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GitHub - NVIDIA-Merlin/HugeCTR: HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training
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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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href=\"https://nvidia-merlin.github.io/HugeCTR/main/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\n\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/sparse_operation_kit/master/index.html\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/5d1cb262f24120fa1d2096b444db990250b22b4f57f50f7db426468d7e445d08/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f534f4b253230446f63756d656e746174696f6e2d626c75653f6c6f676f436f6c6f723d626c7565\" alt=\"SOK Documentation\" data-canonical-src=\"https://img.shields.io/badge/SOK%20Documentation-blue?logoColor=blue\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHugeCTR is a GPU-accelerated recommender framework designed for training and inference of large deep learning models.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDesign Goals:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cstrong\u003eFast\u003c/strong\u003e: HugeCTR performs outstandingly in recommendation \u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/performance.html\" rel=\"nofollow\"\u003ebenchmarks\u003c/a\u003e including MLPerf.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEasy\u003c/strong\u003e: Regardless of whether you are a data scientist or machine learning practitioner, we've made it easy for anybody to use HugeCTR with plenty of \u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html\" rel=\"nofollow\"\u003edocuments\u003c/a\u003e, \u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/notebooks\"\u003enotebooks\u003c/a\u003e and \u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/samples\"\u003esamples\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDomain Specific\u003c/strong\u003e: HugeCTR provides the \u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR#core-features\"\u003eessentials\u003c/a\u003e, so that you can efficiently deploy your recommender models with very large embedding.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: If you have any questions in using HugeCTR, please file an issue or join our \u003ca href=\"https://join.slack.com/t/hugectr/shared_invite/zt-2ji0b305s-SIVB~_XZYtz38JCkT8VFSg\" rel=\"nofollow\"\u003eSlack channel\u003c/a\u003e to have more interactive discussions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTable of Contents\u003c/h2\u003e\u003ca id=\"user-content-table-of-contents\" class=\"anchor\" aria-label=\"Permalink: Table of Contents\" href=\"#table-of-contents\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html\" rel=\"nofollow\"\u003eCore Features\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html#installing-and-building-hugectr\" rel=\"nofollow\"\u003eGetting Started\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html#tools\" rel=\"nofollow\"\u003eHugeCTR SDK\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html\" rel=\"nofollow\"\u003eSupport and Feedback\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html\" rel=\"nofollow\"\u003eContributing to HugeCTR\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/additional_resources.html\" rel=\"nofollow\"\u003eAdditional Resources\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCore Features\u003c/h2\u003e\u003ca id=\"user-content-core-features\" class=\"anchor\" aria-label=\"Permalink: Core Features\" href=\"#core-features\"\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\"\u003eHugeCTR supports a variety of features, including the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/api/python_interface.html\" rel=\"nofollow\"\u003eHigh-Level abstracted Python interface\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#model-parallel-training\" rel=\"nofollow\"\u003eModel parallel training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA-Merlin/HugeCTR/blob/main/performance.md\"\u003eOptimized GPU workflow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#multi-node-training\" rel=\"nofollow\"\u003eMulti-node training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#mixed-precision-training\" rel=\"nofollow\"\u003eMixed precision training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#embedding-training-cache\" rel=\"nofollow\"\u003eEmbedding training cache\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/triton-inference-server/hugectr_backend/blob/main/docs/architecture.md#enabling-the-gpu-embedding-cache\"\u003eGPU embedding cache\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/triton-inference-server/hugectr_backend/blob/main/docs/architecture.md#hugectr-backend-framework\"\u003eGPU / CPU memory sharing mechanism across various inference instances\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#hugectr-to-onnx-converter\" rel=\"nofollow\"\u003eHugeCTR to ONNX Converter\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#hierarchical-parameter-server\" rel=\"nofollow\"\u003eHierarchical Parameter Server\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/sparse_operation_kit\"\u003eSparse Operation Kit\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eTo learn about our latest enhancements, refer to our \u003ca href=\"/NVIDIA-Merlin/HugeCTR/blob/main/release_notes.md\"\u003erelease notes\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\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\"\u003eIf you'd like to quickly train a model using the Python interface, do the following:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eStart a NGC container with your local host directory (/your/host/dir mounted) by running the following command:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"docker run --gpus=all --rm -it --cap-add SYS_NICE -v /your/host/dir:/your/container/dir -w /your/container/dir -it -u $(id -u):$(id -g) nvcr.io/nvidia/merlin/merlin-hugectr:24.06\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003edocker run --gpus=all --rm -it --cap-add SYS_NICE -v /your/host/dir:/your/container/dir -w /your/container/dir -it -u $(id -u):$(id -g) nvcr.io/nvidia/merlin/merlin-hugectr:24.06\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: The \u003cstrong\u003e/your/host/dir\u003c/strong\u003e directory is just as visible as the \u003cstrong\u003e/your/container/dir\u003c/strong\u003e directory. The \u003cstrong\u003e/your/host/dir\u003c/strong\u003e directory is also your starting directory.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: HugeCTR uses NCCL to share data between ranks, and NCCL may requires shared memory for IPC and pinned (page-locked) system memory resources. It is recommended that you increase these resources by issuing the following options in the \u003ccode\u003edocker run\u003c/code\u003e command.