def create_optimizer( task: base_task.Task, params: config_definitions.ExperimentConfig ) -> tf.keras.optimizers.Optimizer: """A create optimizer util to be backward compatability with new args.""" if 'dp_config' in inspect.signature(task.create_optimizer).parameters: dp_config = None if hasattr(params.task, 'differential_privacy_config'): dp_config = params.task.differential_privacy_config optimizer = task.create_optimizer(params.trainer.optimizer_config, params.runtime, dp_config=dp_config) else: if hasattr(params.task, 'differential_privacy_config' ) and params.task.differential_privacy_config is not None: raise ValueError('Differential privacy config is specified but ' 'task.create_optimizer api does not accept it.') optimizer = task.create_optimizer(params.trainer.optimizer_config, params.runtime) return optimizer
def create_trainer(params: config_definitions.ExperimentConfig, task: base_task.Task, train: bool, evaluate: bool, checkpoint_exporter: Optional[BestCheckpointExporter] = None, trainer_cls=base_trainer.Trainer) -> base_trainer.Trainer: """Create trainer.""" logging.info('Running default trainer.') model = task.build_model() optimizer = task.create_optimizer(params.trainer, params.runtime) return trainer_cls( params, task, model=model, optimizer=optimizer, train=train, evaluate=evaluate, checkpoint_exporter=checkpoint_exporter)
def run_experiment_with_multitask_eval( *, distribution_strategy: tf.distribute.Strategy, train_task: base_task.Task, eval_tasks: List[base_task.Task], mode: str, params: configs.MultiEvalExperimentConfig, model_dir: str, run_post_eval: bool = False, save_summary: bool = True, trainer: Optional[core_lib.Trainer] = None) -> tf.keras.Model: """Runs train/eval configured by the experiment params. Args: distribution_strategy: A distribution distribution_strategy. train_task: A base_task.Task instance. eval_tasks: A list of evaluation tasks. mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval' or 'continuous_eval'. params: MultiEvalExperimentConfig instance. model_dir: A 'str', a path to store model checkpoints and summaries. run_post_eval: Whether to run post eval once after training, metrics logs are returned. save_summary: Whether to save train and validation summary. trainer: the core_lib.Trainer instance. It should be created within the strategy.scope(). If not provided, an instance will be created by default if `mode` contains 'train'. Returns: model: `tf.keras.Model` instance. """ is_training = 'train' in mode is_eval = 'eval' in mode with distribution_strategy.scope(): if is_training: trainer = trainer or core_lib.Trainer( config=params, task=train_task, model=train_task.build_model(), optimizer=train_task.create_optimizer(params.trainer.optimizer_config, params.runtime), train=True, evaluate=False) else: trainer = None model = trainer.model if trainer else train_task.build_model() if is_eval: eval_steps = dict([(task_routine.task_config.name, task_routine.eval_steps) for task_routine in params.eval_tasks]) evaluator = evaluator_lib.MultiTaskEvaluator( eval_tasks=eval_tasks, model=model, global_step=trainer.global_step if is_training else None, eval_steps=eval_steps, checkpoint_exporter=train_utils.maybe_create_best_ckpt_exporter( params, model_dir)) else: evaluator = None if trainer: checkpoint = trainer.checkpoint global_step = trainer.global_step else: checkpoint = evaluator.checkpoint global_step = evaluator.global_step checkpoint_manager = tf.train.CheckpointManager( checkpoint, directory=model_dir, max_to_keep=params.trainer.max_to_keep, step_counter=global_step, checkpoint_interval=params.trainer.checkpoint_interval, init_fn=trainer.initialize if trainer else None) controller = orbit.Controller( strategy=distribution_strategy, trainer=trainer, evaluator=evaluator, global_step=global_step, steps_per_loop=params.trainer.steps_per_loop, checkpoint_manager=checkpoint_manager, summary_dir=os.path.join(model_dir, 