- class Controller
Bases:
abc.ABCThis class defines the abstract behavior required of an AutoML Controller.
Controller implements the AutoML strategy that decides how the training is to be conducted. Controller produces recommendations by finding values from a search space with some algorithm.
- abstract initial_recommendation()
This method is called by the AutoML workflow engine to produce the initial set of recommendations. The controller must produce 1 or more recommendations. If no recommendation is produced, the AutoML workflow will stop immediately.
This method is called only once at the beginning of the AutoML process.
- Parameters
ctx – the context that enables across-component data sharing and communication
Returns: a list of recommendations
- abstract refine_recommendation()
This method is called by the AutoML workflow engine to produce a set of recommendations based on the result from a previous job.
The controller can produce 0 or more recommendations.
This method is called every time a job finishes executing a previous recommendation.
- Parameters
outcome – the result of executing the previous recommendation
ctx – the context that enables across-component data sharing and communication
Returns: a list of recommendations, could be empty
- abstract set_search_space(space: automl.defs.SearchSpace, ctx: automl.defs.Context)
Set the search space. This is the search space that the controller will search against to produce recommendations. The controller must keep it for later use.
- Parameters
space – the search space
ctx – the context that enables across-component data sharing and communication
Returns:
NOTE: the controller should validate the search space and makes sure it is acceptable. In case the search space is not acceptable, the controller should either raise an exception or ask to stop the workflow by calling: ctx.ask_to_stop().
- shutdown(ctx: automl.defs.Context)
Called at the end of the AutoML workflow. This provides the opportunity for the controller to clean up if needed.
- Parameters
ctx – the context that enables across-component data sharing and communication
Returns:
- class FloatParameterSearchEngine(search_space: automl.defs.SearchSpace, ctx: automl.defs.Context)
Bases:
objectSets up network for Reinforcement learning to provide recommendations.
- Parameters
search_space – search space definition
ctx – AutoMLContext
- compute_settings(state: list)
Uses state and the network in the engine to calculate the next valid action and then returns it after advancing the state to the next valid action.
Returns: Next valid action based on engine’s state
- initial_recommendation() → list
Calculates initial specific configuration for running first training.
Returns: Specific configuration to use for running training.
- refine_recommendation(score: float, state: list) → list
Receives back the validation score for a completed run and calculates and returns specific configuration to use for the next training.
- Parameters
score – validation score
Returns: Specific configuration to use for running training.
- reinforce
net_manager = NetManager(num_input=784, num_classes=10, learning_rate=0.001, mnist=mnist, batch_size=100)
- random() → x in the interval [0, 1).
- class PolicyNetwork(max_num_layers: int, num_params: int)
Bases:
torch.nn.modules.module.ModuleInitializes internal Module state, shared by both nn.Module and ScriptModule.
- forward(inputs: torch.Tensor) → torch.Tensor
Defines the computation performed at every call.
Should be overridden by all subclasses.
NoteAlthough the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
- reset_parameters() → None
- training: bool
- class Reinforce(max_layers: int, num_params: int, global_step: int = 0, division_rate: float = 100.0, reg_param: float = 0.001, discount_factor: float = 0.99, exploration: float = 0.3)
Bases:
object- get_action(state)
- if random.random() < self.exploration:
return np.array([[random.sample(range(1, 35), 4*self.max_layers)]])
- else:
return self.sess.run(self.predicted_action, {self.states: state})
- storeRollout(state: list, reward: float) → None
- train_step(steps_count: int) → None
- class ReinforcementController(max_rounds=1000, num_workers=16)
Bases:
automl.components.controllers.controller.Controller- initial_recommendation(ctx)
This method is called by the AutoML workflow engine to produce the initial set of recommendations. The controller must produce 1 or more recommendations. If no recommendation is produced, the AutoML workflow will stop immediately.
This method is called only once at the beginning of the AutoML process.
- Parameters
ctx – the context that enables across-component data sharing and communication
Returns: a list of recommendations
- refine_recommendation(outcome: automl.defs.Outcome, ctx: automl.defs.Context)
This method is called by the AutoML workflow engine to produce a set of recommendations based on the result from a previous job.
The controller can produce 0 or more recommendations.
This method is called every time a job finishes executing a previous recommendation.
- Parameters
outcome – the result of executing the previous recommendation
ctx – the context that enables across-component data sharing and communication
Returns: a list of recommendations, could be empty
- set_search_space(space, ctx)
Set the search space. This is the search space that the controller will search against to produce recommendations. The controller must keep it for later use.
- Parameters
space – the search space
ctx – the context that enables across-component data sharing and communication
Returns:
NOTE: the controller should validate the search space and makes sure it is acceptable. In case the search space is not acceptable, the controller should either raise an exception or ask to stop the workflow by calling: ctx.ask_to_stop().