Stats
condaforge/miniforge is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
Miniforge Alternatives
Similar projects and alternatives to miniforge



Scout APM
Scout APM  Leadingedge performance monitoring starting at $39/month. Scout APM uses tracing logic that ties bottlenecks to source code so you know the exact line of code causing performance issues and can get back to building a great product faster.

Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more







Posts

Terminal killing my command to initialize conda for Miniforge3
Following this site's instructions, I tried multiple ways of downloading and installing Miniforge, including Homebrew, CI pipeline, and by downloading the shell files from here.

I’ve bought a baseline MBA M1. Would you guys guide me through the installation of Python and its libraries?
here's the link to installing miniforge3: https://github.com/condaforge/miniforge

Numpy building from source vs pip install on M1 MacBook Air. How does it work?
I’d strongly recommend using miniforge (basically anaconda), which has ARM builds ready to go (and includes numpy, pandas, etc). Compiling the project from scratch is a giant headache.

M1 MacBook Air Hits 900GFlops in the Browser with Safari's Experimental WebGPU
This kind of matches the performance I recorded when numpy is linked to vecLib for large matrix matrix multiplication in float32:
https://gist.github.com/ogrisel/87dcf2c3ab8a304ededf75934b11...
Note however there is currently no way to build and link numpy and scipy against vecLib to get correct results when calling LAPACK routines (to get Singular Value Decomposition for instance). It might be related to low level fortran ABI problems but I am not an expert so I don't know for sure.
It's possible to get a fully working numpy / scipy stack with OpenBLAS and gfortran by using the condaforge distribution:
https://github.com/condaforge/miniforge#download
The performance is not as good as with vecLib (see the linked benchmark) but it's already very good (e.g. compared to a similarly priced Intel or AMD laptop with OpenBLAS and maybe even MKL).

I want to use Python commercially for free. Is Miniconda the best way to go (as opposed to Anaconda)?
Would Miniconda be the best way to go? Or would Miniforge be a good alternative? I guess I'm concerned that I will accidentally install something through Miniconda that would then require me to pay for Anaconda, although this concern may be unfounded.

How to get Python, Numpy and Pandas running natively on Apple Silicon.
We will start by installing Python using Miniforge, download the arm64 (Apple Silicon) version of the software on the miniforge GitHubpage
 [poll] State of package managers in 2021

Installing ScikitLearn on an Apple M1
It turns out the solution is to use Miniforge, a version of Conda that is comparable to Miniconda, but supports various CPU architectures. Whatever that means. As I said, I'm no Python expert, but this tool essentially allows me to create virtual environments and install packages compiling them for the M1 chip! Any packages it doesn't support can then be installed from pip.