def get_args():
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()
return args
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to restore the model from.',
type=str,
default='./checkpoint')
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_PREFIX",
"sagemaker"))
parser.add_argument(
'--num_workers',
help="(int) The number of workers started in this pool",
type=int,
default=int(rospy.get_param("NUM_WORKERS", 1)))
parser.add_argument('--rollout_idx',
help="(int) The index of current rollout worker",
type=int,
default=0)
parser.add_argument('-r',
'--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp',
'--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--reward_file_s3_key',
help='(string) Reward File S3 Key',
type=str,
default=rospy.get_param("REWARD_FILE_S3_KEY", None))
parser.add_argument('--model_metadata_s3_key',
help='(string) Model Metadata File S3 Key',
type=str,
default=rospy.get_param("MODEL_METADATA_FILE_S3_KEY",
None))
# For training job, reset is not allowed. penalty_seconds, off_track_penalty, and
# collision_penalty will all be 0 be default
parser.add_argument('--number_of_resets',
help='(integer) Number of resets',
type=int,
default=int(rospy.get_param("NUMBER_OF_RESETS", 0)))
parser.add_argument('--penalty_seconds',
help='(float) penalty second',
type=float,
default=float(rospy.get_param("PENALTY_SECONDS", 0.0)))
parser.add_argument('--job_type',
help='(string) job type',
type=str,
default=rospy.get_param("JOB_TYPE", "TRAINING"))
parser.add_argument('--is_continuous',
help='(boolean) is continous after lap completion',
type=bool,
default=utils.str2bool(
rospy.get_param("IS_CONTINUOUS", False)))
parser.add_argument('--race_type',
help='(string) Race type',
type=str,
default=rospy.get_param("RACE_TYPE", "TIME_TRIAL"))
parser.add_argument('--off_track_penalty',
help='(float) off track penalty second',
type=float,
default=float(rospy.get_param("OFF_TRACK_PENALTY",
0.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
0.0)))
args = parser.parse_args()
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region)
logger.info("S3 bucket: %s", args.s3_bucket)
logger.info("S3 prefix: %s", args.s3_prefix)
# 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)
# Download and import reward function
if not args.reward_file_s3_key:
log_and_exit(
"Reward function code S3 key not available for S3 bucket {} and prefix {}"
.format(args.s3_bucket, args.s3_prefix),
SIMAPP_SIMULATION_WORKER_EXCEPTION, SIMAPP_EVENT_ERROR_CODE_500)
download_customer_reward_function(s3_client, args.reward_file_s3_key)
try:
from custom_files.customer_reward_function import reward_function
except Exception as e:
log_and_exit("Failed to import user's reward_function: {}".format(e),
SIMAPP_SIMULATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_400)
# Instantiate Cameras
configure_camera(namespaces=['racecar'])
preset_file_success, _ = download_custom_files_if_present(
s3_client, args.s3_prefix)
#! TODO each agent should have own config
_, _, version = utils_parse_model_metadata.parse_model_metadata(
model_metadata_local_path)
agent_config = {
'model_metadata': model_metadata_local_path,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value:
LINK_NAMES,
ConfigParams.VELOCITY_LIST.value:
VELOCITY_TOPICS,
ConfigParams.STEERING_LIST.value:
STEERING_TOPICS,
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION', True)),
ConfigParams.ALT_DIR.value:
utils.str2bool(
rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)),
ConfigParams.ACTION_SPACE_PATH.value:
'custom_files/model_metadata.json',
ConfigParams.REWARD.value:
reward_function,
ConfigParams.AGENT_NAME.value:
'racecar',
ConfigParams.VERSION.value:
version,
ConfigParams.NUMBER_OF_RESETS.value:
args.number_of_resets,
ConfigParams.PENALTY_SECONDS.value:
args.penalty_seconds,
ConfigParams.NUMBER_OF_TRIALS.value:
None,
ConfigParams.IS_CONTINUOUS.value:
args.is_continuous,
ConfigParams.RACE_TYPE.value:
args.race_type,
ConfigParams.COLLISION_PENALTY.value:
args.collision_penalty,
ConfigParams.OFF_TRACK_PENALTY.value:
args.off_track_penalty
}
}
#! TODO each agent should have own s3 bucket
step_metrics_prefix = rospy.get_param('SAGEMAKER_SHARED_S3_PREFIX')
if args.num_workers > 1:
step_metrics_prefix = os.path.join(step_metrics_prefix,
str(args.rollout_idx))
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
rospy.get_param('METRICS_S3_BUCKET'),
MetricsS3Keys.METRICS_KEY.value:
rospy.get_param('METRICS_S3_OBJECT_KEY'),
MetricsS3Keys.REGION.value: rospy.get_param('AWS_REGION')
}
metrics_s3_model_cfg = {
MetricsS3Keys.METRICS_BUCKET.value:
args.s3_bucket,
MetricsS3Keys.METRICS_KEY.value:
os.path.join(args.s3_prefix, DEEPRACER_CHKPNT_KEY_SUFFIX),
MetricsS3Keys.REGION.value:
args.aws_region
}
run_phase_subject = RunPhaseSubject()
agent_list = list()
agent_list.append(
create_rollout_agent(
agent_config,
TrainingMetrics(agent_name='agent',
s3_dict_metrics=metrics_s3_config,
s3_dict_model=metrics_s3_model_cfg,
ckpnt_dir=args.checkpoint_dir,
run_phase_sink=run_phase_subject,
use_model_picker=(args.rollout_idx == 0)),
run_phase_subject))
agent_list.append(create_obstacles_agent())
agent_list.append(create_bot_cars_agent())
# ROS service to indicate all the robomaker markov packages are ready for consumption
signal_robomaker_markov_package_ready()
PhaseObserver('/agent/training_phase', run_phase_subject)
aws_region = rospy.get_param('AWS_REGION', args.aws_region)
simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None)
mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET',
None) if args.rollout_idx == 0 else None
if simtrace_s3_bucket:
simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX')
if args.num_workers > 1:
simtrace_s3_object_prefix = os.path.join(simtrace_s3_object_prefix,
str(args.rollout_idx))
if mp4_s3_bucket:
mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX')
s3_writer_job_info = []
if simtrace_s3_bucket:
s3_writer_job_info.append(
IterationData(
'simtrace', simtrace_s3_bucket, simtrace_s3_object_prefix,
aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, 'agent',
IterationDataLocalFileNames.SIM_TRACE_TRAINING_LOCAL_FILE.