\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"-shm-size=1g -ulimit memlock=-1\"\u003e\u003cpre lang=\"text\" class=\"notranslate\"\u003e\u003ccode\u003e-shm-size=1g -ulimit memlock=-1\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eWrite a simple Python script to generate a synthetic dataset:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"# dcn_parquet_generate.py\nimport hugectr\nfrom hugectr.tools import DataGeneratorParams, DataGenerator\ndata_generator_params = DataGeneratorParams(\n format = hugectr.DataReaderType_t.Parquet,\n label_dim = 1,\n dense_dim = 13,\n num_slot = 26,\n i64_input_key = False,\n source = \u0026quot;./dcn_parquet/file_list.txt\u0026quot;,\n eval_source = \u0026quot;./dcn_parquet/file_list_test.txt\u0026quot;,\n slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, \n 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120,\n 1543 ],\n dist_type = hugectr.Distribution_t.PowerLaw,\n power_law_type = hugectr.PowerLaw_t.Short)\ndata_generator = DataGenerator(data_generator_params)\ndata_generator.generate()\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e# dcn_parquet_generate.py\nimport hugectr\nfrom hugectr.tools import DataGeneratorParams, DataGenerator\ndata_generator_params = DataGeneratorParams(\n format = hugectr.DataReaderType_t.Parquet,\n label_dim = 1,\n dense_dim = 13,\n num_slot = 26,\n i64_input_key = False,\n source = \"./dcn_parquet/file_list.txt\",\n eval_source = \"./dcn_parquet/file_list_test.txt\",\n slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, \n 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120,\n 1543 ],\n dist_type = hugectr.Distribution_t.PowerLaw,\n power_law_type = hugectr.PowerLaw_t.Short)\ndata_generator = DataGenerator(data_generator_params)\ndata_generator.generate()\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGenerate the Parquet dataset for your DCN model by running the following command:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python dcn_parquet_generate.py\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython dcn_parquet_generate.py\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: The generated dataset will reside in the folder \u003ccode\u003e./dcn_parquet\u003c/code\u003e, which contains training and evaluation data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eWrite a simple Python script for training:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"# dcn_parquet_train.py\nimport hugectr\nfrom mpi4py import MPI\nsolver = hugectr.CreateSolver(max_eval_batches = 1280,\n batchsize_eval = 1024,\n batchsize = 1024,\n lr = 0.001,\n vvgpu = [[0]],\n repeat_dataset = True)\nreader = hugectr.DataReaderParams(data_reader_type = hugectr.DataReaderType_t.Parquet,\n source = [\u0026quot;./dcn_parquet/file_list.txt\u0026quot;],\n eval_source = \u0026quot;./dcn_parquet/file_list_test.txt\u0026quot;,\n slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, \n 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ])\noptimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam,\n update_type = hugectr.Update_t.Global)\nmodel = hugectr.Model(solver, reader, optimizer)\nmodel.add(hugectr.Input(label_dim = 1, label_name = \u0026quot;label\u0026quot;,\n dense_dim = 13, dense_name = \u0026quot;dense\u0026quot;,\n data_reader_sparse_param_array =\n [hugectr.DataReaderSparseParam(\u0026quot;data1\u0026quot;, 1, True, 26)]))\nmodel.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.DistributedSlotSparseEmbeddingHash,\n workspace_size_per_gpu_in_mb = 75,\n embedding_vec_size = 16,\n combiner = \u0026quot;sum\u0026quot;,\n sparse_embedding_name = \u0026quot;sparse_embedding1\u0026quot;,\n bottom_name = \u0026quot;data1\u0026quot;,\n optimizer = optimizer))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Reshape,\n bottom_names = [\u0026quot;sparse_embedding1\u0026quot;],\n top_names = [\u0026quot;reshape1\u0026quot;],\n leading_dim=416))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,\n bottom_names = [\u0026quot;reshape1\u0026quot;, \u0026quot;dense\u0026quot;], top_names = [\u0026quot;concat1\u0026quot;]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.MultiCross,\n bottom_names = [\u0026quot;concat1\u0026quot;],\n top_names = [\u0026quot;multicross1\u0026quot;],\n num_layers=6))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,\n bottom_names = [\u0026quot;concat1\u0026quot;],\n top_names = [\u0026quot;fc1\u0026quot;],\n num_output=1024))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,\n bottom_names = [\u0026quot;fc1\u0026quot;],\n top_names = [\u0026quot;relu1\u0026quot;]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,\n bottom_names = [\u0026quot;relu1\u0026quot;],\n top_names = [\u0026quot;dropout1\u0026quot;],\n dropout_rate=0.5))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,\n bottom_names = [\u0026quot;dropout1\u0026quot;, \u0026quot;multicross1\u0026quot;],\n top_names = [\u0026quot;concat2\u0026quot;]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,\n bottom_names = [\u0026quot;concat2\u0026quot;],\n top_names = [\u0026quot;fc2\u0026quot;],\n num_output=1))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,\n bottom_names = [\u0026quot;fc2\u0026quot;, \u0026quot;label\u0026quot;],\n top_names = [\u0026quot;loss\u0026quot;]))\nmodel.compile()\nmodel.summary()\nmodel.graph_to_json(graph_config_file = \u0026quot;dcn.json\u0026quot;)\nmodel.fit(max_iter = 5120, display = 200, eval_interval = 1000, snapshot = 5000, snapshot_prefix = \u0026quot;dcn\u0026quot;)\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e# dcn_parquet_train.py\nimport hugectr\nfrom mpi4py import MPI\nsolver = hugectr.CreateSolver(max_eval_batches = 1280,\n batchsize_eval = 1024,\n batchsize = 1024,\n lr = 0.001,\n vvgpu = [[0]],\n repeat_dataset = True)\nreader = hugectr.DataReaderParams(data_reader_type = hugectr.DataReaderType_t.Parquet,\n source = [\"./dcn_parquet/file_list.txt\"],\n eval_source = \"./dcn_parquet/file_list_test.txt\",\n slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, \n 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ])\noptimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam,\n update_type = hugectr.Update_t.Global)\nmodel = hugectr.Model(solver, reader, optimizer)\nmodel.add(hugectr.Input(label_dim = 1, label_name = \"label\",\n dense_dim = 13, dense_name = \"dense\",\n data_reader_sparse_param_array =\n [hugectr.DataReaderSparseParam(\"data1\", 1, True, 26)]))\nmodel.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.DistributedSlotSparseEmbeddingHash,\n workspace_size_per_gpu_in_mb = 75,\n embedding_vec_size = 16,\n combiner = \"sum\",\n sparse_embedding_name = \"sparse_embedding1\",\n bottom_name = \"data1\",\n optimizer = optimizer))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Reshape,\n bottom_names = [\"sparse_embedding1\"],\n top_names = [\"reshape1\"],\n leading_dim=416))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,\n bottom_names = [\"reshape1\", \"dense\"], top_names = [\"concat1\"]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.MultiCross,\n bottom_names = [\"concat1\"],\n top_names = [\"multicross1\"],\n num_layers=6))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,\n bottom_names = [\"concat1\"],\n top_names = [\"fc1\"],\n num_output=1024))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,\n bottom_names = [\"fc1\"],\n top_names = [\"relu1\"]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout,\n bottom_names = [\"relu1\"],\n top_names = [\"dropout1\"],\n dropout_rate=0.5))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat,\n bottom_names = [\"dropout1\", \"multicross1\"],\n top_names = [\"concat2\"]))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,\n bottom_names = [\"concat2\"],\n top_names = [\"fc2\"],\n num_output=1))\nmodel.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,\n bottom_names = [\"fc2\", \"label\"],\n top_names = [\"loss\"]))\nmodel.compile()\nmodel.summary()\nmodel.graph_to_json(graph_config_file = \"dcn.json\")\nmodel.fit(max_iter = 5120, display = 200, eval_interval = 1000, snapshot = 5000, snapshot_prefix = \"dcn\")\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: Ensure that the paths to the synthetic datasets are correct with respect to this Python script. \u003ccode\u003edata_reader_type\u003c/code\u003e, \u003ccode\u003echeck_type\u003c/code\u003e, \u003ccode\u003elabel_dim\u003c/code\u003e, \u003ccode\u003edense_dim\u003c/code\u003e, and\n\u003ccode\u003edata_reader_sparse_param_array\u003c/code\u003e should be consistent with the generated dataset.