{"id":19134153,"url":"https://github.com/um-arm-lab/gpu_profiling","last_synced_at":"2026-06-19T19:31:08.342Z","repository":{"id":212374718,"uuid":"731257577","full_name":"UM-ARM-Lab/gpu_profiling","owner":"UM-ARM-Lab","description":"GPU Profiling Tools For The Lab","archived":false,"fork":false,"pushed_at":"2023-12-13T21:01:08.000Z","size":15,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-11-12T19:24:27.589Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/UM-ARM-Lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-12-13T17:16:00.000Z","updated_at":"2023-12-13T22:25:07.000Z","dependencies_parsed_at":null,"dependency_job_id":"5ddad89a-ea8f-47d9-a15b-321a9cab95de","html_url":"https://github.com/UM-ARM-Lab/gpu_profiling","commit_stats":null,"previous_names":["um-arm-lab/gpu_profiling"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/UM-ARM-Lab/gpu_profiling","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Fgpu_profiling","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Fgpu_profiling/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Fgpu_profiling/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Fgpu_profiling/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/UM-ARM-Lab","download_url":"https://codeload.github.com/UM-ARM-Lab/gpu_profiling/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/UM-ARM-Lab%2Fgpu_profiling/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34546191,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-19T02:00:06.005Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-09T06:25:41.151Z","updated_at":"2026-06-19T19:31:08.322Z","avatar_url":"https://github.com/UM-ARM-Lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ARM Lab GPU Profiling\n\nThis repo houses the code the UMich ARM Lab uses to benchmark their GPUs.\n\n# Installation\n\n## Hardware\n\nThese benchmarks require a CUDA-capable GPU, meaning a somewhat recent NVIDIA GPU and driver.\n\n## Software Dependencies\n\nThe only software dependencies for running these benchmarks is Python 3.7+ and `pipenv`. Python 3 should be on your path as `python3`. Your system installation of Python is sufficient as the benchmarking functions handle virtual environment creation for you. Additionally, `pipenv` should be installed and on your path.\n\nIf `pipenv` is not installed, install it with `python3 -m pip install pipenv`.\n\n## Installation And Initialization Process\n\nInitialize the repository with:\n\n```bash\n./init_repo.sh\n```\n\nThe script clones the necessary repositories and creates a `pipenv` environment for running the benchmarks. Note that this will ask you which CUDA version you would like for PyTorch to be installed with.\n\n# Usage\n\n## Running Benchmarks\n\n1. Ensure that you have initialized the repository by following instructions in the [Installation](#installation) section.\n2. `cd` to where this repository is located.\n3. Activate the `pipenv` environment with `pipenv shell`.\n4. Run the benchmarks with `python run_benchmarks.py`.\n\n## Comparison With Other Rigs\n\nTo compare the results of your machine with others, run `python compare_results.py`.\n\nWe include the profiling results of a few of our older computers for reference.\n\nHowever, the comparison script isn't all-encompassing so if you want fine-grained control of plotting, you may desire to make your own script or notebook.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fum-arm-lab%2Fgpu_profiling","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fum-arm-lab%2Fgpu_profiling","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fum-arm-lab%2Fgpu_profiling/lists"}