tensorflow_macos
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework. (by apple)
miniforge
A conda-forge distribution. (by conda-forge)
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| tensorflow_macos | miniforge | |
|---|---|---|
| 33 | 56 | |
| 2,887 | 5,306 | |
| - | 7.0% | |
| 3.4 | 7.7 | |
| almost 3 years ago | 3 days ago | |
| Shell | Shell | |
| 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.
tensorflow_macos
Posts with mentions or reviews of tensorflow_macos.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-01-17.
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Updated Apple Silicon Guide for M2 Pro and M2 Max Chips
https://github.com/apple/tensorflow_macos is no longer needed
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The hunt for the M1’s neural engine
Tensorflow has a CoreML enabled version which run on ANE.
https://github.com/apple/tensorflow_macos
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M1 Mac users
Apple released a guide on how to use the M1's integrated Neural Chip in TensorFlow. Have a look at this Apple documentation page (and maybe also this GitHub that talks about TensorFlow together with Apple's own ML Compute platform).
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MacBook Air or Wait for new potential MacBook Air with M2
Tensorflow does work on Apple Silicon
- Kernels dying when using tensorflow in Jupyter Notebooks.
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Main PyTorch maintainer confirms that work is being done to support Apple Silicon GPU acceleration for the popular machine learning framework.
Apple did some work to optimize tensorflow for M1, can be found here https://github.com/apple/tensorflow_macos It's alpha, but works fine, I tried it
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The M1 Max is the fastest GPU we have ever measured in Affinity Photo benchmark
https://github.com/apple/tensorflow_macos/issues/25
https://forums.macrumors.com/threads/apple-silicon-deep-lear...
It is expected that the M1 Max should have similar performance to a RTX-2080 or Titan X.
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MacBook Pro M1 Pro benchmark
In case anyone is interested, in ran a fairly simple MNIST benchmark (proposed here : https://github.com/apple/tensorflow_macos/issues/25) on my recently acquired M1 Pro MBP (16-core GPU, 16GB RAM).
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Error while installing tensorflow on Mac M1
The only method I know of to download tensorflow on M1 macs is the one documented here: https://github.com/apple/tensorflow_macos
- How exactly does the Neural Engine benefit the consumer?
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 tensorflow_macos and miniforge you can also consider the following projects:
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
mamba - The Fast Cross-Platform Package Manager
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
pyenv - Simple Python version management
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
conda - A system-level, binary package and environment manager running on all major operating systems and platforms.
flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
Poetry - Python packaging and dependency management made easy
Python-docker - Docker Official Image packaging for Python
asdf - Extendable version manager with support for Ruby, Node.js, Elixir, Erlang & more
coremltools - Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
pip-tools - A set of tools to keep your pinned Python dependencies fresh.
tensorflow_macos vs Pointnet_Pointnet2_pytorch
miniforge vs mamba
tensorflow_macos vs tinygrad
miniforge vs pyenv
tensorflow_macos vs ROCm
miniforge vs conda
tensorflow_macos vs flamegraph
miniforge vs Poetry
tensorflow_macos vs Python-docker
miniforge vs asdf
tensorflow_macos vs coremltools
miniforge vs pip-tools