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HugeCTR is a high efficiency GPU framework designed for Click-Through-Rate (CTR) estimating training


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Version LICENSE Documentation SOK Documentation

HugeCTR is a GPU-accelerated recommender framework designed for training and inference of large deep learning models.

Design Goals:

  • Fast: HugeCTR performs outstandingly in recommendation benchmarks including MLPerf.
  • Easy: 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 documents, notebooks and samples.
  • Domain Specific: HugeCTR provides the essentials, so that you can efficiently deploy your recommender models with very large embedding.

NOTE: If you have any questions in using HugeCTR, please file an issue or join our Slack channel to have more interactive discussions.

Table of Contents

Core Features

HugeCTR supports a variety of features, including the following:

To learn about our latest enhancements, refer to our release notes.

Getting Started

If you'd like to quickly train a model using the Python interface, do the following:

  1. Start a NGC container with your local host directory (/your/host/dir mounted) by running the following command:

    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)

    NOTE: The /your/host/dir directory is just as visible as the /your/container/dir directory. The /your/host/dir directory is also your starting directory.

    NOTE: 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 docker run command.

    -shm-size=1g -ulimit memlock=-1
  2. Write a simple Python script to generate a synthetic dataset:

    import hugectr
    from 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)
  3. Generate the Parquet dataset for your DCN model by running the following command:


    NOTE: The generated dataset will reside in the folder ./dcn_parquet, which contains training and evaluation data.

  4. Write a simple Python script for training:

    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"],
    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"],
    model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                               bottom_names = ["concat1"],
                               top_names = ["fc1"],
    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"],
    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"],
    model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,
                               bottom_names = ["fc2", "label"],
                               top_names = ["loss"]))
    model.graph_to_json(graph_config_file = "dcn.json") = 5120, display = 200, eval_interval = 1000, snapshot = 5000, snapshot_prefix = "dcn")

    NOTE: Ensure that the paths to the synthetic datasets are correct with respect to this Python script. data_reader_type, check_type, label_dim, dense_dim, and data_reader_sparse_param_array should be consistent with the generated dataset.

  5. Train the model by running the following command:


    NOTE: 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.

For more information, refer to the HugeCTR User Guide.


We're able to support external developers who can't use HugeCTR directly by exporting important HugeCTR components using:

  • Sparse Operation Kit directory | documentation: a python package wrapped with GPU accelerated operations dedicated for sparse training/inference cases.
  • GPU Embedding Cache: embedding cache available on the GPU memory designed for CTR inference workload.

Support and Feedback

If you encounter any issues or have questions, go to 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 survey.

Contributing to HugeCTR

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 HugeCTR Contributor Guide.

Additional Resources



Yingcan Wei, Matthias Langer, Fan Yu, Minseok Lee, Jie Liu, Ji Shi and Zehuan Wang, "A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models," Proceedings of the 16th ACM Conference on Recommender Systems, pp. 408-419, 2022.

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, "Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference," Proceedings of the 16th ACM Conference on Recommender Systems, pp. 534-537, 2022.


Conference / Website Title Date Speaker Language
ACM RecSys 2022 A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models September 2022 Matthias Langer English
Short Videos Episode 1 Merlin HugeCTR:GPU 加速的推荐系统框架 May 2022 Joey Wang 中文
Short Videos Episode 2 HugeCTR 分级参数服务器如何加速推理 May 2022 Joey Wang 中文
Short Videos Episode 3 使用 HugeCTR SOK 加速 TensorFlow 训练 May 2022 Gems Guo 中文
GTC Sping 2022 Merlin HugeCTR: Distributed Hierarchical Inference Parameter Server Using GPU Embedding Cache March 2022 Matthias Langer, Yingcan Wei, Yu Fan English
APSARA 2021 GPU 推荐系统 Merlin Oct 2021 Joey Wang 中文
GTC Spring 2021 Learn how Tencent Deployed an Advertising System on the Merlin GPU Recommender Framework April 2021 Xiangting Kong, Joey Wang English
GTC Spring 2021 Merlin HugeCTR: Deep Dive Into Performance Optimization April 2021 Minseok Lee English
GTC Spring 2021 Integrate HugeCTR Embedding with TensorFlow April 2021 Jianbing Dong English
GTC China 2020 MERLIN HUGECTR :深入研究性能优化 Oct 2020 Minseok Lee English
GTC China 2020 性能提升 7 倍 + 的高性能 GPU 广告推荐加速系统的落地实现 Oct 2020 Xiangting Kong 中文
GTC China 2020 使用 GPU EMBEDDING CACHE 加速 CTR 推理过程 Oct 2020 Fan Yu 中文
GTC China 2020 将 HUGECTR EMBEDDING 集成于 TENSORFLOW Oct 2020 Jianbing Dong 中文
GTC Spring 2020 HugeCTR: High-Performance Click-Through Rate Estimation Training March 2020 Minseok Lee, Joey Wang English
GTC China 2019 HUGECTR: GPU 加速的推荐系统训练 Oct 2019 Joey Wang 中文


Conference / Website Title Date Authors Language
Wechat Blog Merlin HugeCTR 分级参数服务器系列之三:集成到TensorFlow Nov. 2022 Kingsley Liu 中文
NVIDIA Devblog 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 August 2022 Shashank Verma, Wenwen Gao, Yingcan Wei, Matthias Langer, Jerry Shi, Fan Yu, Kingsley Liu, Minseok Lee English/中文
NVIDIA Devblog Merlin HugeCTR Sparse Operation Kit 系列之二 June 2022 Kunlun Li 中文
NVIDIA Devblog Merlin HugeCTR Sparse Operation Kit 系列之一 March 2022 Gems Guo, Jianbing Dong 中文
Wechat Blog Merlin HugeCTR 分级参数服务器系列之二 March 2022 Yingcan Wei, Matthias Langer, Jerry Shi 中文
Wechat Blog Merlin HugeCTR 分级参数服务器系列之一 Jan. 2022 Yingcan Wei, Jerry Shi 中文
NVIDIA Devblog Accelerating Embedding with the HugeCTR TensorFlow Embedding Plugin Sept 2021 Vinh Nguyen, Ann Spencer, Joey Wang and Jianbing Dong English Optimizing Meituan’s Machine Learning Platform: An Interview with Jun Huang Sept 2021 Sheng Luo and Benedikt Schifferer English Leading Design and Development of the Advertising Recommender System at Tencent: An Interview with Xiangting Kong Sept 2021 Xiangting Kong, Ann Spencer English
NVIDIA Devblog 扩展和加速大型深度学习推荐系统 – HugeCTR 系列第 1 部分 June 2021 Minseok Lee 中文
NVIDIA Devblog 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 June 2021 Vinh Nguyen 中文 Training large Deep Learning Recommender Models with Merlin HugeCTR’s Python APIs — HugeCTR Series Part 2 May 2021 Minseok Lee, Joey Wang, Vinh Nguyen and Ashish Sardana English Scaling and Accelerating large Deep Learning Recommender Systems — HugeCTR Series Part 1 May 2021 Minseok Lee English
IRS 2020 Merlin: A GPU Accelerated Recommendation Framework Aug 2020 Even Oldridge etc. English
NVIDIA Devblog Introducing NVIDIA Merlin HugeCTR: A Training Framework Dedicated to Recommender Systems July 2020 Minseok Lee and Joey Wang English