Develop and deploy applications faster with GPU-optimized containers.
Simplifying AI and HPC Workflows
A container is a portable unit of software that combines the application and all its dependencies into a single package that is agnostic to the underlying host OS, removing the need to build complex environments and simplifying the application development-to-deployment process.
The NVIDIA® NGC™ catalog contains a host of GPU-optimized containers for deep learning, machine learning, visualization, and high-performance computing (HPC) applications that are tested for performance, security, and scalability.
For Data Scientists, Researchers, and Developers
Develop Faster with Containers
NGC containers allow you to focus on application development instead of building the environment needed to run your applications.
- Diverse set of containers spanning a multitude of use cases
- Built-in libraries and dependencies for easy compiling of custom applications
- Faster training with Automatic Mixed Precision (AMP) and minimal code changes
- Reduced time to solution by scaling up from single-node to multi-node systems
- Extremely portable, allowing you to develop on the cloud, on premises, or at the edge
For Machine Learning Engineers and IT
Seamlessly Deploy to Production
The containers are tested on various platforms and architectures, enabling seamless deployment on a wide variety of systems and platforms.
- Flexible to run on bare metal, virtual machines (VMs), and Kubernetes, including various architectures such as x86, ARM, and IBM Power
- Highly versatile with support for various container runtimes such as Docker, Singularity, cri-o, and containerd
- Enterprise-ready with containers scanned for common vulnerabilities and exposures (CVEs)
- Backed by optional enterprise support to troubleshoot issues for NVIDIA-built software
NVIDIA-built containers are updated monthly and third-party software is updated regularly to deliver the features needed to extract maximum performance from your existing infrastructure and reduce time to solution.
BERT-Large for Natural Language Processing
BERT-Large leverages mixed precision arithmetic and Tensor Cores on Volta V100 and Ampere A100 GPUs for faster training times while maintaining target accuracy.
BERT-Large and Training performance with TensorFlow on a single node 8x V100 (16GB) & A100 (40GB). Mixed Precision. Batch size for BERT: 3 (V100), 24 (A100)
ResNet50 v1.5 for Image Processing
This model is trained with mixed precision using Tensor Cores on Volta, Turing and NVIDIA Ampere GPU architectures for faster training.
ResNet 50 performance with TensorFlow on single-node 8x V100 (16GB) and A100 (40 GB). Mixed Precision. Batch size for ResNet50: 26
Matlab for Deep Learning
Continuous development of Matlab’s Deep Learning container improves performance for training and inference
Windows 10, Intel Xeon E5-2623 @2.4GHz, NVIDIA Titan V 12GB GPUs
Built By Developers For Developers
Get started today by selecting from over 80 containerized software applications and SDKs, developed by NVIDIA and our ecosystem of partners.
NVIDIA Triton Inference Server
NVIDIA Triton™ Inference Server is an open-source inference solution that maximizes utilization of and performance on GPUs.
NVIDIA TensorRT® is a C++ library that facilitates high-performance inference on NVIDIA GPUs.
NVIDIA HPC SDK
The NVIDIA HPC SDK is a comprehensive suite of compilers, libraries, and tools for building, deploying, and managing HPC applications.
DeepStream is the streaming analytics toolkit for AI-based video, audio, and image understanding for multi-sensor processing.
NVIDIA Riva, is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs.
HugeCTR, a component of NVIDIA Merlin™, is a deep neural network training framework that is capable of distributed training across multiple GPUs and nodes for maximum performance.
NGC Catalog Resources
Learn how to use the NGC catalog with these step-by-step instructions.
Watch all the top NGC sessions on-demand.
Walk through how to use the NGC catalog with these video tutorials.