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Comment by shoo | Hacker News Reader

From a continuum.io blog post about Conda [1]:

> Having been involved in the python world for so long, we are all aware of pip, easy_install, and virtualenv, but these tools did not meet all of our specific requirements. The main problem is that they are focused around Python, neglecting non-Python library dependencies, such as HDF5, MKL, LLVM, etc., which do not have a setup.py in their source code and also do not install files into Python’s site-packages directory.

I haven't used Conda, but I totally get their main point here. Python packages sometimes depend upon non-Python packages. Any Python-specific packaging solution that cannot express that e.g. a Python package depends upon a native library does not really solve this problem.

I don't regard packaging, dependency management, and isolation as programming-language-dependent problems.

[1] http://www.continuum.io/blog/conda

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Conda certainly sounds ambitious -- but I'm doubtful how useful it really is. As I see it, it mainly fills two needs: the lack of a package manager on OS X and the lack of a package manager on Windows.

Windows is getting a package manager now, and OS X arguably have the App Store.

Everywhere else (read: Linux/BSD) -- most of what's packaged by Conda, is already packaged by the vendor.

In short: I don't think it's the best way forward (but it may very well be the best way for many usecases/users right now and in the near future).

I agree.

There's probably also some element of "it isn't the best general solution, but it's super convenient for some particular group of people, e.g. python users".

A big part of Conda's usefulness is not system-wide package management a la apt or yum or whatever, but being able to have reproducible, isolated environments for scientific computing or other times when reproducibility is very important, and/or when you need multiple versions of packages for compatibility with other packages.

I believe it uses virtualenv for this, but I may be wrong. Regardless, having this feature (as well as no interference with the system Python) is one of the killer features for me, and others I'm sure.

This is a great point. Still, whole system snapshots in the form of a vm disk image, vagrant etc might be a better way to serve that use-case. Perhapssome form of appkiance levering something like guix or coreOS?

An additional benefit of Conda is that building or installing packages that require a C/Fortran compiler can be done without using sudo. This is really nice when you're working on a cluster or other shared computer resources, and don't want to have to bother the sysadmin.

I haven't evaluated Conda either, but the wheel vs egg distinction is categorically different from managing build chains for huge tools like Hadoop.

Replace "sometimes" with "nearly always" in the scientific world.

+1 that conda solves "most of our packaging problems" in the science/data science world (I've lived within it for 1.5 years, it really simplifies things). I use conda on Linux, Mac and Windows for r&d and deployment for tools including numpy, sklearn, matplotlib along with flask, pymongo etc.

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