diff --git a/nvidia/nvfp4-quantization/README.md b/nvidia/nvfp4-quantization/README.md index 046460b..94477ed 100644 --- a/nvidia/nvfp4-quantization/README.md +++ b/nvidia/nvfp4-quantization/README.md @@ -6,7 +6,7 @@ - [Overview](#overview) - [NVFP4 on Blackwell](#nvfp4-on-blackwell) -- [Desktop Access](#desktop-access) +- [Instructions](#instructions) --- @@ -71,7 +71,7 @@ huggingface-cli whoami **Rollback**: Remove the output directory and any pulled Docker images to restore original state. -## Desktop Access +## Instructions ## Step 1. Prepare the environment diff --git a/nvidia/rapids/assets/cudf_pandas_demo.ipynb b/nvidia/rapids/assets/cudf_pandas_demo.ipynb new file mode 100644 index 0000000..4399bea --- /dev/null +++ b/nvidia/rapids/assets/cudf_pandas_demo.ipynb @@ -0,0 +1,1000 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "84635d55-68a2-468b-ac09-9029ebdab55f", + "metadata": { + "id": "84635d55-68a2-468b-ac09-9029ebdab55f" + }, + "source": [ + "# Accelerating large string data processing with cudf pandas accelerator mode (cudf.pandas)\n", + "cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API in the style of pandas.\n", + "\n", + "cuDF now provides a pandas accelerator mode (`cudf.pandas`), allowing you to bring accelerated computing to your pandas workflows without requiring any code change.\n", + "\n", + "This notebook demonstrates how cuDF pandas accelerator mode can help accelerate processing of datasets with large string fields (4 GB+) processing by simply adding a `%load_ext` command. We have introduced this feature as part of our Rapids 24.08 release." + ] + }, + { + "cell_type": "markdown", + "id": "bb8fe7ab-c055-40e9-897d-c62c72f28a16", + "metadata": { + "id": "bb8fe7ab-c055-40e9-897d-c62c72f28a16" + }, + "source": [ + "# ⚠️ Verify your setup\n", + "\n", + "First, we'll verify that you are running with an NVIDIA GPU." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a88b8586-cfdd-4d31-9b4d-9be8508f7ba0", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "a88b8586-cfdd-4d31-9b4d-9be8508f7ba0", + "outputId": "18525b64-b34b-40e3-ed3a-1ad56ae794b5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fri Oct 3 23:16:52 2025 \n", + "+-----------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 580.82.09 Driver Version: 580.82.09 CUDA Version: 13.0 |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+========================+======================|\n", + "| 0 NVIDIA GB10 Off | 0000000F:01:00.0 Off | N/A |\n", + "| N/A 44C P0 10W / N/A | Not Supported | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + "\n", + "+-----------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=========================================================================================|\n", + "| 0 N/A N/A 3405 G /usr/lib/xorg/Xorg 242MiB |\n", + "| 0 N/A N/A 3562 G /usr/bin/gnome-shell 53MiB |\n", + "| 0 N/A N/A 214921 C .../envs/rapids-25.10/bin/python 196MiB |\n", + "+-----------------------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi # this should display information about available GPUs" + ] + }, + { + "cell_type": "markdown", + "id": "5cd58071-4371-428b-8a02-9cd66e6cb91f", + "metadata": { + "id": "5cd58071-4371-428b-8a02-9cd66e6cb91f" + }, + "source": [ + "# Download the data" + ] + }, + { + "cell_type": "markdown", + "id": "9eb67713-7cf4-415a-bce7-ff4695862faa", + "metadata": { + "id": "9eb67713-7cf4-415a-bce7-ff4695862faa" + }, + "source": [ + "## Overview\n", + "The data we'll be working with summarizes job postings data that a developer working at a job listing firm might analyze to understand posting trends.\n", + "\n", + "We'll need to download a curated copy of this Kaggle dataset [https://www.kaggle.com/datasets/asaniczka/1-3m-linkedin-jobs-and-skills-2024/data?select=job_summary.csv] directly from the kaggle API. \n", + "\n", + "**Data License and Terms**
\n", + "As this dataset originates from a Kaggle dataset, it's governed by that dataset's license and terms of use, which is the Open Data Commons license. Review here: https://opendatacommons.org/licenses/by/1-0/index.html . For each dataset an user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose.\n", + "\n", + "**Are there restrictions on how I can use this data?
