Reducing inference time for a custom model

AI & Data Science Deep Learning (Training & Inference) TensorRT
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How to further reduce inference time for tensorrt engine after quantization and reduce the no of kernel launches/have tensorrt complete executiion when in different streams without getting effected by tasks in other streams

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Here are several strategies to further reduce inference time for a TensorRT engine after quantization, minimize the number of kernel launches, and ensure efficient execution across multiple streams without performance degradation:

  1. Resource Limitation during Engine Creation: Limit compute resources during the engine creation phase. This allows TensorRT to choose optimal kernels suited for the runtime conditions, enhancing throughput while maintaining latency.

  2. Utilize the TensorFlow-Quantization Toolkit: This toolkit aids in deploying TensorFlow 2-based Keras models at reduced precision. By quantizing layers based on operator names and patterns, you can create a quantized graph, improving the efficiency and inference time of TensorRT engines.

  3. Leverage the PyTorch Quantization Toolkit: Similar to TensorFlow, this toolkit provides facilities for training models at reduced precision in PyTorch, allowing for subsequent optimization in TensorRT, which can lead to faster inference times.

  4. Adjust Conversions: Evaluate adding or removing conversions during the optimization process. Choosing an FP16 kernel implementation or removing non-essential conversions may enhance layer precision and reduce inference time.

  5. Optimize Explicitly Quantized Networks: Focus on optimizing group convolutions in explicitly quantized networks by strategically placing Q/DQ pairs. Ensuring quantization strategies align with network architecture can yield better performance.

  6. Use NVIDIA Nsight™ Systems for Profiling: Leverage this tool for profiling and analyzing performance. It can help identify bottlenecks, optimize kernel launches, and ensure TensorRT operates efficiently in multiple streams without adverse effects from other tasks.

Implementing these strategies can help in achieving lower inference times, minimizing kernel launches, and maintaining performance consistency in multi-stream environments for TensorRT engines.