Tensorflow Serving - 使用tf.contrib.learn.Experiment

Question

训练的模型的“无可用版本”消息我已经使用Google Cloud ML引擎的入门教程作为参考训练了模型。我可以设法在Google Cloud ML上部署和投放此模型，而不会出现任何问题。

现在我想要使用Tensorflow服务来服务，但我得到以下信息错误：

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/ 

我使用开始日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/ 

输出文件夹的内容是：

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    

ù PDATE：我尝试使用冻结模型（.pb文件和变量文件夹），这实际上是我用来在Google Cloud ML Engine上部署模型的文件夹，但得到了相同的错误消息。

这些文件位于下面的文件夹中：

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 

我用来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) 

有没有人有什么我任何提示米做错了吗？

最好的问候！

Answer 1

TensorFlow服务期望您指向包含版本子目录的基础目录。在你的情况下，“伺服”是你想要指向的目录，“1489706933289”是该版本的目录。

下面应该工作：

bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server \ 
    --port=9000 \ 
    --model_base_path=/serving/tf_models/extrato/output/Servo 

（请注意，除了“伺服”的基本路径，并没有“1489706933289”的）

注意，在CloudML你直接部署版本，所以你想要指向一个GCS上的子目录，类似于gs://my_bucket/.../tf_models/extrato/output/Servo/1489706933289