ONNX
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160x Speedup in Nudity Detection with ONNX & PyTorch
Read Full Article: 160x Speedup in Nudity Detection with ONNX & PyTorchAn innovative approach to enhancing the efficiency of a nudity detection pipeline achieved a remarkable 160x speedup by utilizing a "headless" strategy with ONNX and PyTorch. The optimization involved converting the model to an ONNX format, which is more efficient for inference, and removing unnecessary components that do not contribute to the final prediction. This streamlined process not only improves performance but also reduces computational costs, making it more feasible for real-time applications. Such advancements are crucial for deploying AI models in environments where speed and resource efficiency are paramount.
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Guide to Deploying ML Models on Edge Devices
Read Full Article: Guide to Deploying ML Models on Edge Devices
"Ultimate ONNX for Deep Learning Optimization" is a comprehensive guide aimed at ML Engineers and Embedded Developers, focusing on deploying machine learning models to resource-constrained edge devices. The book addresses the challenges of moving models from research to production, offering a detailed workflow from model export to deployment. It covers ONNX fundamentals, optimization techniques such as quantization and pruning, and practical tools like ONNX Runtime. Real-world case studies are included, demonstrating the deployment of models like YOLOv12 and Whisper on devices like the Raspberry Pi. This guide is essential for those looking to optimize deep learning models for speed and efficiency without compromising accuracy. This matters because effectively deploying machine learning models on edge devices can significantly enhance the performance and applicability of AI in real-world scenarios.
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Four Ways to Run ONNX AI Models on GPU with CUDA
Read Full Article: Four Ways to Run ONNX AI Models on GPU with CUDA
Running ONNX AI models on GPUs with CUDA can be achieved through four distinct methods, enhancing flexibility and performance for machine learning operations. These methods include using ONNX Runtime with CUDA execution provider, leveraging TensorRT for optimized inference, employing PyTorch with its ONNX export capabilities, and utilizing the NVIDIA Triton Inference Server for scalable deployment. Each approach offers unique advantages, such as improved speed, ease of integration, or scalability, catering to different needs in AI model deployment. Understanding these options is crucial for optimizing AI workloads and ensuring efficient use of GPU resources.