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTrain the model by running the following command:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"python dcn_parquet_train.py\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003epython dcn_parquet_train.py\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE\u003c/strong\u003e: It is presumed that the evaluation AUC value is incorrect since randomly generated datasets are being used. When the training is done, files that contain the\ndumped graph JSON, saved model weights, and optimizer states will be generated.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003eFor more information, refer to the \u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html\" rel=\"nofollow\"\u003eHugeCTR User Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHugeCTR SDK\u003c/h2\u003e\u003ca id=\"user-content-hugectr-sdk\" class=\"anchor\" aria-label=\"Permalink: HugeCTR SDK\" href=\"#hugectr-sdk\"\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\"\u003eWe're able to support external developers who can't use HugeCTR directly by exporting important HugeCTR components using:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSparse Operation Kit \u003ca href=\"/NVIDIA-Merlin/HugeCTR/blob/main/sparse_operation_kit\"\u003edirectory\u003c/a\u003e | \u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/sparse_operation_kit/master/\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e: a python package wrapped with GPU accelerated operations dedicated for sparse training/inference cases.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA-Merlin/HugeCTR/blob/main/gpu_cache\"\u003eGPU Embedding Cache\u003c/a\u003e: embedding cache available on the GPU memory designed for CTR inference workload.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSupport and Feedback\u003c/h2\u003e\u003ca id=\"user-content-support-and-feedback\" class=\"anchor\" aria-label=\"Permalink: Support and Feedback\" href=\"#support-and-feedback\"\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\"\u003eIf you encounter any issues or have questions, go to \u003ca href=\"https://github.com/NVIDIA/HugeCTR/issues\"\u003ehttps://github.com/NVIDIA/HugeCTR/issues\u003c/a\u003e and submit an issue so that we can provide you with the necessary resolutions and answers. To further advance the HugeCTR Roadmap, we encourage you to share all the details regarding your recommender system pipeline using this \u003ca href=\"https://developer.nvidia.com/merlin-devzone-survey\" rel=\"nofollow\"\u003esurvey\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing to HugeCTR\u003c/h2\u003e\u003ca id=\"user-content-contributing-to-hugectr\" class=\"anchor\" aria-label=\"Permalink: Contributing to HugeCTR\" href=\"#contributing-to-hugectr\"\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\"\u003eWith HugeCTR being an open source project, we welcome contributions from the general public. With your contributions, we can continue to improve HugeCTR's quality and performance. To learn how to contribute, refer to our \u003ca href=\"https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html\" rel=\"nofollow\"\u003eHugeCTR Contributor Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAdditional Resources\u003c/h2\u003e\u003ca id=\"user-content-additional-resources\" class=\"anchor\" aria-label=\"Permalink: Additional Resources\" href=\"#additional-resources\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eWebpages\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-merlin\" rel=\"nofollow\"\u003eNVIDIA Merlin\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/nvidia-merlin/hugectr\" rel=\"nofollow\"\u003eNVIDIA HugeCTR\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePublications\u003c/h3\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\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\u003cem\u003eYingcan Wei, Matthias Langer, Fan Yu, Minseok Lee, Jie Liu, Ji Shi and Zehuan Wang\u003c/em\u003e, \"\u003ca href=\"https://dl.acm.org/doi/10.1145/3523227.3546765\" rel=\"nofollow\"\u003eA GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models\u003c/a\u003e,\" Proceedings of the 16th ACM Conference on Recommender Systems, pp. 408-419, 2022.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eZehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, Fan Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi and Kunlun Li\u003c/em\u003e, \"\u003ca href=\"https://dl.acm.org/doi/10.1145/3523227.3547405\" rel=\"nofollow\"\u003eMerlin HugeCTR: GPU-accelerated Recommender System Training and Inference\u003c/a\u003e,\" Proceedings of the 16th ACM Conference on Recommender Systems, pp. 534-537, 2022.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTalks\u003c/h3\u003e\u003ca id=\"user-content-talks\" class=\"anchor\" aria-label=\"Permalink: Talks\" href=\"#talks\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eConference / Website\u003c/th\u003e\n\u003cth\u003eTitle\u003c/th\u003e\n\u003cth\u003eDate\u003c/th\u003e\n\u003cth\u003eSpeaker\u003c/th\u003e\n\u003cth\u003eLanguage\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eACM RecSys 2022\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://vimeo.com/752339625/6ecec7fa70\" rel=\"nofollow\"\u003eA GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eSeptember 2022\u003c/td\u003e\n\u003ctd\u003eMatthias Langer\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShort Videos Episode 1\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.bilibili.com/video/BV1jT411E7VJ/\" rel=\"nofollow\"\u003eMerlin HugeCTR：GPU 加速的推荐系统框架\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMay 2022\u003c/td\u003e\n\u003ctd\u003eJoey Wang\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShort Videos Episode 2\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.bilibili.com/video/BV1PW4y127UA/\" rel=\"nofollow\"\u003eHugeCTR 分级参数服务器如何加速推理\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMay 2022\u003c/td\u003e\n\u003ctd\u003eJoey Wang\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eShort Videos Episode 3\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.bilibili.com/video/BV1mG411n7XH/\" rel=\"nofollow\"\u003e使用 HugeCTR SOK 加速 TensorFlow 训练\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMay 2022\u003c/td\u003e\n\u003ctd\u003eGems Guo\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC Sping 2022\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41126/\" rel=\"nofollow\"\u003eMerlin HugeCTR: Distributed Hierarchical Inference Parameter Server Using GPU Embedding Cache\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMarch 2022\u003c/td\u003e\n\u003ctd\u003eMatthias Langer, Yingcan Wei, Yu Fan\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAPSARA 2021\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://yunqi.aliyun.com/2021/agenda/session205?spm=5176.23948577a2c4e.J_6988780170.27.5ae7379893BcVp\" rel=\"nofollow\"\u003eGPU 推荐系统 Merlin\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2021\u003c/td\u003e\n\u003ctd\u003eJoey Wang\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC Spring 2021\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31820/\" rel=\"nofollow\"\u003eLearn how Tencent Deployed an Advertising System on the Merlin GPU Recommender Framework\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eApril 2021\u003c/td\u003e\n\u003ctd\u003eXiangting Kong, Joey Wang\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC Spring 2021\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31269/\" rel=\"nofollow\"\u003eMerlin