value)))
if mp4_s3_bucket:
s3_writer_job_info.extend([
IterationData(
'pip', mp4_s3_bucket, mp4_s3_object_prefix, aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, 'agent',
IterationDataLocalFileNames.
CAMERA_PIP_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'45degree', mp4_s3_bucket, mp4_s3_object_prefix, aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, 'agent',
IterationDataLocalFileNames.
CAMERA_45DEGREE_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'topview', mp4_s3_bucket, mp4_s3_object_prefix, aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, 'agent',
IterationDataLocalFileNames.
CAMERA_TOPVIEW_MP4_VALIDATION_LOCAL_PATH.value))
])
s3_writer = S3Writer(job_info=s3_writer_job_info)
redis_ip = s3_client.get_ip()
logger.info("Received IP from SageMaker successfully: %s", redis_ip)
# Download hyperparameters from SageMaker
hyperparameters_file_success = False
hyperparams_s3_key = os.path.normpath(args.s3_prefix +
"/ip/hyperparameters.json")
hyperparameters_file_success = s3_client.download_file(
s3_key=hyperparams_s3_key, local_path="hyperparameters.json")
sm_hyperparams_dict = {}
if hyperparameters_file_success:
logger.info("Received Sagemaker hyperparameters successfully!")
with open("hyperparameters.json") as filepointer:
sm_hyperparams_dict = json.load(filepointer)
else:
logger.info("SageMaker hyperparameters not found.")
enable_domain_randomization = utils.str2bool(
rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False))
if preset_file_success:
preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py")
preset_location += ":graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
logger.info("Using custom preset file!")
else:
graph_manager, _ = get_graph_manager(
hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=run_phase_subject,
enable_domain_randomization=enable_domain_randomization)
# If num_episodes_between_training is smaller than num_workers then cancel worker early.
episode_steps_per_rollout = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps
# Reduce number of workers if allocated more than num_episodes_between_training
if args.num_workers > episode_steps_per_rollout:
logger.info(
"Excess worker allocated. Reducing from {} to {}...".format(
args.num_workers, episode_steps_per_rollout))
args.num_workers = episode_steps_per_rollout
if args.rollout_idx >= episode_steps_per_rollout or args.rollout_idx >= args.num_workers:
err_msg_format = "Exiting excess worker..."
err_msg_format += "(rollout_idx[{}] >= num_workers[{}] or num_episodes_between_training[{}])"
logger.info(
err_msg_format.format(args.rollout_idx, args.num_workers,
episode_steps_per_rollout))
# Close the down the job
utils.cancel_simulation_job(
os.environ.get('AWS_ROBOMAKER_SIMULATION_JOB_ARN'),
rospy.get_param('AWS_REGION'))
memory_backend_params = DeepRacerRedisPubSubMemoryBackendParameters(
redis_address=redis_ip,
redis_port=6379,
run_type=str(RunType.ROLLOUT_WORKER),
channel=args.s3_prefix,
num_workers=args.num_workers,
rollout_idx=args.rollout_idx)
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})
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager)
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.checkpoint_dir
rollout_worker(graph_manager=graph_manager,
num_workers=args.num_workers,
rollout_idx=args.rollout_idx,
task_parameters=task_parameters,
s3_writer=s3_writer)
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"]
)
import rl_coach.core_types
from rl_coach import logger
from rl_coach.logger import screen
import argparse
import copy
import logging
import os
import sys
import shutil
import glob
import re
from .configuration_list import ConfigurationList
from rl_coach.coach import CoachLauncher
screen.set_use_colors(
False) # Simple text logging so it looks good in CloudWatch
class CoachConfigurationList(ConfigurationList):
"""Helper Object for converting CLI arguments (or SageMaker hyperparameters)
into Coach configuration.
"""
# Being security-paranoid and not instantiating any arbitrary string the customer passes in
ALLOWED_TYPES = {
"Frames": rl_coach.core_types.Frames,
"EnvironmentSteps": rl_coach.core_types.EnvironmentSteps,
"EnvironmentEpisodes": rl_coach.core_types.EnvironmentEpisodes,
"TrainingSteps": rl_coach.core_types.TrainingSteps,
"Time": rl_coach.core_types.Time,
}
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)
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(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to restore the model from.',
type=str,
default='./checkpoint')
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=os.environ.get("SAGEMAKER_SHARED_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=os.environ.get("SAGEMAKER_SHARED_S3_PREFIX",
"sagemaker"))
parser.add_argument(
'--num-workers',
help="(int) The number of workers started in this pool",
type=int,
default=1)
parser.add_argument('-r',
'--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp',
'--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("APP_REGION", "us-east-1"))
parser.add_argument('--reward_file_s3_key',
help='(string) Reward File S3 Key',
type=str,
default=os.environ.get("REWARD_FILE_S3_KEY", None))
parser.add_argument('--model_metadata_s3_key',
help='(string) Model Metadata File S3 Key',
type=str,
default=os.environ.get("MODEL_METADATA_FILE_S3_KEY",
None))
args = parser.parse_args()
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region)
print("S3 bucket: %s" % args.s3_bucket)
print("S3 prefix: %s" % args.s3_prefix)
# Load the model metadata
model_metadata_local_path = os.path.join(CUSTOM_FILES_PATH,
'model_metadata.json')
load_model_metadata(s3_client, args.model_metadata_s3_key,
model_metadata_local_path)
redis_ip = s3_client.get_ip()
print("Received IP from SageMaker successfully: %s" % redis_ip)
# Download hyperparameters from SageMaker
hyperparameters_file_success = False
hyperparams_s3_key = os.path.normpath(args.s3_prefix +
"/ip/hyperparameters.json")
hyperparameters_file_success = s3_client.download_file(
s3_key=hyperparams_s3_key, local_path="hyperparameters.json")
sm_hyperparams_dict = {}
if hyperparameters_file_success:
print("Received Sagemaker hyperparameters successfully!")
