Requests to TensorFlow serving's predict API returns error “Missing inputs”










0















I have trained a simple regression model to fit a linear function with the following equation: y = 3x + 1. For testing purposes, I saved the model as checkpoints, so that I could resume training and wouldn't have to start from scratch every time.



Now I want to make this model available via TF serving. For this reason, I had to convert it into the SavedModel format of tensorflow via this script:



import tensorflow as tf
import restoretest as rt ## just the module that contains the linear model

tf.reset_default_graph()

latest_checkpoint = tf.train.latest_checkpoint('path/to/checkpoints')
model = rt.LinearModel()
saver = tf.train.Saver()

export_path = 'path/to/export/folder'

with tf.Session() as sess:

if latest_checkpoint:
saver.restore(sess, latest_checkpoint)
else:
raise ValueError('No checkpoint file found')

print('Exporting trained model to', export_path)

builder = tf.saved_model.builder.SavedModelBuilder(export_path)

## define inputs and outputs

tensor_info_x = tf.saved_model.utils.build_tensor_info(model.x)
tensor_info_y = tf.saved_model.utils.build_tensor_info(model.y_pred)

prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs='xvals': tensor_info_x,
outputs='yvals': tensor_info_y,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))


builder.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.SERVING],
signature_def_map=tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,
main_op=tf.tables_initializer(),
strip_default_attrs=True)

builder.save()

print('Done exporting')


This creates a folder (as expected) with the contents:



export_folder
|-saved_model.pb
|-variables
|-variables.index
|-variables.data-00000-of-00001


To serve this with tf serving and docker, I pulled the tensorflow/serving image from docker and ran the container via the command:



sudo docker run -p 8501:8501 --mount type=bind,source=path/to/export/folder,target=models/linear -e MODEL_NAME=linear -t tensorflow/serving


This seems to execute without problems, as I get a lot of infos. In the last line of the output it says




[evhttp_server.cc : 237] RAW: Entering the event loop ...




I guess the server is waiting for requests. Now, when I try to send a request to it via curl, I get an error:



curl -d '"xvals": [1.0 2.0 5.0]' -X POST http://localhost:8501/v1/models/linear:predict



"error": "Missing 'inputs' or 'instances' key"




What am I doing wrong? The model works when I send dummy values via the saved_model_cli.










share|improve this question


























    0















    I have trained a simple regression model to fit a linear function with the following equation: y = 3x + 1. For testing purposes, I saved the model as checkpoints, so that I could resume training and wouldn't have to start from scratch every time.



    Now I want to make this model available via TF serving. For this reason, I had to convert it into the SavedModel format of tensorflow via this script:



    import tensorflow as tf
    import restoretest as rt ## just the module that contains the linear model

    tf.reset_default_graph()

    latest_checkpoint = tf.train.latest_checkpoint('path/to/checkpoints')
    model = rt.LinearModel()
    saver = tf.train.Saver()

    export_path = 'path/to/export/folder'

    with tf.Session() as sess:

    if latest_checkpoint:
    saver.restore(sess, latest_checkpoint)
    else:
    raise ValueError('No checkpoint file found')

    print('Exporting trained model to', export_path)

    builder = tf.saved_model.builder.SavedModelBuilder(export_path)

    ## define inputs and outputs

    tensor_info_x = tf.saved_model.utils.build_tensor_info(model.x)
    tensor_info_y = tf.saved_model.utils.build_tensor_info(model.y_pred)

    prediction_signature = (
    tf.saved_model.signature_def_utils.build_signature_def(
    inputs='xvals': tensor_info_x,
    outputs='yvals': tensor_info_y,
    method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))


    builder.add_meta_graph_and_variables(sess,
    [tf.saved_model.tag_constants.SERVING],
    signature_def_map=tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,
    main_op=tf.tables_initializer(),
    strip_default_attrs=True)

    builder.save()

    print('Done exporting')


    This creates a folder (as expected) with the contents:



    export_folder
    |-saved_model.pb
    |-variables
    |-variables.index
    |-variables.data-00000-of-00001


    To serve this with tf serving and docker, I pulled the tensorflow/serving image from docker and ran the container via the command:



    sudo docker run -p 8501:8501 --mount type=bind,source=path/to/export/folder,target=models/linear -e MODEL_NAME=linear -t tensorflow/serving


    This seems to execute without problems, as I get a lot of infos. In the last line of the output it says




    [evhttp_server.cc : 237] RAW: Entering the event loop ...




