def validate(s3_bucket, s3_prefix, aws_region):
screen.set_use_colors(False)
screen.log_title(" S3 bucket: {} \n S3 prefix: {}".format(
s3_bucket, s3_prefix))
# download model metadata
model_metadata = ModelMetadata(bucket=s3_bucket,
s3_key=get_s3_key(
s3_prefix, MODEL_METADATA_S3_POSTFIX),
region_name=aws_region,
local_path=MODEL_METADATA_LOCAL_PATH)
# Create model local path
os.makedirs(LOCAL_MODEL_DIR)
try:
# Handle backward compatibility
model_metadata_info = model_metadata.get_model_metadata_info()
observation_list = model_metadata_info[ModelMetadataKeys.SENSOR.value]
version = model_metadata_info[ModelMetadataKeys.VERSION.value]
except Exception as ex:
log_and_exit("Failed to parse model_metadata file: {}".format(ex),
SIMAPP_VALIDATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_400)
# Below get_transition_data function must called before create_training_agent function
# to avoid 500 in case unsupported Sensor is received.
# create_training_agent will exit with 500 if unsupported sensor is received,
# and get_transition_data function below will exit with 400 if unsupported sensor is received.
# We want to return 400 in model validation case if unsupported sensor is received.
# Thus, call this get_transition_data function before create_traning_agent function!
transitions = get_transition_data(observation_list)
checkpoint = Checkpoint(bucket=s3_bucket,
s3_prefix=s3_prefix,
region_name=args.aws_region,
agent_name='agent',
checkpoint_dir=LOCAL_MODEL_DIR)
# make coach checkpoint compatible
if version < SIMAPP_VERSION_2 and not checkpoint.rl_coach_checkpoint.is_compatible(
):
checkpoint.rl_coach_checkpoint.make_compatible(
checkpoint.syncfile_ready)
# add checkpoint into checkpoint_dict
checkpoint_dict = {'agent': checkpoint}
agent_config = {
'model_metadata': model_metadata,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value: {},
ConfigParams.STEERING_LIST.value: {},
ConfigParams.CHANGE_START.value: None,
ConfigParams.ALT_DIR.value: None,
ConfigParams.MODEL_METADATA.value: model_metadata,
ConfigParams.REWARD.value: None,
ConfigParams.AGENT_NAME.value: 'racecar'
}
}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
sm_hyperparams_dict = {}
graph_manager, _ = get_graph_manager(hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=None)
ds_params_instance = S3BotoDataStoreParameters(
checkpoint_dict=checkpoint_dict)
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager,
ignore_lock=True)
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = LOCAL_MODEL_DIR
_validate(graph_manager=graph_manager,
task_parameters=task_parameters,
transitions=transitions,
s3_bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region)
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument('-pk',
'--preset_s3_key',
help="(string) Name of a preset to download from S3",
type=str,
required=False)
parser.add_argument(
'-ek',
'--environment_s3_key',
help="(string) Name of an environment file to download from S3",
type=str,
required=False)
parser.add_argument('--model_metadata_s3_key',
help="(string) Model Metadata File S3 Key",
type=str,
required=False)
parser.add_argument(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to write the model to.',
type=str,
default='./checkpoint')
parser.add_argument(
'--pretrained_checkpoint_dir',
help='(string) Path to a folder for downloading a pre-trained model',
type=str,
default=PRETRAINED_MODEL_DIR)
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=os.environ.get(
"SAGEMAKER_SHARED_S3_BUCKET_PATH", "gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default='sagemaker')
parser.add_argument('--framework',
help='(string) tensorflow or mxnet',
type=str,
default='tensorflow')
parser.add_argument('--pretrained_s3_bucket',
help='(string) S3 bucket for pre-trained model',
type=str)
parser.add_argument('--pretrained_s3_prefix',
help='(string) S3 prefix for pre-trained model',
type=str,
default='sagemaker')
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("AWS_REGION", "us-east-1"))
args, _ = parser.parse_known_args()
s3_client = S3Client(region_name=args.aws_region, max_retry_attempts=0)
# download model metadata
# TODO: replace 'agent' with name of each agent
model_metadata_download = ModelMetadata(
bucket=args.s3_bucket,
s3_key=args.model_metadata_s3_key,
region_name=args.aws_region,
local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format('agent'))
model_metadata_info = model_metadata_download.get_model_metadata_info()
network_type = model_metadata_info[ModelMetadataKeys.NEURAL_NETWORK.value]
version = model_metadata_info[ModelMetadataKeys.VERSION.value]
# upload model metadata
model_metadata_upload = ModelMetadata(
bucket=args.s3_bucket,
s3_key=get_s3_key(args.s3_prefix, MODEL_METADATA_S3_POSTFIX),
region_name=args.aws_region,
local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format('agent'))
model_metadata_upload.persist(
s3_kms_extra_args=utils.get_s3_kms_extra_args())
shutil.copy2(model_metadata_download.local_path, SM_MODEL_OUTPUT_DIR)
success_custom_preset = False
if args.preset_s3_key:
preset_local_path = "./markov/presets/preset.py"
try:
s3_client.download_file(bucket=args.s3_bucket,
s3_key=args.preset_s3_key,
local_path=preset_local_path)
success_custom_preset = True
except botocore.exceptions.ClientError:
pass
if not success_custom_preset:
logger.info(
"Could not download the preset file. Using the default DeepRacer preset."