with open("hyperparameters.json") as fp:
sm_hyperparams_dict = json.load(fp)
else:
print("SageMaker hyperparameters not found.")
preset_file_success = False
environment_file_success = False
preset_file_success, environment_file_success = download_custom_files_if_present(
s3_client, args.s3_prefix)
if not environment_file_success:
# Download reward function if environment file is not downloaded
if not args.reward_file_s3_key:
raise ValueError("Customer reward S3 key not supplied!")
download_customer_reward_function(s3_client, args.reward_file_s3_key)
import markov.environments
print("Using default environment!")
else:
register_custom_environments()
print("Using custom environment!")
if preset_file_success:
preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py")
preset_location += ":graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
print("Using custom preset file!")
else:
from markov.sagemaker_graph_manager import get_graph_manager
graph_manager, _ = get_graph_manager(**sm_hyperparams_dict)
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address=redis_ip,
redis_port=6379,
run_type='worker',
channel=args.s3_prefix)
graph_manager.agent_params.memory.register_var('memory_backend_params',
memory_backend_params)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
aws_region=args.aws_region,
s3_folder=args.s3_prefix)
data_store = S3BotoDataStore(ds_params_instance)
data_store.graph_manager = graph_manager
graph_manager.data_store = data_store
rollout_worker(
graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
data_store=data_store,
num_workers=args.num_workers,
)
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to restore the model from.',
type=str,
default='./checkpoint')
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=os.environ.get("SAGEMAKER_SHARED_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=os.environ.get("SAGEMAKER_SHARED_S3_PREFIX",
"sagemaker"))
parser.add_argument(
'--num-workers',
help="(int) The number of workers started in this pool",
type=int,
default=1)
parser.add_argument('-r',
'--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp',
'--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("APP_REGION", "us-east-1"))
parser.add_argument('--reward_file_s3_key',
help='(string) Reward File S3 Key',
type=str,
default=os.environ.get("REWARD_FILE_S3_KEY", None))
parser.add_argument('--model_metadata_s3_key',
help='(string) Model Metadata File S3 Key',
type=str,
default=os.environ.get("MODEL_METADATA_FILE_S3_KEY",
None))
parser.add_argument('--aws_endpoint_url',
help='(string) AWS region',
type=str,
default=os.environ.get("AWS_ENDPOINT_URL", None))
args = parser.parse_args()
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region,
endpoint_url=args.aws_endpoint_url)
logger.info("S3 bucket: %s" % args.s3_bucket)
logger.info("S3 prefix: %s" % args.s3_prefix)
# Load the model metadata
model_metadata_local_path = os.path.join(CUSTOM_FILES_PATH,
'model_metadata.json')
load_model_metadata(s3_client, args.model_metadata_s3_key,
model_metadata_local_path)
# Download reward function
if not args.reward_file_s3_key:
utils.json_format_logger(
"Reward function code S3 key not available for S3 bucket {} and prefix {}"
.format(args.s3_bucket, args.s3_prefix),
**utils.build_system_error_dict(
utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500))
traceback.print_exc()
utils.simapp_exit_gracefully()
download_customer_reward_function(s3_client, args.reward_file_s3_key)
# Register the gym enviroment, this will give clients the ability to creat the enviroment object
register(id=defaults.ENV_ID,
entry_point=defaults.ENTRY_POINT,
max_episode_steps=defaults.MAX_STEPS,
reward_threshold=defaults.THRESHOLD)
redis_ip = s3_client.get_ip()
logger.info("Received IP from SageMaker successfully: %s" % redis_ip)
# Download hyperparameters from SageMaker
hyperparameters_file_success = False
hyperparams_s3_key = os.path.normpath(args.s3_prefix +
"/ip/hyperparameters.json")
hyperparameters_file_success = s3_client.download_file(
s3_key=hyperparams_s3_key, local_path="hyperparameters.json")
sm_hyperparams_dict = {}
if hyperparameters_file_success:
logger.info("Received Sagemaker hyperparameters successfully!")
with open("hyperparameters.json") as fp:
sm_hyperparams_dict = json.load(fp)
else:
logger.info("SageMaker hyperparameters not found.")
preset_file_success, _ = download_custom_files_if_present(
s3_client, args.s3_prefix)
if preset_file_success:
preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py")
preset_location += ":graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
logger.info("Using custom preset file!")
