How to prepare warmup request file for tensorflow serving? - tensorflow

Question

Current version of tensorflow-serving try to load warmup request from assets.extra/tf_serving_warmup_requests file.
2018-08-16 16:05:28.513085: I tensorflow_serving/servables/tensorflow/saved_model_warmup.cc:83] No warmup data file found at /tmp/faster_rcnn_inception_v2_coco_2018_01_28_string_input_version-export/1/assets.extra/tf_serving_warmup_requests
I wonder if tensorflow provides common api to export request to the location or not? Or should we write request to the location manually?

Answer 1

At this point there is no common API for exporting the warmup data into the assets.extra. It's relatively simple to write a script (similar to below):
import tensorflow as tf
from tensorflow_serving.apis import model_pb2
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_log_pb2
def main():
with tf.python_io.TFRecordWriter("tf_serving_warmup_requests") as writer:
request = predict_pb2.PredictRequest(
model_spec=model_pb2.ModelSpec(name="<add here>"),
inputs={"examples": tf.make_tensor_proto([<add here>])}
)
log = prediction_log_pb2.PredictionLog(
predict_log=prediction_log_pb2.PredictLog(request=request))
writer.write(log.SerializeToString())
if __name__ == "__main__":
main()

Answer 2

We refered to the official doc
Specially, we used Classification instead of Prediction, so we altered that code to be
log = prediction_log_pb2.PredictionLog(
classify_log=prediction_log_pb2.ClassifyLog(request=<request>))

Answer 3

This is a complete example of an object detection system using a ResNet model. The prediction consist of an image.
import tensorflow as tf
import requests
import base64
from tensorflow.python.framework import tensor_util
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_log_pb2
IMAGE_URL = 'https://tensorflow.org/images/blogs/serving/cat.jpg'
NUM_RECORDS = 100
def get_image_bytes():
image_content = requests.get(IMAGE_URL, stream=True)
image_content.raise_for_status()
return image_content.content
def main():
"""Generate TFRecords for warming up."""
with tf.io.TFRecordWriter("tf_serving_warmup_requests") as writer:
image_bytes = get_image_bytes()
predict_request = predict_pb2.PredictRequest()
predict_request.model_spec.name = 'resnet'
predict_request.model_spec.signature_name = 'serving_default'
predict_request.inputs['image_bytes'].CopyFrom(
tensor_util.make_tensor_proto([image_bytes], tf.string))
log = prediction_log_pb2.PredictionLog(
predict_log=prediction_log_pb2.PredictLog(request=predict_request))
for r in range(NUM_RECORDS):
writer.write(log.SerializeToString())
if __name__ == "__main__":
main()
This script will create a file called “tf_serving_warmup_requests”
I moved this file to /your_model_location/resnet/1538687457/assets.extra/ and then restart my docker image to pickup the new changes.