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GitHub - NVIDIA-Merlin/Merlin: NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production.
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0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon 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0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg><span data-view-component="true" class="d-inline"> Star </span> </a></div> </div> <div class="flex-1"> <a href="/login?return_to=%2FNVIDIA-Merlin%2FMerlin" rel="nofollow" id="files-overview-watch-button" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"notification subscription menu watch","repository_id":null,"auth_type":"LOG_IN","originating_url":"https://github.com/NVIDIA-Merlin/Merlin","user_id":null}}" data-hydro-click-hmac="27e408572f09eccc345b0176c09b8d9c43f0af075366669078b9258c8ab84a92" aria-label="You must be signed in to change notification settings" data-view-component="true" class="btn-sm btn btn-block"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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href=\"https://github.com/NVIDIA-Merlin\"\u003eNVIDIA Merlin\u003c/a\u003e\u003c/h1\u003e\u003ca id=\"user-content-nvidia-merlin\" class=\"anchor\" aria-label=\"Permalink: NVIDIA Merlin\" href=\"#nvidia-merlin\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca 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href=\"https://camo.githubusercontent.com/e7d7eb7035b9903ca9021a506dd4b0f911b7a2561f1d13da83f27e31e35c0203/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f4d65726c696e\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e7d7eb7035b9903ca9021a506dd4b0f911b7a2561f1d13da83f27e31e35c0203/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f4d65726c696e\" alt=\"GitHub License\" data-canonical-src=\"https://img.shields.io/github/license/NVIDIA-Merlin/Merlin\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/Merlin/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\"Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNVIDIA Merlin is an open source library that accelerates recommender systems on\nNVIDIA GPUs. The library enables data scientists, machine learning engineers,\nand researchers to build high-performing recommenders at scale. Merlin includes\ntools to address common feature engineering, training, and inference challenges.\nEach stage of the Merlin pipeline is optimized to support hundreds of terabytes\nof data, which is all accessible through easy-to-use APIs. For more information,\nsee \u003ca href=\"https://developer.nvidia.com/nvidia-merlin\" rel=\"nofollow\"\u003eNVIDIA Merlin\u003c/a\u003e on the NVIDIA\ndeveloper web site.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenefits\u003c/h2\u003e\u003ca id=\"user-content-benefits\" class=\"anchor\" aria-label=\"Permalink: Benefits\" href=\"#benefits\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNVIDIA Merlin is a scalable and GPU-accelerated solution, making it easy to\nbuild recommender systems from end to end. With NVIDIA Merlin, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTransform data (ETL) for preprocessing and engineering features.\u003c/li\u003e\n\u003cli\u003eAccelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI\nby leveraging optimized, custom-built data loaders.\u003c/li\u003e\n\u003cli\u003eScale large deep learning recommender models by distributing large embedding\ntables that exceed available GPU and CPU memory.\u003c/li\u003e\n\u003cli\u003eDeploy data transformations and trained models to production with only a few\nlines of code.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComponents of NVIDIA Merlin\u003c/h2\u003e\u003ca id=\"user-content-components-of-nvidia-merlin\" class=\"anchor\" aria-label=\"Permalink: Components of NVIDIA Merlin\" href=\"#components-of-nvidia-merlin\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNVIDIA Merlin consists of the following open source libraries:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/NVTabular\"\u003eNVTabular\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://pypi.org/project/nvtabular/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/59bfb037a45166f34e31da80f552d2fce18741c2ef610acd8cdaa12e1278df96/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6e76746162756c61722e737667\" alt=\"PyPI version shields.io\" data-canonical-src=\"https://img.shields.io/pypi/v/nvtabular.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/NVTabular/stable/Introduction.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003cbr\u003e NVTabular is a feature engineering and preprocessing library for tabular\ndata. The library can quickly and easily manipulate terabyte-size datasets that\nare used to train deep learning based recommender systems. The library offers a\nhigh-level API that can define complex data transformation workflows. With\nNVTabular, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePrepare datasets quickly and easily for experimentation so that you can train\nmore models.\u003c/li\u003e\n\u003cli\u003eProcess datasets that exceed GPU and CPU memory without having to worry about\nscale.