Difference between revisions of "Edge AI SDK/AI Framework/Nvidia x86 64"

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= RTX-A5000 AI Suite rc =
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= DeepStream =
  
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<span style="font-size:larger;">The&nbsp;[https://www.nvidia.com/en-us/design-visualization/rtx-a5000/ NVIDIA RTX A5000]&nbsp;is supported by an advanced software suite designed to accelerate AI, data science, and graphics applications on professional workstations.</span>
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DeepStream&nbsp;is a complete streaming analytics toolkit based on GStreamer for AI-based multi-sensor processing, video, audio, and image understanding. It’s ideal for vision AI developers, software partners, startups, and OEMs building IVA apps and services. Developers can now create stream processing pipelines that incorporate neural networks and other complex processing tasks such as tracking, video encoding/decoding, and video rendering. DeepStream pipelines enable real-time analytics on video, image, and sensor data.
  
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<span style="font-size:larger;">Spearhead innovation from your desktop with the NVIDIA RTX<sup>™</sup>&nbsp;A5000 graphics card, the perfect balance of power, performance, and reliability to tackle complex workflows. Built on the latest NVIDIA Ampere architecture and featuring 24 gigabytes (GB) of GPU memory, it’s everything designers, engineers, and artists need to realize their visions for the future, today.</span>
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DeepStream’s multi-platform support gives you a faster, easier way to develop vision AI applications and services. You can even deploy them on-premises, on the edge, and in the cloud with the click of a button.&nbsp;
  
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&nbsp;
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More Info refer to&nbsp;[https://developer.nvidia.com/deepstream-sdk https://developer.nvidia.com/deepstream-sdk]
  
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&nbsp;
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=== DeepStream 6.4 Prerequisites: ===
  
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= Applications =
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You must install the following components:
  
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<span style="font-size:larger;">[https://hailo.ai/products/hailo-software/hailo-ai-software-suite/#sw-tappas TAPPAS] is a solution designed to streamline the development and deployment of edge applications demanding high AI performance. This reference application software package empowers users to expedite their time-to-market by minimizing the development workload. TAPPAS encompasses a user-friendly set of fully operational application examples based on GStreamer, featuring pipeline elements and pre-trained AI tasks. These examples leverage advanced Deep Neural Networks, highlighting Hailo's AI processors' top-notch throughput and power efficiency. Furthermore, TAPPAS serves as a demonstration of Hailo's system integration capabilities, showcasing specific use cases on predefined software and hardware platforms. Utilizing TAPPAS simplifies integration with Hailo's runtime software stack and offers a starting point for users to fine-tune their applications. By demonstrating Hailo's system integration scenarios on both predefined software and hardware platforms, it can be used for evaluations, reference code, and demos. This approach effectively accelerates time to market, streamlines integration with Hailo's runtime software stack, and provides customers with a foundation to fine-tune their applications.</span>
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*Ubuntu 22.04
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*GStreamer 1.20.3
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*Nvidia Driver R535.104.12
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*CUDA 12.2
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*TensorRT 8.6.1.6
  
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<span style="font-size:larger;">Refer to [https://github.com/hailo-ai/tappas github-TAPPAS]</span>
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= TensorRT =
  
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&nbsp;
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[https://developer.nvidia.com/tensorrt TensorRT]&nbsp;is a high performance deep learning inference runtime for image classification, segmentation, and object detection neural networks. TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference for all deep learning frameworks. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.
  
 
&nbsp;
 
&nbsp;
  
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== <span style="font-size:larger;">Edge AI SDK&nbsp;/ Application</span> ==
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= <span style="font-size:larger;">Edge AI SDK&nbsp;/ Application</span> =
  
 
<span style="font-size:larger;">Quick Start (Vision) / Application / Video or WebCam / dGPU</span>
 
<span style="font-size:larger;">Quick Start (Vision) / Application / Video or WebCam / dGPU</span>
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| style="width: 154px;" | <span style="font-size:larger;">Object Detection</span>
 
| style="width: 154px;" | <span style="font-size:larger;">Object Detection</span>
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| style="width: 179px;" | &nbsp;
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| style="width: 179px;" | yolov3.weights
 
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| style="width: 154px;" | <span style="font-size:larger;">Person Detection</span>
 
| style="width: 154px;" | <span style="font-size:larger;">Person Detection</span>
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| style="width: 179px;" | &nbsp;
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| style="width: 179px;" | sample_ssd_relu6.uff
 
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| style="width: 154px;" | <span style="font-size:larger;">Face Detection</span>
 
| style="width: 154px;" | <span style="font-size:larger;">Face Detection</span>
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| style="width: 179px;" | &nbsp;
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| style="width: 179px;" | facenet.etlt
 
