{"id":19705732,"url":"https://github.com/llnl/thicket-tutorial","last_synced_at":"2025-04-29T15:31:19.074Z","repository":{"id":66083037,"uuid":"591860681","full_name":"LLNL/thicket-tutorial","owner":"LLNL","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-02T21:00:18.000Z","size":42180,"stargazers_count":1,"open_issues_count":6,"forks_count":5,"subscribers_count":4,"default_branch":"develop","last_synced_at":"2024-11-02T21:17:04.675Z","etag":null,"topics":["tutorial"],"latest_commit_sha":null,"homepage":"https://thicket.readthedocs.io","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LLNL.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2023-01-22T05:34:57.000Z","updated_at":"2024-11-02T21:00:24.000Z","dependencies_parsed_at":"2024-04-11T21:29:37.544Z","dependency_job_id":"99fdedee-0a55-499c-a9ae-efc2b8e2d70c","html_url":"https://github.com/LLNL/thicket-tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fthicket-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fthicket-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fthicket-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LLNL%2Fthicket-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LLNL","download_url":"https://codeload.github.com/LLNL/thicket-tutorial/tar.gz/refs/heads/develop","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224178664,"owners_count":17268926,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["tutorial"],"created_at":"2024-11-11T21:29:56.101Z","updated_at":"2024-11-11T21:29:56.710Z","avatar_url":"https://github.com/LLNL.png","language":"Jupyter Notebook","readme":"# \u003cimg src=\"https://raw.githubusercontent.com/llnl/thicket/develop/logo-notext.png\" width=\"64\" valign=\"middle\" alt=\"thicket\"/\u003e Thicket Tutorial\n\n\nThicket is a python-based toolkit for Exploratory Data Analysis (EDA) of\nparallel performance data that enables performance optimization and\nunderstanding of applications' performance on supercomputers. It bridges the\nperformance tool gap between being able to consider only a single instance of a\nsimulation run (e.g., single platform, single measurement tool, or single\nscale) and finding actionable insights in multi-dimensional, multi-scale,\nmulti-architecture, and multi-tool performance datasets.\n\nYou can find detailed documentation, along with non-interactive tutorials from this repository of Thicket in the\n[ReadTheDocs](https://thicket.readthedocs.io/en/latest/).\n\nThis repository contains materials for Thicket's hands-on tutorial. You can do\nall of the exercises on your own laptop using BinderHub.\n\nYou find Thicket itself in its Github repository: https://github.com/llnl/thicket\n\n### Running the notebooks\n\n#### Docker\n\nWe provide a Dockerfile for users to run the notebooks locally. To run locally *and interactively*, you must first build the Docker container with:\n\n```bash\ndocker build -t thicket-tutorial -f Dockerfile.local .\n```\n\nThen, you must create a Docker network with:\n\n```bash\ndocker network create jupyterhub\n```\n\nFinally, you can launch the tutorial. To launch the tutorial without preserving any changes, run:\n\n```bash\ndocker run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial --NotebookApp.token=''\n```\n\nIf you would rather your changes be preserved, run:\n\n```bash\ndocker run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock -v .:/home/jovyan --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial\n```\n\nAlternatively, if you want to run the notebooks automatically (i.e., non-interactive), you can simply run the `dev_scripts/autorun.sh` script. This script executes the same commands as above, but it uses the `run_all.sh` script as an entrypoint instead of `start.sh`.\n\nThe Docker-based code for running this tutorial locally was derived from the material from the 2023 RADIUSS tutorial for Flux, which can be found here: https://github.com/flux-framework/Tutorials/tree/master/2023-RADIUSS-AWS/JupyterNotebook\n\n#### Podman\n\nIf you want to use podman instead of docker, you can replace \"docker\" with \"podman\" for most steps.\n\nFirst initialize and start podman:\n\n```bash\npodman machine init\npodman machine start\n```\n\nThen build the container:\n\n```bash\npodman build -t thicket-tutorial -f Dockerfile.local .\n```\n\nThen create a network:\n\n```bash\npodman network create jupyterhub\n```\n\nThen launch the tutorial:\n\n```bash\npodman run --rm -it --entrypoint /start.sh -v /var/run/docker.sock:/var/run/docker.sock --net jupyterhub --name jupyterhub -p 8888:8888 thicket-tutorial\n```\n\nClean up after you are done:\n```bash\npodman machine stop\n```\n\n### License\n\nThis repository is distributed under the terms of the MIT license.\n\nAll contributions must be made under the MIT license.  Copyrights are retained\nby contributors. No copyright assignment is required to contribute to this\nproject.\n\nSee [LICENSE](https://github.com/llnl/thicket-tutorial/blob/develop/LICENSE).\n\nSPDX-License-Identifier: MIT\n\nLLNL-CODE-834749\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fthicket-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fllnl%2Fthicket-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fllnl%2Fthicket-tutorial/lists"}