)
else:
preset_location = "markov.presets.preset:graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
s3_client.upload_file(
bucket=args.s3_bucket,
s3_key=os.path.normpath("%s/presets/preset.py" %
args.s3_prefix),
local_path=preset_local_path,
s3_kms_extra_args=utils.get_s3_kms_extra_args())
if success_custom_preset:
logger.info("Using preset: %s" % args.preset_s3_key)
if not success_custom_preset:
params_blob = os.environ.get('SM_TRAINING_ENV', '')
if params_blob:
params = json.loads(params_blob)
sm_hyperparams_dict = params["hyperparameters"]
else:
sm_hyperparams_dict = {}
#! TODO each agent should have own config
agent_config = {
'model_metadata': model_metadata_download,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value: {},
ConfigParams.STEERING_LIST.value: {},
ConfigParams.CHANGE_START.value: None,
ConfigParams.ALT_DIR.value: None,
ConfigParams.MODEL_METADATA.value: model_metadata_download,
ConfigParams.REWARD.value: None,
ConfigParams.AGENT_NAME.value: 'racecar'
}
}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
graph_manager, robomaker_hyperparams_json = get_graph_manager(
hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=None,
run_type=str(RunType.TRAINER))
# Upload hyperparameters to SageMaker shared s3 bucket
hyperparameters = Hyperparameters(bucket=args.s3_bucket,
s3_key=get_s3_key(
args.s3_prefix,
HYPERPARAMETER_S3_POSTFIX),
region_name=args.aws_region)
hyperparameters.persist(
hyperparams_json=robomaker_hyperparams_json,
s3_kms_extra_args=utils.get_s3_kms_extra_args())
# Attach sample collector to graph_manager only if sample count > 0
max_sample_count = int(sm_hyperparams_dict.get("max_sample_count", 0))
if max_sample_count > 0:
sample_collector = SampleCollector(
bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region_name=args.aws_region,
max_sample_count=max_sample_count,
sampling_frequency=int(
sm_hyperparams_dict.get("sampling_frequency", 1)))
graph_manager.sample_collector = sample_collector
# persist IP config from sagemaker to s3
ip_config = IpConfig(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region_name=args.aws_region)
ip_config.persist(s3_kms_extra_args=utils.get_s3_kms_extra_args())
training_algorithm = model_metadata_download.training_algorithm
output_head_format = FROZEN_HEAD_OUTPUT_GRAPH_FORMAT_MAPPING[
training_algorithm]
use_pretrained_model = args.pretrained_s3_bucket and args.pretrained_s3_prefix
# Handle backward compatibility
if use_pretrained_model:
# checkpoint s3 instance for pretrained model
# TODO: replace 'agent' for multiagent training
checkpoint = Checkpoint(bucket=args.pretrained_s3_bucket,
s3_prefix=args.pretrained_s3_prefix,
region_name=args.aws_region,
agent_name='agent',
checkpoint_dir=args.pretrained_checkpoint_dir,
output_head_format=output_head_format)
# make coach checkpoint compatible
if version < SIMAPP_VERSION_2 and not checkpoint.rl_coach_checkpoint.is_compatible(
):
checkpoint.rl_coach_checkpoint.make_compatible(
checkpoint.syncfile_ready)
# get best model checkpoint string
model_checkpoint_name = checkpoint.deepracer_checkpoint_json.get_deepracer_best_checkpoint(
)
# Select the best checkpoint model by uploading rl coach .coach_checkpoint file
checkpoint.rl_coach_checkpoint.update(
model_checkpoint_name=model_checkpoint_name,
s3_kms_extra_args=utils.get_s3_kms_extra_args())
# add checkpoint into checkpoint_dict
checkpoint_dict = {'agent': checkpoint}
# load pretrained model
ds_params_instance_pretrained = S3BotoDataStoreParameters(
checkpoint_dict=checkpoint_dict)
data_store_pretrained = S3BotoDataStore(ds_params_instance_pretrained,
graph_manager, True)
data_store_pretrained.load_from_store()
memory_backend_params = DeepRacerRedisPubSubMemoryBackendParameters(
redis_address="localhost",
