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Search thousands of GPU-optimized Containers, pretrained Models, SDKs, and Helm charts—ready to accelerate AI, digital twins, and HPC from cloud to edge.
Accelerate custom generative AI app deployment using pre-built containers with optimized AI models.

Displaying 24 results

PyG
NVIDIA
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
Merlin PyTorch
NVIDIA
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
Merlin Tensorflow Training
NVIDIA
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
Wide & Deep for TensorFlow2
NVIDIA Deep Learning Examples
Wide & Deep Recommender model.
Resource
Merlin PyTorch Training
NVIDIA
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
Merlin HugeCTR
NVIDIA
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
Merlin Training
NVIDIA
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
Merlin PyTorch Inference
NVIDIA
This container allows users to deploy NVTabular workflows and PyTorch models to Triton Inference server for production.
Container
Merlin Tensorflow Inference
NVIDIA
This container allows users to deploy NVTabular workflows and TensorFlow models to Triton Inference server for production.
Container
Kumo Relational Foundation Model (NIM)
NVIDIA
Structured data relational foundation model
Container
DLI Recommender Systems Course - Base Environment
NVIDIA
Base environment used in the NVIDIA Deep Learning Institute (DLI) Course Building Intelligent Recommender Systems, along with Next Steps project.
Container
DLRM checkpoint (PyTorch, FP32, BS64k, Base, FL15)
NVIDIA Deep Learning Examples
DLRM PyTorch checkpoint trained on Criteo Dataset with FreqLimit=15 on A100 without AMP
Model
NCF for TensorFlow1
NVIDIA Deep Learning Examples
The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.
Resource
SIM checkpoint (TensorFlow2, prebatch4096)
NVIDIA Deep Learning Examples
SIM TensorFlow2 checkpoint trained on Amazon Books 2014 Dataset prebatched with size of 4096
Model
DLRM checkpoint (TensorFlow2, TF32, BS64k, Base, FL15)
NVIDIA Deep Learning Examples
DLRM TensorFlow2 checkpoint trained on Criteo Dataset with FreqLimit=15 on A100 with TF32
Model
SIM for TensorFlow2
NVIDIA Deep Learning Examples
Search-based Interest Model (SIM) is a system for predicting user behavior given sequences of previous interactions.
Resource
DLRM for PyTorch
NVIDIA Deep Learning Examples
The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
Resource
Wide&Deep TF2 checkpoint (Base, 128k, AMP, NVTabular, Multihot)
NVIDIA Deep Learning Examples
Wide&Deep Base TensorFlow2 checkpoint trained with AMP on NVTabular preprocessed dataset with multihot embeddings
Model
VAE for TensorFlow1
NVIDIA Deep Learning Examples
The Variational Autoencoder for collaborative filtering focuses on providing recommendations. This is an optimized implementation.
Resource
DLRM for TensorFlow2
NVIDIA Deep Learning Examples
The Deep Learning Recommendation Model (DLRM) is a recommendation model designed to make use of both categorical and numerical inputs.
Resource
Wide & Deep TF checkpoint (Base, 128k, v1, AMP)
NVIDIA Deep Learning Examples
Wide & Deep Base TensorFlow checkpoint trained with AMP
Model
NCF for PyTorch
NVIDIA Deep Learning Examples
The NCF model focuses on providing recommendations. This is a modified implementation with improved overfitting and better accuracy.
Resource
Wide & Deep for TensorFlow1
NVIDIA Deep Learning Examples
Wide & Deep Recommender model.
Resource
Deep Learning Frameworks
NVIDIA
This collection contains performance-optimized Deep Learning frameworks.
Collection
1-24 of 24 items

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