News, updates, and handy materials related to the Savant ecosystem
PyTorch and Savant are both Python-first and CUDA-capable, yet they solve fundamentally different problems. This article explains the design philosophies, target audiences, and use cases of each — and when...
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A step-by-step guide to building a fan-out/fan-in video analytics pipeline that distributes a stream across multiple parallel modules, runs independent inference, and merges results back into a single frame using...
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Savant ecosystem gains Meta-Merge, a new service that completes the Fan-Out/Fan-In pattern by merging metadata from parallel inference branches back into a single unified frame.
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A step-by-step guide to exporting Ultralytics models to the ONNX format for use in Savant's TensorRT-based inference pipeline.
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Savant 0.6.0 brings Blackwell GPU support through custom-built DeepStream 7.1 with TensorRT 10.9, plus Python 3.12 for improved CPU-bound performance.
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An in-depth landscape overview of current and legacy tech stacks for computer vision applications, covering frameworks from OpenCV to DeepStream to Savant.
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Savant ecosystem expands with Router, a new microservice for simplifying and optimizing the routing of video streams across complex CV pipelines.
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Retina RTSP Service enables seamless integration of real-time video streaming into Savant-powered AI video pipelines with low latency and high performance.
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Savant 0.5.1 brings DeepStream 7.0 support, 34 real-world samples, Key-Value API, original resolution processing, and many more enhancements.
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We explain the advantages of USB/CSI cameras over RTSP cameras for projects working on Nvidia Jetson Orin hardware.
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A deep dive into camera properties that matter for computer vision: sensor quality, lens selection, chip processing, and software encoding.
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A minor bugfix update to Savant 0.4.x, fixing issues with JSON/image/video sink adapters.
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A minor bugfix update to Savant 0.4.x for JSON/image/video sink adapters.
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Savant 0.4.1 continues the 0.4.x release cycle with new features, bug fixes, and a new watchdog service for pipeline health monitoring.
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Learn how to record and replay video streaming traffic for reproducible testing, troubleshooting, and code improvement in Savant pipelines.
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Savant now supports hardware-accelerated video transcoding and composition, enabling custom dashboards, resolution changes, and video walls.
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Savant 0.4.0 focuses on system usability, interfacing, advanced computer vision, and video analytics on top of DeepStream 6.4.
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New adapters integrate Amazon Kinesis Video Streams with Savant, combining video-optimized streaming and storage in the cloud.
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Learn how to dynamically attach and detach video sources to a running Savant pipeline using Docker, including Unix domain sockets and TCP/IP.
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Savant 0.3.11 adds MJPEG USB camera support -- a popular format for high-resolution, high-FPS streaming and stereo cameras.
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How to simulate an MJPEG USB camera in Linux using FFmpeg and a V4L2 loopback device for development and testing.
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Benchmark results demonstrating Savant's performance advantage over PyTorch for video inference, including hardware-accelerated decoding.
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Learn how to implement keypoint detection for pose estimation, facial recognition alignment, and action recognition using Savant.
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Using Nvidia PeopleNet and Savant to accurately count and monitor people in user-defined polygonal areas for various applications.
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Building a license plate detection and recognition pipeline using YOLOv8 and Nvidia NGC models with the Savant framework.
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Media Pass-Through allows copying the incoming video stream to output without transcoding, optimizing pipelines that don't need draw functionality.
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Why you may find it beneficial to use Savant instead of DeepStream, OpenVINO, PyTorch, or OpenCV in your next computer vision project.
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How to build scalable computer vision applications using Kafka and KeyDB for handling real-time and delayed video processing at scale.
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Exploring the challenges of real-time video analytics and practical approaches to overcome them using modern frameworks and hardware acceleration.
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Build a pipeline that calculates vehicles passing through metering zones and sends statistics to Grafana for visualization.
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Introducing RocksQ, a high-performance persistent queue built on RocksDB for buffering video frames and metadata in Savant.
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How to dynamically reconfigure pipeline parameters at runtime using Etcd for adaptive computer vision applications.
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Savant 0.2.5 brings significant changes, new features, and improvements to developer experience and deployment.
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How to use OpenTelemetry to trace, log, and collect metrics for deep learning computer vision pipelines in Savant.
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A high-performance facial re-identification pipeline using Savant for doorbell security and video content annotation systems.
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Running real-time instance segmentation with the YOLOv8 family on Savant, enabling efficient object segmentation on edge and data center hardware.
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How Savant handles video encoding on hardware that lacks NVENC, such as Jetson Orin Nano, Tesla A100, and Tesla H100.
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Element Groups allow conditionally loading pipeline elements during initialization based on environment variables and expressions.
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