Review and add GPU-accelerated notebook samples #243
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| # GPU-Accelerated Samples | ||
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| Jupyter notebooks demonstrating GPU acceleration using NVIDIA RAPIDS libraries on Microsoft Fabric and Azure VMs with NVIDIA GPUs. | ||
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| > Based on examples from [NVIDIA RAPIDS](https://github.com/rapidsai) (Apache 2.0 License, Copyright NVIDIA Corporation). Modified and tested for Azure GPU VM environment. | ||
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| ## Requirements | ||
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| - NVIDIA GPU (Tesla T4 or better) | ||
| - CUDA 12.x | ||
| - Python 3.12+ | ||
| - RAPIDS 25.x (cuDF, cuML, cuCIM) | ||
| - Conda environment recommended | ||
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| ## Notebooks | ||
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| | Notebook | Description | | ||
| |----------|-------------| | ||
| | [cudf-pandas-accelerator-demo.ipynb](cudf-pandas-accelerator-demo.ipynb) | Drop-in GPU acceleration for pandas with `cudf.pandas` — no code changes needed | | ||
| | [cudf-pandas-stock-analysis.ipynb](cudf-pandas-stock-analysis.ipynb) | Stock market data analysis using GPU-accelerated pandas (read, merge, resample, plot) | | ||
| | [cuml-scikit-learn-accelerator-demo.ipynb](cuml-scikit-learn-accelerator-demo.ipynb) | Drop-in GPU acceleration for scikit-learn (PCA, UMAP, KNN, HDBSCAN on activity recognition data) | | ||
| | [gpu-vs-cpu-compute-benchmark.ipynb](gpu-vs-cpu-compute-benchmark.ipynb) | Side-by-side GPU vs CPU benchmark for common compute operations | | ||
| | [rapids-dataframe-gpu-vs-cpu.ipynb](rapids-dataframe-gpu-vs-cpu.ipynb) | RAPIDS cuDF DataFrame operations compared to pandas on larger datasets | | ||
| | [multi-gpu-embedding-and-knn-search.ipynb](multi-gpu-embedding-and-knn-search.ipynb) | Multi-GPU text embedding generation and KNN similarity search using Ray + RAPIDS | | ||
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| ## cuCIM Medical Imaging | ||
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| GPU-accelerated image processing for digital pathology and microscopy using [cuCIM](https://github.com/rapidsai/cucim). | ||
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| > Based on examples from [rapidsai/cucim](https://github.com/rapidsai/cucim) (Apache 2.0 License, Copyright NVIDIA Corporation). Modified for Azure GPU VM environment. | ||
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| | Notebook | Description | | ||
| |----------|-------------| | ||
| | [01-whole-slide-image-reading.ipynb](cucim_medical_imaging/01-whole-slide-image-reading.ipynb) | Reading and displaying multi-resolution whole-slide pathology images | | ||
| | [02-image-cache-performance.ipynb](cucim_medical_imaging/02-image-cache-performance.ipynb) | Tile caching strategies for faster repeated region access | | ||
| | [03-gabor-texture-classification.ipynb](cucim_medical_imaging/03-gabor-texture-classification.ipynb) | GPU vs CPU Gabor filter bank for texture classification | | ||
| | [04-random-walker-segmentation.ipynb](cucim_medical_imaging/04-random-walker-segmentation.ipynb) | GPU-accelerated random walker image segmentation | | ||
| | [05-vesselness-filter.ipynb](cucim_medical_imaging/05-vesselness-filter.ipynb) | GPU-accelerated vessel detection using Frangi/Sato filters | | ||
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| ### Setup for cuCIM notebooks | ||
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| **Notebooks 01 and 02** require a whole-slide image file. Before running them, create the `input/` folder and download a sample image: | ||
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| ```bash | ||
| cd cucim_medical_imaging | ||
| mkdir -p input | ||
| # Download a sample Aperio SVS whole-slide image (~170MB) | ||
| wget -O input/image.tif https://openslide.cs.cmu.edu/download/openslide-testdata/Aperio/CMU-1.svs | ||
| ``` | ||
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| **Notebooks 03, 04, and 05** are self-contained — they use synthetic data from scikit-image and do not require any additional files. | ||
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| ### Notes | ||
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| - **multi-gpu-embedding-and-knn-search.ipynb** requires an Azure OpenAI API key and endpoint. Update the `api_key` and `azure_endpoint` values in Step 1 before running. | ||
| - **cuml-scikit-learn-accelerator-demo.ipynb** downloads the UCI HAR dataset to `/tmp/HAR_data/` on first run (requires internet access). | ||
| - **cudf-pandas-stock-analysis.ipynb** downloads stock price data from Yahoo Finance (requires internet access). | ||
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "eb420212", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Basic Usage\n", | ||
| "\n", | ||
| "This notebook shows basic usage of cuCIM with a whole-slide pathology image." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "39389384", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Prerequisites" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "93157b57", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "# !conda install -c conda-forge pillow" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "6378c4a3", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Read image" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "c02bf93e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from cucim import CuImage\n", | ||
| "\n", | ||
| "img = CuImage(\"input/image.tif\")\n", | ||
|
Contributor
There was a problem hiding this comment. Better to auto download the data from a blob storage to local path. |
||
| "print(f\"Image loaded: {img.path}\")\n", | ||
| "print(f\"Dimensions (XY): {img.size('XY')}\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2b371170", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### See metadata" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "4ff52186", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import json\n", | ||
| "\n", | ||
| "metadata = img.metadata\n", | ||
| "print(json.dumps(metadata, indent=2)[:2000])" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "5ffeeeed", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Read region\n", | ||
| "\n", | ||
