Skip to main content

SYCL-accelerated H3 geospatial library

Project description

H3 SYCL Bridge

Docker Prerequisites

To run the Docker images with GPU acceleration enabled (h3-turbo), you must ensure your host machine is correctly configured with NVIDIA drivers and Docker support.

Specifically, the following must be installed:

  1. NVIDIA Drivers: Ensure you have the NVIDIA GPU drivers installed on your host (compatible with CUDA 12.0+).
  2. nvidia-container-toolkit: This toolkit enables the Docker engine to access the GPU.

Installation Guide

For the NVIDIA Container Toolkit, please follow the official installation guide.

After installing the toolkit, remember to restart the Docker daemon:

sudo systemctl restart docker

You can then verify your setup by running:

docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi

Running Automated Benchmarks via Docker

To run the automated benchmarks using Docker, you can use the provided docker-compose.benchmark.yml file. This setup automatically builds the necessary environment and executes benchmark_runner.py with GPU support enabled.

Make sure you have your H3_TURBO_LICENSE environment variable set, or pass it directly. Run the following command:

H3_TURBO_LICENSE=your_license_here docker compose -f docker-compose.benchmark.yml up --build

Note: Generated 1-month licenses are available within the Docker images published at https://hub.docker.com/repositories/cflockhart.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

h3_turbo_sm89-1.0.4-cp312-cp312-manylinux_2_39_x86_64.whl (60.4 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.39+ x86-64

File details

Details for the file h3_turbo_sm89-1.0.4-cp312-cp312-manylinux_2_39_x86_64.whl.

File metadata

File hashes

Hashes for h3_turbo_sm89-1.0.4-cp312-cp312-manylinux_2_39_x86_64.whl
Algorithm Hash digest
SHA256 4ac314c9b943cc268f56d815f637d9666cb9939d16b1208cf93773ddde2c7b29
MD5 48aace4c57818a8619f8bf2749a70e24
BLAKE2b-256 094b31a80255eff071d269dd16cdeab34ec7e94a4ad9d29971f27ce5aaf45039

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page