My computer recently had an unfortunate interface with dihydrogen monoxide. To be determined if it will come back to life but it's not looking good. So, I bought a new Macbook which means, the M3 chip (arm64). I had a nice experience using the M1 from my previous job so I was looking forward to it.

Of course 😩 the x86 vs arm architecture issues started immediately when I tried using TensorFlow.

Here's how I fixed it. The pull request: deepcell-imaging#229

DeepCell uses TF 2.8 so that's what we have to use. Unfortunately the 2.8.4 package doesn't come with ARM binaries. Incidentally TF 2.16.1 does have arm64 binaries ... but I can't use it here 😑

Apple has some documentation for installing TensorFlow and the "metal" plugin. In particular,

For TensorFlow version 2.12 or earlier:

python -m pip install tensorflow-macos

In our case we need tensorflow-macos==2.8.0 as found in the tensorflow-macos release history. Unfortunately the files list reveals there's no Python 3.10 distribution so I need to downgrade to Python 3.9.

As for tensorflow-metal the package documentation says we need v0.4.0.

I packaged a new requirements file for Mac arm64 users:

$ cat requirements-mac-arm64.txt
tensorflow-macos==2.8.0
tensorflow-metal==0.4.0

Then you install the mac requirements:

pip install -r requirements-mac-arm64.txt

Of course, the shenanigans don't stop! Running pip install -r requirements.txt fails to install DeepCell, because it depends on tensorflow – not tensorflow-macos (which provides the same Python module tensorflow).

So I ran it this way to skip dependencies after installing the ones we could:

pip install -r requirements-mac-arm64.txt
pip install -r requirements.txt
pip install -r requirements.txt --no-deps

Then I got an interesting protobuf failure.

If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).

More information: https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates

Quick fix: grab the most recent 3.20.x protobuf version, 3.20.3.

Apple provides a test script:

import tensorflow as tf

cifar = tf.keras.datasets.cifar100
(x_train, y_train), (x_test, y_test) = cifar.load_data()
model = tf.keras.applications.ResNet50(
    include_top=True,
    weights=None,
    input_shape=(32, 32, 3),
    classes=100,)

loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False)
model.compile(optimizer="adam", loss=loss_fn, metrics=["accuracy"])
model.fit(x_train, y_train, epochs=5, batch_size=64)

One 180 MB model download later … we're golden.

2024-06-05 23:10:30.794862: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.

Just to confirm, let's check Activity Monitor – and yes, it's using the gpu. 🎉 😤

Phew. Well, hopefully this is a one-time thing. Most of our development is cloud which is x86, the more common binary format.

Until our next adventure with binaries ✌

Cover image by Kari Shea on Unsplash