我使用Google Cloud ML Engine的入门教程作为参考，培训了一个模型。我可以毫无问题地在Google Cloud ML上部署和提供此模型。
现在我尝试使用Tensorflow服务来提供服务，但我收到以下消息错误：
2017-03-17 19:20:17.064146: W tensorflow_serving/sources/storage_path/file_system_storage_path_source.cc:204] No versions of servable default found under base path /serving/tf_models/extrato/output/
用于启动de Tensorflow服务的命令行调用是：
root@df98954689a1:/serving# bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=9000 --model_base_path=/serving/tf_models/extrato/output/
输出文件夹的内容为：
root@df98954689a1:/serving# ls -la tf_models/extrato/output
total 119740
drwxr-xr-x 4 root root 4096 Mar 17 17:02 .
drwxr-xr-x 3 root root 4096 Mar 17 17:02 ..
-rw-r--r-- 1 root root 184 Mar 17 17:02 checkpoint
drwxr-xr-x 2 root root 4096 Mar 17 17:02 eval
-rw-r--r-- 1 root root 96390060 Mar 17 17:02 events.out.tfevents.1489705843.elio-MS-7A66
drwxr-xr-x 3 root root 4096 Mar 17 17:02 export
-rw-r--r-- 1 root root 1362798 Mar 17 17:02 graph.pbtxt
-rw-r--r-- 1 root root 7633781 Mar 17 17:02 model.ckpt-1000001.data-00000-of-00001
-rw-r--r-- 1 root root 1975 Mar 17 17:02 model.ckpt-1000001.index
-rw-r--r-- 1 root root 637623 Mar 17 17:02 model.ckpt-1000001.meta
-rw-r--r-- 1 root root 7633781 Mar 17 17:02 model.ckpt-2.data-00000-of-00001
-rw-r--r-- 1 root root 1975 Mar 17 17:02 model.ckpt-2.index
-rw-r--r-- 1 root root 637623 Mar 17 17:02 model.ckpt-2.meta
-rw-r--r-- 1 root root 7633781 Mar 17 17:02 model.ckpt-566170.data-00000-of-00001
-rw-r--r-- 1 root root 1975 Mar 17 17:02 model.ckpt-566170.index
-rw-r--r-- 1 root root 637623 Mar 17 17:02 model.ckpt-566170.meta
更新：我尝试使用冻结的模型（.pb文件和变量文件夹），这些模型确实是我用来在Google Cloud ML Engine上部署模型的文件夹，但是收到了相同的错误消息
这些文件位于以下文件夹中：
root@d4f1c917b59d:/serving# ls -la tf_models/extrato/output/export/Servo/1489706933289/
total 356
drwxr-xr-x 3 root root 4096 Mar 17 17:02 .
drwxr-xr-x 3 root root 4096 Mar 17 17:02 ..
-rw-r--r-- 1 root root 348848 Mar 17 17:02 saved_model.pb
drwxr-xr-x 2 root root 4096 Mar 17 17:02 variables
我用于训练和导出模型的代码是：
import argparse
import model
import tensorflow as tf
from tensorflow.contrib.learn.python.learn import learn_runner
from tensorflow.contrib.learn.python.learn.utils import (
saved_model_export_utils)
def generate_experiment_fn(train_files,
eval_files,
num_epochs=None,
train_batch_size=40,
eval_batch_size=40,
embedding_size=8,
first_layer_size=100,
num_layers=4,
scale_factor=0.7,
**experiment_args):
"""Create an experiment function given hyperparameters.
See command line help text for description of args.
Returns:
A function (output_dir) -> Experiment where output_dir is a string
representing the location of summaries, checkpoints, and exports.
this function is used by learn_runner to create an Experiment which
executes model code provided in the form of an Estimator and
input functions.
All listed arguments in the outer function are used to create an
Estimator, and input functions (training, evaluation, serving).
Unlisted args are passed through to Experiment.