else:
from markov.sagemaker_graph_manager import get_graph_manager
graph_manager, _ = get_graph_manager(**sm_hyperparams_dict)
logger.info("Connecting to redis at %s:%d" % (redis_ip, args.redis_port))
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address=redis_ip,
redis_port=6379,
run_type='worker',
channel=args.s3_prefix)
logger.info("Connecting to s3 boto data store at %s" %
args.aws_endpoint_url)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
aws_region=args.aws_region,
s3_folder=args.s3_prefix,
aws_endpoint_url=args.aws_endpoint_url)
data_store = S3BotoDataStore(ds_params_instance)
data_store.graph_manager = graph_manager
graph_manager.data_store = data_store
rollout_worker(graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
data_store=data_store,
num_workers=args.num_workers,
memory_backend_params=memory_backend_params)
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--checkpoint-dir',
help=
'(string) Path to a local folder containing a checkpoint to write the model to.',
type=str,
default='./checkpoint')
parser.add_argument(
'--pretrained-checkpoint-dir',
help=
'(string) Path to a local 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('--RLCOACH_PRESET',
help='(string) Default preset to use',
type=str,
default='object_tracker')
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
required=True)
args, unknown = parser.parse_known_args()
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region)
# Import to register the environment with Gym
import robomaker.environments
preset_location = "robomaker.presets.%s:graph_manager" % args.RLCOACH_PRESET
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
host_ip_address = get_ip_from_host()
s3_client.write_ip_config(host_ip_address)
print("Uploaded IP address information to S3: %s" % host_ip_address)
use_pretrained_model = False
if args.pretrained_s3_bucket and args.pretrained_s3_prefix:
s3_client_pretrained = SageS3Client(
bucket=args.pretrained_s3_bucket,
s3_prefix=args.pretrained_s3_prefix,
aws_region=args.aws_region)
s3_client_pretrained.download_model(PRETRAINED_MODEL_DIR)
use_pretrained_model = True
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address="localhost",
redis_port=6379,
run_type='trainer',
channel=args.s3_prefix)
graph_manager.agent_params.memory.register_var('memory_backend_params',
memory_backend_params)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
s3_folder=args.s3_prefix,
aws_region=args.aws_region)
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
training_worker(graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
use_pretrained_model=use_pretrained_model,
framework=args.framework)
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--checkpoint-dir',
help='(string) Path to a local folder containing a checkpoint to write the model to.',
type=str,
default='./checkpoint')
parser.add_argument('--pretrained-checkpoint-dir',
help='(string) Path to a local 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('--RLCOACH_PRESET',
help='(string) Default preset to use',
type=str,
default='deepracer')
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
required=True)
args, unknown = parser.parse_known_args()
s3_client = SageS3Client(bucket=args.s3_bucket, s3_prefix=args.s3_prefix, aws_region=args.aws_region)
# Import to register the environment with Gym
import robomaker.environments
preset_location = "robomaker.presets.%s:graph_manager" % args.RLCOACH_PRESET
graph_manager = short_dynamic_import(preset_location, ignore_module_case=True)
host_ip_address = get_ip_from_host()
s3_client.write_ip_config(host_ip_address)
print("Uploaded IP address information to S3: %s" % host_ip_address)
use_pretrained_model = False
if args.pretrained_s3_bucket and args.pretrained_s3_prefix:
s3_client_pretrained = SageS3Client(bucket=args.pretrained_s3_bucket,
s3_prefix=args.pretrained_s3_prefix,
aws_region=args.aws_region)
s3_client_pretrained.download_model(PRETRAINED_MODEL_DIR)
use_pretrained_model = True
memory_backend_params = RedisPubSubMemoryBackendParameters(redis_address="localhost",
redis_port=6379,
run_type='trainer',
channel=args.s3_prefix)
graph_manager.agent_params.memory.register_var('memory_backend_params', memory_backend_params)
ds_params_instance = S3BotoDataStoreParameters(bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
s3_folder=args.s3_prefix,
aws_region=args.aws_region)
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
training_worker(
graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
use_pretrained_model=use_pretrained_model,
framework=args.framework
)
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to restore the model from.',
type=str,
default='./checkpoint')
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_PREFIX",
"sagemaker"))
parser.add_argument(
'--num-workers',
help="(int) The number of workers started in this pool",
type=int,
default=1)
parser.add_argument('-r',
'--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp',
'--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--reward_file_s3_key',
help='(string) Reward File S3 Key',
type=str,
default=rospy.get_param("REWARD_FILE_S3_KEY", None))
parser.add_argument('--model_metadata_s3_key',
help='(string) Model Metadata File S3 Key',
type=str,
default=rospy.get_param("MODEL_METADATA_FILE_S3_KEY",
None))
args = parser.parse_args()
s3_client = SageS3Client(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region)
logger.info("S3 bucket: %s" % args.s3_bucket)
logger.info("S3 prefix: %s" % args.s3_prefix)
# 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)
# Download and import reward function
if not args.reward_file_s3_key:
utils.log_and_exit(
"Reward function code S3 key not available for S3 bucket {} and prefix {}"
.format(args.s3_bucket,
args.s3_prefix), utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500)
download_customer_reward_function(s3_client, args.reward_file_s3_key)
try:
from custom_files.customer_reward_function import reward_function
except Exception as e:
utils.log_and_exit(
"Failed to import user's reward_function: {}".format(e),
utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_400)
# Instantiate Cameras
configure_camera()
redis_ip = s3_client.get_ip()
logger.info("Received IP from SageMaker successfully: %s" % redis_ip)
# Download hyperparameters from SageMaker
hyperparameters_file_success = False
hyperparams_s3_key = os.path.normpath(args.s3_prefix +
"/ip/hyperparameters.json")
hyperparameters_file_success = s3_client.download_file(
s3_key=hyperparams_s3_key, local_path="hyperparameters.json")
sm_hyperparams_dict = {}
if hyperparameters_file_success:
logger.info("Received Sagemaker hyperparameters successfully!")
with open("hyperparameters.json") as fp:
sm_hyperparams_dict = json.load(fp)
else:
logger.info("SageMaker hyperparameters not found.")