redis_port=6379,
run_type=str(RunType.TRAINER),
channel=args.s3_prefix,
network_type=network_type)
graph_manager.memory_backend_params = memory_backend_params
# checkpoint s3 instance for training model
checkpoint = Checkpoint(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region_name=args.aws_region,
agent_name='agent',
checkpoint_dir=args.checkpoint_dir,
output_head_format=output_head_format)
checkpoint_dict = {'agent': checkpoint}
ds_params_instance = S3BotoDataStoreParameters(
checkpoint_dict=checkpoint_dict)
graph_manager.data_store_params = ds_params_instance
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager)
task_parameters = TaskParameters()
task_parameters.experiment_path = SM_MODEL_OUTPUT_DIR
task_parameters.checkpoint_save_secs = 20
if use_pretrained_model:
task_parameters.checkpoint_restore_path = args.pretrained_checkpoint_dir
task_parameters.checkpoint_save_dir = args.checkpoint_dir
training_worker(
graph_manager=graph_manager,
task_parameters=task_parameters,
user_batch_size=json.loads(robomaker_hyperparams_json)["batch_size"],
user_episode_per_rollout=json.loads(
robomaker_hyperparams_json)["num_episodes_between_training"],
training_algorithm=training_algorithm)
def main():
screen.set_use_colors(False)
logger.info("src/training_worker.py - INIZIO MAIN")
parser = argparse.ArgumentParser()
parser.add_argument('-pk',
'--preset_s3_key',
help="(string) Name of a preset to download from S3",
type=str,
required=False)
parser.add_argument(
'-ek',
'--environment_s3_key',
help="(string) Name of an environment file to download from S3",
type=str,
required=False)
parser.add_argument('--model_metadata_s3_key',
help="(string) Model Metadata File S3 Key",
type=str,
required=False)
parser.add_argument(
'-c',
'--checkpoint-dir',
help=
'(string) Path to a folder containing a checkpoint to write the model to.',
type=str,
default='./checkpoint')
parser.add_argument(
'--pretrained-checkpoint-dir',
help='(string) Path to a folder for downloading a pre-trained model',
type=str,
default=PRETRAINED_MODEL_DIR)
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=os.environ.get(
"SAGEMAKER_SHARED_S3_BUCKET_PATH", "gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default='sagemaker')
parser.add_argument('--s3_endpoint_url',
help='(string) S3 endpoint URL',
type=str,
default=os.environ.get("S3_ENDPOINT_URL", None))
parser.add_argument('--framework',
help='(string) tensorflow or mxnet',
type=str,
default='tensorflow')
parser.add_argument('--pretrained_s3_bucket',
help='(string) S3 bucket for pre-trained model',
type=str)
parser.add_argument('--pretrained_s3_prefix',
help='(string) S3 prefix for pre-trained model',
type=str,
default='sagemaker')
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("AWS_REGION", "us-east-1"))
args, _ = parser.parse_known_args()
logger.info("S3 bucket: %s \n S3 prefix: %s \n S3 endpoint URL: %s",
args.s3_bucket, args.s3_prefix, args.s3_endpoint_url)
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region,
s3_endpoint_url=args.s3_endpoint_url)
# Load the model metadata
model_metadata_local_path = os.path.join(CUSTOM_FILES_PATH,
'model_metadata.json')
utils.load_model_metadata(s3_client, args.model_metadata_s3_key,
model_metadata_local_path)
s3_client.upload_file(
os.path.normpath("%s/model/model_metadata.json" % args.s3_prefix),
model_metadata_local_path)
shutil.copy2(model_metadata_local_path, SM_MODEL_OUTPUT_DIR)
success_custom_preset = False
if args.preset_s3_key:
preset_local_path = "./markov/presets/preset.py"
success_custom_preset = s3_client.download_file(
s3_key=args.preset_s3_key, local_path=preset_local_path)
if not success_custom_preset:
logger.info(
"Could not download the preset file. Using the default DeepRacer preset."
