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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/integrations/tensorrt.md
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@ -13,7 +13,7 @@ By using the TensorRT export format, you can enhance your [Ultralytics YOLOv8](h
## TensorRT
<p align="center">
<img width="100%" src="https://github.com/ultralytics/ultralytics/assets/26833433/7fea48c2-9709-4deb-8d04-eaf95d12a91d" alt="TensorRT Overview">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/tensorrt-overview.avif" alt="TensorRT Overview">
</p>
[TensorRT](https://developer.nvidia.com/tensorrt), developed by NVIDIA, is an advanced software development kit (SDK) designed for high-speed deep learning inference. It's well-suited for real-time applications like object detection.
@ -31,7 +31,7 @@ TensorRT models offer a range of key features that contribute to their efficienc
- **Layer Fusion**: The TensorRT optimization process includes layer fusion, where multiple layers of a neural network are combined into a single operation. This reduces computational overhead and improves inference speed by minimizing memory access and computation.
<p align="center">
<img width="100%" src="https://developer-blogs.nvidia.com/wp-content/uploads/2017/12/pasted-image-0-3.png" alt="TensorRT Layer Fusion">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/tensorrt-layer-fusion.avif" alt="TensorRT Layer Fusion">
</p>
- **Dynamic Tensor Memory Management**: TensorRT efficiently manages tensor memory usage during inference, reducing memory overhead and optimizing memory allocation. This results in more efficient GPU memory utilization.
@ -47,7 +47,7 @@ TensorRT offers several deployment options, and each option balances ease of int
- **Deploying within TensorFlow**: This method integrates TensorRT into TensorFlow, allowing optimized models to run in a familiar TensorFlow environment. It's useful for models with a mix of supported and unsupported layers, as TF-TRT can handle these efficiently.
<p align="center">
<img width="100%" src="https://docs.nvidia.com/deeplearning/tensorrt/quick-start-guide/graphics/tf-trt-workflow.png" alt="TensorRT Overview">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/tf-trt-workflow.avif" alt="TensorRT Overview">
</p>
- **Standalone TensorRT Runtime API**: Offers granular control, ideal for performance-critical applications. It's more complex but allows for custom implementation of unsupported operators.