HugeCTR: Deep Dive Into Performance Optimization\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eApril 2021\u003c/td\u003e\n\u003ctd\u003eMinseok Lee\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC Spring 2021\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31425/\" rel=\"nofollow\"\u003eIntegrate HugeCTR Embedding with TensorFlow\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eApril 2021\u003c/td\u003e\n\u003ctd\u003eJianbing Dong\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC China 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.cn/on-demand/session/gtccn2020-cns20516/\" rel=\"nofollow\"\u003eMERLIN HUGECTR ：深入研究性能优化\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2020\u003c/td\u003e\n\u003ctd\u003eMinseok Lee\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC China 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.cn/on-demand/session/gtccn2020-cns20483/\" rel=\"nofollow\"\u003e性能提升 7 倍 + 的高性能 GPU 广告推荐加速系统的落地实现\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2020\u003c/td\u003e\n\u003ctd\u003eXiangting Kong\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC China 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.cn/on-demand/session/gtccn2020-cns20626/\" rel=\"nofollow\"\u003e使用 GPU EMBEDDING CACHE 加速 CTR 推理过程\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2020\u003c/td\u003e\n\u003ctd\u003eFan Yu\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC China 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.cn/on-demand/session/gtccn2020-cns20377/\" rel=\"nofollow\"\u003e将 HUGECTR EMBEDDING 集成于 TENSORFLOW\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2020\u003c/td\u003e\n\u003ctd\u003eJianbing Dong\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC Spring 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21455/\" rel=\"nofollow\"\u003eHugeCTR: High-Performance Click-Through Rate Estimation Training\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMarch 2020\u003c/td\u003e\n\u003ctd\u003eMinseok Lee, Joey Wang\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGTC China 2019\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://www.nvidia.cn/on-demand/session/gtcchina2019-cn9794/\" rel=\"nofollow\"\u003eHUGECTR: GPU 加速的推荐系统训练\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eOct 2019\u003c/td\u003e\n\u003ctd\u003eJoey Wang\u003c/td\u003e\n\u003ctd\u003e中文\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\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBlogs\u003c/h3\u003e\u003ca id=\"user-content-blogs\" class=\"anchor\" aria-label=\"Permalink: Blogs\" href=\"#blogs\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eConference / Website\u003c/th\u003e\n\u003cth\u003eTitle\u003c/th\u003e\n\u003cth\u003eDate\u003c/th\u003e\n\u003cth\u003eAuthors\u003c/th\u003e\n\u003cth\u003eLanguage\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eWechat Blog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mp.weixin.qq.com/s/sFmJXZ53Qj4J7iGkzGvQbw\" rel=\"nofollow\"\u003eMerlin HugeCTR 分级参数服务器系列之三：集成到TensorFlow\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eNov. 2022\u003c/td\u003e\n\u003ctd\u003eKingsley Liu\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/zh-cn/blog/scaling-recommendation-system-inference-with-merlin-hierarchical-parameter-server/\" rel=\"nofollow\"\u003eScaling Recommendation System Inference with Merlin Hierarchical Parameter Server/使用 Merlin 分层参数服务器扩展推荐系统推理\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eAugust 2022\u003c/td\u003e\n\u003ctd\u003eShashank Verma, Wenwen Gao, Yingcan Wei, Matthias Langer, Jerry Shi, Fan Yu, Kingsley Liu, Minseok Lee\u003c/td\u003e\n\u003ctd\u003eEnglish/中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.cn/zh-cn/blog/merlin-hugectr-sparse-operation-kit-series-2/\" rel=\"nofollow\"\u003eMerlin HugeCTR Sparse Operation Kit 系列之二\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eJune 2022\u003c/td\u003e\n\u003ctd\u003eKunlun Li\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/zh-cn/blog/merlin-hugectr-sparse-operation-kit-part-1/\" rel=\"nofollow\"\u003eMerlin HugeCTR Sparse Operation Kit 系列之一\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMarch 2022\u003c/td\u003e\n\u003ctd\u003eGems Guo, Jianbing Dong\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWechat Blog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mp.weixin.qq.com/s/z-K3UNg6-ysrfKe3C6McZg\" rel=\"nofollow\"\u003eMerlin HugeCTR 分级参数服务器系列之二\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMarch 2022\u003c/td\u003e\n\u003ctd\u003eYingcan Wei, Matthias Langer, Jerry Shi\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eWechat Blog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mp.weixin.qq.com/s/5_AKe6f_nJjddCLZU28P2A\" rel=\"nofollow\"\u003eMerlin HugeCTR 分级参数服务器系列之一\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eJan. 2022\u003c/td\u003e\n\u003ctd\u003eYingcan Wei, Jerry Shi\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/blog/accelerating-embedding-with-the-hugectr-tensorflow-embedding-plugin/\" rel=\"nofollow\"\u003eAccelerating Embedding with the HugeCTR TensorFlow Embedding Plugin\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eSept 2021\u003c/td\u003e\n\u003ctd\u003eVinh Nguyen, Ann Spencer, Joey Wang and Jianbing Dong\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emedium.com\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://medium.com/nvidia-merlin/optimizing-meituans-machine-learning-platform-an-interview-with-jun-huang-7e046143131f\" rel=\"nofollow\"\u003eOptimizing Meituan’s Machine Learning Platform: An Interview with Jun Huang\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eSept 2021\u003c/td\u003e\n\u003ctd\u003eSheng Luo and Benedikt Schifferer\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emedium.com\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://medium.com/nvidia-merlin/leading-design-and-development-of-the-advertising-recommender-system-at-tencent-an-interview-with-37f1eed898a7\" rel=\"nofollow\"\u003eLeading Design and Development of the Advertising Recommender System at Tencent: An Interview with Xiangting Kong\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eSept 2021\u003c/td\u003e\n\u003ctd\u003eXiangting Kong, Ann Spencer\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/zh-cn/blog/scaling-and-accelerating-large-deep-learning-recommender-systems-hugectr-series-part-1/\" rel=\"nofollow\"\u003e扩展和加速大型深度学习推荐系统 – HugeCTR 系列第 1 部分\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eJune 2021\u003c/td\u003e\n\u003ctd\u003eMinseok Lee\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/zh-cn/blog/training-large-deep-learning-recommender-models-with-merlin-hugectrs-python-apis-hugectr-series-part2/\" rel=\"nofollow\"\u003e使用 Merlin HugeCTR 的 Python API 训练大型深度学习推荐模型 – HugeCTR 系列第 2 部分\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eJune 2021\u003c/td\u003e\n\u003ctd\u003eVinh Nguyen\u003c/td\u003e\n\u003ctd\u003e中文\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emedium.com\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://medium.com/nvidia-merlin/training-large-deep-learning-recommender-models-with-merlin-hugectrs-python-apis-hugectr-series-69a666e0bdb7\" rel=\"nofollow\"\u003eTraining large Deep Learning Recommender Models with Merlin HugeCTR’s Python APIs — HugeCTR Series Part 2\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMay 2021\u003c/td\u003e\n\u003ctd\u003eMinseok Lee, Joey Wang, Vinh Nguyen and Ashish Sardana\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003emedium.com\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://medium.com/nvidia-merlin/scaling-and-accelerating-large-deep-learning-recommender-systems-hugectr-series-part-1-c19577acfe9d\" rel=\"nofollow\"\u003eScaling and Accelerating large Deep Learning Recommender Systems — HugeCTR Series Part 1\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eMay 2021\u003c/td\u003e\n\u003ctd\u003eMinseok Lee\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIRS 2020\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://irsworkshop.github.io/2020/publications/paper_21_Oldridge_Merlin.pdf\" rel=\"nofollow\"\u003eMerlin: A GPU Accelerated Recommendation Framework\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eAug 2020\u003c/td\u003e\n\u003ctd\u003eEven Oldridge etc.