'train') if save_summary else None, eval_summary_dir=os.path.join(model_dir, 'validation') if (save_summary) else None, summary_interval=params.trainer.summary_interval if (save_summary) else None) logging.info('Starts to execute mode: %s', mode) with distribution_strategy.scope(): if mode == 'train': controller.train(steps=params.trainer.train_steps) elif mode == 'train_and_eval': controller.train_and_evaluate( train_steps=params.trainer.train_steps, eval_steps=params.trainer.validation_steps, eval_interval=params.trainer.validation_interval) elif mode == 'eval': controller.evaluate(steps=params.trainer.validation_steps) elif mode == 'continuous_eval': def timeout_fn(): if evaluator.global_step.numpy() >= params.trainer.train_steps: return True return False controller.evaluate_continuously( steps=params.trainer.validation_steps, timeout=params.trainer.continuous_eval_timeout, timeout_fn=timeout_fn) else: raise NotImplementedError('The mode is not implemented: %s' % mode) if run_post_eval: return model, evaluator.evaluate( tf.convert_to_tensor(params.trainer.validation_steps)) else: return model, {}
def run_experiment_wtih_multitask_eval( *, distribution_strategy: tf.distribute.Strategy, train_task: base_task.Task, eval_tasks: multitask.MultiTask, mode: str, params: configs.MultiEvalExperimentConfig, model_dir: str) -> tf.keras.Model: """Runs train/eval configured by the experiment params. Args: distribution_strategy: A distribution distribution_strategy. train_task: A base_task.Task instance. eval_tasks: A multitask.MultiTask with evaluation tasks. mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval' or 'continuous_eval'. params: MultiEvalExperimentConfig instance. model_dir: A 'str', a path to store model checkpoints and summaries. Returns: model: `tf.keras.Model` instance. """ is_training = 'train' in mode is_eval = 'eval' in mode with distribution_strategy.scope(): optimizer = train_task.create_optimizer(params.trainer, params.runtime) model = train_task.build_model() if is_training: trainer = core_lib.Trainer(config=params, task=train_task, model=model, optimizer=optimizer, train=True, evaluate=False) else: trainer = None if is_eval: evaluator = evaluator_lib.MultiTaskEvaluator( task=eval_tasks, model=model, global_step=trainer.global_step if is_training else None) else: evaluator = None if trainer: checkpoint = trainer.checkpoint global_step = trainer.global_step else: checkpoint = evaluator.checkpoint global_step = evaluator.global_step checkpoint_manager = tf.train.CheckpointManager( checkpoint, directory=model_dir, max_to_keep=params.trainer.max_to_keep, step_counter=global_step, checkpoint_interval=params.trainer.checkpoint_interval, init_fn=trainer.initialize if trainer else None) controller = orbit.Controller( strategy=distribution_strategy, trainer=trainer, evaluator=evaluator, global_step=global_step, steps_per_loop=params.trainer.steps_per_loop, checkpoint_manager=checkpoint_manager, summary_dir=os.path.join(model_dir, 'train'), eval_summary_dir=os.path.join(model_dir, 'validation'), summary_interval=params.trainer.summary_interval) logging.info('Starts to execute mode: %s', mode) with distribution_strategy.scope(): if mode == 'train': controller.train(steps=params.trainer.train_steps) elif mode == 'train_and_eval': controller.train_and_evaluate( train_steps=params.trainer.train_steps, eval_steps=params.trainer.validation_steps, eval_interval=params.trainer.validation_interval) elif mode == 'eval': controller.evaluate(steps=params.trainer.validation_steps) elif mode == 'continuous_eval': def timeout_fn(): if evaluator.global_step.numpy() >= params.trainer.train_steps: return True return False controller.evaluate_continuously( steps=params.trainer.validation_steps, timeout=params.trainer.continuous_eval_timeout, timeout_fn=timeout_fn) else: raise NotImplementedError('The mode is not implemented: %s' % mode) return model