**\n", + "For each dataset an user elects to use, the user is responsible for checking if the dataset license is fit for the intended purpose.\n", + "\n", + "## Get the Data\n", + "First, [please follow these instructions from Kaggle to download and/or updating your Kaggle API token to get acces the dataset](https://www.kaggle.com/discussions/general/74235). \n", + "- If you're using Colab, you can skip Step #1\n", + "- If you're working on your local system, you can skip the Step #2.\n", + "\n", + "This should take about 1-2 minutes.\n", + "\n", + "Next, run this code below, which should also take 1-2 minutes:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "406838c6-267c-423e-82ab-ea13d5fa9c90", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: kaggle in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (1.7.4.5)\n", + "Requirement already satisfied: bleach in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (6.2.0)\n", + "Requirement already satisfied: certifi>=14.05.14 in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (2025.8.3)\n", + "Requirement already satisfied: charset-normalizer in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (3.4.3)\n", + "Requirement already satisfied: idna in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (3.10)\n", + "Requirement already satisfied: protobuf in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (6.32.1)\n", + "Requirement already satisfied: python-dateutil>=2.5.3 in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (2.9.0.post0)\n", + "Requirement already satisfied: python-slugify in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (8.0.4)\n", + "Requirement already satisfied: requests in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (2.32.5)\n", + "Requirement already satisfied: setuptools>=21.0.0 in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (80.9.0)\n", + "Requirement already satisfied: six>=1.10 in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (1.17.0)\n", + "Requirement already satisfied: text-unidecode in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (1.3)\n", + "Requirement already satisfied: tqdm in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (4.67.1)\n", + "Requirement already satisfied: urllib3>=1.15.1 in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (2.5.0)\n", + "Requirement already satisfied: webencodings in /home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages (from kaggle) (0.5.1)\n" + ] + } + ], + "source": [ + "!pip install kaggle\n", + "!mkdir -p ~/.kaggle\n", + "!cp kaggle.json ~/.kaggle/\n", + "!chmod 600 ~/.kaggle/kaggle.json" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3efacb3c-5f3d-4ff0-b32a-76bbb80b5f74", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3efacb3c-5f3d-4ff0-b32a-76bbb80b5f74", + "outputId": "5fe4a878-cf57-44f9-e40e-ed413035b150" + }, + "outputs": [], + "source": [ + "# Download the dataset through kaggle API-\n", + "!kaggle datasets download -d asaniczka/1-3m-linkedin-jobs-and-skills-2024\n", + "#unzip the file to access contents\n", + "!unzip 1-3m-linkedin-jobs-and-skills-2024.zip" + ] + }, + { + "cell_type": "markdown", + "id": "2__ZMVe6LaBJ", + "metadata": { + "id": "2__ZMVe6LaBJ" + }, + "source": [ + "# Analysis with cuDF Pandas" + ] + }, + { + "cell_type": "markdown", + "id": "df47f304-2b30-4380-afd5-0613b63d103d", + "metadata": {}, + "source": [ + "The magic command `%load_ext cudf.pandas` enables GPU acceleration for pandas data processing in a Jupyter notebook, allowing most pandas operations to automatically execute on NVIDIA GPUs for improved performance. \n", + "\n", + "With this extension loaded before importing pandas, your code can use standard pandas syntax while gaining the benefits of GPU speedup, automatically falling back to CPU execution for operations not supported on the GPU. This provides a seamless way to accelerate existing pandas workflows with zero code changes, especially for large data analytics tasks or machine learning preprocessing." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e5cd2520-30a6-41c1-b7c5-5abe0eb90d82", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cudf.pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eadb8d77-cb45-4c7c-ae9f-77e47a4f29b3", + "metadata": { + "id": "eadb8d77-cb45-4c7c-ae9f-77e47a4f29b3" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "196268f2-6169-4ed7-a9e6-db9078caa6ab", + "metadata": { + "id": "196268f2-6169-4ed7-a9e6-db9078caa6ab" + }, + "source": [ + "We'll run a piece of code to get a feel what GPU-acceleration brings to pandas workflows." