Keras - tensorflow serving - Iris example

iris_client.py

"""Manual test client for tensorflow_model_server."""
from __future__ import print_function

import os
import sys

import numpy as np
import tensorflow as tf
from grpc.beta import implementations
from tensorflow.python.platform import flags

from tensorflow_serving.apis import predict_pb2, prediction_service_pb2

FLAGS = tf.app.flags.FLAGS


def main(_):
    # Prepare request
    request = predict_pb2.PredictRequest()
    request.model_spec.name = 'iris'
    request.model_spec.signature_name = 'predict_iris'

    X = np.asarray([np.asarray([1, 2, 3, 4])]).astype(np.float32)
    request.inputs['input'].CopyFrom(
        tf.contrib.util.make_tensor_proto(X, shape=X.shape))
    request.output_filter.append('output')
    # Send request

    channel = implementations.insecure_channel('localhost', int(8500))
    stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)

    print(stub.Predict(request, 5.0))


if __name__ == '__main__':
    tf.app.run()

iris_serving.py

"""
Usage: iris.py [--training_iteration=x] [--export_version=y] export_dir
"""

from __future__ import print_function

import os
import sys

import tensorflow as tf
from keras import backend as K
from keras.layers import Dense, Input
from keras.models import Model
from keras.utils.np_utils import to_categorical
from sklearn.datasets import load_iris
from tensorflow.python.saved_model import builder as saved_model_builder
from tensorflow.python.saved_model import (signature_constants,
                                           signature_def_utils, tag_constants,
                                           utils)
from tensorflow.python.util import compat

tf.app.flags.DEFINE_integer('training_iteration', 1000,
                            'number of training iterations.')
tf.app.flags.DEFINE_integer('export_version', 1,
                            'version number of the model.')
tf.app.flags.DEFINE_string('work_dir', '/tmp', 'Working directory.')
FLAGS = tf.app.flags.FLAGS


def main(_):
    if len(sys.argv) < 2 or sys.argv[-1].startswith('-'):
        print('Usage: iris.py [--training_iteration=x] '
              '[--export_version=y] export_dir')
        sys.exit(-1)
    if FLAGS.training_iteration <= 0:
        print('Please specify a positive value for training iteration.')
        sys.exit(-1)
    if FLAGS.export_version <= 0:
        print('Please specify a positive value for version number.')
        sys.exit(-1)

    # Train model
    print('Training model...')

    sess = tf.Session()

    K.set_session(sess)
    K.set_learning_phase(1)

    iris = load_iris()
    _in = Input(shape=(4, ), name='input')
    x = Dense(10, activation='relu')(_in)
    out = Dense(3, activation='softmax', name='output')(x)
    model = Model(input=_in, output=out)
    model.compile(
        optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])

    labels = to_categorical(iris.target, 3)
    model.fit(iris.data, labels, nb_epoch=FLAGS.training_iteration)
    #score = model.evaluate(iris.data, labels)

    #print(score)
    #print(model.metrics_names)

    print('Done training!')

    K.set_learning_phase(
        0)  # all new operations will be in test mode from now on

    # serialize the model and get its weights, for quick re-building
    config = model.to_json()
    weights = model.get_weights()
    del model

    # re-build a model where the learning phase is now hard-coded to 0
    from keras.models import model_from_json
    new_model = model_from_json(config)
    new_model.set_weights(weights)
    export_path = os.path.join(
        compat.as_bytes(sys.argv[-1]),
        compat.as_bytes(str(FLAGS.export_version)))
    #export_version =...  # version number (integer)

    builder = saved_model_builder.SavedModelBuilder(export_path)

    inputs = {
        in_.name: utils.build_tensor_info(in_.output)
        for in_ in new_model.input_layers
    }
    outputs = {
        out.name: utils.build_tensor_info(out.output)
        for out in new_model.output_layers
    }

    prediction_signature = signature_def_utils.build_signature_def(
        inputs=inputs,
        outputs=outputs,
        method_name=signature_constants.PREDICT_METHOD_NAME)

    legacy_init_op = tf.group(
        tf.initialize_all_tables(), name='legacy_init_op')

    builder.add_meta_graph_and_variables(
        sess, [tag_constants.SERVING],
        signature_def_map={
            'predict_iris': prediction_signature,
        },
        legacy_init_op=legacy_init_op)

    builder.save()

    print('Done exporting!')


if __name__ == '__main__':
    tf.app.run()
# tensorflow_model_server --model_name=iris --model_base_path=/path/to/exported/model

Hey man, I have the problem of root@d843340a2a0b:/serving/tf_serving# /serving/bazel-bin/tf_serving/client --server=localhost:8888 --image=/serving/image/car_snow.jpg Traceback (most recent call last): File "/serving/bazel-bin/tf_serving/client.runfiles/tf_serving/tf_serving/client.py", line 59, in <module> tf.app.run() File "/serving/bazel-bin/tf_serving/client.runfiles/org_tensorflow/tensorflow/python/platform/app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "/serving/bazel-bin/tf_serving/client.runfiles/tf_serving/tf_serving/client.py", line 54, in main result = stub.Predict(request, 10.0) # 10 secs timeout File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 324, in __call__ self._request_serializer, self._response_deserializer) File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 210, in _blocking_unary_unary raise _abortion_error(rpc_error_call) grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.FAILED_PRECONDITION, details="Default serving signature key not found.")

when exporting model this way, do you have any ideas why?

Try to export your keras model as described on this page tensorflow/serving#310 (comment).