\u003c/li\u003e\n\u003cli\u003eFocus on what to do with the data and not how to do it by using abstraction at\nthe operation level.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR\"\u003eHugeCTR\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/stable/hugectr_user_guide.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nHugeCTR is a GPU-accelerated training framework that can scale large deep learning\nrecommendation models by distributing training across multiple GPUs and nodes.\nHugeCTR contains optimized data loaders with GPU-acceleration and provides\nstrategies for scaling large embedding tables beyond available memory. With\nHugeCTR, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eScale embedding tables over multiple GPUs or nodes.\u003c/li\u003e\n\u003cli\u003eLoad a subset of an embedding table into a GPU in a coarse-grained, on-demand\nmanner during the training stage.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/models\"\u003eMerlin Models\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://pypi.org/project/merlin-models/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/63bfc3066013a49a0d03c50da43f0ef286bdf0506afd4a5658869718a72ad4d0/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d6d6f64656c732e737667\" alt=\"PyPI version shields.io\" data-canonical-src=\"https://img.shields.io/pypi/v/merlin-models.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/models/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nThe Merlin Models library provides standard models for recommender systems with\nan aim for high-quality implementations that range from classic machine learning\nmodels to highly-advanced deep learning models. With Merlin Models, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAccelerate your ranking model training by up to 10x by using performant data\nloaders for TensorFlow, PyTorch, and HugeCTR.\u003c/li\u003e\n\u003cli\u003eIterate rapidly on featuring engineering and model exploration by mapping\ndatasets created with NVTabular into a model input layer automatically. The\nmodel input layer enables you to change either without impacting the other.\u003c/li\u003e\n\u003cli\u003eAssemble connectable building blocks for common RecSys architectures so that\nyou can create of new models quickly and easily.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/Transformers4Rec\"\u003eTransformers4Rec\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://pypi.org/project/Transformers4Rec/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/78a0017021a73fb4dd8faa3e9f7016eed3cce545e8c9d54759452c0f71822093/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f5472616e73666f726d657273345265632e737667\" alt=\"PyPI version shields.io\" data-canonical-src=\"https://img.shields.io/pypi/v/Transformers4Rec.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/Transformers4Rec/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nThe Transformers4Rec library provides sequential and session-based recommendation.\nThe library provides modular building blocks that are compatible with standard PyTorch modules.\nYou can use the building blocks to design custom architectures such as multiple towers, multiple heads and tasks, and losses.\nWith Transformers4Rec, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBuild sequential and session-based recommenders from any sequential tabular data.\u003c/li\u003e\n\u003cli\u003eTake advantage of the integration with NVTabular for seamless data preprocessing and feature engineering.\u003c/li\u003e\n\u003cli\u003ePerform next-item prediction as well as classic binary classification or regression tasks.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/systems\"\u003eMerlin Systems\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://pypi.org/project/merlin-systems/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/52f94ad21a3db4b11d35bdf983eb4c7993d719824bf6faf0f80efedfbadccbbb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d73797374656d732e737667\" alt=\"PyPI version shields.io\" data-canonical-src=\"https://img.shields.io/pypi/v/merlin-systems.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/systems/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nMerlin Systems provides tools for combining recommendation models with other\nelements of production recommender systems like feature stores, nearest neighbor\nsearch, and exploration strategies into end-to-end recommendation pipelines that\ncan be served with Triton Inference Server. With Merlin Systems, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eStart with an integrated platform for serving recommendations built on Triton\nInference Server.\u003c/li\u003e\n\u003cli\u003eCreate graphs that define the end-to-end process of generating\nrecommendations.\u003c/li\u003e\n\u003cli\u003eBenefit from existing integrations with popular tools that are commonly found\nin recommender system pipelines.