This project is dedicated to optimizing TensorFlow models to improve inference performance on GPUs by leveraging TensorRT (TF-TRT). The primary focus is on the efficient deployment of deep learning models, ensuring they meet real-time processing requirements in various applications.
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Model Optimization: Explore the optimization of different deep learning architectures, particularly InceptionV3, through the implementation of TensorRT. The project aims to enhance performance by reducing latency and increasing throughput.
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Precision Levels: Investigate the impact of various precision settings (FP32, FP16, and INT8) on model accuracy and performance. By tuning these settings, the project seeks to balance computational efficiency with model fidelity.
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Parameter Tuning: Conduct a thorough analysis of TensorRT parameter tuning and its effects on inference speed and resource utilization. This involves experimenting with batch sizes, workspace sizes, and layer fusion techniques.
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Performance Benchmarking: Establish benchmarks to quantify the improvements achieved through optimization. This will include detailed performance metrics, such as inference time and GPU memory usage, to evaluate the effectiveness of the optimization strategies.
The project aims to deliver optimized TensorFlow models that can be deployed efficiently in production environments, significantly enhancing their performance capabilities.