Deploy retrained inception SavedModel to google cl

Question

I am trying to deploy a retrained version of the inception model on google cloud ml-engine. Gathering informations from the SavedModel documentation, this reference, and this post of rhaertel80, I exported successfully my retrained model to a SavedModel, uploaded it to a bucket and tried to deploy it to a ml-engine version.

This last task actually creates a version, but it outputs this error:

Create Version failed. Bad model detected with error: "Error loading the model: Unexpected error when loading the model"

And when I try to get predictions from the model via commandline I get this error message: "message": "Field: name Error: Online prediction is unavailable for this version. Please verify that CreateVersion has completed successfully."

I have made several attempts, trying different method_name and tag options but none worked.

The code added to the original inception code is

  ### DEFINE SAVED MODEL SIGNATURE

  in_image = graph.get_tensor_by_name('DecodeJpeg/contents:0')
  inputs = {'image_bytes': tf.saved_model.utils.build_tensor_info(in_image)}

  out_classes = graph.get_tensor_by_name('final_result:0')
  outputs = {'prediction': tf.saved_model.utils.build_tensor_info(out_classes)}

  signature = tf.saved_model.signature_def_utils.build_signature_def(
      inputs=inputs,
      outputs=outputs,
      method_name='tensorflow/serving/predict'
  )


  ### SAVE OUT THE MODEL

  b = saved_model_builder.SavedModelBuilder('new_export_dir')
  b.add_meta_graph_and_variables(sess,
                                 [tf.saved_model.tag_constants.SERVING],
                                 signature_def_map={'predict_images': signature})
  b.save() 

Another consideration that might help: I have used an exported a trained_graph.pb with graph_def.SerializeToString() to get the predictions locally and it works fine, but when I substitute it with the saved_model.pb it fails.

Any suggestions on what the issue might be?

Answer 1

In your signature_def_map, use the key 'serving_default', which is defined in signature_constants as DEFAULT_SERVING_SIGNATURE_DEF_KEY:

b.add_meta_graph_and_variables(sess,
                               [tf.saved_model.tag_constants.SERVING],
                               signature_def_map={'serving_default': signature})