"""
# Check verbose logging flag
verbose_logging = experiment_args.pop('verbose_logging')
model.set_verbose_logging(verbose_logging)
def _experiment_fn(output_dir):
# num_epochs can control duration if train_steps isn't
# passed to Experiment
train_input = model.generate_input_fn(
train_files,
num_epochs=num_epochs,
batch_size=train_batch_size,
)
# Don't shuffle evaluation data
eval_input = model.generate_input_fn(
eval_files,
batch_size=eval_batch_size,
shuffle=False
)
return tf.contrib.learn.Experiment(
model.build_estimator(
output_dir,
embedding_size=embedding_size,
# Construct layers sizes with exponetial decay
hidden_units=[
max(2, int(first_layer_size * scale_factor**i))
for i in range(num_layers)
]
),
train_input_fn=train_input,
eval_input_fn=eval_input,
# export strategies control the prediction graph structure
# of exported binaries.
export_strategies=[saved_model_export_utils.make_export_strategy(
model.serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1
)],
**experiment_args
)
return _experiment_fn
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Input Arguments
parser.add_argument(
'--train-files',
help='GCS or local paths to training data',
nargs='+',
required=True
)
parser.add_argument(
'--num-epochs',
help="""\
Maximum number of training data epochs on which to train.
If both --max-steps and --num-epochs are specified,
the training job will run for --max-steps or --num-epochs,
whichever occurs first. If unspecified will run for --max-steps.\
""",
type=int,
)
parser.add_argument(
'--train-batch-size',
help='Batch size for training steps',
type=int,
default=40
)
parser.add_argument(
'--eval-batch-size',
help='Batch size for evaluation steps',
type=int,
default=40
)
parser.add_argument(
'--train-steps',
help="""\
Steps to run the training job for. If --num-epochs is not specified,
this must be. Otherwise the training job will run indefinitely.\
""",
type=int
)
parser.add_argument(
'--eval-steps',
help='Number of steps to run evalution for at each checkpoint',
default=100,
type=int
)
parser.add_argument(
'--eval-files',
help='GCS or local paths to evaluation data',
nargs='+',
required=True
)
# Training arguments
parser.add_argument(
'--embedding-size',
help='Number of embedding dimensions for categorical columns',
default=8,
type=int
)
parser.add_argument(
'--first-layer-size',
help='Number of nodes in the first layer of the DNN',
default=100,
type=int
)
parser.add_argument(
'--num-layers',
help='Number of layers in the DNN',
default=4,
type=int
)
parser.add_argument(
'--scale-factor',
help='How quickly should the size of the layers in the DNN decay',
default=0.7,
type=float
)
parser.add_argument(
'--job-dir',
help='GCS location to write checkpoints and export models',
required=True
)
# Argument to turn on all logging
parser.add_argument(
'--verbose-logging',
default=False,
type=bool,
help='Switch to turn on or off verbose logging and warnings'
)
# Experiment arguments
parser.add_argument(
'--eval-delay-secs',
help='How long to wait before running first evaluation',
default=10,
type=int
)
parser.add_argument(
'--min-eval-frequency',
help='Minimum number of training steps between evaluations',
default=1,
type=int
)
args = parser.parse_args()
arguments = args.__dict__
job_dir = arguments.pop('job_dir')
print('Starting Census: Please lauch tensorboard to see results: tensorboard --logdir=$MODEL_DIR')
# Run the training job
# learn_runner pulls configuration information from environment
# variables using tf.learn.RunConfig and uses this configuration
# to conditionally execute Experiment, or param server code
learn_runner.run(generate_experiment_fn(**arguments), job_dir)
有没有人对我做错了什么有任何提示？
最诚挚的问候！
答案 0 :(得分：3)
TensorFlow服务期望您指向包含版本子目录的基本目录。在您的情况下，“Servo”是您要指向的目录，“1489706933289”是该版本的目录。
以下内容应该有效：
bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server \
--port=9000 \
--model_base_path=/serving/tf_models/extrato/output/Servo
（注意在基本路径中添加“Servo”，并且没有“1489706933289”）
请注意，在CloudML中，您可以直接部署版本，因此您需要指向GCS上类似于gs://my_bucket/.../tf_models/extrato/output/Servo/1489706933289的子目录