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| style="width: 154px;" | <span style="font-size:larger;">Pose Estimation</span>
 
| style="width: 154px;" | <span style="font-size:larger;">Pose Estimation</span>
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| style="width: 179px;" | &nbsp;
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| style="width: 179px;" | model.etlt
 
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|}
  
 
= <span style="font-size:larger;">Benchmark</span> =
 
= <span style="font-size:larger;">Benchmark</span> =
  
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<span style="font-size:larger;"><span style="font-size:larger;">In order to measure FPS, power and latency of the RTX-A5000 you can use the docker to run the&nbsp;command "trtexec" . For more information please refer to the ''trtexec''&nbsp;documentation in [https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec link].</span></span>
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<span style="font-size:larger;"><span style="font-size:larger;">In order to measure FPS, power and latency of the RTX-A5000 you can use&nbsp;the&nbsp;command "trtexec" . For more information please refer to the ''trtexec''&nbsp;documentation in [https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec link].</span></span>
  
 
<span style="font-size:larger;"><span style="font-size:larger;">&nbsp;</span></span>
 
<span style="font-size:larger;"><span style="font-size:larger;">&nbsp;</span></span>
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== <span style="font-size:larger;"><span style="font-size:larger;">RTX-A5000 Benchmark</span></span> ==
 
== <span style="font-size:larger;"><span style="font-size:larger;">RTX-A5000 Benchmark</span></span> ==
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<pre><span style="font-size:x-small;">docker run</span>
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<pre>trtexec --loadEngine=models/model_fp16.engine --batch=16</pre>
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trtexec --loadEngine=models/model_fp16.engine --batch=16</pre>
 
  
 
[[File:EdgeAISDK rtxa5000 trtexec.png|1000x300px|EdgeAISDK rtxa5000 trtexec.png]]
 
[[File:EdgeAISDK rtxa5000 trtexec.png|1000x300px|EdgeAISDK rtxa5000 trtexec.png]]
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[[File:EdgeAISDK rtxa5000 UI.png|800x450px|EdgeAISDK rtxa5000 UI.png]]
 
[[File:EdgeAISDK rtxa5000 UI.png|800x450px|EdgeAISDK rtxa5000 UI.png]]
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[[Category:Editor]]
 

Latest revision as of 09:45, 2 July 2024

DeepStream

DeepStream is a complete streaming analytics toolkit based on GStreamer for AI-based multi-sensor processing, video, audio, and image understanding. It’s ideal for vision AI developers, software partners, startups, and OEMs building IVA apps and services. Developers can now create stream processing pipelines that incorporate neural networks and other complex processing tasks such as tracking, video encoding/decoding, and video rendering. DeepStream pipelines enable real-time analytics on video, image, and sensor data.

DeepStream’s multi-platform support gives you a faster, easier way to develop vision AI applications and services. You can even deploy them on-premises, on the edge, and in the cloud with the click of a button. 

More Info refer to https://developer.nvidia.com/deepstream-sdk

DeepStream 6.4 Prerequisites:

You must install the following components:

  • Ubuntu 22.04
  • GStreamer 1.20.3
  • Nvidia Driver R535.104.12
  • CUDA 12.2
  • TensorRT 8.6.1.6

TensorRT

TensorRT is a high performance deep learning inference runtime for image classification, segmentation, and object detection neural networks. TensorRT is built on CUDA, NVIDIA’s parallel programming model, and enables you to optimize inference for all deep learning frameworks. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications.

 

Edge AI SDK / Application

Quick Start (Vision) / Application / Video or WebCam / dGPU

EdgeAISDK rtxa5000.png

 

Application Model
Object Detection yolov3.weights
Person Detection sample_ssd_relu6.uff
Face Detection facenet.etlt
Pose Estimation model.etlt

Benchmark

In order to measure FPS, power and latency of the RTX-A5000 you can use the command "trtexec" . For more information please refer to the trtexec documentation in link.

 

 

 

RTX-A5000 Benchmark

trtexec --loadEngine=models/model_fp16.engine --batch=16

EdgeAISDK rtxa5000 trtexec.png

 

 

Edge AI SDK / Benchmark

Evaluate the RTX-A5000 performance with Edge AI SDK.

EdgeAISDK rtxa5000 benchmark.png

 

NVIDIA System Management Interface

The NVIDIA System Management Interface (nvidia-smi) is a command line utility, based on top of the NVIDIA Management Library (NVML), intended to aid in the management and monitoring of NVIDIA GPU devices. 

This utility allows administrators to query GPU device state and with the appropriate privileges, permits administrators to modify GPU device state. 

nvidia-smi

 

 

 

RTX-A5000 Utilization

nvidia-smi

EdgeAISDK rtxa5000 utility.png

 

 

RTX-A5000 Temperature

nvidia-smi

  EdgeAISDK rtxa5000 thermal.png

Edge AI SDK / Monitoring

EdgeAISDK rtxa5000 UI.png

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