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2688bfeb-58c4-4fc0-9233-8d7e2759ec46", + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start_time = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ae3b6a16-ff72-4421-b43c-06c33f57ec12", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ae3b6a16-ff72-4421-b43c-06c33f57ec12", + "outputId": "656acbf7-078f-42b3-832d-ad4e84e01c70" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 185 ms, sys: 2.08 s, total: 2.27 s\n", + "Wall time: 2.95 s\n", + "Dataset Size (in GB): 4.76\n" + ] + } + ], + "source": [ + "%time job_summary_df = pd.read_csv(\"job_summary.csv\", dtype=('str'))\n", + "print(\"Dataset Size (in GB):\",round(job_summary_df.memory_usage(\n", + " deep=True).sum()/(1024**3),2))" + ] + }, + { + "cell_type": "markdown", + "id": "01c506e1-f135-4afb-8fc7-23e72c05d73c", + "metadata": { + "id": "01c506e1-f135-4afb-8fc7-23e72c05d73c" + }, + "source": [ + "The same dataset takes about around 1.5 minutes to load with pandas. That's around **5x speedup** with no changes to the code!" + ] + }, + { + "cell_type": "markdown", + "id": "d9d0a0e1-1d74-494d-bd12-b829f11eeede", + "metadata": { + "id": "d9d0a0e1-1d74-494d-bd12-b829f11eeede" + }, + "source": [ + "Let's load the remaining two datasets as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "12e4cf7e-8824-4822-9d30-46b81ba2acd7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "12e4cf7e-8824-4822-9d30-46b81ba2acd7", + "outputId": "5ca1be17-09e3-40ab-928b-82176bf597bf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 45.3 ms, sys: 199 ms, total: 244 ms\n", + "Wall time: 354 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "job_skills_df = pd.read_csv(\"job_skills.csv\", dtype=('str'))\n", + "job_postings_df = pd.read_csv(\"linkedin_job_postings.csv\", dtype=('str'))" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "13c8f9da-121f-4311-8a79-274425363e5e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 276 + }, + "id": "13c8f9da-121f-4311-8a79-274425363e5e", + "outputId": "a73599c1-05b2-4f56-a190-c69c017bb330" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 4.46 ms, sys: 3.1 ms, total: 7.56 ms\n", + "Wall time: 46.3 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "0 957\n", + "1 3816\n", + "2 5314\n", + "3 2774\n", + "4 2749\n", + "Name: summary_length, dtype: int32" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "job_summary_df['summary_length'] = job_summary_df['job_summary'].str.len()\n", + "job_summary_df['summary_length'].head()" + ] + }, + { + "cell_type": "markdown", + "id": "67b68792-5c64-4ebd-9d80-cf6ff55baeef", + "metadata": { + "id": "67b68792-5c64-4ebd-9d80-cf6ff55baeef" + }, + "source": [ + "That was lightning fast! We went from around 10+ (with pandas) to a few milliseconds." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "31e1cc84-debb-4da7-bc20-5c7139f786f7", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 504 + }, + "id": "31e1cc84-debb-4da7-bc20-5c7139f786f7", + "outputId": "2d89fc49-7e5b-41db-c25b-441d54480711" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 39.8 ms, sys: 30 ms, total: 69.8 ms\n", + "Wall time: 211 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " summary_length\n", + "company job_title \n", + "ClickJobs.io Adolescent Behavioral Health Therapist - Substa... 23748.0\n", + "Mt. San Antonio College Chief, Police and Campus Safety 22998.0\n", + "CareerBeacon Airside/Groundside Project Manager [Halifax Int... 22938.0\n", + "Tacoma Community College Anthropology Professor - Part-time 22790.0\n", + "IRS, Office of Chief Counsel Program Analyst (12-Month Roster) 22774.0\n", + "... ...\n", + "鴻海精密工業股份有限公司 HR Specialist - Payroll & Benefit 0.0\n", + " Material Planner 0.0\n", + " RFQ Specialist 0.0\n", + " Supply Chain Program Manager 0.0\n", + "🌟Daniel-Scott Recruitment Ltd🌟 IT Manager 0.0\n", + "\n", + "[801276 rows x 1 columns]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "df_merged.groupby(['company',\"job_title\"]).agg({\n", + " \"summary_length\":\"mean\"}).sort_values(by='summary_length', ascending = False).fillna(0)" + ] + }, + { + "cell_type": "markdown", + "id": "IME4urGYQ3qS", + "metadata": { + "id": "IME4urGYQ3qS" + }, + "source": [ + "We went down from around 5 seconds to less than a second here. This is in line with our speedups on other operations!" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "adc00726-f151-41f4-8731-a1ce1f83eea2", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 458 + }, + "id": "adc00726-f151-41f4-8731-a1ce1f83eea2", + "outputId": "46423696-b167-4ffe-bb3b-9de7f3e6d668" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 13.7 ms, sys: 20.3 ms, total: 34 ms\n", + "Wall time: 156 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
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job_titlejob_locationsummary_length
0🔥Nurse Manager, Patient Services - Operating RoomLake George, NY7342.0
1🔥Behavioral Health RN 3 12sGlens Falls, NY2787.0
2🔥 Surgical Technologist - EveningsLake George, NY2920.0
3🔥 Physician Practice Clinical Lead RNSaratoga Springs, NY2945.0
4🔥 Physican Practice LPN - GreenLake George, NY2969.0
............