\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA Devblog\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://developer.nvidia.com/blog/introducing-merlin-hugectr-training-framework-dedicated-to-recommender-systems/\" rel=\"nofollow\"\u003eIntroducing NVIDIA Merlin HugeCTR: A Training Framework Dedicated to Recommender Systems\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eJuly 2020\u003c/td\u003e\n\u003ctd\u003eMinseok Lee and Joey Wang\u003c/td\u003e\n\u003ctd\u003eEnglish\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"HugeCTR","anchor":"hugectr","htmlText":"HugeCTR"},{"level":2,"text":"Table of Contents","anchor":"table-of-contents","htmlText":"Table of Contents"},{"level":2,"text":"Core Features","anchor":"core-features","htmlText":"Core Features"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":2,"text":"HugeCTR SDK","anchor":"hugectr-sdk","htmlText":"HugeCTR SDK"},{"level":2,"text":"Support and Feedback","anchor":"support-and-feedback","htmlText":"Support and Feedback"},{"level":2,"text":"Contributing to HugeCTR","anchor":"contributing-to-hugectr","htmlText":"Contributing to HugeCTR"},{"level":2,"text":"Additional Resources","anchor":"additional-resources","htmlText":"Additional 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aria-label="Permalink: HugeCTR" href="#hugectr"><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 href="/NVIDIA-Merlin/HugeCTR/blob/main/release_notes.md"><img 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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> <a href="https://nvidia-merlin.github.io/HugeCTR/sparse_operation_kit/master/index.html" rel="nofollow"><img src="https://camo.githubusercontent.com/5d1cb262f24120fa1d2096b444db990250b22b4f57f50f7db426468d7e445d08/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f534f4b253230446f63756d656e746174696f6e2d626c75653f6c6f676f436f6c6f723d626c7565" alt="SOK Documentation" data-canonical-src="https://img.shields.io/badge/SOK%20Documentation-blue?logoColor=blue" style="max-width: 100%;"></a></p> <p dir="auto">HugeCTR is a GPU-accelerated recommender framework designed for training and inference of large deep learning models.</p> <p dir="auto">Design Goals:</p> <ul dir="auto"> <li><strong>Fast</strong>: HugeCTR performs outstandingly in recommendation <a href="https://nvidia-merlin.github.io/HugeCTR/main/performance.html" rel="nofollow">benchmarks</a> including MLPerf.</li> <li><strong>Easy</strong>: Regardless of whether you are a data scientist or machine learning practitioner, we've made it easy for anybody to use HugeCTR with plenty of <a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html" rel="nofollow">documents</a>, <a href="https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/notebooks">notebooks</a> and <a href="https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/samples">samples</a>.</li> <li><strong>Domain Specific</strong>: HugeCTR provides the <a href="https://github.com/NVIDIA-Merlin/HugeCTR#core-features">essentials</a>, so that you can efficiently deploy your recommender models with very large embedding.</li> </ul> <p dir="auto"><strong>NOTE</strong>: If you have any questions in using HugeCTR, please file an issue or join our <a href="https://join.slack.com/t/hugectr/shared_invite/zt-2ji0b305s-SIVB~_XZYtz38JCkT8VFSg" rel="nofollow">Slack channel</a> to have more interactive discussions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Table of Contents</h2><a id="user-content-table-of-contents" class="anchor" aria-label="Permalink: Table of Contents" href="#table-of-contents"><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> <ul dir="auto"> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html" rel="nofollow">Core Features</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html#installing-and-building-hugectr" rel="nofollow">Getting Started</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html#tools" rel="nofollow">HugeCTR SDK</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html" rel="nofollow">Support and Feedback</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html" rel="nofollow">Contributing to HugeCTR</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/additional_resources.html" rel="nofollow">Additional Resources</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Core Features</h2><a id="user-content-core-features" class="anchor" aria-label="Permalink: Core Features" href="#core-features"><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">HugeCTR supports a variety of features, including the following:</p> <ul dir="auto"> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/api/python_interface.html" rel="nofollow">High-Level abstracted Python interface</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#model-parallel-training" rel="nofollow">Model parallel training</a></li> <li><a href="/NVIDIA-Merlin/HugeCTR/blob/main/performance.md">Optimized GPU workflow</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#multi-node-training" rel="nofollow">Multi-node training</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#mixed-precision-training" rel="nofollow">Mixed precision training</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#embedding-training-cache" rel="nofollow">Embedding training cache</a></li> <li><a href="https://github.com/triton-inference-server/hugectr_backend/blob/main/docs/architecture.md#enabling-the-gpu-embedding-cache">GPU embedding cache</a></li> <li><a href="https://github.com/triton-inference-server/hugectr_backend/blob/main/docs/architecture.md#hugectr-backend-framework">GPU / CPU memory sharing mechanism across various inference instances</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#hugectr-to-onnx-converter" rel="nofollow">HugeCTR to ONNX Converter</a></li> <li><a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_core_features.html#hierarchical-parameter-server" rel="nofollow">Hierarchical Parameter Server</a></li> <li><a href="https://github.com/NVIDIA-Merlin/HugeCTR/tree/main/sparse_operation_kit">Sparse Operation Kit</a></li> </ul> <p dir="auto">To learn about our latest enhancements, refer to our <a href="/NVIDIA-Merlin/HugeCTR/blob/main/release_notes.md">release notes</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><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">If you'd like to quickly train a model using the Python interface, do the following:</p> <ol dir="auto"> <li> <p dir="auto">Start a NGC container with your local host directory (/your/host/dir mounted) by running the following command:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="docker run --gpus=all --rm -it --cap-add SYS_NICE -v /your/host/dir:/your/container/dir -w /your/container/dir -it -u $(id -u):$(id -g) nvcr.io/nvidia/merlin/merlin-hugectr:24.06"><pre class="notranslate"><code>docker run --gpus=all --rm -it --cap-add SYS_NICE -v /your/host/dir:/your/container/dir -w /your/container/dir -it -u $(id -u):$(id -g) nvcr.io/nvidia/merlin/merlin-hugectr:24.06 </code></pre></div> <p dir="auto"><strong>NOTE</strong>: The <strong>/your/host/dir</strong> directory is just as visible as the <strong>/your/container/dir</strong> directory. The <strong>/your/host/dir</strong> directory is also your starting directory.