Use tflearn.DNN with google cloud ml-engine

Question

Is there a good way to deploy a model built using tflearn.DNN class to Google Cloud ML Engine? It seems like SavedModel requires input and output tensors to be defined in the prediction signature definition but unsure how to get that from tflearn.DNN.

Answer 1

I figured this out later at least for my specific case. This snippet lets you export your DNN as a SavedModel which can then be deployed to Google Cloud ML Engine.

Snippet is below with the following arguments

• filename is the export directory
• input_tensor is the input_data layer given to tflearn.DNN
• output_tensor is the entire network passed to tflearn.DNN

• session is an attribute of the object returned by tflearn.DNN

  builder = tf.saved_model.builder.SavedModelBuilder(filename)
  
  signature = tf.saved_model.signature_def_utils.predict_signature_def(
      inputs={'in':input_tensor}, outputs={'out':output_tensor})
  builder.add_meta_graph_and_variables(session,
                                       [tf.saved_model.tag_constants.SERVING],
                                       signature_def_map={'serving_default':signature})
  
  builder.save()
  
  serving_vars = {
      'name':self.name
  }
  
  assets = filename + '/assets.extra'
  os.makedirs(assets)
  
  with open(assets + '/serve.pkl', 'wb') as f:
      pickle.dump(serving_vars, f, pickle.HIGHEST_PROTOCOL)