The TensorFlow runtime has components that are lazily initialized, which can cause high latency for the first request/s sent to a model after it is loaded. This latency can be several orders of magnitude higher than that of a single inference request.
To reduce the impact of lazy initialization on request latency, it's possible to trigger the initialization of the sub-systems and components at model load time by providing a sample set of inference requests along with the SavedModel. This process is known as "warming up" the model.
SavedModel Warmup is supported for Regress, Classify, MultiInference and Predict. To trigger warmup of the model at load time, attach a warmup data file under the assets.extra subfolder of the SavedModel directory.
Requirements for model warmup to work correctly:
- Warmup file name: 'tf_serving_warmup_requests'
- File location: assets.extra/
- File format: TFRecord with each record as a PredictionLog.
- Number of warmup records <= 1000.
- The warmup data must be representative of the inference requests used at serving.
Example code snippet producing warmup data:
import tensorflow as tf from tensorflow_serving.apis import classification_pb2 from tensorflow_serving.apis import inference_pb2 from tensorflow_serving.apis import model_pb2 from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_log_pb2 from tensorflow_serving.apis import regression_pb2 def main(): with tf.io.TFRecordWriter("tf_serving_warmup_requests") as writer: # replace <request> with one of: # predict_pb2.PredictRequest(..) # classification_pb2.ClassificationRequest(..) # regression_pb2.RegressionRequest(..) # inference_pb2.MultiInferenceRequest(..) log = prediction_log_pb2.PredictionLog( predict_log=prediction_log_pb2.PredictLog(request=<request>)) writer.write(log.SerializeToString()) if __name__ == "__main__": main()