</p> <p dir="auto"><strong>NOTE</strong>: HugeCTR uses NCCL to share data between ranks, and NCCL may requires shared memory for IPC and pinned (page-locked) system memory resources. It is recommended that you increase these resources by issuing the following options in the <code>docker run</code> command.</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="-shm-size=1g -ulimit memlock=-1"><pre lang="text" class="notranslate"><code>-shm-size=1g -ulimit memlock=-1 </code></pre></div> </li> <li> <p dir="auto">Write a simple Python script to generate a synthetic dataset:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="# dcn_parquet_generate.py import hugectr from hugectr.tools import DataGeneratorParams, DataGenerator data_generator_params = DataGeneratorParams( format = hugectr.DataReaderType_t.Parquet, label_dim = 1, dense_dim = 13, num_slot = 26, i64_input_key = False, source = "./dcn_parquet/file_list.txt", eval_source = "./dcn_parquet/file_list_test.txt", slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ], dist_type = hugectr.Distribution_t.PowerLaw, power_law_type = hugectr.PowerLaw_t.Short) data_generator = DataGenerator(data_generator_params) data_generator.generate()"><pre class="notranslate"><code># dcn_parquet_generate.py import hugectr from hugectr.tools import DataGeneratorParams, DataGenerator data_generator_params = DataGeneratorParams( format = hugectr.DataReaderType_t.Parquet, label_dim = 1, dense_dim = 13, num_slot = 26, i64_input_key = False, source = "./dcn_parquet/file_list.txt", eval_source = "./dcn_parquet/file_list_test.txt", slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ], dist_type = hugectr.Distribution_t.PowerLaw, power_law_type = hugectr.PowerLaw_t.Short) data_generator = DataGenerator(data_generator_params) data_generator.generate() </code></pre></div> </li> <li> <p dir="auto">Generate the Parquet dataset for your DCN model by running the following command:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python dcn_parquet_generate.py"><pre class="notranslate"><code>python dcn_parquet_generate.py </code></pre></div> <p dir="auto"><strong>NOTE</strong>: The generated dataset will reside in the folder <code>./dcn_parquet</code>, which contains training and evaluation data.</p> </li> <li> <p dir="auto">Write a simple Python script for training:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="# dcn_parquet_train.py import hugectr from mpi4py import MPI solver = hugectr.CreateSolver(max_eval_batches = 1280, batchsize_eval = 1024, batchsize = 1024, lr = 0.001, vvgpu = [[0]], repeat_dataset = True) reader = hugectr.DataReaderParams(data_reader_type = hugectr.DataReaderType_t.Parquet, source = ["./dcn_parquet/file_list.txt"], eval_source = "./dcn_parquet/file_list_test.txt", slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ]) optimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam, update_type = hugectr.Update_t.Global) model = hugectr.Model(solver, reader, optimizer) model.add(hugectr.Input(label_dim = 1, label_name = "label", dense_dim = 13, dense_name = "dense", data_reader_sparse_param_array = [hugectr.DataReaderSparseParam("data1", 1, True, 26)])) model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.DistributedSlotSparseEmbeddingHash, workspace_size_per_gpu_in_mb = 75, embedding_vec_size = 16, combiner = "sum", sparse_embedding_name = "sparse_embedding1", bottom_name = "data1", optimizer = optimizer)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Reshape, bottom_names = ["sparse_embedding1"], top_names = ["reshape1"], leading_dim=416)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat, bottom_names = ["reshape1", "dense"], top_names = ["concat1"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.MultiCross, bottom_names = ["concat1"], top_names = ["multicross1"], num_layers=6)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct, bottom_names = ["concat1"], top_names = ["fc1"], num_output=1024)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU, bottom_names = ["fc1"], top_names = ["relu1"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout, bottom_names = ["relu1"], top_names = ["dropout1"], dropout_rate=0.5)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat, bottom_names = ["dropout1", "multicross1"], top_names = ["concat2"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct, bottom_names = ["concat2"], top_names = ["fc2"], num_output=1)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss, bottom_names = ["fc2", "label"], top_names = ["loss"])) model.compile() model.summary() model.graph_to_json(graph_config_file = "dcn.json") model.fit(max_iter = 5120, display = 200, eval_interval = 1000, snapshot = 5000, snapshot_prefix = "dcn")"><pre class="notranslate"><code># dcn_parquet_train.py import hugectr from mpi4py import MPI solver = hugectr.CreateSolver(max_eval_batches = 1280, batchsize_eval = 1024, batchsize = 1024, lr = 0.001, vvgpu = [[0]], repeat_dataset = True) reader = hugectr.DataReaderParams(data_reader_type = hugectr.DataReaderType_t.Parquet, source = ["./dcn_parquet/file_list.txt"], eval_source = "./dcn_parquet/file_list_test.txt", slot_size_array = [39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543, 63, 63, 39884, 39043, 17289, 7420, 20263, 3, 7120, 1543 ]) optimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.Adam, update_type = hugectr.Update_t.Global) model = hugectr.Model(solver, reader, optimizer) model.add(hugectr.Input(label_dim = 1, label_name = "label", dense_dim = 13, dense_name = "dense", data_reader_sparse_param_array = [hugectr.DataReaderSparseParam("data1", 1, True, 26)])) model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.DistributedSlotSparseEmbeddingHash, workspace_size_per_gpu_in_mb = 75, embedding_vec_size = 16, combiner = "sum", sparse_embedding_name = "sparse_embedding1", bottom_name = "data1", optimizer = optimizer)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Reshape, bottom_names = ["sparse_embedding1"], top_names = ["reshape1"], leading_dim=416)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat, bottom_names = ["reshape1", "dense"], top_names = ["concat1"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.MultiCross, bottom_names = ["concat1"], top_names = ["multicross1"], num_layers=6)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct, bottom_names = ["concat1"], top_names = ["fc1"], num_output=1024)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU, bottom_names = ["fc1"], top_names = ["relu1"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Dropout, bottom_names = ["relu1"], top_names = ["dropout1"], dropout_rate=0.5)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Concat, bottom_names = ["dropout1", "multicross1"], top_names = ["concat2"])) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct, bottom_names = ["concat2"], top_names = ["fc2"], num_output=1)) model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss, bottom_names = ["fc2", "label"], top_names = ["loss"])) model.compile() model.summary() model.graph_to_json(graph_config_file = "dcn.json") model.fit(max_iter = 5120, display = 200, eval_interval = 1000, snapshot = 5000, snapshot_prefix = "dcn") </code></pre></div> <p dir="auto"><strong>NOTE</strong>: Ensure that the paths to the synthetic datasets are correct with respect to this Python script. <code>data_reader_type</code>, <code>check_type</code>, <code>label_dim</code>, <code>dense_dim</code>, and <code>data_reader_sparse_param_array</code> should be consistent with the generated dataset.