class_docstr: Functionality for deploying a model to a filename
docstr: Deploy a model to be used later or in a different system.
  
  NOTE: for a metric that is to be minimized, set asc=True or otherwise
  you will end up with the model that has the highest loss.
  
  Deploy() takes in the object from Scan() and creates a package locally
  that can be later activated with Restore().
  
  scan_object | object | The object that is returned from Scan() upon
                         completion.
  model_name | str | Name for the .zip file to be created.
  metric | str | The metric to be used for picking the best model.
  asc | bool | Make this True for metrics that are to be minimized
               (e.g. loss), and False when the metric is to be
               maximized (e.g. acc).
  saved | bool | if a model saved on local machine should be used
  custom_objects | dict | if the model has a custom object, pass it here
code: def __init__(
      self,
      scan_object,
      model_name,
      metric,
      asc=False,
      saved=False,
      custom_objects=None,
  ):
      """Deploy a model to be used later or in a different system.
  
      NOTE: for a metric that is to be minimized, set asc=True or otherwise
      you will end up with the model that has the highest loss.
  
      Deploy() takes in the object from Scan() and creates a package locally
      that can be later activated with Restore().
  
      scan_object | object | The object that is returned from Scan() upon
                             completion.
      model_name | str | Name for the .zip file to be created.
      metric | str | The metric to be used for picking the best model.
      asc | bool | Make this True for metrics that are to be minimized
                   (e.g. loss), and False when the metric is to be
                   maximized (e.g. acc).
      saved | bool | if a model saved on local machine should be used
      custom_objects | dict | if the model has a custom object, pass it here
  
      """
  
      import os
  
      self.scan_object = scan_object
      os.mkdir(model_name)
      self.path = model_name + "/" + model_name
      self.model_name = model_name
      self.metric = metric
      self.asc = asc
      self.data = scan_object.data
  
      from ..utils.best_model import best_model, activate_model
  
      self.best_model = best_model(scan_object, metric, asc)
      self.model = activate_model(
          scan_object, self.best_model, saved, custom_objects
      )
  
      # runtime
      self.save_model_as()
      self.save_details()
      self.save_data()
      self.save_results()
      self.save_params()
      self.save_readme()
      self.package()
  
file_path: talos/talos/commands/deploy.py
label_desc: Use image node_id 1 to create a new Deploy object  with example: obj = Deploy(scan_object, model_name, metric, asc, saved, custom_objects)