def __init__(
self,
model_params: ContinuousActionModelParameters,
preprocess_handler: PreprocessHandler,
state_normalization: Dict[int, NormalizationParameters],
action_normalization: Dict[int, NormalizationParameters],
use_gpu: bool,
use_all_avail_gpus: bool,
):
logger.info("Running continuous workflow with params:")
logger.info(model_params)
model_params = model_params
min_action_range_tensor_serving, max_action_range_tensor_serving = construct_action_scale_tensor(
action_normalization, model_params.action_rescale_map)
trainer = DDPGTrainer(
model_params,
state_normalization,
action_normalization,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
use_gpu=use_gpu,
use_all_avail_gpus=use_all_avail_gpus,
)
trainer = update_model_for_warm_start(trainer)
assert type(
trainer) == DDPGTrainer, "Warm started wrong model type: " + str(
type(trainer))
evaluator = Evaluator(
None,
model_params.rl.gamma,
trainer,
metrics_to_score=trainer.metrics_to_score,
)
super(ContinuousWorkflow, self).__init__(
preprocess_handler,
trainer,
evaluator,
model_params.shared_training.minibatch_size,
)
def train_network(params):
logger.info("Running Parametric DQN workflow with params:")
logger.info(params)
# Set minibatch size based on # of devices being used to train
params["shared_training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
dataset = JSONDataset(params["training_data_path"],
batch_size=training_parameters.minibatch_size)
state_normalization = read_norm_file(params["state_norm_data_path"])
action_normalization = read_norm_file(params["action_norm_data_path"])
num_batches = int(len(dataset) / training_parameters.minibatch_size)
logger.info("Read in batch data set {} of size {} examples. Data split "
"into {} batches of size {}.".format(
params["training_data_path"],
len(dataset),
num_batches,
training_parameters.minibatch_size,
))
min_action_range_tensor_serving, max_action_range_tensor_serving = construct_action_scale_tensor(
action_normalization, trainer_params.action_rescale_map)
trainer = DDPGTrainer(
trainer_params,
state_normalization,
action_normalization,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
use_gpu=params["use_gpu"],
use_all_avail_gpus=params["use_all_avail_gpus"],
)
trainer = update_model_for_warm_start(trainer)
state_preprocessor = Preprocessor(state_normalization, params["use_gpu"])
action_preprocessor = Preprocessor(action_normalization, params["use_gpu"])
start_time = time.time()
for epoch in range(params["epochs"]):
dataset.reset_iterator()
for batch_idx in range(num_batches):
report_training_status(batch_idx, num_batches, epoch,
params["epochs"])
batch = dataset.read_batch(batch_idx)
tdp = preprocess_batch_for_training(
state_preprocessor,
batch,
action_preprocessor=action_preprocessor)
tdp.set_type(trainer.dtype)
trainer.train(tdp)
through_put = (len(dataset) * params["epochs"]) / (time.time() -
start_time)
logger.info("Training finished. Processed ~{} examples / s.".format(
round(through_put)))
return export_trainer_and_predictor(trainer, params["model_output_path"])
def train_network(params):
logger.info("Running Parametric DQN workflow with params:")
logger.info(params)
# Set minibatch size based on # of devices being used to train
params["shared_training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"]
)
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
trainer_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
dataset = JSONDataset(
params["training_data_path"], batch_size=training_parameters.minibatch_size
)
state_normalization = read_norm_file(params["state_norm_data_path"])
action_normalization = read_norm_file(params["action_norm_data_path"])
num_batches = int(len(dataset) / training_parameters.minibatch_size)
logger.info(
"Read in batch data set {} of size {} examples. Data split "
"into {} batches of size {}.".format(
params["training_data_path"],
len(dataset),
num_batches,
training_parameters.minibatch_size,
)
)
min_action_range_tensor_serving, max_action_range_tensor_serving = construct_action_scale_tensor(
action_normalization, trainer_params.action_rescale_map
)
trainer = DDPGTrainer(
trainer_params,
state_normalization,
action_normalization,
min_action_range_tensor_serving,
max_action_range_tensor_serving,
use_gpu=params["use_gpu"],
use_all_avail_gpus=params["use_all_avail_gpus"],
)
trainer = update_model_for_warm_start(trainer)
state_preprocessor = Preprocessor(state_normalization, False)
action_preprocessor = Preprocessor(action_normalization, False)
start_time = time.time()
for epoch in range(params["epochs"]):
dataset.reset_iterator()
batch_idx = -1
while True:
batch_idx += 1
report_training_status(batch_idx, num_batches, epoch, params["epochs"])
batch = dataset.read_batch()
if batch is None:
break
tdp = preprocess_batch_for_training(
state_preprocessor, batch, action_preprocessor=action_preprocessor
)
tdp.set_type(trainer.dtype)
trainer.train(tdp)
through_put = (len(dataset) * params["epochs"]) / (time.time() - start_time)
logger.info(
"Training finished. Processed ~{} examples / s.".format(round(through_put))
)
return export_trainer_and_predictor(trainer, params["model_output_path"])