</p> </li> <li> <p dir="auto">Train the model by running the following command:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="python dcn_parquet_train.py"><pre class="notranslate"><code>python dcn_parquet_train.py </code></pre></div> <p dir="auto"><strong>NOTE</strong>: It is presumed that the evaluation AUC value is incorrect since randomly generated datasets are being used. When the training is done, files that contain the dumped graph JSON, saved model weights, and optimizer states will be generated.</p> </li> </ol> <p dir="auto">For more information, refer to the <a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_user_guide.html" rel="nofollow">HugeCTR User Guide</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">HugeCTR SDK</h2><a id="user-content-hugectr-sdk" class="anchor" aria-label="Permalink: HugeCTR SDK" href="#hugectr-sdk"><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">We're able to support external developers who can't use HugeCTR directly by exporting important HugeCTR components using:</p> <ul dir="auto"> <li>Sparse Operation Kit <a href="/NVIDIA-Merlin/HugeCTR/blob/main/sparse_operation_kit">directory</a> | <a href="https://nvidia-merlin.github.io/HugeCTR/sparse_operation_kit/master/" rel="nofollow">documentation</a>: a python package wrapped with GPU accelerated operations dedicated for sparse training/inference cases.</li> <li><a href="/NVIDIA-Merlin/HugeCTR/blob/main/gpu_cache">GPU Embedding Cache</a>: embedding cache available on the GPU memory designed for CTR inference workload.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Support and Feedback</h2><a id="user-content-support-and-feedback" class="anchor" aria-label="Permalink: Support and Feedback" href="#support-and-feedback"><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">If you encounter any issues or have questions, go to <a href="https://github.com/NVIDIA/HugeCTR/issues">https://github.com/NVIDIA/HugeCTR/issues</a> and submit an issue so that we can provide you with the necessary resolutions and answers. To further advance the HugeCTR Roadmap, we encourage you to share all the details regarding your recommender system pipeline using this <a href="https://developer.nvidia.com/merlin-devzone-survey" rel="nofollow">survey</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributing to HugeCTR</h2><a id="user-content-contributing-to-hugectr" class="anchor" aria-label="Permalink: Contributing to HugeCTR" href="#contributing-to-hugectr"><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">With HugeCTR being an open source project, we welcome contributions from the general public. With your contributions, we can continue to improve HugeCTR's quality and performance. To learn how to contribute, refer to our <a href="https://nvidia-merlin.github.io/HugeCTR/main/hugectr_contributor_guide.html" rel="nofollow">HugeCTR Contributor Guide</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Additional Resources</h2><a id="user-content-additional-resources" class="anchor" aria-label="Permalink: Additional Resources" href="#additional-resources"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>Webpages</th> </tr> </thead> <tbody> <tr> <td><a href="https://developer.nvidia.com/nvidia-merlin" rel="nofollow">NVIDIA Merlin</a></td> </tr> <tr> <td><a href="https://developer.nvidia.com/nvidia-merlin/hugectr" rel="nofollow">NVIDIA HugeCTR</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Publications</h3><a id="user-content-publications" class="anchor" aria-label="Permalink: Publications" href="#publications"><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"><em>Yingcan Wei, Matthias Langer, Fan Yu, Minseok Lee, Jie Liu, Ji Shi and Zehuan Wang</em>, "<a href="https://dl.acm.org/doi/10.1145/3523227.3546765" rel="nofollow">A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models</a>," Proceedings of the 16th ACM Conference on Recommender Systems, pp. 408-419, 2022.</p> <p dir="auto"><em>Zehuan Wang, Yingcan Wei, Minseok Lee, Matthias Langer, Fan Yu, Jie Liu, Shijie Liu, Daniel G. Abel, Xu Guo, Jianbing Dong, Ji Shi and Kunlun Li</em>, "<a href="https://dl.acm.org/doi/10.1145/3523227.3547405" rel="nofollow">Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference</a>," Proceedings of the 16th ACM Conference on Recommender Systems, pp. 534-537, 2022.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Talks</h3><a id="user-content-talks" class="anchor" aria-label="Permalink: Talks" href="#talks"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>Conference / Website</th> <th>Title</th> <th>Date</th> <th>Speaker</th> <th>Language</th> </tr> </thead> <tbody> <tr> <td>ACM RecSys 2022</td> <td><a href="https://vimeo.com/752339625/6ecec7fa70" rel="nofollow">A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models</a></td> <td>September 2022</td> <td>Matthias Langer</td> <td>English</td> </tr> <tr> <td>Short Videos Episode 1</td> <td><a href="https://www.bilibili.com/video/BV1jT411E7VJ/" rel="nofollow">Merlin HugeCTR：GPU 加速的推荐系统框架</a></td> <td>May 2022</td> <td>Joey Wang</td> <td>中文</td> </tr> <tr> <td>Short Videos Episode 2</td> <td><a href="https://www.bilibili.com/video/BV1PW4y127UA/" rel="nofollow">HugeCTR 分级参数服务器如何加速推理</a></td> <td>May 2022</td> <td>Joey Wang</td> <td>中文</td> </tr> <tr> <td>Short Videos Episode 3</td> <td><a href="https://www.bilibili.com/video/BV1mG411n7XH/" rel="nofollow">使用 HugeCTR SOK 加速 TensorFlow 训练</a></td> <td>May 2022</td> <td>Gems Guo</td> <td>中文</td> </tr> <tr> <td>GTC Sping 2022</td> <td><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41126/" rel="nofollow">Merlin HugeCTR: Distributed Hierarchical Inference Parameter Server Using GPU Embedding Cache</a></td> <td>March 2022</td> <td>Matthias Langer, Yingcan Wei, Yu Fan</td> <td>English</td> </tr> <tr> <td>APSARA 2021</td> <td><a href="https://yunqi.aliyun.com/2021/agenda/session205?spm=5176.23948577a2c4e.J_6988780170.27.5ae7379893BcVp" rel="nofollow">GPU 推荐系统 Merlin</a></td> <td>Oct 2021</td> <td>Joey Wang</td> <td>中文</td> </tr> <tr> <td>GTC Spring 2021</td> <td><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31820/" rel="nofollow">Learn how Tencent Deployed an Advertising System on the Merlin GPU Recommender Framework</a></td> <td>April 2021</td> <td>Xiangting Kong, Joey Wang</td> <td>English</td> </tr> <tr> <td>GTC Spring 2021</td> <td><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31269/" rel="nofollow">Merlin HugeCTR: Deep Dive Into Performance Optimization</a></td> <td>April 2021</td> <td>Minseok Lee</td> <td>English</td> </tr> <tr> <td>GTC Spring 2021</td> <td><a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31425/" rel="nofollow">Integrate HugeCTR Embedding with TensorFlow</a></td> <td>April 2021</td> <td>Jianbing Dong</td> <td>English</td> </tr> <tr> <td>GTC China 2020</td> <td><a href="https://www.nvidia.cn/on-demand/session/gtccn2020-cns20516/" rel="nofollow">MERLIN HUGECTR ：深入研究性能优化</a></td> <td>Oct 2020</td> <td>Minseok Lee</td> <td>English</td> </tr> <tr> <td>GTC China 2020</td> <td><a href="https://www.nvidia.cn/on-demand/session/gtccn2020-cns20483/" rel="nofollow">性能提升 7 倍 + 的高性能 GPU 广告推荐加速系统的落地实现</a></td> <td>Oct 2020</td> <td>Xiangting Kong</td> <td>中文</td> </tr> <tr> <td>GTC China 2020</td> <td><a href="https://www.nvidia.cn/on-demand/session/gtccn2020-cns20626/" rel="nofollow">使用 GPU EMBEDDING CACHE 加速 CTR 推理过程</a></td> <td>Oct 2020</td> <td>Fan Yu</td> <td>中文</td> </tr> <tr> <td>GTC China 2020</td> <td><a href="https://www.nvidia.cn/on-demand/session/gtccn2020-cns20377/" rel="nofollow">将 HUGECTR EMBEDDING 集成于 