preset_file_success, _ = download_custom_files_if_present(
s3_client, args.s3_prefix)
#! TODO each agent should have own config
_, _, version = utils_parse_model_metadata.parse_model_metadata(
model_metadata_local_path)
agent_config = {
'model_metadata': model_metadata_local_path,
'car_ctrl_cnfig': {
ConfigParams.LINK_NAME_LIST.value:
LINK_NAMES,
ConfigParams.VELOCITY_LIST.value:
VELOCITY_TOPICS,
ConfigParams.STEERING_LIST.value:
STEERING_TOPICS,
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION', True)),
ConfigParams.ALT_DIR.value:
utils.str2bool(
rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)),
ConfigParams.ACTION_SPACE_PATH.value:
'custom_files/model_metadata.json',
ConfigParams.REWARD.value:
reward_function,
ConfigParams.AGENT_NAME.value:
'racecar',
ConfigParams.VERSION.value:
version
}
}
#! TODO each agent should have own s3 bucket
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
rospy.get_param('METRICS_S3_BUCKET'),
MetricsS3Keys.METRICS_KEY.value:
rospy.get_param('METRICS_S3_OBJECT_KEY'),
MetricsS3Keys.REGION.value:
rospy.get_param('AWS_REGION'),
MetricsS3Keys.STEP_BUCKET.value:
rospy.get_param('SAGEMAKER_SHARED_S3_BUCKET'),
MetricsS3Keys.STEP_KEY.value:
os.path.join(rospy.get_param('SAGEMAKER_SHARED_S3_PREFIX'),
TRAINING_SIMTRACE_DATA_S3_OBJECT_KEY)
}
agent_list = list()
agent_list.append(
create_rollout_agent(agent_config, TrainingMetrics(metrics_s3_config)))
agent_list.append(create_obstacles_agent())
agent_list.append(create_bot_cars_agent())
if preset_file_success:
preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py")
preset_location += ":graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
logger.info("Using custom preset file!")
else:
graph_manager, _ = get_graph_manager(sm_hyperparams_dict, agent_list)
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address=redis_ip,
redis_port=6379,
run_type=str(RunType.ROLLOUT_WORKER),
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)
data_store = S3BotoDataStore(ds_params_instance)
data_store.graph_manager = graph_manager
graph_manager.data_store = data_store
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.checkpoint_dir
rollout_worker(graph_manager=graph_manager,
data_store=data_store,
num_workers=args.num_workers,
task_parameters=task_parameters)
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)
try:
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, unknown = parser.parse_known_args()
start_redis_server()
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')
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)
# Register the gym enviroment, this will give clients the ability to creat the enviroment object
register(id=defaults.ENV_ID,
entry_point=defaults.ENTRY_POINT,
max_episode_steps=defaults.MAX_STEPS,
reward_threshold=defaults.THRESHOLD)
user_batch_size, user_episode_per_rollout = None, None
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:
agent_param_loc = "markov.presets.preset:agent_params"
agent_params = short_dynamic_import(
agent_param_loc, ignore_module_case=True)
user_batch_size = agent_params.network_wrappers[
'main'].batch_size
user_episode_per_rollout = agent_params.algorithm.num_consecutive_playing_steps.num_steps
logger.info("Using preset: %s" % args.preset_s3_key)
if not success_custom_preset:
from markov.sagemaker_graph_manager import get_graph_manager
user_batch_size = json.loads(
robomaker_hyperparams_json)["batch_size"],
user_episode_per_rollout = json.loads(
robomaker_hyperparams_json)["num_episodes_between_training"]
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 = {}
graph_manager, robomaker_hyperparams_json = get_graph_manager(
**sm_hyperparams_dict)
s3_client.upload_hyperparameters(robomaker_hyperparams_json)
logger.info("Uploaded hyperparameters.json to S3")
host_ip_address = 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:
s3_client_pretrained = SageS3Client(
bucket=args.pretrained_s3_bucket,
s3_prefix=args.pretrained_s3_prefix,
aws_region=args.aws_region)
s3_client_pretrained.download_model(args.pretrained_checkpoint_dir)
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address="localhost",
redis_port=6379,
run_type='trainer',
channel=args.s3_prefix)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
aws_region=args.aws_region,
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
training_worker(graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
use_pretrained_model=use_pretrained_model,
framework=args.framework,
memory_backend_params=memory_backend_params,
user_batch_size=user_batch_size,
user_episode_per_rollout=user_episode_per_rollout)
except Exception as ex:
utils.json_format_logger(
"Training worker exited with exception: {}".format(ex),
**utils.build_system_error_dict(
utils.SIMAPP_TRAINING_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500))
utils.simapp_exit_gracefully()
def get_config_args(self,
parser: argparse.ArgumentParser) -> argparse.Namespace:
"""
Returns a Namespace object with all the user-specified configuration options needed to launch.
This implementation uses argparse to take arguments from the CLI, but this can be over-ridden by
another method that gets its configuration from elsewhere. An equivalent method however must
return an identically structured Namespace object, which conforms to the structure defined by
get_argument_parser.
This method parses the arguments that the user entered, does some basic validation, and
modification of user-specified values in short form to be more explicit.
:param parser: a parser object which implicitly defines the format of the Namespace that
is expected to be returned.
:return: the parsed arguments as a Namespace
"""
args = parser.parse_args()
if args.nocolor:
screen.set_use_colors(False)
# if no arg is given
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
# list available presets
if args.list:
self.display_all_presets_and_exit()
# Read args from config file for distributed Coach.
if args.distributed_coach and args.distributed_coach_run_type == RunType.ORCHESTRATOR:
coach_config = ConfigParser({
'image': '',
'memory_backend': 'redispubsub',
'data_store': 's3',
's3_end_point': 's3.amazonaws.com',
's3_bucket_name': '',
's3_creds_file': ''
})
try:
coach_config.read(args.distributed_coach_config_path)
args.image = coach_config.get('coach', 'image')
args.memory_backend = coach_config.get('coach',
'memory_backend')
args.data_store = coach_config.get('coach', 'data_store')
if args.data_store == 's3':
args.s3_end_point = coach_config.get(
'coach', 's3_end_point')
args.s3_bucket_name = coach_config.get(
'coach', 's3_bucket_name')
args.s3_creds_file = coach_config.get(
'coach', 's3_creds_file')
except Error as e:
screen.error(
"Error when reading distributed Coach config file: {}".