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/core\"\u003eMerlin Core\u003c/a\u003e\u003c/strong\u003e\n\u003ca href=\"https://pypi.org/project/merlin-core/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/ce07c9baaa53903e2f2c2ab637c8c14110dbfe3686abeb1a2110f0aaed7709fb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d636f72652e737667\" alt=\"PyPI version shields.io\" data-canonical-src=\"https://img.shields.io/pypi/v/merlin-core.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://nvidia-merlin.github.io/core/stable/README.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667\" alt=\" Documentation\" data-canonical-src=\"https://img.shields.io/badge/documentation-blue.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\nMerlin Core provides functionality that is used throughout the Merlin ecosystem.\nWith Merlin Core, you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUse a standard dataset abstraction for processing large datasets across\nmultiple GPUs and nodes.\u003c/li\u003e\n\u003cli\u003eBenefit from a common schema that identifies key dataset features and enables\nMerlin to automate routine modeling and serving tasks.\u003c/li\u003e\n\u003cli\u003eSimplify your code by using a shared API for constructing graphs of data\ntransformation operators.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h2\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe simplest way to use Merlin is to run a docker container. NVIDIA GPU Cloud (NGC) provides containers that include all the Merlin component libraries, dependencies, and receive unit and integration testing. For more information, see the \u003ca href=\"https://nvidia-merlin.github.io/Merlin/stable/containers.html\" rel=\"nofollow\"\u003eContainers\u003c/a\u003e page.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo develop and contribute to Merlin, review the installation documentation for each component library. The development environment for each Merlin component is easily set up with \u003ccode\u003econda\u003c/code\u003e or \u003ccode\u003epip\u003c/code\u003e:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eComponent\u003c/th\u003e\n\u003cth\u003eInstallation Steps\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHugeCTR\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/master/hugectr_contributor_guide.html\" rel=\"nofollow\"\u003ehttps://nvidia-merlin.github.io/HugeCTR/master/hugectr_contributor_guide.html\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMerlin Core\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/core/blob/stable/README.md#installation\"\u003ehttps://github.com/NVIDIA-Merlin/core/blob/stable/README.md#installation\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMerlin Models\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/models/blob/stable/README.md#installation\"\u003ehttps://github.com/NVIDIA-Merlin/models/blob/stable/README.md#installation\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMerlin Systems\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/systems/blob/stable/README.md#installation\"\u003ehttps://github.com/NVIDIA-Merlin/systems/blob/stable/README.md#installation\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVTabular\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/NVTabular/blob/stable/README.md#installation\"\u003ehttps://github.com/NVIDIA-Merlin/NVTabular/blob/stable/README.md#installation\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTransformers4Rec\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/README.md#installation\"\u003ehttps://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/README.md#installation\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExample Notebooks and Tutorials\u003c/h2\u003e\u003ca id=\"user-content-example-notebooks-and-tutorials\" class=\"anchor\" aria-label=\"Permalink: Example Notebooks and Tutorials\" href=\"#example-notebooks-and-tutorials\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA collection of \u003ca href=\"/NVIDIA-Merlin/Merlin/blob/main/examples\"\u003eend-to-end examples\u003c/a\u003e are available in the form of Jupyter notebooks.\nThe example notebooks demonstrate how to:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDownload and prepare a dataset.\u003c/li\u003e\n\u003cli\u003eUse preprocessing and engineering features.\u003c/li\u003e\n\u003cli\u003eTrain deep-learning recommendation models with TensorFlow, PyTorch, FastAI, HugeCTR or Merlin Models.\u003c/li\u003e\n\u003cli\u003eDeploy the models to production with Triton Inference Server.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eThese examples are based on different datasets and provide a wide range of\nreal-world use cases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMerlin Is Built On\u003c/h2\u003e\u003ca id=\"user-content-merlin-is-built-on\" class=\"anchor\" aria-label=\"Permalink: Merlin Is Built On\" href=\"#merlin-is-built-on\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/rapidsai/cudf\"\u003eRAPIDS cuDF\u003c/a\u003e\u003c/strong\u003e\u003cbr\u003e Merlin relies on cuDF for\nGPU-accelerated DataFrame operations used in feature engineering.