1104106\"Attorney\" (Gov Appt/Non-Merit) JobsKentucky, United States2427.0
1104107\"Accountant\"Shavano Park, TX1497.0
1104108\"Accountant\"Basking Ridge, NJ1073.0
1104109\"Accountant\"Austin, TX1993.0
1104110\"A\" Softball Coach - Central Middle SchoolEast Corinth, ME718.0
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1104111 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " job_title \\\n", + "0 🔥Nurse Manager, Patient Services - Operating Room \n", + "1 🔥Behavioral Health RN 3 12s \n", + "2 🔥 Surgical Technologist - Evenings \n", + "3 🔥 Physician Practice Clinical Lead RN \n", + "4 🔥 Physican Practice LPN - Green \n", + "... ... \n", + "1104106 \"Attorney\" (Gov Appt/Non-Merit) Jobs \n", + "1104107 \"Accountant\" \n", + "1104108 \"Accountant\" \n", + "1104109 \"Accountant\" \n", + "1104110 \"A\" Softball Coach - Central Middle School \n", + "\n", + " job_location summary_length \n", + "0 Lake George, NY 7342.0 \n", + "1 Glens Falls, NY 2787.0 \n", + "2 Lake George, NY 2920.0 \n", + "3 Saratoga Springs, NY 2945.0 \n", + "4 Lake George, NY 2969.0 \n", + "... ... ... \n", + "1104106 Kentucky, United States 2427.0 \n", + "1104107 Shavano Park, TX 1497.0 \n", + "1104108 Basking Ridge, NJ 1073.0 \n", + "1104109 Austin, TX 1993.0 \n", + "1104110 East Corinth, ME 718.0 \n", + "\n", + "[1104111 rows x 3 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# Group by company, job_title, and month, and calculate the mean of summary_length\n", + "grouped_df = df_merged.groupby(['job_title', 'job_location']).agg({'summary_length': 'mean'})\n", + "\n", + "# Reset index to sort by job_title and month\n", + "grouped_df = grouped_df.reset_index()\n", + "\n", + "# Sort by job_title and month\n", + "sorted_df = grouped_df.sort_values(by=['job_title', 'job_location','summary_length'],\n", + " ascending=False).reset_index(drop=True).fillna(0)\n", + "sorted_df" + ] + }, + { + "cell_type": "markdown", + "id": "08c97b81-64c5-48fb-8fe0-d36789cf3deb", + "metadata": { + "id": "08c97b81-64c5-48fb-8fe0-d36789cf3deb" + }, + "source": [ + "The acceleration is consistently 10x+ for complex aggregations and sorting that involve multiple columns." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4560fe8f-61f9-4c23-bf43-ed6a82e5456e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.182934522628784\n" + ] + } + ], + "source": [ + "end_time = time.time()\n", + "execution_time = end_time - start_time\n", + "print(execution_time)" + ] + }, + { + "cell_type": "markdown", + "id": "9bcc719b-666a-4bc9-97d6-16f448b5c707", + "metadata": { + "id": "9bcc719b-666a-4bc9-97d6-16f448b5c707" + }, + "source": [ + "# Summary\n", + "\n", + "With cudf.pandas, you can keep using pandas as your primary dataframe library. When things start to get a little slow, just load the `cudf.pandas` extension and enjoy the incredible speedups.\n", + "\n", + "If you like Google Colab and want to get peak `cudf.pandas` performance to process even larger datasets, Google Colab's paid tier includes both L4 and A100 GPUs (in addition to the T4 GPU this demo notebook is using).\n", + "\n", + "To learn more about cudf.pandas, we encourage you to visit https://rapids.ai/cudf-pandas." + ] + }, + { + "cell_type": "markdown", + "id": "76bfe45d-dee5-435a-86ce-e3c945692a40", + "metadata": { + "id": "76bfe45d-dee5-435a-86ce-e3c945692a40" + }, + "source": [ + "# Do you have any feedback for us?" + ] + }, + { + "cell_type": "markdown", + "id": "0d5ad763-961a-453b-ab65-f95c4b8c78df", + "metadata": { + "id": "0d5ad763-961a-453b-ab65-f95c4b8c78df" + }, + "source": [ + "Fill this quick survey HERE\n", + "\n", + "Raise an issue on our github repo HERE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cbf6c1f-497c-4077-b3b9-e21514c859aa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "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": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/nvidia/rapids/assets/cuml_sklearn_demo.ipynb b/nvidia/rapids/assets/cuml_sklearn_demo.ipynb new file mode 100644 index 0000000..b423bc6 --- /dev/null +++ b/nvidia/rapids/assets/cuml_sklearn_demo.ipynb @@ -0,0 +1,2395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "ZhTWAPo7X5-I" + }, + "source": [ + "# Getting Started with cuML's accelerator mode (cuml.accel)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BinKvOgMYOCp" + }, + "source": [ + "cuML is a Python GPU library for accelerating machine learning models using a scikit-learn-like API.\n", + "\n", + "cuML now has an accelerator mode (cuml.accel) which allows you to bring accelerated computing to existing workflows with zero code changes required. In addition to scikit-learn, cuml.accel also provides acceleration to algorithms found in umap-learn (UMAP) and hdbscan (HDBSCAN).\n", + "\n", + "This notebook is a brief introduction to cuml.accel.\n", + "\n", + "**Author:** Mitesh Patel, Allison Ding
