tensorflow: Cannot run TensorFlow 2.7 in Docker on M1 (Apple Silicon)

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): No
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): macOS 12.0 Monterey
• TensorFlow installed from (source or binary): pre-built binary (Docker and pip)
• TensorFlow version (use command below): 2.7
• Python version: 3.8.10

Describe the current behavior

Our team needs to run TensorFlow as part of a larger application in Docker. However, this doesn’t seem possible on an M1 Mac.

For example, if I use the default TF Docker image (for x86-64 only, an ARM64 image is not available):

> docker run -it tensorflow/tensorflow /bin/bash
> python -c "import tensorflow"
The TensorFlow library was compiled to use AVX instructions, but these aren't available on your machine.
qemu: uncaught target signal 6 (Aborted) - core dumped
Aborted

I get the same error when installing from pip on an x86-64 Linux container:

> docker run -it --platform=linux/amd64 python:3.8-buster /bin/bash
> pip install --upgrade pip && pip install tensorflow
> python -c "import tensorflow"
The TensorFlow library was compiled to use AVX instructions, but these aren't available on your machine.
qemu: uncaught target signal 6 (Aborted) - core dumped
Aborted

Ostensibly, this is because the pre-built TensorFlow requires the CPU to support AVX instructions, but this is not supported by Docker / QEMU when emulating an x86-64 container on M1.

Describe the expected behavior

There should be a way to run TensorFlow in Docker on M1! (Without building from source.)

Every other ML/DS library works with on Docker on M1: PyTorch, Scikit-Learn, Numpy, Scipy, etc.

Standalone code to reproduce the issue

See code snippets above.