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://www.dask.org/\" rel=\"nofollow\"\u003eDask\u003c/a\u003e\u003c/strong\u003e\u003cbr\u003e Merlin relies on Dask to distribute and scale\nfeature engineering and preprocessing within NVTabular and to accelerate\ndataloading in Merlin Models and HugeCTR.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/triton-inference-server/server\"\u003eTriton Inference Server\u003c/a\u003e\u003c/strong\u003e\u003cbr\u003e\nMerlin leverages Triton Inference Server to provide GPU-accelerated serving for\nrecommender system pipelines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeedback and Support\u003c/h2\u003e\u003ca id=\"user-content-feedback-and-support\" class=\"anchor\" aria-label=\"Permalink: Feedback and Support\" href=\"#feedback-and-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo report bugs or get help, please\n\u003ca href=\"https://github.com/NVIDIA-Merlin/Merlin/issues/new/choose\"\u003eopen an issue\u003c/a\u003e.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"NVIDIA Merlin","anchor":"nvidia-merlin","htmlText":"NVIDIA Merlin"},{"level":2,"text":"Benefits","anchor":"benefits","htmlText":"Benefits"},{"level":2,"text":"Components of NVIDIA Merlin","anchor":"components-of-nvidia-merlin","htmlText":"Components of NVIDIA Merlin"},{"level":2,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Example Notebooks and Tutorials","anchor":"example-notebooks-and-tutorials","htmlText":"Example Notebooks and Tutorials"},{"level":2,"text":"Merlin Is Built On","anchor":"merlin-is-built-on","htmlText":"Merlin Is Built On"},{"level":2,"text":"Feedback and Support","anchor":"feedback-and-support","htmlText":"Feedback and Support"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA-Merlin%2FMerlin"}},{"displayName":"LICENSE","repoName":"Merlin","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FNVIDIA-Merlin%2FMerlin"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> <div 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2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><a href="https://github.com/NVIDIA-Merlin">NVIDIA Merlin</a></h1><a id="user-content-nvidia-merlin" class="anchor" aria-label="Permalink: NVIDIA Merlin" href="#nvidia-merlin"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/7ffd26436214e09f22afd1f01453e153aa09e81efb1b9719df9f63e05d673008/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f4e56494449412d4d65726c696e2f4d65726c696e3f736f72743d73656d766572"><img src="https://camo.githubusercontent.com/7ffd26436214e09f22afd1f01453e153aa09e81efb1b9719df9f63e05d673008/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f7461672f4e56494449412d4d65726c696e2f4d65726c696e3f736f72743d73656d766572" alt="GitHub tag (latest SemVer)" data-canonical-src="https://img.shields.io/github/v/tag/NVIDIA-Merlin/Merlin?sort=semver" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/e7d7eb7035b9903ca9021a506dd4b0f911b7a2561f1d13da83f27e31e35c0203/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f4d65726c696e"><img src="https://camo.githubusercontent.com/e7d7eb7035b9903ca9021a506dd4b0f911b7a2561f1d13da83f27e31e35c0203/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f6c6963656e73652f4e56494449412d4d65726c696e2f4d65726c696e" alt="GitHub License" data-canonical-src="https://img.shields.io/github/license/NVIDIA-Merlin/Merlin" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/Merlin/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt="Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a></p> <p dir="auto">NVIDIA Merlin is an open source library that accelerates recommender systems on NVIDIA GPUs. The library enables data scientists, machine learning engineers, and researchers to build high-performing recommenders at scale. Merlin includes tools to address common feature engineering, training, and inference challenges. Each stage of the Merlin pipeline is optimized to support hundreds of terabytes of data, which is all accessible through easy-to-use APIs. For more information, see <a href="https://developer.nvidia.com/nvidia-merlin" rel="nofollow">NVIDIA Merlin</a> on the NVIDIA developer web site.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Benefits</h2><a id="user-content-benefits" class="anchor" aria-label="Permalink: Benefits" href="#benefits"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NVIDIA Merlin is a scalable and GPU-accelerated solution, making it easy to build recommender systems from end to end. With NVIDIA Merlin, you can:</p> <ul dir="auto"> <li>Transform data (ETL) for preprocessing and engineering features.</li> <li>Accelerate your existing training pipelines in TensorFlow, PyTorch, or FastAI by leveraging optimized, custom-built data loaders.</li> <li>Scale large deep learning recommender models by distributing large embedding tables that exceed available GPU and CPU memory.</li> <li>Deploy data transformations and trained models to production with only a few lines of code.