与针对Google CloudML部署的Tensorflow培训分开创建服务图吗? [英] Creating a serving graph separately from training in tensorflow for Google CloudML deployment?

问题描述

我正在尝试将tf.keras图像分类模型部署到Google CloudML Engine.我是否必须包含代码来与培训分开创建服务图，以使其能够在网络应用程序中为我的模型提供服务?我已经有SavedModel格式(saved_model.pb和可变文件)的模型，所以我不确定是否需要执行此额外步骤才能使其正常工作.

I am trying to deploy a tf.keras image classification model to Google CloudML Engine. Do I have to include code to create serving graph separately from training to get it to serve my models in a web app? I already have my model in SavedModel format (saved_model.pb & variable files), so I'm not sure if I need to do this extra step to get it to work.

例如这是直接来自GCP Tensorflow部署模型的代码文档

e.g. this is code directly from GCP Tensorflow Deploying models documentation

def json_serving_input_fn():
  """Build the serving inputs."""
  inputs = {}
  for feat in INPUT_COLUMNS:
    inputs[feat.name] = tf.placeholder(shape=[None], dtype=feat.dtype)

  return tf.estimator.export.ServingInputReceiver(inputs, inputs)

推荐答案

您可能正在使用实际的图像文件来训练模型，而最好将图像作为已编码的字节字符串发送到CloudML上托管的模型.因此，如上所述，导出模型时需要指定ServingInputReceiver函数.一些用于Keras模型的样板代码:

You are probably training your model with actual image files, while it is best to send images as encoded byte-string to a model hosted on CloudML. Therefore you'll need to specify a ServingInputReceiver function when exporting the model, as you mention. Some boilerplate code to do this for a Keras model:

# Convert keras model to TF estimator
tf_files_path = './tf'
estimator =\
    tf.keras.estimator.model_to_estimator(keras_model=model,
                                          model_dir=tf_files_path)

# Your serving input function will accept a string
# And decode it into an image
def serving_input_receiver_fn():
    def prepare_image(image_str_tensor):
        image = tf.image.decode_png(image_str_tensor,
                                    channels=3)
        return image  # apply additional processing if necessary

    # 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 the estimator - deploy it to CloudML afterwards
export_path = './export'
estimator.export_savedmodel(
    export_path,
    serving_input_receiver_fn=serving_input_receiver_fn)

您可以参考

You can refer to this very helpful answer for a more complete reference and other options for exporting your model.

:如果此方法引发ValueError: Couldn't find trained model at ./tf.错误，您可以尝试一下我在

If this approach throws a ValueError: Couldn't find trained model at ./tf. error, you can try it the workaround solution that I documented in this answer.

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