Local virtualenv, like this:
python -m venv venv
source venv/bin/activate
pip install -U pip wheel setuptools
pip install -r requirements.txt
Thanks for your helpful reply.
I have tried this and yes - it does indeed make a build. In a Python env.
However, learn = cnn_learner(dls, resnet18, metrics=error_rate)
… kills the kernal. I have tried multiple times.
Configuration is: Sonoma Macbook Pro Max M2.
So - guess have to wait until something improves with the FastAi distro.
I believe FastAI is incompatible with Pytorch versions above 1.13. I haven’t used FastAI recently, so my input may not be relevant.
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yes - it is pretty clear to me now. thanks for the reply.
I know Jeremy is a Windows guy (not that there is any wrong with that …LOL)
Really need to get Fast AI working well on M1/2 Apple Silicon.
Lots of people do ML on this platform.
‘cnn_learner’ is deprecated. Have you tried ‘vision_learner’ ?
it is irrespective. tried each method.
it is ok - if M1/2 is a priority it will get updated.
until then - just review FastAi but use Pytorch Independently on Apple silicon.
I managed to get both my M2 Mac Book air and M1 Mac Mini to run the chapter1 code with instructions from this address:
with one exception, instead of the overnight channel, I used the pytorch channel to install. M1 is much slower than M2, about 10x. But M2 is quiet decent.
conda install pytorch torchvision torchaudio -c pytorch
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Yes, fastai has been successfully used on Apple M1 chips. Users report smooth performance and compatibility, leveraging the chip’s power for efficient machine learning tasks.
Yes, fastai runs on Apple M1 chips with ARM-compatible dependencies. Users report good performance and suggest using Miniforge for setup.