MINICONDA VS ANACONDA PYTHON INSTALL
MINICONDA VS ANACONDA PYTHON MANUAL
MINICONDA VS ANACONDA PYTHON SOFTWARE

Pip packages are Python libraries like NumPy or matplotlib.The fundamental difference between pip and Conda packaging is what they put in packages. The starting point: which kind of dependencies? Focusing on the Conda-Forge package repository Conda has multiple package repositories, or “channels”.īy the end you should understand why Conda exists, when you might want to use it, and the tradeoffs between choosing each one.Linux, including running on Docker, though with some mention of macOS and Windows.Python only Conda has support for other languages but I won’t go into that.While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to: What are the tradeoffs between the two?.This means you can save time and effort in building your images.If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Best of all? It’s only ~13 MiB to download. with no dependency on Conda, and that doesn’t include a default Python version making it perfect for setting up fresh environments with as small a footprint as possible. Micromamba is a standalone binary version of Mamba, i.e. This is where Micromamba comes into play!

MINICONDA VS ANACONDA PYTHON MANUAL

The download size for the mambaforge package is ~100 MiB, which is a fair bit larger than Miniconda is.įor regular use, this isn’t a big deal, but when you’re trying to create lean docker images in a production environment without requiring lots of space-saving manual intervention, it helps to have as small a download as possible. Because it depends on Conda, either way of installing it means you end up with both package managers on your system. Mamba is definitely faster than Miniconda, but unfortunately it is still quite a wedge to download. This may be important if the computers where your CI jobs are running are particularly weak. In addition, on the smallest instance Miniconda failed to complete its task because it ran out of memory and the process was unceremoniously terminated. This may seem like a significant step down from the massive gains seen previously, but factor this out over a year’s worth of CI runs and one starts to see how this could be beneficial, especially if you’re paying for CI time. In general, even for this relatively small environment, Mamba was approximately half a minute quicker to set everything up. Mamba is most akin to Miniconda, in that it comes with Python, but doesn’t ship with a whole load of extra software. This is where Mamba comes in, the fast drop-in replacement for conda, which reimplements the slow bits in in C++.

Whilst Miniconda is small as compared with full-fat anaconda, the latest Miniconda3 Linux 64-bit Python 3.9 download size is 58.6 MiB, could this be better? How big will the docker image be? We want to make sure that our images are as small as possible to make it quicker to download for our users.How long does a CI job take? Conda has a reputation for taking its time when dealing with complex sets of dependencies and we owe it to ourselves to make sure that CI jobs don’t take longer than they need to.Hence, we need to think about the following:

In order to test and distribute our workflows, we have CI (continuous integration) pipelines set-up to automatically build docker images each time a workflow is updated, which can be many times a day for the development version of a given project.

MINICONDA VS ANACONDA PYTHON SOFTWARE

in research, but when it comes to moving bioinformatics software into production, there are some extra considerations we have to make.

Miniconda is great for general purpose use, e.g.

MINICONDA VS ANACONDA PYTHON INSTALL

Conda install python=3.8 jupyter -c conda-forge Workflows in production

Miniconda vs anaconda python