| "Let's read the whole slide at the lowest resolution and save as a thumbnail." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "ec613ae8", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "resolutions = img.resolutions\n", | ||
| "level_dimensions = resolutions[\"level_dimensions\"]\n", | ||
| "level_count = resolutions[\"level_count\"]\n", | ||
| "\n", | ||
| "print(f\"Number of levels: {level_count}\")\n", | ||
| "print(f\"Level dimensions: {level_dimensions}\")\n", | ||
| "\n", | ||
| "# Read the whole slide at lowest resolution\n", | ||
| "region = img.read_region(\n", | ||
| " location=[0, 0], size=level_dimensions[level_count - 1], level=level_count - 1\n", | ||
| ")\n", | ||
| "\n", | ||
| "# Save and display\n", | ||
| "region.save(\"input/thumbnail.ppm\")\n", | ||
| "Image.open(\"input/thumbnail.ppm\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "2f6da447", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Now let's read a 512x512 region at full resolution (level 0)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "daacf8cf", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "region = img.read_region([10000, 20000], [512, 512], 0)\n", | ||
| "region.save(\"input/test.ppm\")\n", | ||
| "Image.open(\"input/test.ppm\")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "6c367337", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### `__array_interface__` support\n", | ||
| "\n", | ||
| "A NumPy array has the `__array_interface__` property. Here is a simple example:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "e09b53d6", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "\n", | ||
| "np_arr = np.array([1, 2, 3])\n", | ||
| "print(np_arr.__array_interface__)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "f7a3f798", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "As you can see from the above result, a NumPy array has `__array_interface__` property which describes the memory layout.\n", | ||
| "\n", | ||
| "**cuCIM also supports `__array_interface__`**, so you can directly convert a cuCIM region to a NumPy array and display it with PIL:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "0804834e", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "region = img.read_region([10000, 20000], [512, 512], 0)\n", | ||
| "np_img_arr = np.asarray(region)\n", | ||
| "\n", | ||
| "print(f\"Array shape: {np_img_arr.shape}, dtype: {np_img_arr.dtype}\")\n", | ||
| "arr_iface = np_img_arr.__array_interface__\n", | ||
| "print(f\"__array_interface__ shape: {arr_iface['shape']}\")\n", | ||
| "\n", | ||
| "Image.fromarray(np_img_arr)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "0b702b4e", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### `__cuda_array_interface__` support\n", | ||
| "\n", | ||
| "A CuPy array has `__cuda_array_interface__` property. Here is a simple example:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "0990d4d4", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import cupy as cp\n", | ||
| "\n", | ||
| "cp_arr = cp.array([1, 2, 3])\n", | ||
| "print(cp_arr.__cuda_array_interface__)" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "a130d04f", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "A Python object that has `__cuda_array_interface__` property is considered as a CUDA array-like object and can be converted to CuPy array through `cp.asarray(obj)` method.\n", | ||
| "\n", | ||
| "**`__cuda_array_interface__` is also supported in CuImage** if you specify `device='cuda'` in `read_region()`. The following code loads image data directly to GPU memory and visualizes it in the Jupyter notebook:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "5a43d245", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import cupy as cp\n", | ||
| "from cucim import CuImage\n", | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "img = CuImage(\"input/image.tif\")\n", | ||
| "resolutions = img.resolutions\n", | ||
| "level_dimensions = resolutions[\"level_dimensions\"]\n", | ||
| "level_count = resolutions[\"level_count\"]\n", | ||
| "\n", | ||
| "# Read region directly to GPU memory\n", | ||
| "region_gpu = img.read_region(\n", | ||
| " [0, 0], level_dimensions[level_count - 1], level_count - 1, device=\"cuda\"\n", | ||
| ")\n", | ||
| "\n", | ||
| "print(f\"Device: {region_gpu.device}\")\n", | ||
| "cuda_iface = region_gpu.__cuda_array_interface__\n", | ||
| "print(f\"__cuda_array_interface__ shape: {cuda_iface['shape']}\")\n", | ||
| "\n", | ||
| "# Convert to CuPy array, then to CPU for display\n", | ||
| "cupy_arr = cp.asarray(region_gpu)\n", | ||
| "print(f\"CuPy array shape: {cupy_arr.shape}\")\n", | ||
| "Image.fromarray(cupy_arr.get())" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "499f2e79", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Associated images\n", | ||
| "\n", | ||
| "Some image formats such as Philips TIFF and Aperio SVS have associated images (Macro or Label images) in addition to the multi-resolution images.\n", | ||
| "\n", | ||
| "Let's check what associated images are available:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "id": "b9714d55", | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "from PIL import Image\n", | ||
| "\n", | ||
| "print(f\"Associated images: {img.associated_images}\")\n", | ||
| "\n", | ||
| "for name in img.associated_images:\n", | ||
| " assoc_img = img.associated_image(name)\n", | ||
| " np_arr = np.asarray(assoc_img)\n", | ||
| " print(f\" '{name}': shape={np_arr.shape}\")\n", | ||
| "\n", | ||
| "# Display the macro image if available\n", | ||
| "if 'macro' in img.associated_images:\n", | ||
| " macro_image = img.associated_image('macro')\n", | ||
| " np_img_arr = np.asarray(macro_image)\n", | ||
| " print(f\"\\nDisplaying macro image: {np_img_arr.shape}\")\n", | ||
| " display(Image.fromarray(np_img_arr))\n", | ||
| "\n", | ||
| "# Check for label image\n", | ||
| "if 'label' in img.associated_images:\n", | ||
| " print(\"Label image exists\")\n", | ||
| "else:\n", | ||
| " print(\"\\nThere is no associated image named 'label'!\")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3 (ipykernel)", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.13.11" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 5 | ||
| } | ||
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these notebooks seem to be a little bit heavy as sample notebooks, I'd suggest we combine them into one notebook and keep core components in the notebooks.