format(e))
if args.image == '':
screen.error("Image cannot be empty.")
data_store_choices = ['s3', 'nfs']
if args.data_store not in data_store_choices:
screen.warning("{} data store is unsupported.".format(
args.data_store))
screen.error(
"Supported data stores are {}.".format(data_store_choices))
memory_backend_choices = ['redispubsub']
if args.memory_backend not in memory_backend_choices:
screen.warning("{} memory backend is not supported.".format(
args.memory_backend))
screen.error("Supported memory backends are {}.".format(
memory_backend_choices))
if args.data_store == 's3':
if args.s3_bucket_name == '':
screen.error("S3 bucket name cannot be empty.")
if args.s3_creds_file == '':
args.s3_creds_file = None
if args.play and args.distributed_coach:
screen.error("Playing is not supported in distributed Coach.")
# replace a short preset name with the full path
if args.preset is not None:
args.preset = self.expand_preset(args.preset)
# validate the checkpoints args
if args.checkpoint_restore_dir is not None and not os.path.exists(
args.checkpoint_restore_dir):
screen.error(
"The requested checkpoint folder to load from does not exist.")
# validate the checkpoints args
if args.checkpoint_restore_file is not None and not glob(
args.checkpoint_restore_file + '*'):
screen.error(
"The requested checkpoint file to load from does not exist.")
# no preset was given. check if the user requested to play some environment on its own
if args.preset is None and args.play and not args.environment_type:
screen.error(
'When no preset is given for Coach to run, and the user requests human control over '
'the environment, the user is expected to input the desired environment_type and level.'
'\nAt least one of these parameters was not given.')
elif args.preset and args.play:
screen.error(
"Both the --preset and the --play flags were set. These flags can not be used together. "
"For human control, please use the --play flag together with the environment type flag (-et)"
)
elif args.preset is None and not args.play:
screen.error(
"Please choose a preset using the -p flag or use the --play flag together with choosing an "
"environment type (-et) in order to play the game.")
# get experiment name and path
args.experiment_name = logger.get_experiment_name(args.experiment_name)
args.experiment_path = logger.get_experiment_path(args.experiment_name)
if args.play and args.num_workers > 1:
screen.warning(
"Playing the game as a human is only available with a single worker. "
"The number of workers will be reduced to 1")
args.num_workers = 1
args.framework = Frameworks[args.framework.lower()]
# checkpoints
args.checkpoint_save_dir = os.path.join(
args.experiment_path,
'checkpoint') if args.checkpoint_save_secs is not None else None
if args.export_onnx_graph and not args.checkpoint_save_secs:
screen.warning(
"Exporting ONNX graphs requires setting the --checkpoint_save_secs flag. "
"The --export_onnx_graph will have no effect.")
return args
def main():
screen.set_use_colors(False)
parser = argparse.ArgumentParser()
parser.add_argument(
'-c',
'--checkpoint_dir',
help=
'(string) Path to a folder containing a checkpoint to restore the model from.',
type=str,
default='./checkpoint')
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=rospy.get_param("SAGEMAKER_SHARED_S3_PREFIX",
"sagemaker"))
parser.add_argument(
'--num_workers',
help="(int) The number of workers started in this pool",
type=int,
default=int(rospy.get_param("NUM_WORKERS", 1)))
parser.add_argument('--rollout_idx',
help="(int) The index of current rollout worker",
type=int,
default=0)
parser.add_argument('-r',
'--redis_ip',
help="(string) IP or host for the redis server",
default='localhost',
type=str)
parser.add_argument('-rp',
'--redis_port',
help="(int) Port of the redis server",
default=6379,
type=int)
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--reward_file_s3_key',
help='(string) Reward File S3 Key',
type=str,
default=rospy.get_param("REWARD_FILE_S3_KEY", None))
parser.add_argument('--model_metadata_s3_key',
help='(string) Model Metadata File S3 Key',
type=str,
default=rospy.get_param("MODEL_METADATA_FILE_S3_KEY",
None))
# For training job, reset is not allowed. penalty_seconds, off_track_penalty, and
# collision_penalty will all be 0 be default
parser.add_argument('--number_of_resets',
help='(integer) Number of resets',
type=int,
default=int(rospy.get_param("NUMBER_OF_RESETS", 0)))
parser.add_argument('--penalty_seconds',
help='(float) penalty second',
type=float,
default=float(rospy.get_param("PENALTY_SECONDS", 0.0)))
parser.add_argument('--job_type',
help='(string) job type',
type=str,
default=rospy.get_param("JOB_TYPE", "TRAINING"))
parser.add_argument('--is_continuous',
help='(boolean) is continous after lap completion',
type=bool,
default=utils.str2bool(
rospy.get_param("IS_CONTINUOUS", False)))
parser.add_argument('--race_type',
help='(string) Race type',
type=str,
default=rospy.get_param("RACE_TYPE", "TIME_TRIAL"))
parser.add_argument('--off_track_penalty',
help='(float) off track penalty second',
type=float,
default=float(rospy.get_param("OFF_TRACK_PENALTY",
0.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
0.0)))
args = parser.parse_args()
logger.info("S3 bucket: %s", args.s3_bucket)
logger.info("S3 prefix: %s", args.s3_prefix)
# Download and import reward function
# TODO: replace 'agent' with name of each agent for multi-agent training
reward_function_file = RewardFunction(
bucket=args.s3_bucket,
s3_key=args.reward_file_s3_key,
region_name=args.aws_region,
local_path=REWARD_FUCTION_LOCAL_PATH_FORMAT.format('agent'))
reward_function = reward_function_file.get_reward_function()
# Instantiate Cameras
configure_camera(namespaces=['racecar'])