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Components of NVIDIA Merlin</h2><a id="user-content-components-of-nvidia-merlin" class="anchor" aria-label="Permalink: Components of NVIDIA Merlin" href="#components-of-nvidia-merlin"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NVIDIA Merlin consists of the following open source libraries:</p> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/NVTabular">NVTabular</a></strong> <a href="https://pypi.org/project/nvtabular/" rel="nofollow"><img src="https://camo.githubusercontent.com/59bfb037a45166f34e31da80f552d2fce18741c2ef610acd8cdaa12e1278df96/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6e76746162756c61722e737667" alt="PyPI version shields.io" data-canonical-src="https://img.shields.io/pypi/v/nvtabular.svg" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/NVTabular/stable/Introduction.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a> <br> NVTabular is a feature engineering and preprocessing library for tabular data. The library can quickly and easily manipulate terabyte-size datasets that are used to train deep learning based recommender systems. The library offers a high-level API that can define complex data transformation workflows. With NVTabular, you can:</p> <ul dir="auto"> <li>Prepare datasets quickly and easily for experimentation so that you can train more models.</li> <li>Process datasets that exceed GPU and CPU memory without having to worry about scale.</li> <li>Focus on what to do with the data and not how to do it by using abstraction at the operation level.</li> </ul> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/HugeCTR">HugeCTR</a></strong> <a href="https://nvidia-merlin.github.io/HugeCTR/stable/hugectr_user_guide.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a><br> HugeCTR is a GPU-accelerated training framework that can scale large deep learning recommendation models by distributing training across multiple GPUs and nodes. HugeCTR contains optimized data loaders with GPU-acceleration and provides strategies for scaling large embedding tables beyond available memory. With HugeCTR, you can:</p> <ul dir="auto"> <li>Scale embedding tables over multiple GPUs or nodes.</li> <li>Load a subset of an embedding table into a GPU in a coarse-grained, on-demand manner during the training stage.</li> </ul> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/models">Merlin Models</a></strong> <a href="https://pypi.org/project/merlin-models/" rel="nofollow"><img src="https://camo.githubusercontent.com/63bfc3066013a49a0d03c50da43f0ef286bdf0506afd4a5658869718a72ad4d0/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d6d6f64656c732e737667" alt="PyPI version shields.io" data-canonical-src="https://img.shields.io/pypi/v/merlin-models.svg" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/models/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a><br> The Merlin Models library provides standard models for recommender systems with an aim for high-quality implementations that range from classic machine learning models to highly-advanced deep learning models. With Merlin Models, you can:</p> <ul dir="auto"> <li>Accelerate your ranking model training by up to 10x by using performant data loaders for TensorFlow, PyTorch, and HugeCTR.</li> <li>Iterate rapidly on featuring engineering and model exploration by mapping datasets created with NVTabular into a model input layer automatically. The model input layer enables you to change either without impacting the other.</li> <li>Assemble connectable building blocks for common RecSys architectures so that you can create of new models quickly and easily.</li> </ul> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/Transformers4Rec">Transformers4Rec</a></strong> <a href="https://pypi.org/project/Transformers4Rec/" rel="nofollow"><img src="https://camo.githubusercontent.com/78a0017021a73fb4dd8faa3e9f7016eed3cce545e8c9d54759452c0f71822093/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f5472616e73666f726d657273345265632e737667" alt="PyPI version shields.io" data-canonical-src="https://img.shields.io/pypi/v/Transformers4Rec.svg" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/Transformers4Rec/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a><br> The Transformers4Rec library provides sequential and session-based recommendation. The library provides modular building blocks that are compatible with standard PyTorch modules. You can use the building blocks to design custom architectures such as multiple towers, multiple heads and tasks, and losses. With Transformers4Rec, you can:</p> <ul dir="auto"> <li>Build sequential and session-based recommenders from any sequential tabular data.</li> <li>Take advantage of the integration with NVTabular for seamless data preprocessing and feature engineering.</li> <li>Perform next-item prediction as well as classic binary classification or regression tasks.