Using Google Coral TPU on OpenSUSE

Installing the TPU driver

The official guide only covers steps for Debian. Third-party compiled binaries are available for Fedora, RHEL, and OpenSUSE etc. The RPM for OpenSUSE Tumbleweed can be downloaded directly here. 

Once downloaded, install the RPM file with "sudo zypper install gasket..."

Installing the userspace runtime driver

The userspace driver can be built from source inside docker:

git clone https://github.com/google-coral/libedgetpu.git

cd libedgetpu

CPU=k8 DOCKER_CPUS="k8" DOCKER_IMAGE="ubuntu:18.04" DOCKER_TARGETS=libedgetpu make docker-build

Copy the files from "out" folder and install using ldconfig:

sudo mkdir -p /usr/lib/tpu/lib

sudo cp --preserve=links out/direct/k8/* /usr/lib/tpu/lib

echo /usr/lib/tpu/lib | sudo tee -a /etc/ld.so.conf.d/tpu.conf

sudo ldconfig

Running the examples

The "pycoral" package mentioned on the officail guide is kind of confusing. The "pycoral" from pypi is actually a totally unrelated package. What we need for the TPU can be found here.

Also, there is no support for Python 3.10 yet. So needed to use 3.9. To install packages in order to run the example:

python3.9 -m venv venv

. venv/bin/activate

pip install numpy pillow

python -m pip install --extra-index-url https://google-coral.github.io/py-repo/ pycoral~=2.0

Then follow the steps on the official guide to run the example.

$ python3 examples/classify_image.py --model test_data/mobilenet_v2_1.0_224_inat_bird_quant_edgetpu.tflite --labels test_data/inat_bird_labels.txt --input test_data/parrot.jpg
----INFERENCE TIME----
Note: The first inference on Edge TPU is slow because it includes loading the model into Edge TPU memory.
12.8ms
2.8ms
2.8ms
2.8ms
2.8ms
-------RESULTS--------
Ara macao (Scarlet Macaw): 0.75781