)
else:
preset_location = "markov.presets.preset:graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
success_custom_preset = s3_client.upload_file(
s3_key=os.path.normpath("%s/presets/preset.py" %
args.s3_prefix),
local_path=preset_local_path)
if success_custom_preset:
logger.info("Using preset: %s" % args.preset_s3_key)
if not success_custom_preset:
params_blob = os.environ.get('SM_TRAINING_ENV', '')
if params_blob:
params = json.loads(params_blob)
sm_hyperparams_dict = params["hyperparameters"]
else:
sm_hyperparams_dict = {}
#configurazione agente: metadati del modello impostati dall'utente (angolo di sterzo + velocità) + nome
#! TODO each agent should have own config
agent_config = {
'model_metadata': model_metadata_local_path,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value: {},
ConfigParams.STEERING_LIST.value: {},
ConfigParams.CHANGE_START.value: None,
ConfigParams.ALT_DIR.value: None,
ConfigParams.ACTION_SPACE_PATH.value:
'custom_files/model_metadata.json',
ConfigParams.REWARD.value: None,
ConfigParams.AGENT_NAME.value: 'racecar'
}
}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
logger.info(
"src/training_worker.py - ora chiamo la get_graph_manager, che recupera l'agente"
)
graph_manager, robomaker_hyperparams_json = get_graph_manager(
hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=None)
logger.info("src/training_worker.py - ho l'agente")
s3_client.upload_hyperparameters(robomaker_hyperparams_json)
logger.info("Uploaded hyperparameters.json to S3")
# Attach sample collector to graph_manager only if sample count > 0
max_sample_count = int(sm_hyperparams_dict.get("max_sample_count", 0))
if max_sample_count > 0:
sample_collector = SampleCollector(
s3_client=s3_client,
s3_prefix=args.s3_prefix,
max_sample_count=max_sample_count,
sampling_frequency=int(
sm_hyperparams_dict.get("sampling_frequency", 1)))
graph_manager.sample_collector = sample_collector
host_ip_address = utils.get_ip_from_host()
s3_client.write_ip_config(host_ip_address)
logger.info("Uploaded IP address information to S3: %s" % host_ip_address)
use_pretrained_model = args.pretrained_s3_bucket and args.pretrained_s3_prefix
# Handle backward compatibility
_, network_type, version = parse_model_metadata(model_metadata_local_path)
if use_pretrained_model:
if float(version) < float(SIMAPP_VERSION) and \
not utils.has_current_ckpnt_name(args.pretrained_s3_bucket, args.pretrained_s3_prefix, args.aws_region, args.s3_endpoint_url):
utils.make_compatible(args.pretrained_s3_bucket,
args.pretrained_s3_prefix, args.aws_region,
SyncFiles.TRAINER_READY.value)
#Select the optimal model for the starting weights
utils.do_model_selection(s3_bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region=args.aws_region,
s3_endpoint_url=args.s3_endpoint_url)
ds_params_instance_pretrained = S3BotoDataStoreParameters(
aws_region=args.aws_region,
bucket_names={'agent': args.pretrained_s3_bucket},
base_checkpoint_dir=args.pretrained_checkpoint_dir,
s3_folders={'agent': args.pretrained_s3_prefix},
s3_endpoint_url=args.s3_endpoint_url)
data_store_pretrained = S3BotoDataStore(ds_params_instance_pretrained,
graph_manager, True)
data_store_pretrained.load_from_store()
memory_backend_params = DeepRacerRedisPubSubMemoryBackendParameters(
redis_address="localhost",
redis_port=6379,
run_type=str(RunType.TRAINER),
