{"id":13477181,"url":"https://github.com/fastmachinelearning/hls4ml-tutorial","last_synced_at":"2025-05-16T05:04:15.720Z","repository":{"id":37019569,"uuid":"268801687","full_name":"fastmachinelearning/hls4ml-tutorial","owner":"fastmachinelearning","description":"Tutorial notebooks for hls4ml ","archived":false,"fork":false,"pushed_at":"2025-05-01T18:15:00.000Z","size":20838,"stargazers_count":340,"open_issues_count":25,"forks_count":154,"subscribers_count":20,"default_branch":"main","last_synced_at":"2025-05-16T05:04:07.627Z","etag":null,"topics":["fpga","hls4ml","machine-learning","pruning","quantization-aware-training","tutorial"],"latest_commit_sha":null,"homepage":"http://fastmachinelearning.org/hls4ml-tutorial/","language":"Jupyter Notebook","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/fastmachinelearning.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,"zenodo":null}},"created_at":"2020-06-02T12:59:21.000Z","updated_at":"2025-05-15T13:10:31.000Z","dependencies_parsed_at":"2025-01-10T15:27:00.736Z","dependency_job_id":"3c71421b-1cc1-4624-ae12-8841accddb78","html_url":"https://github.com/fastmachinelearning/hls4ml-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/fastmachinelearning%2Fhls4ml-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fhls4ml-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fhls4ml-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fastmachinelearning%2Fhls4ml-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fastmachinelearning","download_url":"https://codeload.github.com/fastmachinelearning/hls4ml-tutorial/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254471061,"owners_count":22076585,"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":["fpga","hls4ml","machine-learning","pruning","quantization-aware-training","tutorial"],"created_at":"2024-07-31T16:01:39.124Z","updated_at":"2025-05-16T05:04:15.703Z","avatar_url":"https://github.com/fastmachinelearning.png","language":"Jupyter Notebook","readme":"# hls4ml-tutorial: Tutorial notebooks for `hls4ml`\n\n\n[![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://fastmachinelearning.org/hls4ml-tutorial)\n![deploy-book](https://github.com/fastmachinelearning/hls4ml-tutorial/actions/workflows/deploy.yml/badge.svg)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)\n[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit\u0026logoColor=white)](https://github.com/pre-commit/pre-commit)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastmachinelearning/hls4ml-tutorial)\n\n\nThere are several ways to run the tutorial notebooks:\n## Online\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/fastmachinelearning/hls4ml-tutorial/HEAD)\n\n## Conda\nRunning the tutorials requires AMD Vitis HLS to be installed, see [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vitis.html).\nAfter the installation, the necessary environmental variables can be set using\n```\nsource /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh\n```\n\nThe Python environment used for the tutorials is specified in the `environment.yml` file.\nIt can be setup like:\n```bash\nconda env create -f environment.yml\nconda activate hls4ml-tutorial\nsource /path/to/your/installtion/Xilinx/Vitis_HLS/202X.X/settings64.(c)sh\n```\n\nNote that part 7 of the tutorial makes use of the `VivadoAccelator` backend of hls4ml for which no Vitis equivalent is available yet. For this part of the tutorial it is therefore necesary to install and source Vivado HLS version 2019.2 or 2020.1, which can be obtained [here](https://www.xilinx.com/support/download/index.html/content/xilinx/en/downloadNav/vivado-design-tools/archive.html).\n\n## Companion material\nWe have prepared a set of slides with some introduction and more details on each of the exercises.\nPlease find them [here](https://docs.google.com/presentation/d/1c4LvEc6yMByx2HJs8zUP5oxLtY6ACSizQdKvw5cg5Ck/edit?usp=sharing).\n\n\n## Notebooks\n```{tableofcontents}\n```\n","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffastmachinelearning%2Fhls4ml-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffastmachinelearning%2Fhls4ml-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffastmachinelearning%2Fhls4ml-tutorial/lists"}