\n", + "**Date:** October 3, 2025" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ylUzZjM-mRMH" + }, + "source": [ + "# ⚠️ Verify your setup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e_AtDRMEZQd-" + }, + "source": [ + "First, we'll verfiy that we are running on an NVIDIA GPU:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sun Oct 5 21:26:06 2025 \n", + "+-----------------------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 580.82.09 Driver Version: 580.82.09 CUDA Version: 13.0 |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + "| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|=========================================+========================+======================|\n", + "| 0 NVIDIA GB10 Off | 0000000F:01:00.0 Off | N/A |\n", + "| N/A 38C P0 5W / N/A | Not Supported | 0% Default |\n", + "| | | N/A |\n", + "+-----------------------------------------+------------------------+----------------------+\n", + "\n", + "+-----------------------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=========================================================================================|\n", + "| 0 N/A N/A 3405 G /usr/lib/xorg/Xorg 242MiB |\n", + "| 0 N/A N/A 3562 G /usr/bin/gnome-shell 50MiB |\n", + "+-----------------------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "!nvidia-smi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qC3fevZecnns" + }, + "source": [ + "With classical machine learning, there is a wide range of interesting problems we can explore. In this tutorial we'll examine 3 of the more popular use cases: classification, clustering, and dimensionality reduction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# GPU-Accelerated Machine Learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The notebook magic command `%load_ext cuml.accel` enables automatic GPU acceleration for machine learning code written with libraries like UMAP and HDBSCAN, so models can train and run on NVIDIA GPUs without code changes or manual conversions. \n", + "\n", + "After activating this extension, supported estimators and algorithms are dispatched to the GPU for faster computation, and any operations not yet accelerated will seamlessly fall back to CPU execution. This approach is ideal for accelerating existing workflows in Jupyter or IPython environments, allowing significant speedups with minimal effort" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext cuml.accel" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Classification" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load a dataset and see how we can use scikit-learn to classify that data. For this example we'll use the Coverage Type dataset, which contains a number of features that can be used to predict forest cover type, such as elevation, aspect, slope, and soil-type.\n", + "\n", + "More information on this dataset can be found at https://archive.ics.uci.edu/dataset/31/covertype." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.svm import SVC, LinearSVC\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import classification_report, accuracy_score" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz\"\n", + "\n", + "# Column names for the dataset (from UCI Covertype description)\n", + "columns = ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology',\n", + " 'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm',\n", + " 'Horizontal_Distance_To_Fire_Points', 'Wilderness_Area1', 'Wilderness_Area2', 'Wilderness_Area3',\n", + " 'Wilderness_Area4', 'Soil_Type1', 'Soil_Type2', 'Soil_Type3', 'Soil_Type4', 'Soil_Type5', 'Soil_Type6',\n", + " 'Soil_Type7', 'Soil_Type8', 'Soil_Type9', 'Soil_Type10', 'Soil_Type11', 'Soil_Type12', 'Soil_Type13',\n", + " 'Soil_Type14', 'Soil_Type15', 'Soil_Type16', 'Soil_Type17', 'Soil_Type18', 'Soil_Type19', 'Soil_Type20',\n", + " 'Soil_Type21', 'Soil_Type22', 'Soil_Type23', 'Soil_Type24', 'Soil_Type25', 'Soil_Type26', 'Soil_Type27',\n", + " 'Soil_Type28', 'Soil_Type29', 'Soil_Type30', 'Soil_Type31', 'Soil_Type32', 'Soil_Type33', 'Soil_Type34',\n", + " 'Soil_Type35', 'Soil_Type36', 'Soil_Type37', 'Soil_Type38', 'Soil_Type39', 'Soil_Type40', 'Cover_Type']\n", + "\n", + "data = pd.read_csv(url, header=None)\n", + "data.columns=columns" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(581012, 55)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll separate out the classification variable (Cover_Type) from the rest of the data. This is what we will aim to predict with our classification model. We can also split our dataset into training and test data using the scikit-learn train_test_split function." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "X, y = data.drop('Cover_Type', axis=1), data['Cover_Type']\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have our dataset split, we're ready to run a model. To start, we will just run the model using the sklearn library with a starting max depth of 5 and all of the features. Note that we can set n_jobs=-1 to utilize all available CPU cores for fitting the trees -- this will ensure we get the best performance possible on our system's CPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "clf = LinearSVC()\n", + "clf.