</li> </ul> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/systems">Merlin Systems</a></strong> <a href="https://pypi.org/project/merlin-systems/" rel="nofollow"><img src="https://camo.githubusercontent.com/52f94ad21a3db4b11d35bdf983eb4c7993d719824bf6faf0f80efedfbadccbbb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d73797374656d732e737667" alt="PyPI version shields.io" data-canonical-src="https://img.shields.io/pypi/v/merlin-systems.svg" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/systems/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a><br> Merlin Systems provides tools for combining recommendation models with other elements of production recommender systems like feature stores, nearest neighbor search, and exploration strategies into end-to-end recommendation pipelines that can be served with Triton Inference Server. With Merlin Systems, you can:</p> <ul dir="auto"> <li>Start with an integrated platform for serving recommendations built on Triton Inference Server.</li> <li>Create graphs that define the end-to-end process of generating recommendations.</li> <li>Benefit from existing integrations with popular tools that are commonly found in recommender system pipelines.</li> </ul> <p dir="auto"><strong><a href="https://github.com/NVIDIA-Merlin/core">Merlin Core</a></strong> <a href="https://pypi.org/project/merlin-core/" rel="nofollow"><img src="https://camo.githubusercontent.com/ce07c9baaa53903e2f2c2ab637c8c14110dbfe3686abeb1a2110f0aaed7709fb/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f6d65726c696e2d636f72652e737667" alt="PyPI version shields.io" data-canonical-src="https://img.shields.io/pypi/v/merlin-core.svg" style="max-width: 100%;"></a> <a href="https://nvidia-merlin.github.io/core/stable/README.html" rel="nofollow"><img src="https://camo.githubusercontent.com/c6ff5992c6aebec30ac94d38e92d15d9a1c178718f0101a35c131519c35ce1d1/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f646f63756d656e746174696f6e2d626c75652e737667" alt=" Documentation" data-canonical-src="https://img.shields.io/badge/documentation-blue.svg" style="max-width: 100%;"></a><br> Merlin Core provides functionality that is used throughout the Merlin ecosystem. With Merlin Core, you can:</p> <ul dir="auto"> <li>Use a standard dataset abstraction for processing large datasets across multiple GPUs and nodes.</li> <li>Benefit from a common schema that identifies key dataset features and enables Merlin to automate routine modeling and serving tasks.</li> <li>Simplify your code by using a shared API for constructing graphs of data transformation operators.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installation</h2><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The simplest way to use Merlin is to run a docker container. NVIDIA GPU Cloud (NGC) provides containers that include all the Merlin component libraries, dependencies, and receive unit and integration testing. For more information, see the <a href="https://nvidia-merlin.github.io/Merlin/stable/containers.html" rel="nofollow">Containers</a> page.</p> <p dir="auto">To develop and contribute to Merlin, review the installation documentation for each component library. The development environment for each Merlin component is easily set up with <code>conda</code> or <code>pip</code>:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Component</th> <th>Installation Steps</th> </tr> </thead> <tbody> <tr> <td>HugeCTR</td> <td><a href="https://nvidia-merlin.github.io/HugeCTR/master/hugectr_contributor_guide.html" rel="nofollow">https://nvidia-merlin.github.io/HugeCTR/master/hugectr_contributor_guide.html</a></td> </tr> <tr> <td>Merlin Core</td> <td><a href="https://github.com/NVIDIA-Merlin/core/blob/stable/README.md#installation">https://github.com/NVIDIA-Merlin/core/blob/stable/README.md#installation</a></td> </tr> <tr> <td>Merlin Models</td> <td><a href="https://github.com/NVIDIA-Merlin/models/blob/stable/README.md#installation">https://github.com/NVIDIA-Merlin/models/blob/stable/README.md#installation</a></td> </tr> <tr> <td>Merlin Systems</td> <td><a href="https://github.com/NVIDIA-Merlin/systems/blob/stable/README.md#installation">https://github.com/NVIDIA-Merlin/systems/blob/stable/README.md#installation</a></td> </tr> <tr> <td>NVTabular</td> <td><a href="https://github.com/NVIDIA-Merlin/NVTabular/blob/stable/README.md#installation">https://github.com/NVIDIA-Merlin/NVTabular/blob/stable/README.md#installation</a></td> </tr> <tr> <td>Transformers4Rec</td> <td><a href="https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/README.md#installation">https://github.com/NVIDIA-Merlin/Transformers4Rec/blob/stable/README.md#installation</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Example Notebooks and Tutorials</h2><a id="user-content-example-notebooks-and-tutorials" class="anchor" aria-label="Permalink: Example Notebooks and Tutorials" href="#example-notebooks-and-tutorials"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A collection of <a href="/NVIDIA-Merlin/Merlin/blob/main/examples">end-to-end examples</a> are available in the form of Jupyter notebooks. The example notebooks demonstrate how to:</p> <ul dir="auto"> <li>Download and prepare a dataset.</li> <li>Use preprocessing and engineering features.</li> <li>Train deep-learning recommendation models with TensorFlow, PyTorch, FastAI, HugeCTR or Merlin Models.</li> <li>Deploy the models to production with Triton Inference Server.</li> </ul> <p dir="auto">These examples are based on different datasets and provide a wide range of real-world use cases.