ML engine serving seems to not be working as intended

问题

While using the following code and doing a gcloud ml-engine local predict I get:

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder' with dtype string and shape [?] [[Node: Placeholder = Placeholderdtype=DT_STRING, shape=[?], _device="/job:localhost/replica:0/task:0/device:CPU:0"]] (Error code: 2)

tf_files_path = './tf'
# os.makedirs(tf_files_path)  # temp dir
estimator =\
    tf.keras.estimator.model_to_estimator(keras_model_path="model_data/yolo.h5",
                                            model_dir=tf_files_path)

#up_one_dir(os.path.join(tf_files_path, 'keras'))

def serving_input_receiver_fn():
    def prepare_image(image_str_tensor):
        image = tf.image.decode_jpeg(image_str_tensor,
                                    channels=3)
        image = tf.divide(image, 255)
        image = tf.image.convert_image_dtype(image, tf.float32)
        return image

    # Ensure model is batchable
    # https://stackoverflow.com/questions/52303403/
    input_ph = tf.placeholder(tf.string, shape=[None])
    images_tensor = tf.map_fn(
                prepare_image, input_ph, back_prop=False, dtype=tf.float32)

    return tf.estimator.export.ServingInputReceiver(
        {model.input_names[0]: images_tensor},
        {'image_bytes': input_ph})

export_path = './export'
estimator.export_savedmodel(
    export_path,
    serving_input_receiver_fn=serving_input_receiver_fn)

The json I am sending to the ml engine looks like this:

{"image_bytes": {"b64": "/9j/4AAQSkZJRgABAQAAAQABAAD/2w..."}}

When not doing a local prediction, but sending it to ML engine itself, I get:

ERROR: (gcloud.ml-engine.predict) HTTP request failed. Response: {
"error": {
"code": 500,
"message": "Internal error encountered.",
"status": "INTERNAL"
}
}

The saved_model_cli gives:

saved_model_cli show --all --dir export/1547848897/

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['image_bytes'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder:0
The given SavedModel SignatureDef contains the following output(s):
outputs['conv2d_59'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_59/BiasAdd:0
outputs['conv2d_67'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_67/BiasAdd:0
outputs['conv2d_75'] tensor_info:
dtype: DT_FLOAT
shape: (-1, -1, -1, 255)
name: conv2d_75/BiasAdd:0
Method name is: tensorflow/serving/predict

Does anyone see what is going wrong here?

回答1:

The issue has been resolved. The output of the model appeared to be too big for ml-engine to send it back and it didn't capture it in a more relevant exception than 500 internal error. We added some post-processing steps in the model and it works fine now.

For the gcloud ml-engine local predict command that is returning an error, it seems to be a bug. As the model works on ml-engine now, but still does return this error with local prediction.

来源：https://stackoverflow.com/questions/54287997/ml-engine-serving-seems-to-not-be-working-as-intended

标签

tensorflow

tensorflow-serving

google-cloud-ml