“MLX” is more than just a technical solution; it is an innovative and user-friendly framework inspired by popular frameworks like PyTorch, Jax, and ArrayFire. It facilitates the training and deployment of AI models on Apple devices without sacrificing performance or compatibility.

Kubeflow 1.8 adds ARM processor support which simplifies adoption for Apple Silicon users and IoT servers.

Rosetta is now Generally Available for all users on macOS 13 or later. It provides faster emulation of Intel-based images on Apple Silicon. To use Rosetta, see Settings. Rosetta is enabled by default on macOS 14.1 and later.

Solution #3: Switch to Conda-Forge

In general, the environment variable is too heavy-handed and should be avoided, since it will impact all images you build or run. Given the speed impact, you don’t for example want to run your postgres image with emulation, to no benefit.

The problem with this approach is that the emulation slows down your runtime. How much slower it is? Once benchmark I ran was only 6% of the speed of the host machine!

The other option is to run x86_64 Docker images on your ARM64 Mac machine, using emulation. Docker is packaged with software that will translate or emulate x86_64 machine code into ARM64 machine code on the fly; it’s slow, but the code will run.

Third, you can pre-compile wheels, store them somewhere, and install those directly instead of downloading the packages from PyPI.

If you have a compiler installed in your Docker image and any required native libraries and development headers, you can compile a native package from the source code. Basically, you add a RUN apt-get upgrade && apt-get install -y gcc and iterate until the package compiles successfully.

First, it’s possible the relevant wheels are available in newer versions of the libraries.

When you pip install murmurhash==1.0.6 on a M1/M2 Mac inside Docker, again it looks at the available files

When you pip install filprofiler==2022.05.0 on a M1/M2 Mac inside Docker, pip will look at the available files for that version, and then

In either case, pure Python will Just Work, because it’s interpreted at runtime: there’s no CPU-specific machine code, it’s just text that the Python interpreter knows how to run. The problems start when we start using compiled Python extensions. These are machine code, and therefore you need a version that is specific to your particular CPU instruction set.

on an Intel/AMD PC or Mac, docker pull will pull the linux/amd64 image. On a newer Mac using M1/M2/Silicon chips, docker pull will the pull the linux/arm64/v8 image.

This is a neat Docker trick for those who have an ARM development machine (Apple M1), but sometimes need to build x86/amd64 images locally to push up to a registry.

So, we can create this builder on our local machine. The nice part about this creation is that it is idempotent, so you can run this command many times without changing the result. All we have to do is to create a builder profile and in this case I have named it amd64_builder.

flipou83 · 24dI have a M1 but all my storage is on a TrueNAS and Unraid server. Doesn't trust external drive storage for the problems you listed on your post. I have a share for it's TimeMachine backups and big Photos library and iMovie Library which don't even fit on the internal SSD of the Mac mini.I would put the SSD you bought into an independent computer that acts as a NAS on your network. What about this solution?