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Merlin Is Built On</h2><a id="user-content-merlin-is-built-on" class="anchor" aria-label="Permalink: Merlin Is Built On" href="#merlin-is-built-on"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong><a href="https://github.com/rapidsai/cudf">RAPIDS cuDF</a></strong><br> Merlin relies on cuDF for GPU-accelerated DataFrame operations used in feature engineering.</p> <p dir="auto"><strong><a href="https://www.dask.org/" rel="nofollow">Dask</a></strong><br> Merlin relies on Dask to distribute and scale feature engineering and preprocessing within NVTabular and to accelerate dataloading in Merlin Models and HugeCTR.</p> <p dir="auto"><strong><a href="https://github.com/triton-inference-server/server">Triton Inference Server</a></strong><br> Merlin leverages Triton Inference Server to provide GPU-accelerated serving for recommender system pipelines.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Feedback and Support</h2><a id="user-content-feedback-and-support" class="anchor" aria-label="Permalink: Feedback and Support" href="#feedback-and-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To report bugs or get help, please <a href="https://github.com/NVIDIA-Merlin/Merlin/issues/new/choose">open an issue</a>.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="crfccoqRztyADI0xiOWlHUqk0GBwDdK1+HAoHKwNTaR32GJAE3DhX55AZdPrHZZuQeg0ZHtUonuHquyWM6TrnA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> NVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems, from feature engineering and preprocessing to training deep learning models and running inference in production. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:machine-learning" href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:deep-learning" href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:end-to-end" href="/topics/end-to-end" title="Topic: end-to-end" data-view-component="true" class="topic-tag topic-tag-link"> end-to-end </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:recommendation-system" href="/topics/recommendation-system" title="Topic: recommendation-system" data-view-component="true" class="topic-tag topic-tag-link"> recommendation-system </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:gpu-acceleration" href="/topics/gpu-acceleration" title="Topic: gpu-acceleration" data-view-component="true" class="topic-tag topic-tag-link"> gpu-acceleration </a> <a data-ga-click="Topic, repository page" data-octo-click="topic_click" data-octo-dimensions="topic:recommender-system" href="/topics/recommender-system" title="Topic: recommender-system" data-view-component="true" class="topic-tag topic-tag-link"> recommender-system </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository Overview","action":"click","label":"location:sidebar;file:readme"}" href="#readme-ov-file"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-book mr-2"> <path d="M0 1.75A.75.75 0 0 1 .75 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class="octicon octicon-note mr-2"> <path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25Zm1.75-.25a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25ZM3.5 6.25a.75.75 0 0 1 .75-.75h7a.75.75 0 0 1 0 1.5h-7a.75.75 0 0 1-.75-.75Zm.75 2.25h4a.75.75 0 0 1 0 1.5h-4a.75.75 0 0 1 0-1.5Z"></path> </svg> <span class="color-fg-muted">Custom properties</span> </a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/NVIDIA-Merlin/Merlin/stargazers" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 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6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>34</strong> watching </a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/NVIDIA-Merlin/Merlin/forks" data-view-component="true" class="Link Link--muted"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>118</strong> forks </a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FNVIDIA-Merlin%2FMerlin&report=NVIDIA-Merlin+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/NVIDIA-Merlin/Merlin/releases" data-view-component="true" class="Link--primary no-underline Link"> Releases <span title="10" data-view-component="true" class="Counter">10</span> </a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA-Merlin/Merlin/releases/tag/v24.06.00"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">v24.06.00</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2024-06-14T12:19:07Z" class="no-wrap">Jun 14, 2024</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA-Merlin/Merlin/releases" 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14.082 15H1.918a1.75 1.75 0 0 1-1.543-2.575Zm1.763.707a.25.25 0 0 0-.44 0L1.698 13.132a.25.25 0 0 0 .22.368h12.164a.25.25 0 0 0 .22-.368Zm.53 3.996v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 11a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path> </svg> <button type="button" class="flash-close js-ajax-error-dismiss" aria-label="Dismiss error"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left 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aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> 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