Get Started With NVIDIA Metropolis Microservices
Metropolis microservices give you powerful, customizable, cloud-native building blocks for developing vision AI applications and solutions—built to run on NVIDIA Cloud and data center GPUs.
Release Highlights
This latest release of NVIDIA Metropolis microservices (v2) for enterprise GPUs features:
- New reference workflows and guides on using digital twin and synthetic data generation to improve model robustness and facilitate end-to-end application development, tuning, and validation.
- Advanced new transformer-based detection and ReID models to significantly boost accuracy and robustness in varied tracking scenarios.
- The new Real-Time Location System (RTLS) mode in multi-camera tracking for real-time, accurate location updates.
- Enhanced overall system performance with the introduction of Single-View 3D Tracking (SV3DT) and improved visualization tools across single- and multi-camera tracking environments.
- Upgraded underlying systems to support dynamic configurations, including more efficient Kafka message consumption and the ability to update configurations and calibrations on the fly.
- Edge-to-cloud connectivity features for seamless streaming and inferencing using WebRTC and OpenVPN, alongside dynamic camera stream management in Docker and Kubernetes deployments.
- Enhanced user interfaces with new visualization options, including thumbnails for event cards and polygonal field of view drawings, improving interaction and accessibility.
- Critical bug fixes across various components to enhance stability and user experience, with updates to memory management and UI responsiveness.
Featured Resources
Optimizing End-to-End Vision Workflow Development
The multi-camera tracking reference workflow brings the entire development pipeline from data generation to model training to application development. It helps developers build complex vision AI applications for large spaces.
Read the BlogUsing Microservices to Build Complex and Large-Scale Vision AI Applications
Explore a suite of tailored microservices for vision AI, including multi-camera analytics, generative AI integration, and industry-specific automation—all designed for scalability and state-of-the-art accuracy.
Watch the VideoSolving Computer Vision Grand Challenges in One Click
Discover the process of building advanced cloud-native vision AI applications through an end-to-end workflow using NVIDIA's essential tools. These include using synthetic data generation with NVIDIA Isaac Sim™, transfer learning with NVIDIA TAO, and pre-built microservices from NVIDIA Metropolis.
Watch the VideoGet Started Video Tutorial
Additional Resources
Documentation
Software and Forum
- Download the Software
- Forum ( Please note: Forum access for technical questions requires prior application approval to access the software )
| Ethical AI |
| NVIDIA platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Also, work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended. |