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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Shared utils among inference plugins that are platform-specific."""
from glob import glob
from grpc.beta import implementations
import random
from six.moves.urllib.parse import urlparse
import tensorflow as tf
from tensorboard.plugins.interactive_inference.utils import common_utils
from tensorboard._vendor.tensorflow_serving.apis import classification_pb2
from tensorboard._vendor.tensorflow_serving.apis import prediction_service_pb2
from tensorboard._vendor.tensorflow_serving.apis import regression_pb2
def filepath_to_filepath_list(file_path):
"""Returns a list of files given by a filepath.
Args:
file_path: A path, possibly representing a single file, or containing a
wildcard or sharded path.
Returns:
A list of files represented by the provided path.
"""
file_path = file_path.strip()
if '*' in file_path:
return glob(file_path)
else:
return [file_path]
def throw_if_file_access_not_allowed(file_path, logdir, has_auth_group):
"""Throws an error if a file cannot be loaded for inference.
Args:
file_path: A file path.
logdir: The path to the logdir of the TensorBoard context.
has_auth_group: True if TensorBoard was started with an authorized group,
in which case we allow access to all visible files.
Raises:
InvalidUserInputError: If the file is not in the logdir and is not globally
readable.
"""
return
def example_protos_from_path(path,
num_examples=10,
start_index=0,
parse_examples=True,
sampling_odds=1,
example_class=tf.train.Example):
"""Returns a number of examples from the provided path.
Args:
path: A string path to the examples.
num_examples: The maximum number of examples to return from the path.
parse_examples: If true then parses the serialized proto from the path into
proto objects. Defaults to True.
sampling_odds: Odds of loading an example, used for sampling. When >= 1
(the default), then all examples are loaded.
example_class: tf.train.Example or tf.train.SequenceExample class to load.
Defaults to tf.train.Example.
Returns:
A list of Example protos or serialized proto strings at the path.
Raises:
InvalidUserInputError: If examples cannot be procured from the path.
"""
def append_examples_from_iterable(iterable, examples):
for value in iterable:
if sampling_odds >= 1 or random.random() < sampling_odds:
examples.append(
example_class.FromString(value) if parse_examples else value)
if len(examples) >= num_examples:
return
filenames = filepath_to_filepath_list(path)
examples = []
compression_types = [
tf.python_io.TFRecordCompressionType.NONE,
tf.python_io.TFRecordCompressionType.GZIP,
tf.python_io.TFRecordCompressionType.ZLIB,
]
current_compression_idx = 0
current_file_index = 0
while (current_file_index < len(filenames) and
current_compression_idx < len(compression_types)):
try:
record_iterator = tf.python_io.tf_record_iterator(
path=filenames[current_file_index],
options=tf.python_io.TFRecordOptions(
compression_types[current_compression_idx]))
append_examples_from_iterable(record_iterator, examples)
current_file_index += 1
if len(examples) >= num_examples:
break
except tf.errors.DataLossError:
current_compression_idx += 1
except (IOError, tf.errors.NotFoundError) as e:
raise common_utils.InvalidUserInputError(e)
if examples:
return examples
else:
raise common_utils.InvalidUserInputError(
'No examples found at ' + path +
'. Valid formats are TFRecord files.')
def call_servo(examples, serving_bundle):
"""Send an RPC request to the Servomatic prediction service.
Args:
examples: A list of examples that matches the model spec.
serving_bundle: A `ServingBundle` object that contains the information to
make the serving request.
Returns:
A ClassificationResponse or RegressionResponse proto.
"""
parsed_url = urlparse('http://' + serving_bundle.inference_address)
channel = implementations.insecure_channel(parsed_url.hostname,
parsed_url.port)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
if serving_bundle.model_type == 'classification':
request = classification_pb2.ClassificationRequest()
else:
request = regression_pb2.RegressionRequest()
request.model_spec.name = serving_bundle.model_name
if serving_bundle.model_version is not None:
request.model_spec.version.value = serving_bundle.model_version
if serving_bundle.signature is not None:
request.model_spec.signature_name = serving_bundle.signature
request.input.example_list.examples.extend(examples)
if serving_bundle.model_type == 'classification':
return stub.Classify(request, 30.0) # 30 secs timeout
else:
return stub.Regress(request, 30.0) # 30 secs timeout
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