Optimize prediction thoughtput of tensorflow keras

General Discussion
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#1

Recently, I built a model that runs on a CPU—a vanilla neural network. Following that, I implemented serving using gRPC and Kubernetes for deployment. Despite configuring the number of CPUs, memory, and service threads, the RPS is bottlenecked around 10 during prediction. I load model during service initialization and run a prediction for each incoming request, as shown below:

pythonCopy code

def GetPredict(self, request, context):
    predict = model.predict()
    return service_pb2.Predict(predict=predict)

I have verified that the prediction process, as indicated by [=============] verbosity, takes around 100ms. However, even with increased CPU or memory, the RPS not increase. Something the Kubernetes pods are crashing when calling the service so much. I think some memory leak in the prediction. Additionally, I want this to be a single prediction, not a batch prediction. Any way to optimize directly without using some tool like ONNX? I use Tensorflow 2.13.

#3

Optimizing the throughput of a TensorFlow Keras model on CPU with gRPC and Kubernetes involves several strategies:

  1. Model Optimization: Simplify your model, consider model quantization, or use TensorFlow Lite for more efficient CPU inference.
  2. Service Code Optimization: Implement asynchronous processing to handle multiple requests simultaneously and consider small-scale batching for predictions.
  3. Kubernetes Tuning: Fine-tune your Kubernetes deployment for optimal resource utilization, efficient load balancing, and effective autoscaling.
  4. gRPC Optimization: Adjust gRPC parameters for better performance and manage connections efficiently.
  5. Memory Leak Investigation: Use profiling tools to identify and fix any memory leaks, and consider manual garbage collection.
  6. Monitoring: Implement robust monitoring and logging to identify performance bottlenecks and errors.
  7. Advanced TensorFlow Features: Explore TensorFlow Serving as an optimized alternative for deploying TensorFlow models.
  8. Hardware Review: Although you’re using CPUs, evaluate if other hardware options like GPUs might offer better performance for your workload.

By applying these strategies, you can potentially increase the request-per-second (RPS) throughput and address issues like service crashes and memory leaks, even without using tools like ONNX. Remember to test changes thoroughly to ensure they positively impact performance.