How to improve the latency of inference for a Deep Learning model using python programming?

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200ms is definitely high for real-time needs. In the world of <python programming> and AI, the bottleneck is often the data pipeline. First, try converting your model to OpenVINO or TensorRT; these engines are specifically designed to optimize weights for the hardware. Secondly, look at your pre-processing script. If you are using NumPy or OpenCV functions in a loop, try to vectorize them. You can also use the multiprocessing module to handle image decoding on a separate CPU core so that the GPU isn't waiting for the next frame.

   Answered 2025-11-12 by Michelle Adams

Have you checked if your <python programming> environment is utilizing the GPU correctly, or is it defaulting to CPU for certain layers in your CNN?

   Answered 2025-11-14 by Gregory Scott

• Gregory, I confirmed it's using the GPU via tf.config.list_physical_devices. However, I noticed the CPU usage spikes during the resize and normalization steps. Would moving those specific operations into a custom Lambda layer inside the model help, or should I just use a faster C++ based library to feed the data into the script?

     Commented 2025-11-15 by Paul Nelson

Try switching to ONNX Runtime. It’s a standard for high-performance inference and works seamlessly with <python programming> across different hardware backends.

   Answered 2025-11-16 by Sandra Mitchell

• Sandra is right. I moved my models to ONNX and saw an immediate 3x speedup on my edge devices. It's much lighter than carrying the whole TensorFlow runtime.

     Commented 2025-11-17 by Susan Gree