    I guess the server is waiting for requests. Now, when I try to send a request to it via curl, I get an error:



    curl -d '"xvals": [1.0 2.0 5.0]' -X POST http://localhost:8501/v1/models/linear:predict



    "error": "Missing 'inputs' or 'instances' key"




    What am I doing wrong? The model works when I send dummy values via the saved_model_cli.










    share|improve this question
























      0












      0








      0








      I have trained a simple regression model to fit a linear function with the following equation: y = 3x + 1. For testing purposes, I saved the model as checkpoints, so that I could resume training and wouldn't have to start from scratch every time.



      Now I want to make this model available via TF serving. For this reason, I had to convert it into the SavedModel format of tensorflow via this script:



      import tensorflow as tf
      import restoretest as rt ## just the module that contains the linear model

      tf.reset_default_graph()

      latest_checkpoint = tf.train.latest_checkpoint('path/to/checkpoints')
      model = rt.LinearModel()
      saver = tf.train.Saver()

      export_path = 'path/to/export/folder'

      with tf.Session() as sess:

      if latest_checkpoint:
      saver.restore(sess, latest_checkpoint)
      else:
      raise ValueError('No checkpoint file found')

      print('Exporting trained model to', export_path)

      builder = tf.saved_model.builder.SavedModelBuilder(export_path)

      ## define inputs and outputs

      tensor_info_x = tf.saved_model.utils.build_tensor_info(model.x)
      tensor_info_y = tf.saved_model.utils.build_tensor_info(model.y_pred)

      prediction_signature = (
      tf.saved_model.signature_def_utils.build_signature_def(
      inputs='xvals': tensor_info_x,
      outputs='yvals': tensor_info_y,
      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))


      builder.add_meta_graph_and_variables(sess,
      [tf.saved_model.tag_constants.SERVING],
      signature_def_map=tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,
      main_op=tf.tables_initializer(),
      strip_default_attrs=True)

      builder.save()

      print('Done exporting')


      This creates a folder (as expected) with the contents:



      export_folder
      |-saved_model.pb
      |-variables
      |-variables.index
      |-variables.data-00000-of-00001


      To serve this with tf serving and docker, I pulled the tensorflow/serving image from docker and ran the container via the command:



      sudo docker run -p 8501:8501 --mount type=bind,source=path/to/export/folder,target=models/linear -e MODEL_NAME=linear -t tensorflow/serving


      This seems to execute without problems, as I get a lot of infos. In the last line of the output it says




      [evhttp_server.cc : 237] RAW: Entering the event loop ...




      I guess the server is waiting for requests. Now, when I try to send a request to it via curl, I get an error:



      curl -d '"xvals": [1.0 2.0 5.0]' -X POST http://localhost:8501/v1/models/linear:predict



      "error": "Missing 'inputs' or 'instances' key"




      What am I doing wrong? The model works when I send dummy values via the saved_model_cli.










      share|improve this question














      I have trained a simple regression model to fit a linear function with the following equation: y = 3x + 1. For testing purposes, I saved the model as checkpoints, so that I could resume training and wouldn't have to start from scratch every time.