channel=args.s3_prefix,
network_type=network_type)
graph_manager.memory_backend_params = memory_backend_params
ds_params_instance = S3BotoDataStoreParameters(
aws_region=args.aws_region,
bucket_names={'agent': args.s3_bucket},
base_checkpoint_dir=args.checkpoint_dir,
s3_folders={'agent': args.s3_prefix},
s3_endpoint_url=args.s3_endpoint_url)
graph_manager.data_store_params = ds_params_instance
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager)
task_parameters = TaskParameters()
task_parameters.experiment_path = SM_MODEL_OUTPUT_DIR
task_parameters.checkpoint_save_secs = 20
if use_pretrained_model:
task_parameters.checkpoint_restore_path = args.pretrained_checkpoint_dir
task_parameters.checkpoint_save_dir = args.checkpoint_dir
#funzione riga 48
#prende in input:
# - il grafo (creato con la get_graph_manager)
# - robomaker_hyperparams_json (ritornato dalla get_graph_manager)
training_worker(
graph_manager=graph_manager,
task_parameters=task_parameters,
user_batch_size=json.loads(robomaker_hyperparams_json)["batch_size"],
user_episode_per_rollout=json.loads(
robomaker_hyperparams_json)["num_episodes_between_training"])
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument('-pk', '--preset_s3_key',
help="(string) Name of a preset to download from S3",
type=str,
required=False)
parser.add_argument('-ek', '--environment_s3_key',
help="(string) Name of an environment file to download from S3",
type=str,
required=False)
parser.add_argument('--model_metadata_s3_key',
help="(string) Model Metadata File S3 Key",
type=str,
required=False)
parser.add_argument('-c', '--checkpoint-dir',
help='(string) Path to a folder containing a checkpoint to write the model to.',
type=str,
default='./checkpoint')
parser.add_argument('--pretrained-checkpoint-dir',
help='(string) Path to a folder for downloading a pre-trained model',
type=str,
default=PRETRAINED_MODEL_DIR)
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=os.environ.get("SAGEMAKER_SHARED_S3_BUCKET_PATH", "gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default='sagemaker')
parser.add_argument('--framework',
help='(string) tensorflow or mxnet',
type=str,
default='tensorflow')
parser.add_argument('--pretrained_s3_bucket',
help='(string) S3 bucket for pre-trained model',
type=str)
parser.add_argument('--pretrained_s3_prefix',
help='(string) S3 prefix for pre-trained model',
type=str,
default='sagemaker')
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("AWS_REGION", "us-east-1"))
start_redis_server()
args, _ = parser.parse_known_args()
s3_client = SageS3Client(bucket=args.s3_bucket, s3_prefix=args.s3_prefix, aws_region=args.aws_region)
# Load the model metadata
model_metadata_local_path = os.path.join(CUSTOM_FILES_PATH, 'model_metadata.json')
utils.load_model_metadata(s3_client, args.model_metadata_s3_key, model_metadata_local_path)
s3_client.upload_file(os.path.normpath("%s/model/model_metadata.json" % args.s3_prefix), model_metadata_local_path)
shutil.copy2(model_metadata_local_path, SM_MODEL_OUTPUT_DIR)
success_custom_preset = False
if args.preset_s3_key:
preset_local_path = "./markov/presets/preset.py"
success_custom_preset = s3_client.download_file(s3_key=args.preset_s3_key, local_path=preset_local_path)
if not success_custom_preset:
logger.info("Could not download the preset file. Using the default DeepRacer preset.")