preset_file_success, _ = download_custom_files_if_present(
s3_bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
aws_region=args.aws_region)
# download model metadata
# TODO: replace 'agent' with name of each agent
model_metadata = 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.get_model_metadata_info()
version = model_metadata_info[ModelMetadataKeys.VERSION.value]
agent_config = {
'model_metadata': model_metadata,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value:
LINK_NAMES,
ConfigParams.VELOCITY_LIST.value:
VELOCITY_TOPICS,
ConfigParams.STEERING_LIST.value:
STEERING_TOPICS,
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION', True)),
ConfigParams.ALT_DIR.value:
utils.str2bool(
rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)),
ConfigParams.MODEL_METADATA.value:
model_metadata,
ConfigParams.REWARD.value:
reward_function,
ConfigParams.AGENT_NAME.value:
'racecar',
ConfigParams.VERSION.value:
version,
ConfigParams.NUMBER_OF_RESETS.value:
args.number_of_resets,
ConfigParams.PENALTY_SECONDS.value:
args.penalty_seconds,
ConfigParams.NUMBER_OF_TRIALS.value:
None,
ConfigParams.IS_CONTINUOUS.value:
args.is_continuous,
ConfigParams.RACE_TYPE.value:
args.race_type,
ConfigParams.COLLISION_PENALTY.value:
args.collision_penalty,
ConfigParams.OFF_TRACK_PENALTY.value:
args.off_track_penalty
}
}
#! TODO each agent should have own s3 bucket
metrics_key = rospy.get_param('METRICS_S3_OBJECT_KEY')
if args.num_workers > 1 and args.rollout_idx > 0:
key_tuple = os.path.splitext(metrics_key)
metrics_key = "{}_{}{}".format(key_tuple[0], str(args.rollout_idx),
key_tuple[1])
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
rospy.get_param('METRICS_S3_BUCKET'),
MetricsS3Keys.METRICS_KEY.value: metrics_key,
MetricsS3Keys.REGION.value: rospy.get_param('AWS_REGION')
}
run_phase_subject = RunPhaseSubject()
agent_list = list()
#TODO: replace agent for multi agent training
# checkpoint s3 instance
# TODO replace agent with agent_0 and so on for multiagent case
checkpoint = Checkpoint(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region_name=args.aws_region,
agent_name='agent',
checkpoint_dir=args.checkpoint_dir)
agent_list.append(
create_rollout_agent(
agent_config,
TrainingMetrics(
agent_name='agent',
s3_dict_metrics=metrics_s3_config,
deepracer_checkpoint_json=checkpoint.deepracer_checkpoint_json,
ckpnt_dir=os.path.join(args.checkpoint_dir, 'agent'),
run_phase_sink=run_phase_subject,
use_model_picker=(args.rollout_idx == 0)), run_phase_subject))
agent_list.append(create_obstacles_agent())
agent_list.append(create_bot_cars_agent())
# ROS service to indicate all the robomaker markov packages are ready for consumption
signal_robomaker_markov_package_ready()
PhaseObserver('/agent/training_phase', run_phase_subject)
aws_region = rospy.get_param('AWS_REGION', args.aws_region)
simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None)
mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET',
None) if args.rollout_idx == 0 else None
if simtrace_s3_bucket:
simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX')
if args.num_workers > 1:
simtrace_s3_object_prefix = os.path.join(simtrace_s3_object_prefix,
str(args.rollout_idx))
if mp4_s3_bucket:
mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX')
simtrace_video_s3_writers = []
#TODO: replace 'agent' with 'agent_0' for multi agent training and
# mp4_s3_object_prefix, mp4_s3_bucket will be a list, so need to access with index
if simtrace_s3_bucket:
simtrace_video_s3_writers.append(
SimtraceVideo(
upload_type=SimtraceVideoNames.SIMTRACE_TRAINING.value,
bucket=simtrace_s3_bucket,
s3_prefix=simtrace_s3_object_prefix,
region_name=aws_region,
local_path=SIMTRACE_TRAINING_LOCAL_PATH_FORMAT.format(
'agent')))
if mp4_s3_bucket:
simtrace_video_s3_writers.extend([
SimtraceVideo(
upload_type=SimtraceVideoNames.PIP.value,
bucket=mp4_s3_bucket,
s3_prefix=mp4_s3_object_prefix,
region_name=aws_region,
local_path=CAMERA_PIP_MP4_LOCAL_PATH_FORMAT.format('agent')),
SimtraceVideo(
upload_type=SimtraceVideoNames.DEGREE45.value,
bucket=mp4_s3_bucket,
s3_prefix=mp4_s3_object_prefix,
region_name=aws_region,
local_path=CAMERA_45DEGREE_LOCAL_PATH_FORMAT.format('agent')),
SimtraceVideo(
upload_type=SimtraceVideoNames.TOPVIEW.value,
bucket=mp4_s3_bucket,
s3_prefix=mp4_s3_object_prefix,
region_name=aws_region,
local_path=CAMERA_TOPVIEW_LOCAL_PATH_FORMAT.format('agent'))
])
# TODO: replace 'agent' with specific agent name for multi agent training
ip_config = IpConfig(bucket=args.s3_bucket,
s3_prefix=args.s3_prefix,
region_name=args.aws_region,
local_path=IP_ADDRESS_LOCAL_PATH.format('agent'))
redis_ip = ip_config.get_ip_config()
# Download hyperparameters from SageMaker shared s3 bucket
# TODO: replace 'agent' with name of each agent
hyperparameters = Hyperparameters(
bucket=args.s3_bucket,
s3_key=get_s3_key(args.s3_prefix, HYPERPARAMETER_S3_POSTFIX),
region_name=args.aws_region,
local_path=HYPERPARAMETER_LOCAL_PATH_FORMAT.format('agent'))
sm_hyperparams_dict = hyperparameters.get_hyperparameters_dict()
enable_domain_randomization = utils.str2bool(
rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False))
# Make the clients that will allow us to pause and unpause the physics
rospy.wait_for_service('/gazebo/pause_physics_dr')
rospy.wait_for_service('/gazebo/unpause_physics_dr')
pause_physics = ServiceProxyWrapper('/gazebo/pause_physics_dr', Empty)
unpause_physics = ServiceProxyWrapper('/gazebo/unpause_physics_dr', Empty)
if preset_file_success:
preset_location = os.path.join(CUSTOM_FILES_PATH, "preset.py")
preset_location += ":graph_manager"
graph_manager = short_dynamic_import(preset_location,
ignore_module_case=True)
logger.info("Using custom preset file!")