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In about 2 minutes, we were able to fit our tree model using scikit-learn. This is not bad! Let's use the model we just trained to predict coverage types in our test dataset and take a look at the accuracy of our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y_pred = clf.predict(X_test)\n", + "accuracy_score(y_test, y_pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also print out a full classification report to better understand how we predicted different Coverage_Type categories." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 1 0.39 0.28 0.33 42371\n", + " 2 0.54 0.75 0.63 56772\n", + " 3 0.65 0.61 0.63 7146\n", + " 4 0.00 0.00 0.00 546\n", + " 5 0.00 0.00 0.00 1907\n", + " 6 0.00 0.00 0.00 3454\n", + " 7 0.00 0.00 0.00 4007\n", + "\n", + " accuracy 0.51 116203\n", + " macro avg 0.23 0.23 0.23 116203\n", + "weighted avg 0.45 0.51 0.46 116203\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n", + "/home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n", + "/home/nvidia/miniconda3/envs/rapids-25.10/lib/python3.12/site-packages/sklearn/metrics/_classification.py:1731: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", + " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", result.shape[0])\n" + ] + } + ], + "source": [ + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12.401145458221436\n" + ] + } + ], + "source": [ + "end_time = time.time()\n", + "execution_time = end_time - start_time\n", + "print(execution_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With scikit-learn, we built a model that was able to be trained in just a couple minutes. From the accuracy report, we can see that we predicted the correct class around 70% of the time, which is not bad but could certainly be improved.\n", + "\n", + "Often we want to run several different random forest models in order to optimize our hyperparameters. For example, we may want to increase the number of estimators, or modify the maximum depth of our tree. When running dozens or hundreds of different hyperparameter combinations, things start to become quite slow and iteration takes a lot longer.\n", + "\n", + "We provide some sample code utilizing GridSearchCV below to show what this process might look like. All of these combinations would take a LONG time to run if we spend 2 minutes fitting each model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"\\nfrom sklearn.model_selection import GridSearchCV\\n\\n# Define the parameter grid to search over\\nparam_grid = {\\n 'n_estimators': [50, 100, 200],\\n 'max_depth': [None, 10, 20, 30],\\n 'min_samples_split': [2, 5, 10],\\n 'min_samples_leaf': [1, 2, 4],\\n 'max_features': ['auto', 'sqrt', 'log2'],\\n 'bootstrap': [True, False]\\n}\\n\\ngrid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5)\\ngrid_search.fit(X_train, y_train)\\n\"" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\"\"\"\n", + "from sklearn.model_selection import GridSearchCV\n", + "\n", + "# Define the parameter grid to search over\n", + "param_grid = {\n", + " 'n_estimators': [50, 100, 200],\n", + " 'max_depth': [None, 10, 20, 30],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4],\n", + " 'max_features': ['auto', 'sqrt', 'log2'],\n", + " 'bootstrap': [True, False]\n", + "}\n", + "\n", + "grid_search = GridSearchCV(estimator=clf, param_grid=param_grid, cv=5)\n", + "grid_search.fit(X_train, y_train)\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a model that runs in just seconds, we can perform hyperparameter optimization using a method like the grid search shown above, and have results in just minutes instead of hours." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CPU Fallback" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are some algorithms and functionality from scikit-learn, UMAP, and HDBSCAN that are *not* implemented in cuML. For cases where the underlying functionality is not supported on GPU, the cuML accelerator will gracefully fall back and execute on the CPU instead." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.neighbors import KernelDensity\n", + "import numpy as np\n", + "\n", + "X = np.concatenate((np.random.normal(0, 1, 10000),\n", + " np.random.normal(5, 1, 10000)))[:, np.newaxis]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KernelDensity(bandwidth=0.5)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KernelDensity(bandwidth=0.5)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kde = KernelDensity(kernel='gaussian', bandwidth=0.5)\n", + "kde.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2.03555686 -1.81195669 -1.78476911 ... -3.20381041 -1.73643757\n", + " -2.82360998]\n" + ] + } + ], + "source": [ + "print(kde.score_samples(X))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9Ql7UCNUW1AO" + }, + "source": [ + "We'll now take a look at a clustering example using HDBSCAN." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pdjvwlZ-DaoW" + }, + "source": [ + "# Clustering" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "70sdcTIdfzkK" + }, + "source": [ + "Clustering is an important data science workflow because it helps uncover hidden patterns and structures within data without requiring labeled outcomes. In practice, with high dimensional data it can be difficult to discern whether the clusters we've chosen are good or not. One way to determine the quality of our clustering is with sklearn's [silhouette score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score), which we'll examine shortly.\n", + "\n", + "HDBSCAN is a popular density-based clustering algorithm that is highly flexible. We'll load a toy sklearn dataset to illustrate how HDBSCAN can be accelerated with cuml.accel." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start_time = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "oIlRPHZ1DZ7q" + }, + "outputs": [], + "source": [ + "import hdbscan\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.datasets import make_blobs\n", + "from sklearn.metrics import silhouette_score" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 449 + }, + "id": "RqXkNWRjDZ9n", + "outputId": "57435e6c-a303-4531-d64b-87ba14cce410" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "N = 20000\n", + "K = 100\n", + "\n", + "X, y = make_blobs(\n", + " n_samples=N,\n", + " n_features=K,\n", + " centers=5,\n", + " cluster_std=[3,1,2,1.5,0.5],\n", + " random_state=42\n", + ")\n", + "\n", + "plt.scatter(X[:, 0], X[:, 1], c=y)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 184 + }, + "id": "536jWwBWg1Ou", + "outputId": "c589e021-e14e-4848-d8b5-2302c00d4ef7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 213 ms, sys: 208 ms, total: 421 ms\n", + "Wall time: 597 ms\n" + ] + }, + { + "data": { + "text/html": [ + "
HDBSCAN()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "HDBSCAN()" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clusterer = hdbscan.HDBSCAN()\n", + "%time clusterer.fit(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wrAzGgqrxHtY", + "outputId": "05493c84-d6cf-469a-e533-db36558c76ec" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7352694189002873\n" + ] + } + ], + "source": [ + "print(silhouette_score(X, clusterer.labels_))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.456059455871582\n" + ] + } + ], + "source": [ + "end_time = time.time()\n", + "execution_time = end_time - start_time\n", + "print(execution_time)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "93qD18LqDiOj" + }, + "source": [ + "# Dimensionality Reduction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n0mwvzC0rdq4" + }, + "source": [ + "UMAP is a popular dimensionality reduction technique that is used for both data visualization and as preprocessing for downstream modeling due to its ability to balance preserving both local and global structure of high-dimensional data. To learn more about how it works, visit the [UMAP documentation](https://umap-learn.readthedocs.io/en/latest/).\n", + "\n", + "To explore how cuML can accelerate UMAP, let's load in another dataset from UCI. We'll use the Human Activity Recognition (HAR) dataset, which was created from recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JOeW2dt9D2IZ", + "outputId": "3f687e7c-8652-4b8e-bfc3-43cd1b291531" + }, + "outputs": [], + "source": [ + "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip -O /tmp/HAR_data.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TR78I9VuEGlu", + "outputId": "aad52a74-9c40-4be5-dc30-d6cda2b0f971" + }, + "outputs": [], + "source": [ + "!unzip /tmp/HAR_data.zip -d /tmp/HAR_data/" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start_time = time.time()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "QjGw0WDwCu2_" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:4: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:6: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:7: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:3: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:4: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:5: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:6: SyntaxWarning: invalid escape sequence '\\s'\n", + "<>:7: SyntaxWarning: invalid escape sequence '\\s'\n", + "/tmp/ipykernel_238331/4099239257.py:3: SyntaxWarning: invalid escape sequence '\\s'\n", + " X_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/X_train.txt\", sep=\"\\s+\", header=None)\n", + "/tmp/ipykernel_238331/4099239257.py:4: SyntaxWarning: invalid escape sequence '\\s'\n", + " y_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/y_train.txt\", sep=\"\\s+\", header=None)\n", + "/tmp/ipykernel_238331/4099239257.py:5: SyntaxWarning: invalid escape sequence '\\s'\n", + " X_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/X_test.txt\", sep=\"\\s+\", header=None)\n", + "/tmp/ipykernel_238331/4099239257.py:6: SyntaxWarning: invalid escape sequence '\\s'\n", + " y_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/y_test.txt\", sep=\"\\s+\", header=None)\n", + "/tmp/ipykernel_238331/4099239257.py:7: SyntaxWarning: invalid escape sequence '\\s'\n", + " labels = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/activity_labels.txt\", sep=\"\\s+\", header=None)\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "X_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/X_train.txt\", sep=\"\\s+\", header=None)\n", + "y_train = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/train/y_train.txt\", sep=\"\\s+\", header=None)\n", + "X_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/X_test.txt\", sep=\"\\s+\", header=None)\n", + "y_test = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/test/y_test.txt\", sep=\"\\s+\", header=None)\n", + "labels = pd.read_csv(\"/tmp/HAR_data/UCI HAR Dataset/activity_labels.txt\", sep=\"\\s+\", header=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oesYc97pQ1vr", + "outputId": "e7dca260-5abb-4f27-d61e-e336955b5271" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(7352, 561)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F5Cle6mGOH7S" + }, + "source": [ + "Let's take a look at the activity labels to better understand the data we're working with. We can see that the sensors have grouped activities into 6 different classes." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 238 + }, + "id": "22YYMRvXOUUz", + "outputId": "b15af6d7-8c52-474e-f102-f573dd7f2d2e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " 0 1\n", + "0 1 WALKING\n", + "1 2 WALKING_UPSTAIRS\n", + "2 3 WALKING_DOWNSTAIRS\n", + "3 4 SITTING\n", + "4 5 STANDING\n", + "5 6 LAYING" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "8rA7SsEpCu5M" + }, + "outputs": [], + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "# Scale the data before applying UMAP\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_9ltJhG7tQt0" + }, + "source": [ + "Let's run UMAP with some basic parameters and explore a lower-dimensionality projection of this dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "t4sR7x3sF_9a" + }, + "outputs": [], + "source": [ + "import umap\n", + "umap_model = umap.UMAP(n_neighbors=15, n_components=2, random_state=42, min_dist=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cGD49PFVlyrz", + "outputId": "81f5a19c-d2c6-498c-c673-facc3ced964d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2025-10-05 21:26:31.169] [CUML] [info] build_algo set to brute_force_knn because random_state is given\n", + "CPU times: user 170 ms, sys: 61.5 ms, total: 232 ms\n", + "Wall time: 481 ms\n" + ] + } + ], + "source": [ + "%%time\n", + "\n", + "# Fit UMAP model to the data\n", + "X_train_umap = umap_model.fit_transform(X_train_scaled)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kwJC2woSl4U9" + }, + "source": [ + "It's often quite interesting to visualize the resulting projection of the embeddings created by UMAP. In this case, let's take a look at the now 2-dimensional dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 718 + }, + "id": "PDRxaBEdF__X", + "outputId": "2ddd2b8b-8b1a-48e4-e06b-b5ffe0245f27" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Plot the UMAP result\n", + "plt.figure(figsize=(10, 8))\n", + "plt.scatter(X_train_umap[:, 0], X_train_umap[:, 1], c=y_train.values.ravel(), cmap='Spectral', s=10)\n", + "plt.colorbar(label=\"Activity\")\n", + "plt.title(\"UMAP projection of the UCI HAR dataset\")\n", + "plt.xlabel(\"UMAP Component 1\")\n", + "plt.ylabel(\"UMAP Component 2\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gKFBLuE7mFrs" + }, + "source": [ + "It's interesting to see how our different categories are grouped in relation to one another.\n", + "\n", + "We can look at the trustworthiness score to better understand how well the structure of the original dataset was preserved by our 2D projection" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Say1EIdqMLoq", + "outputId": "3c3f1623-8ef2-4703-9dcb-1e2ba3e148d7" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(0.9709711206340002)" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import trustworthiness\n", + "trustworthiness(X_train, X_train_umap, n_neighbors=15)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1OzvVajcm2jX" + }, + "source": [ + "It looks like this projection is a great representation of our full dataset.\n", + "\n", + "Let's now run the same thing with the accelerator turned on." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4.420806407928467\n" + ] + } + ], + "source": [ + "end_time = time.time()\n", + "execution_time = end_time - start_time\n", + "print(execution_time)" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "rapids-25.10", + "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": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/nvidia/rapids/assets/example b/nvidia/rapids/assets/example deleted file mode 100644 index e69de29..0000000