[Webinar] How to Efficiently Scale Video Analytics at the Edge

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Tackling the complexities of scaling video analytics on edge devices requires a multifaceted approach, combining advanced algorithmic solutions with effective runtime and pipeline optimization strategies.

This webinar is designed to guide you through this intricate process. It delves into selecting efficient models tailored for object detection on edge hardware and enhancing throughput and hardware utilization with advanced tools and techniques. Watch now to uncover the keys to achieving superior real-time analytics performance at the edge.

Webinar Overview

Challenges in Edge-Based Video Analytics

  • Computational Constraints: Understand the limitations of processing power, memory, and peripherals in edge environments.
  • Accuracy vs. Latency: Techniques to balance high accuracy with low latency, crucial for applications in security and healthcare.
  • Real-Time Processing: Strategies for achieving effective real-time analytics within the constraints of edge computing.
  • Hardware Diversity: Addressing the challenges of portability and performance across evolving edge devices.
  • Cost-Performance Optimization: Finding the optimal balance between hardware costs and the performance demands of high-quality models.

Strategies for Maximizing Hardware Utilization

  • Case Study: NVIDIA Jetson AGX Orin: Detailed walkthrough on leveraging NVIDIA DeepStream SDK for running multiple video streams on a single device for object detection.
  • Hardware and Software Setup: In-depth guide on configuring the NVIDIA Jetson AGX Orin, including hardware requirements (Active Heat Sink, Logitec cameras) and software dependencies (JetPack, NVIDIA DeepStream, Video 4 Linux Tools).
  • Performance Optimization: Practical steps for model preparation, compilation to NVIDIA TensorRT, and pipeline construction using GStreamer and NVIDIA plugins.

Deep Dive into Optimization Techniques

Model and Pipeline Tuning:

  • Environment-Specific Tuning: Tailoring your setup to the unique demands of your operating environment, ensuring optimal performance under varying conditions.
  • Model-Specific Adjustments: Techniques for selecting and refining models to achieve high accuracy and speed on edge devices, including the impact of different models on GPU utilization.
  • Pipeline-Specific Optimization: A granular look at constructing and fine-tuning your NVIDIA DeepStream pipeline, leveraging GStreamer, NVIDIA plugins, and Deci’s pipeline strategies for both hardware and software utilization.
  • Profiling for Optimization: Utilizing NVIDIA Nsight for in-depth analysis. This analysis informs strategic adjustments such as configuring hardware plugin allocations, optimizing stream formats and conversions, parallelizing tasks, or changing the model entirely.

Batch Size Variation:

Delve into how varying batch sizes can affect performance metrics and learn a methodical approach to testing and identifying the optimal batch size for your specific use case. This session highlights the direct impact of batch size adjustments on throughput and latency, providing a framework for iterative optimization.

Key Learning Outcomes

✅ Advanced Model Deployment Techniques: This segment offers expert insights into deploying state-of-the-art models on edge devices like the NVIDIA Jetson Orin. Discover the intricacies of model selection, adaptation, and optimization that ensure effective, efficient, and scalable solutions without compromising speed or accuracy.

✅ Comprehensive Hardware Utilization Strategies: Learn to fully exploit the NVIDIA DeepStream SDK for edge video analytics. The session covers managing multiple video streams on a single device with maximum efficiency and throughput, including hardware-specific optimizations, software tuning, and maximizing GPU capabilities for extensive video analytics workloads.

✅ Diverse Real-World Applications and Case Studies: Explore case studies demonstrating the successful implementation of optimization strategies. These real-world examples illustrate how tailored approaches can significantly enhance performance, potentially leading to a 5x increase in throughput for AI video analytics solutions.

Don’t miss this opportunity to gain access to invaluable expertise and start applying these advanced strategies to your projects today. If you’re looking to delve deeper into efficiently scaling your AI video analytics project with Deci’s innovative solutions, book a demo today.

Access Webinar Now

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					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")