TENSORFLOW</a></td> <td>Oct 2020</td> <td>Jianbing Dong</td> <td>中文</td> </tr> <tr> <td>GTC Spring 2020</td> <td><a href="https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21455/" rel="nofollow">HugeCTR: High-Performance Click-Through Rate Estimation Training</a></td> <td>March 2020</td> <td>Minseok Lee, Joey Wang</td> <td>English</td> </tr> <tr> <td>GTC China 2019</td> <td><a href="https://www.nvidia.cn/on-demand/session/gtcchina2019-cn9794/" rel="nofollow">HUGECTR: GPU 加速的推荐系统训练</a></td> <td>Oct 2019</td> <td>Joey Wang</td> <td>中文</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Blogs</h3><a id="user-content-blogs" class="anchor" aria-label="Permalink: Blogs" href="#blogs"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>Conference / Website</th> <th>Title</th> <th>Date</th> <th>Authors</th> <th>Language</th> </tr> </thead> <tbody> <tr> <td>Wechat Blog</td> <td><a href="https://mp.weixin.qq.com/s/sFmJXZ53Qj4J7iGkzGvQbw" rel="nofollow">Merlin HugeCTR 分级参数服务器系列之三：集成到TensorFlow</a></td> <td>Nov. 2022</td> <td>Kingsley Liu</td> <td>中文</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/zh-cn/blog/scaling-recommendation-system-inference-with-merlin-hierarchical-parameter-server/" rel="nofollow">Scaling Recommendation System Inference with Merlin Hierarchical Parameter Server/使用 Merlin 分层参数服务器扩展推荐系统推理</a></td> <td>August 2022</td> <td>Shashank Verma, Wenwen Gao, Yingcan Wei, Matthias Langer, Jerry Shi, Fan Yu, Kingsley Liu, Minseok Lee</td> <td>English/中文</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.cn/zh-cn/blog/merlin-hugectr-sparse-operation-kit-series-2/" rel="nofollow">Merlin HugeCTR Sparse Operation Kit 系列之二</a></td> <td>June 2022</td> <td>Kunlun Li</td> <td>中文</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/zh-cn/blog/merlin-hugectr-sparse-operation-kit-part-1/" rel="nofollow">Merlin HugeCTR Sparse Operation Kit 系列之一</a></td> <td>March 2022</td> <td>Gems Guo, Jianbing Dong</td> <td>中文</td> </tr> <tr> <td>Wechat Blog</td> <td><a href="https://mp.weixin.qq.com/s/z-K3UNg6-ysrfKe3C6McZg" rel="nofollow">Merlin HugeCTR 分级参数服务器系列之二</a></td> <td>March 2022</td> <td>Yingcan Wei, Matthias Langer, Jerry Shi</td> <td>中文</td> </tr> <tr> <td>Wechat Blog</td> <td><a href="https://mp.weixin.qq.com/s/5_AKe6f_nJjddCLZU28P2A" rel="nofollow">Merlin HugeCTR 分级参数服务器系列之一</a></td> <td>Jan. 2022</td> <td>Yingcan Wei, Jerry Shi</td> <td>中文</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/blog/accelerating-embedding-with-the-hugectr-tensorflow-embedding-plugin/" rel="nofollow">Accelerating Embedding with the HugeCTR TensorFlow Embedding Plugin</a></td> <td>Sept 2021</td> <td>Vinh Nguyen, Ann Spencer, Joey Wang and Jianbing Dong</td> <td>English</td> </tr> <tr> <td>medium.com</td> <td><a href="https://medium.com/nvidia-merlin/optimizing-meituans-machine-learning-platform-an-interview-with-jun-huang-7e046143131f" rel="nofollow">Optimizing Meituan’s Machine Learning Platform: An Interview with Jun Huang</a></td> <td>Sept 2021</td> <td>Sheng Luo and Benedikt Schifferer</td> <td>English</td> </tr> <tr> <td>medium.com</td> <td><a href="https://medium.com/nvidia-merlin/leading-design-and-development-of-the-advertising-recommender-system-at-tencent-an-interview-with-37f1eed898a7" rel="nofollow">Leading Design and Development of the Advertising Recommender System at Tencent: An Interview with Xiangting Kong</a></td> <td>Sept 2021</td> <td>Xiangting Kong, Ann Spencer</td> <td>English</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/zh-cn/blog/scaling-and-accelerating-large-deep-learning-recommender-systems-hugectr-series-part-1/" rel="nofollow">扩展和加速大型深度学习推荐系统 – HugeCTR 系列第 1 部分</a></td> <td>June 2021</td> <td>Minseok Lee</td> <td>中文</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/zh-cn/blog/training-large-deep-learning-recommender-models-with-merlin-hugectrs-python-apis-hugectr-series-part2/" rel="nofollow">使用 Merlin HugeCTR 的 Python API 训练大型深度学习推荐模型 – HugeCTR 系列第 2 部分</a></td> <td>June 2021</td> <td>Vinh Nguyen</td> <td>中文</td> </tr> <tr> <td>medium.com</td> <td><a href="https://medium.com/nvidia-merlin/training-large-deep-learning-recommender-models-with-merlin-hugectrs-python-apis-hugectr-series-69a666e0bdb7" rel="nofollow">Training large Deep Learning Recommender Models with Merlin HugeCTR’s Python APIs — HugeCTR Series Part 2</a></td> <td>May 2021</td> <td>Minseok Lee, Joey Wang, Vinh Nguyen and Ashish Sardana</td> <td>English</td> </tr> <tr> <td>medium.com</td> <td><a href="https://medium.com/nvidia-merlin/scaling-and-accelerating-large-deep-learning-recommender-systems-hugectr-series-part-1-c19577acfe9d" rel="nofollow">Scaling and Accelerating large Deep Learning Recommender Systems — HugeCTR Series Part 1</a></td> <td>May 2021</td> <td>Minseok Lee</td> <td>English</td> </tr> <tr> <td>IRS 2020</td> <td><a href="https://irsworkshop.github.io/2020/publications/paper_21_Oldridge_Merlin.pdf" rel="nofollow">Merlin: A GPU Accelerated Recommendation Framework</a></td> <td>Aug 2020</td> <td>Even Oldridge etc.</td> <td>English</td> </tr> <tr> <td>NVIDIA Devblog</td> <td><a href="https://developer.nvidia.com/blog/introducing-merlin-hugectr-training-framework-dedicated-to-recommender-systems/" rel="nofollow">Introducing NVIDIA Merlin HugeCTR: A Training Framework Dedicated to Recommender Systems</a></td> <td>July 2020</td> <td>Minseok Lee and Joey Wang</td> <td>English</td> </tr> </tbody> </table></markdown-accessiblity-table> </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="UnoP/lHl2nagdq/0FadbW/BK7wMK2G/BKaVVbp6WGmCOW1oUPxBhdqoH7rNiSIwD7b0wIF8SNRkJGtLda8f13Q==" /> </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"> HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/cpp" title="Topic: cpp" data-view-component="true" class="topic-tag topic-tag-link"> cpp </a> <a href="/topics/recommendation-system" title="Topic: recommendation-system" data-view-component="true" class="topic-tag topic-tag-link"> recommendation-system </a> <a 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<h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/NVIDIA-Merlin/HugeCTR/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>200</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%2FHugeCTR&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/HugeCTR/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="41" data-view-component="true" class="Counter">41</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/HugeCTR/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;">Merlin: HugeCTR 24.06</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="2024-06-14T07:14:49Z" 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/HugeCTR/releases" data-view-component="true" class="Link">+ 40 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <include-fragment src="/NVIDIA-Merlin/HugeCTR/packages_list?current_repository=HugeCTR" aria-busy="true" aria-label="Loading latest packages"> <h2 class="h4 mb-3"> <a 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data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 15.8%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 1.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 0.5%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA-Merlin/HugeCTR/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>39.7%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA-Merlin/HugeCTR/search?l=cuda" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3A4E3A;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cuda</span> <span>26.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA-Merlin/HugeCTR/search?l=jupyter-notebook" data-ga-click="Repository, language stats 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