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  • SearchSearch thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
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  • Displaying 23 results
    NVIDIA
    NVIDIA
    PyG
    PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data.
    Container
    NVIDIA
    NVIDIA
    Merlin PyTorch
    The Merlin PyTorch container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch, and serve the trained model on Triton Inference Server.
    Container
    NVIDIA
    NVIDIA
    Merlin Tensorflow Training
    This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with TensorFlow.
    Container
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    Wide & Deep for TensorFlow2
    Wide & Deep Recommender model.
    Resource
    NVIDIA
    NVIDIA
    Merlin PyTorch Training
    This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with PyTorch.
    Container
    NVIDIA
    NVIDIA
    Merlin HugeCTR
    The Merlin HugeCTR container enables you to perform data preprocessing, feature engineering, train models with HugeCTR, and then serve the trained model with Triton Inference Server.
    Container
    NVIDIA
    NVIDIA
    Merlin Training
    This container allows users to do preprocessing and feature engineering with NVTabular, and then train a deep-learning based recommender system model with HugeCTR.
    Container
    NVIDIA
    NVIDIA
    Merlin PyTorch Inference
    This container allows users to deploy NVTabular workflows and PyTorch models to Triton Inference server for production.
    Container
    NVIDIA
    NVIDIA
    Merlin Tensorflow Inference
    This container allows users to deploy NVTabular workflows and TensorFlow models to Triton Inference server for production.
    Container
    NVIDIA
    NVIDIA
    DLI Recommender Systems Course - Base Environment
    Base environment used in the NVIDIA Deep Learning Institute (DLI) Course Building Intelligent Recommender Systems, along with Next Steps project.
    Container
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM checkpoint (PyTorch, FP32, BS64k, Base, FL15)
    DLRM PyTorch checkpoint trained on Criteo Dataset with FreqLimit=15 on A100 without AMP
    Model
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    NCF for TensorFlow1
    The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    SIM checkpoint (TensorFlow2, prebatch4096)
    SIM TensorFlow2 checkpoint trained on Amazon Books 2014 Dataset prebatched with size of 4096
    Model
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM checkpoint (TensorFlow2, TF32, BS64k, Base, FL15)
    DLRM TensorFlow2 checkpoint trained on Criteo Dataset with FreqLimit=15 on A100 with TF32
    Model
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    SIM for TensorFlow2
    Search-based Interest Model (SIM) is a system for predicting user behavior given sequences of previous interactions.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM for PyTorch
    The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    Wide&Deep TF2 checkpoint (Base, 128k, AMP, NVTabular, Multihot)
    Wide&Deep Base TensorFlow2 checkpoint trained with AMP on NVTabular preprocessed dataset with multihot embeddings
    Model
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    VAE for TensorFlow1
    The Variational Autoencoder for collaborative filtering focuses on providing recommendations. This is an optimized implementation.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    DLRM for TensorFlow2
    The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    Wide & Deep TF checkpoint (Base, 128k, v1, AMP)
    Wide & Deep Base TensorFlow checkpoint trained with AMP
    Model
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    NCF for PyTorch
    The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.
    Resource
    NVIDIA Deep Learning Examples
    NVIDIA Deep Learning Examples
    Wide & Deep for TensorFlow1
    Wide & Deep Recommender model.
    Resource
    Collection
    NVIDIA
    NVIDIA
    Deep Learning Frameworks
    This collection contains performance-optimized Deep Learning frameworks.
    9