This is a demonstration of the performance benefits of using SciPy, scikit-learn and scikit-image on GPUs (AMD or NVIDIA) using Array API.
The changes to SciPy, scikit-learn, scikit-image and CuPy can be seen here:
First login to Docker Registry
export CR_PAT=YOUR_PERSONAL_ACCESS_TOKEN_FOR_GITHUB echo $CR_PAT | docker login ghcr.io -u USERNAME --password-stdin
docker run --gpus all -it -p 8788:8788 ghcr.io/quansight-labs/array-api-gpu-demo-cuda:latest bash
and then run jupyterlab inside the container:
jupyter lab --ip=0.0.0.0 --port=8788 --allow-root
docker run -it --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --group-add video ghcr.io/quansight-labs/array-api-gpu-demo-rocm:latest bash
and then run jupyterlab inside the container:
jupyter lab --ip=0.0.0.0 --port=8788 --allow-root
Now run the plot_coin_segmentation.ipynb notebook.
Get into the docker container for either of the above mentioned GPU platform and run the following script:
python segmentation_performance.py
This will run the segmentation for various proportions of the greek coins image for cupy and numpy array, i.e. on CPU and GPU.
This will also create a plot of the performance comparison between numpy and cupy, which would look something like:
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