miniforge
A conda-forge distribution. (by conda-forge)
conda
A system-level, binary package and environment manager running on all major operating systems and platforms. (by conda)
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miniforge | conda | |
---|---|---|
56 | 30 | |
5,078 | 6,051 | |
6.2% | 1.7% | |
7.7 | 9.8 | |
5 days ago | 1 day ago | |
Shell | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
miniforge
Posts with mentions or reviews of miniforge.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-10-02.
- Python 3.12
- Installing Anaconda on ChromeOS using Linux
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PSA: conda-libmamba-solver can cut two hours off of your Anaconda install, but has only 47 GitHub stars. It deserves more praise.
Mambaforge!
See the section on the miniforge github repo. It's just like miniconda/miniforge in that its quite a small download vs full-fat Anaconda.
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A quick guide to using mamba-forge for python virtual environment management
Just to further clarify: you don't need mamba to avoid the Anaconda distribution. The place you get mambaforge also supplies (and originally supplied) miniforge, which is miniconda with conda-forge set as the default channel. All the *forge installers do in this regard is automatically set conda-forge as the default (and only) channel, which is something one can do manually with miniconda.
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How to get the best Conda environment experience in Codespaces
Tip 1: To use less of your Codespaces resources start with a smaller image like Miniconda or Miniforge and install only what you need.
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Ask HN: Programs that saved you 100 hours? (2022 edition)
miniforge, no need to deal with conda environments anymore. https://github.com/conda-forge/miniforge
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Using my Steam Deck as my main personal Desktop PC.
gcc yes but IIRC it's missing a lot of build-essential, and the collection of available headers and development libraries is fairly small. I used conda / mambaforge to build my own toolchain, and I know others have had succeess with Homebrew or pacman - r.
Since you mentioned Homebrew, I'll shout out conda / mamba as well. While it was developed for data science and numerical python, conda-forge has a surprising breadth of packages, and I managed to get the entire gcc toolchain plus development libraries installed, all not only in the userspace, but isolated to a single virtual environment.
Talk about easy mode. conda mamba or poetry, and you're done. I think SteamOS even ships with vim and not just vi, but if you need a different version, it's available via conda.
conda
Posts with mentions or reviews of conda.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-09.
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How to Create Virtual Environments in Python
Python's venv module is officially recommended for creating virtual environments since Python 3.5 comes packaged with your Python installation. While there still are additional older tools available, such as conda and virtualenv, if you are new to virtual environments, it is best to use venv now.
- Installing Anaconda on ChromeOS using Linux
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PSA: conda-libmamba-solver can cut two hours off of your Anaconda install, but has only 47 GitHub stars. It deserves more praise.
conda's dependency solver solves a harder problem than pip's. This quote alludes to it "Conda will never be as fast as pip, so long as we're doing real environment solves and pip satisfies itself only for the current operation." (from https://github.com/conda/conda/issues/7239). Thus mamba was created to improve performance and now conda is bringing in that performance boost.
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How to get the best Conda environment experience in Codespaces
The other challenge I ran into sometimes was that if I was running a lower memory/storage Codespace instance, when I tried to use Conda from the command line to modify environments, the process would be killed after a few seconds. This turns out to be related to some performance issues Conda has that make it consume a lot of memory when trying to work with the conda-forge installation channel. You can always then just increase the size of the Codespace your are working with (just go to your Codespaces list and use the triple dots to change the settings for a Codespace).
- Python 3.11.0 final is now available
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Processing JSON 2.5x faster than simdjson with msgspec
I wrote this up as a demo of some potential optimization work in conda, but the same principle could be applied to other projects.
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One-Click Install Stable Diffusion GUI App for M1 Mac. No Dependencies Needed
Which would be less of an issue if I didn't have to fix a conda bug while installing StableDiffusion.
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Issues during install with Miniconda3
Yeah, it's most likely that. Try reinstalling, and if that doesn't fix it, see this issue and related ones for other possible solutions.
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For you, data scientist
Thanks for letting us know about that. I will try this since conda was giving me some problems like causing powershell startup delays of over 4 seconds. See here: https://github.com/conda/conda/issues/11648 .
- Recommendations when publishing a WASM library
What are some alternatives?
When comparing miniforge and conda you can also consider the following projects:
mamba - The Fast Cross-Platform Package Manager
pyenv - Simple Python version management
Poetry - Python packaging and dependency management made easy
PDM - A modern Python package and dependency manager supporting the latest PEP standards
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
pip - The Python package installer
wheel - Adoption analysis of Python Wheels: https://pythonwheels.com/
PyInstaller - Freeze (package) Python programs into stand-alone executables
rez - An integrated package configuration, build and deployment system for software
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.