      Now I want to make this model available via TF serving. For this reason, I had to convert it into the SavedModel format of tensorflow via this script:



      import tensorflow as tf
      import restoretest as rt ## just the module that contains the linear model

      tf.reset_default_graph()

      latest_checkpoint = tf.train.latest_checkpoint('path/to/checkpoints')
      model = rt.LinearModel()
      saver = tf.train.Saver()

      export_path = 'path/to/export/folder'

      with tf.Session() as sess:

      if latest_checkpoint:
      saver.restore(sess, latest_checkpoint)
      else:
      raise ValueError('No checkpoint file found')

      print('Exporting trained model to', export_path)

      builder = tf.saved_model.builder.SavedModelBuilder(export_path)

      ## define inputs and outputs

      tensor_info_x = tf.saved_model.utils.build_tensor_info(model.x)
      tensor_info_y = tf.saved_model.utils.build_tensor_info(model.y_pred)

      prediction_signature = (
      tf.saved_model.signature_def_utils.build_signature_def(
      inputs='xvals': tensor_info_x,
      outputs='yvals': tensor_info_y,
      method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))


      builder.add_meta_graph_and_variables(sess,
      [tf.saved_model.tag_constants.SERVING],
      signature_def_map=tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: prediction_signature,
      main_op=tf.tables_initializer(),
      strip_default_attrs=True)

      builder.save()

      print('Done exporting')


      This creates a folder (as expected) with the contents:



      export_folder
      |-saved_model.pb
      |-variables
      |-variables.index
      |-variables.data-00000-of-00001


      To serve this with tf serving and docker, I pulled the tensorflow/serving image from docker and ran the container via the command:



      sudo docker run -p 8501:8501 --mount type=bind,source=path/to/export/folder,target=models/linear -e MODEL_NAME=linear -t tensorflow/serving


      This seems to execute without problems, as I get a lot of infos. In the last line of the output it says




      [evhttp_server.cc : 237] RAW: Entering the event loop ...




      I guess the server is waiting for requests. Now, when I try to send a request to it via curl, I get an error:



      curl -d '"xvals": [1.0 2.0 5.0]' -X POST http://localhost:8501/v1/models/linear:predict



      "error": "Missing 'inputs' or 'instances' key"




      What am I doing wrong? The model works when I send dummy values via the saved_model_cli.







      python python-3.x tensorflow tensorflow-serving






      share|improve this question













      share|improve this question











      share|improve this question




      share|improve this question










      asked Nov 12 '18 at 13:47









      DocDrivenDocDriven

      1,0661520




      1,0661520






















          1 Answer
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          Looks like the body of the POST request should be modified. According to documentation the format should be



          "inputs": "xvals": [1.0 2.0 5.0]






          share|improve this answer























          • Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

            – DocDriven
            Nov 12 '18 at 15:59










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          1 Answer
          1






          active

          oldest

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          active

          oldest

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          active

          oldest

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          1














          Looks like the body of the POST request should be modified. According to documentation the format should be



          "inputs": "xvals": [1.0 2.0 5.0]






          share|improve this answer























          • Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

            – DocDriven
            Nov 12 '18 at 15:59















          1














          Looks like the body of the POST request should be modified. According to documentation the format should be



          "inputs": "xvals": [1.0 2.0 5.0]






          share|improve this answer























          • Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

            – DocDriven
            Nov 12 '18 at 15:59













          1












          1








          1







          Looks like the body of the POST request should be modified. According to documentation the format should be



          "inputs": "xvals": [1.0 2.0 5.0]






          share|improve this answer













          Looks like the body of the POST request should be modified. According to documentation the format should be



          "inputs": "xvals": [1.0 2.0 5.0]







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 12 '18 at 15:45









          Vlad-HCVlad-HC

          826815




          826815












          • Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

            – DocDriven
            Nov 12 '18 at 15:59

















          • Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

            – DocDriven
            Nov 12 '18 at 15:59
















          Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

          – DocDriven
          Nov 12 '18 at 15:59





          Thanks, aside from my original issue I also realized how to choose different signatures due to your link.

          – DocDriven
          Nov 12 '18 at 15:59

















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