gfortran-for-macOS
miniforge
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gfortran-for-macOS | miniforge | |
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3 | 56 | |
324 | 5,208 | |
- | 6.3% | |
0.0 | 7.7 | |
4 months ago | 4 days ago | |
Perl | Shell | |
- | GNU General Public License v3.0 or later |
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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.
gfortran-for-macOS
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Installing gfortran on MacOS Monterey
I need Fortran for my summer internship, so I'm trying to get ahead of it. However, I have Mac Monterey and I've tried multiple methods of installing gfortran and gcc and nothing has worked. I tried the downloadable package (https://github.com/fxcoudert/gfortran-for-macOS/releases), installing it via homebrew, and using Xcode and the Xcode command line developer tools, and I keep getting the error "library not found." When I do "which gcc" or "which gfortran" I get a response, so it's definitely installed.
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Any easy way of using Fortran on my Macbook Air M1?
You can get your gfortran from here: https://github.com/fxcoudert/gfortran-for-macOS/releases. As far as I know, there is no native M1 support yet so it will run under rosetta.
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Fortran on M1
There are some other Fortran development options for macOS. gfortran is free software, and it looks like someone has built an experimental version of it for the M1. You might also be able to install the Intel version of it via Homebrew (or maybe even compile a working M1 native version that doesn't require Rosetta; I'm not sure whether GCC has Apple Silicon target support yet). I believe there are some commercial Fortran compilers available on the Mac as well.
miniforge
- Python 3.12
- Installing Anaconda on ChromeOS using Linux
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What is the difference between chat, cai-chat, and instruct, and how to use them?
Nope they don't use venv for any of the oobabooga# variants nor is it recommended for the git version. I'm using https://github.com/conda-forge/miniforge#mambaforge-pypy3 (better version of the recommended conda) for the git variant. The oobabooga* variant uses micro/miniconda (I suck with names) which you can easily drop into with cmd_?something? and does it all internally. Like whoever built that whole environment setup for the _windows/linux/mac did a great job.
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Build llama.cpp on Jetson Nano 2GB
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh .
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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!
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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.
- Recommendations for Data Science Workflow
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path issue - cannot import modules in jupyter installed via pip3 (m1 mac)
I'd recommend using miniforge if you're comfortable with CLIs, otherwise https://www.anaconda.com/.
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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
What are some alternatives?
HomeBrew - 🍺 The missing package manager for macOS (or Linux)
mamba - The Fast Cross-Platform Package Manager
homebrew-i386-elf-toolchain - Homebrew formulas for buildling a valid GCC toolchain for the i386-elf target.
pyenv - Simple Python version management
quickstart-fortran - An easy Windows installer for GFortran and the Fortran Package Manager
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
Poetry - Python packaging and dependency management made easy
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
pyright - Static Type Checker for Python
Vcpkg - C++ Library Manager for Windows, Linux, and MacOS