining the two tools, and since mamba became the default solver conda is pretty fast, even when building a new env from scratch.
It feels like conda and it's ability to have multiple Python versions, with env management built in, gives me more than uv, just without the package installation speed. But I am certainly open to someone explaining uv to me in a way to disprove that
When I read the uv docs and see other people's examples, I have a hard time understanding how it works for my workflow. It seems I could continue using conda for environment management and only use uv for package installation and it would be much faster, but that also feels a little shaky and potential for error combining the two tools, and since mamba became the default solver conda is pretty fast, even when building a new env from scratch.
It feels like conda and it's ability to have multiple Python versions, with env management built in, gives me more than uv, just without the package installation speed. But I am certainly open to someone explaining uv to me in a way to disprove that
These days I see Conda (and micromamba) used as a reliable cross-platform winget/apt/yum/brew. For example, to install GDAL.
uv replaces pip, conda and pip have been complementary for a long time. But I would be surprised if uv does not take on conda at some point, e.g. with a micromamba subcommand.
uv replaces pip, conda and pip have been complementary for a long time. But I would be surprised if uv does not take on conda at some point, e.g. with a micromamba subcommand.
Because conda has been around for years and uv is more or less brand new. It's pretty much as simple as that.
The fact that this exists shows that there is a serious problem in the python ecosystem. I'm sure it solves a real problem, so I'm not knocking the author. It's more of a "state of our industry" problem.
I maintain there is no issue. It's really not hard. conda is a smell for me though.
pyenv is all you need. it manages python versions and python virtual environments. you can create and destroy them just as easily as git branches.
pyenv + good ol' requirements.txt is really all you need.
if your env dictates containers, it's even easier to work with. FROM python:version and done.
pyenv is all you need. it manages python versions and python virtual environments. you can create and destroy them just as easily as git branches.
pyenv + good ol' requirements.txt is really all you need.
if your env dictates containers, it's even easier to work with. FROM python:version and done.
The issue is you think pyenv has solved everything, someone else thinks poetry solves everything, I think uv solves everything, and someone else is apt installing things. And then there is installing torch and cuda...
I think having a very widely accepted and supported default would let the community rally around it and improve the experience and documentation (though I am not a fan of the centralized hosting that npm and cargo push, I much prefer the go approach, but we are already there with PyPI)
I think having a very widely accepted and supported default would let the community rally around it and improve the experience and documentation (though I am not a fan of the centralized hosting that npm and cargo push, I much prefer the go approach, but we are already there with PyPI)
uv + poetry are a higher level in the stack than something like pyenv.
pip, uv, poetry are all analagous. they ensure the correct packages are installed. we have some internal apps that devs decided to start with poetry and it has some nice ergonomics ... but on the other hand I find the simplicity of a requirements file to be so ... simple. People get caught up on the file, too, but really its just a convention. you can call it whatever you want, deps.txt, deps.foo, packages.bar ... its just a plaintext file with newline delimited packages. since its "just a file" and "just unix" this has the added perk of being able to cat many files together and use that with pip. it's all just lines of text, pip does not care.
pyenv + pip works. pyenv + poetry works. pyenv + uv works. those become inconsequential decisions you can make on a case by case basis and try or forget as needed.
pip, uv, poetry are all analagous. they ensure the correct packages are installed. we have some internal apps that devs decided to start with poetry and it has some nice ergonomics ... but on the other hand I find the simplicity of a requirements file to be so ... simple. People get caught up on the file, too, but really its just a convention. you can call it whatever you want, deps.txt, deps.foo, packages.bar ... its just a plaintext file with newline delimited packages. since its "just a file" and "just unix" this has the added perk of being able to cat many files together and use that with pip. it's all just lines of text, pip does not care.
pyenv + pip works. pyenv + poetry works. pyenv + uv works. those become inconsequential decisions you can make on a case by case basis and try or forget as needed.
You're right not all of those need to be conflated but they aren't entirely orthogonal either, uv can install/manage python installations for you and poetry can manage environments as well if you want. On top of that, you have tools like mise that are more cross cutting.
I agree that there is a really nice simplicity to requirements.txt but I've myself enjoying uv enough to just fully embrace it (and not farm out to uv pip) and as a result I now find myself in pyproject.toml land.
I agree that there is a really nice simplicity to requirements.txt but I've myself enjoying uv enough to just fully embrace it (and not farm out to uv pip) and as a result I now find myself in pyproject.toml land.
I just install the libraries I need using the operating systems package manager. Works perfectly fine. In development I do use virtualenvs because I need to keep track of which dependencies are required, but in production I just apt-get install.
that will 100% come to bite you in the ass one day
Probably, but it keeps my dependencies low and packages are automatically updated with backported security patches. So far it has never been an issue, but there has been frustrations when the features aren't available, because the Debian packages aren't the newest versions.
I wouldn't recommend it for something with hundreds of dependencies, but I also wouldn't recommend having hundreds of dependencies.
Poetry has messed up packages more often apt ever did, in my use cases. So far using apt as by package manager has failed me exactly zero times.
I wouldn't recommend it for something with hundreds of dependencies, but I also wouldn't recommend having hundreds of dependencies.
Poetry has messed up packages more often apt ever did, in my use cases. So far using apt as by package manager has failed me exactly zero times.
If it works, it works. Just be mindful and prepared for the day where it stops working.
> Just be mindful and prepared
Absolutely, but that's true for pretty much all the package managers, for all languages. They all break at some point.
One thing I would note is: Don't install your dependencies with apt in a development environment, you need to have a clean environment to avoid dragging unneeded dependencies into your production environments. That does mean that you need to find the exact version of your dependency in Debian, but it's a good exercise and ensures that you're mindful of your dependencies.
Be mindful, be prepared is good advise, in all aspects of life really.
Absolutely, but that's true for pretty much all the package managers, for all languages. They all break at some point.
One thing I would note is: Don't install your dependencies with apt in a development environment, you need to have a clean environment to avoid dragging unneeded dependencies into your production environments. That does mean that you need to find the exact version of your dependency in Debian, but it's a good exercise and ensures that you're mindful of your dependencies.
Be mindful, be prepared is good advise, in all aspects of life really.
> If the user doesn't have pixi or micromamba installed, Wetlands will download and set it up automatically.
Please don't. Never have a tool that automatically reaches out onto the internet to get a binary and then run it. Just let the user know that they need to install either pixi or micromamba. It's inherently unsafe and you don't know what will be put into those binaries in the future.
Maybe it's because I don't have a use case for this, but I don't really get what this is for. It's interesting, but I'm not really sure where I'd use it.
Please don't. Never have a tool that automatically reaches out onto the internet to get a binary and then run it. Just let the user know that they need to install either pixi or micromamba. It's inherently unsafe and you don't know what will be put into those binaries in the future.
Maybe it's because I don't have a use case for this, but I don't really get what this is for. It's interesting, but I'm not really sure where I'd use it.
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Wetlands not only simplifies the creation of isolated Conda environments with specific dependencies, but also allows you to run arbitrary Python code within those environments and retrieve the results. It uses the multiprocessing.connection and pickle modules for inter-process communication. Additionally, one can easily use shared memory between the environments, making data exchange more efficient.
Docs: https://arthursw.github.io/wetlands/latest/ Source: https://github.com/arthursw/wetlands/
I’d really appreciate any feedback. Thanks!