else:
preset_location = "markov.presets.preset:graph_manager"
graph_manager = short_dynamic_import(preset_location, ignore_module_case=True)
success_custom_preset = s3_client.upload_file(
s3_key=os.path.normpath("%s/presets/preset.py" % args.s3_prefix), local_path=preset_local_path)
if success_custom_preset:
logger.info("Using preset: %s" % args.preset_s3_key)
if not success_custom_preset:
params_blob = os.environ.get('SM_TRAINING_ENV', '')
if params_blob:
params = json.loads(params_blob)
sm_hyperparams_dict = params["hyperparameters"]
else:
sm_hyperparams_dict = {}
#! TODO each agent should have own config
agent_config = {'model_metadata': model_metadata_local_path,
'car_ctrl_cnfig': {ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value : {},
ConfigParams.STEERING_LIST.value : {},
ConfigParams.CHANGE_START.value : None,
ConfigParams.ALT_DIR.value : None,
ConfigParams.ACTION_SPACE_PATH.value : 'custom_files/model_metadata.json',
ConfigParams.REWARD.value : None,
ConfigParams.AGENT_NAME.value : 'racecar'}}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
#agent_list.append(create_training_agent(agent_config))
graph_manager, robomaker_hyperparams_json = get_graph_manager(sm_hyperparams_dict, agent_list)
s3_client.upload_hyperparameters(robomaker_hyperparams_json)
logger.info("Uploaded hyperparameters.json to S3")
host_ip_address = utils.get_ip_from_host()
s3_client.write_ip_config(host_ip_address)
logger.info("Uploaded IP address information to S3: %s" % host_ip_address)
use_pretrained_model = args.pretrained_s3_bucket and args.pretrained_s3_prefix
if use_pretrained_model:
# Handle backward compatibility
_, _, version = parse_model_metadata(model_metadata_local_path)
if float(version) < float(utils.SIMAPP_VERSION) and \
not utils.has_current_ckpnt_name(args.pretrained_s3_bucket, args.pretrained_s3_prefix, args.aws_region):
utils.make_compatible(args.pretrained_s3_bucket, args.pretrained_s3_prefix,
args.aws_region, SyncFiles.TRAINER_READY.value)
ds_params_instance_pretrained = S3BotoDataStoreParameters(aws_region=args.aws_region,
bucket_name=args.pretrained_s3_bucket,
checkpoint_dir=args.pretrained_checkpoint_dir,
s3_folder=args.pretrained_s3_prefix)
data_store_pretrained = S3BotoDataStore(ds_params_instance_pretrained)
data_store_pretrained.load_from_store()
memory_backend_params = RedisPubSubMemoryBackendParameters(redis_address="localhost",
redis_port=6379,
run_type=str(RunType.TRAINER),
channel=args.s3_prefix)
graph_manager.memory_backend_params = memory_backend_params
ds_params_instance = S3BotoDataStoreParameters(aws_region=args.aws_region,
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
s3_folder=args.s3_prefix)
graph_manager.data_store_params = ds_params_instance
data_store = S3BotoDataStore(ds_params_instance)
data_store.graph_manager = graph_manager
graph_manager.data_store = data_store
task_parameters = TaskParameters()
task_parameters.experiment_path = SM_MODEL_OUTPUT_DIR
task_parameters.checkpoint_save_secs = 20
if use_pretrained_model:
task_parameters.checkpoint_restore_path = args.pretrained_checkpoint_dir
task_parameters.checkpoint_save_dir = args.checkpoint_dir
training_worker(
graph_manager=graph_manager,
task_parameters=task_parameters,
user_batch_size=json.loads(robomaker_hyperparams_json)["batch_size"],
user_episode_per_rollout=json.loads(robomaker_hyperparams_json)["num_episodes_between_training"]
)
def validate(s3_bucket, s3_prefix, custom_files_path, aws_region):
screen.set_use_colors(False)
logger.info("S3 bucket: %s \n S3 prefix: %s", s3_bucket, s3_prefix)
if not os.path.exists(custom_files_path):
os.makedirs(custom_files_path)
else:
GenericValidatorException(
"Custom Files Path already exists!").log_except_and_exit()
s3_client = SageS3Client(bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region)
# Load the model metadata
model_metadata_local_path = os.path.join(custom_files_path,
'model_metadata.json')
utils.load_model_metadata(
s3_client,
os.path.normpath("%s/model/model_metadata.json" % s3_prefix),
model_metadata_local_path)
# Create model local path
local_model_dir = os.path.join(custom_files_path, 'checkpoint')
os.makedirs(local_model_dir)
