m1-machine-learning-test
Code for testing various M1 Chip benchmarks with TensorFlow. (by mrdbourke)
miniforge
A conda-forge distribution. (by conda-forge)
| m1-machine-learning-test | miniforge | |
|---|---|---|
| 3 | 56 | |
| 509 | 5,597 | |
| - | 5.2% | |
| 0.0 | 7.7 | |
| 2 months ago | 26 days ago | |
| Jupyter Notebook | Shell | |
| MIT License | 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.
m1-machine-learning-test
Posts with mentions or reviews of m1-machine-learning-test.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-10.
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Need help with successfully installing tensorflow on a Mac running M1 chip
Here's a pretty decent walkthrough in the readme: https://github.com/mrdbourke/m1-machine-learning-test
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TensorFlow won’t work with new M1 Mac. Please help please please please!
I got a new M1 Mac a few weeks ago and I’m having issues downloading and using tensorflow. I used this method to install it but once I get to the Jupyter Notebook test step, it says that the tensorflow file isn’t found. The tutorial that I found seemed really new so I am super confused! Could someone please help me?? I’m feeling very dumb and incompetent.
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M1 Ultra for deep learning
I found this repo which has some benchmarks for other M1s: https://github.com/mrdbourke/m1-machine-learning-test/tree/main/results
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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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?
When comparing m1-machine-learning-test and miniforge you can also consider the following projects:
TensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos
mamba - The Fast Cross-Platform Package Manager
swift - Swift for TensorFlow
pyenv - Simple Python version management
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
Poetry - Python packaging and dependency management made easy
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
pyright - Static Type Checker for Python
m1-machine-learning-test vs TensorFlow-Tutorials
miniforge vs mamba
m1-machine-learning-test vs swift
miniforge vs pyenv
m1-machine-learning-test vs pyprobml
miniforge vs conda
m1-machine-learning-test vs TensorFlow-Examples
miniforge vs Poetry
miniforge vs tensorflow_macos
miniforge vs asdf
miniforge vs pip-tools
miniforge vs pyright