else:
graph_manager, _ = get_graph_manager(
hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=run_phase_subject,
enable_domain_randomization=enable_domain_randomization,
pause_physics=pause_physics,
unpause_physics=unpause_physics)
# If num_episodes_between_training is smaller than num_workers then cancel worker early.
episode_steps_per_rollout = graph_manager.agent_params.algorithm.num_consecutive_playing_steps.num_steps
# Reduce number of workers if allocated more than num_episodes_between_training
if args.num_workers > episode_steps_per_rollout:
logger.info(
"Excess worker allocated. Reducing from {} to {}...".format(
args.num_workers, episode_steps_per_rollout))
args.num_workers = episode_steps_per_rollout
if args.rollout_idx >= episode_steps_per_rollout or args.rollout_idx >= args.num_workers:
err_msg_format = "Exiting excess worker..."
err_msg_format += "(rollout_idx[{}] >= num_workers[{}] or num_episodes_between_training[{}])"
logger.info(
err_msg_format.format(args.rollout_idx, args.num_workers,
episode_steps_per_rollout))
# Close the down the job
utils.cancel_simulation_job()
memory_backend_params = DeepRacerRedisPubSubMemoryBackendParameters(
redis_address=redis_ip,
redis_port=6379,
run_type=str(RunType.ROLLOUT_WORKER),
channel=args.s3_prefix,
num_workers=args.num_workers,
rollout_idx=args.rollout_idx)
graph_manager.memory_backend_params = memory_backend_params
checkpoint_dict = {'agent': checkpoint}
ds_params_instance = S3BotoDataStoreParameters(
checkpoint_dict=checkpoint_dict)
graph_manager.data_store = S3BotoDataStore(ds_params_instance,
graph_manager)
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.checkpoint_dir
rollout_worker(graph_manager=graph_manager,
num_workers=args.num_workers,
rollout_idx=args.rollout_idx,
task_parameters=task_parameters,
simtrace_video_s3_writers=simtrace_video_s3_writers,
pause_physics=pause_physics,
unpause_physics=unpause_physics)
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, unknown = 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')
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_environment = False
if args.environment_s3_key:
environment_local_path = "./markov/environments/deepracer_racetrack_env.py"
success_custom_environment = s3_client.download_file(
s3_key=args.environment_s3_key, local_path=environment_local_path)
if not success_custom_environment:
print(
"Could not download the environment file. Using the default DeepRacer environment."
)
else:
success_custom_environment = s3_client.upload_file(
s3_key=os.path.normpath(
"%s/environments/deepracer_racetrack_env.py" %
args.s3_prefix),
local_path=environment_local_path)
if success_custom_environment:
print("Using environment: %s" % args.environment_s3_key)
# Import to register the environment with Gym
import markov.environments
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:
print(
"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:
print("Using preset: %s" % args.preset_s3_key)
if not success_custom_preset:
from markov.sagemaker_graph_manager import get_graph_manager
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 = {}
graph_manager, robomaker_hyperparams_json = get_graph_manager(
**sm_hyperparams_dict)
s3_client.upload_hyperparameters(robomaker_hyperparams_json)
print("Uploaded hyperparameters.json to S3")
host_ip_address = get_ip_from_host()
s3_client.write_ip_config(host_ip_address)
print("Uploaded IP address information to S3: %s" % host_ip_address)
use_pretrained_model = False
if args.pretrained_s3_bucket and args.pretrained_s3_prefix:
s3_client_pretrained = SageS3Client(
bucket=args.pretrained_s3_bucket,
s3_prefix=args.pretrained_s3_prefix,
aws_region=args.aws_region)
s3_client_pretrained.download_model(args.pretrained_checkpoint_dir)
use_pretrained_model = True
memory_backend_params = RedisPubSubMemoryBackendParameters(
redis_address="localhost",
redis_port=6379,
run_type='trainer',
channel=args.s3_prefix)
graph_manager.agent_params.memory.register_var('memory_backend_params',
memory_backend_params)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.checkpoint_dir,
aws_region=args.aws_region,
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
training_worker(graph_manager=graph_manager,
checkpoint_dir=args.checkpoint_dir,
use_pretrained_model=use_pretrained_model,
framework=args.framework)
import rl_coach.core_types
from rl_coach import logger
from rl_coach.logger import screen
import argparse
import copy
import logging
import os
import sys
import shutil
import glob
import re
from .configuration_list import ConfigurationList
from rl_coach.coach import CoachLauncher
screen.set_use_colors(False) # Simple text logging so it looks good in CloudWatch
class CoachConfigurationList(ConfigurationList):
"""Helper Object for converting CLI arguments (or SageMaker hyperparameters)
into Coach configuration.
"""
# Being security-paranoid and not instantiating any arbitrary string the customer passes in
ALLOWED_TYPES = {
'Frames': rl_coach.core_types.Frames,
'EnvironmentSteps': rl_coach.core_types.EnvironmentSteps,
'EnvironmentEpisodes': rl_coach.core_types.EnvironmentEpisodes,
'TrainingSteps': rl_coach.core_types.TrainingSteps,
'Time': rl_coach.core_types.Time,
}