try:
# Handle backward compatibility
observation_list, _, version = parse_model_metadata(
model_metadata_local_path)
except Exception as ex:
log_and_exit("Failed to parse model_metadata file: {}".format(ex),
SIMAPP_VALIDATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_400)
transitions = get_transition_data(observation_list)
if float(version) < float(SIMAPP_VERSION) and \
not utils.has_current_ckpnt_name(s3_bucket, s3_prefix, aws_region):
utils.make_compatible(s3_bucket, s3_prefix, aws_region,
SyncFiles.TRAINER_READY.value)
agent_config = {
'model_metadata': model_metadata_local_path,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value: {},
ConfigParams.STEERING_LIST.value: {},
ConfigParams.CHANGE_START.value: None,
ConfigParams.ALT_DIR.value: None,
ConfigParams.ACTION_SPACE_PATH.value: model_metadata_local_path,
ConfigParams.REWARD.value: None,
ConfigParams.AGENT_NAME.value: 'racecar'
}
}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
sm_hyperparams_dict = {}
graph_manager, _ = get_graph_manager(hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=None)
ds_params_instance = S3BotoDataStoreParameters(
aws_region=aws_region,
bucket_names={'agent': s3_bucket},
s3_folders={'agent': s3_prefix},
base_checkpoint_dir=local_model_dir)
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager,
ignore_lock=True)
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = local_model_dir
_validate(graph_manager=graph_manager,
task_parameters=task_parameters,
transitions=transitions,
s3_bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region)
def validate(s3_bucket, s3_prefix, aws_region):
screen.set_use_colors(False)
screen.log_title(" S3 bucket: {} \n S3 prefix: {}".format(
s3_bucket, s3_prefix))
s3_client = SageS3Client(bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region)
# Load the model metadata
utils.load_model_metadata(
s3_client,
os.path.normpath("%s/model/model_metadata.json" % s3_prefix),
MODEL_METADATA_LOCAL_PATH)
# Create model local path
os.makedirs(LOCAL_MODEL_DIR)
try:
# Handle backward compatibility
observation_list, _, version = parse_model_metadata(
MODEL_METADATA_LOCAL_PATH)
except Exception as ex:
log_and_exit("Failed to parse model_metadata file: {}".format(ex),
SIMAPP_VALIDATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_400)
# Below get_transition_data function must called before create_training_agent function
# to avoid 500 in case unsupported Sensor is received.
# create_training_agent will exit with 500 if unsupported sensor is received,
# and get_transition_data function below will exit with 400 if unsupported sensor is received.
# We want to return 400 in model validation case if unsupported sensor is received.
# Thus, call this get_transition_data function before create_traning_agent function!
transitions = get_transition_data(observation_list)
if float(version) < float(SIMAPP_VERSION) and \
not utils.has_current_ckpnt_name(s3_bucket, s3_prefix, aws_region):
utils.make_compatible(s3_bucket, s3_prefix, aws_region,
SyncFiles.TRAINER_READY.value)
agent_config = {
'model_metadata': MODEL_METADATA_LOCAL_PATH,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [],
ConfigParams.VELOCITY_LIST.value: {},
ConfigParams.STEERING_LIST.value: {},
ConfigParams.CHANGE_START.value: None,
ConfigParams.ALT_DIR.value: None,
ConfigParams.ACTION_SPACE_PATH.value: MODEL_METADATA_LOCAL_PATH,
ConfigParams.REWARD.value: None,
ConfigParams.AGENT_NAME.value: 'racecar'
}
}
agent_list = list()
agent_list.append(create_training_agent(agent_config))
sm_hyperparams_dict = {}
graph_manager, _ = get_graph_manager(hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=None)
ds_params_instance = S3BotoDataStoreParameters(
aws_region=aws_region,
bucket_names={'agent': s3_bucket},
s3_folders={'agent': s3_prefix},
base_checkpoint_dir=LOCAL_MODEL_DIR)
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager,
ignore_lock=True)
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = LOCAL_MODEL_DIR
_validate(graph_manager=graph_manager,
task_parameters=task_parameters,
transitions=transitions,
s3_bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region)
