def __init__(self):
# Read ros parameters
# OBJECT_POSITIONS will overwrite NUMBER_OF_OBSTACLES and RANDOMIZE_OBSTACLE_LOCATIONS
self.object_locations = rospy.get_param("OBJECT_POSITIONS", [])
self.num_obstacles = int(rospy.get_param("NUMBER_OF_OBSTACLES", 0)) \
if not self.object_locations else len(self.object_locations)
self.min_obstacle_dist = float(rospy.get_param("MIN_DISTANCE_BETWEEN_OBSTACLES", 2.0))
self.randomize = utils.str2bool(rospy.get_param("RANDOMIZE_OBSTACLE_LOCATIONS", False))
self.use_bot_car = utils.str2bool(rospy.get_param("IS_OBSTACLE_BOT_CAR", False))
self.obstacle_names = ["{}_{}".format(OBSTACLE_NAME_PREFIX, i) for i in range(self.num_obstacles)]
self.obstacle_dimensions = ObstacleDimensions.BOT_CAR_DIMENSION if self.use_bot_car \
else ObstacleDimensions.BOX_OBSTACLE_DIMENSION
# track data
self.track_data = TrackData.get_instance()
# Wait for ros services
rospy.wait_for_service(SPAWN_SDF_MODEL)
rospy.wait_for_service(SPAWN_URDF_MODEL)
self.spawn_sdf_model = ServiceProxyWrapper(SPAWN_SDF_MODEL, SpawnModel)
self.spawn_urdf_model = ServiceProxyWrapper(SPAWN_URDF_MODEL, SpawnModel)
# Load the obstacle sdf/urdf
obstacle_model_folder = "bot_car" if self.use_bot_car else "box_obstacle"
rospack = rospkg.RosPack()
deepracer_path = rospack.get_path("deepracer_simulation_environment")
obstacle_sdf_path = os.path.join(deepracer_path, "models", obstacle_model_folder, "model.sdf")
with open(obstacle_sdf_path, "r") as fp:
self.obstacle_sdf = fp.read()
# Set obstacle poses and spawn the obstacles
self.obstacle_poses = self._compute_obstacle_poses()
self._spawn_obstacles()
self._configure_randomizer()
def _get_agent_list(self, model_metadata, version):
"""Setup agent and get the agents list.
Args:
model_metadata (ModelMetadata): Current racer's model metadata
version (str): The current racer's simapp version in the model metadata
Returns:
agent_list (list): The list of agents for the current racer
"""
# setup agent
agent_config = {
"model_metadata": model_metadata,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [
link_name.replace("racecar", self._current_car_model_state.model_name)
for link_name in LINK_NAMES
],
ConfigParams.VELOCITY_LIST.value: [
velocity_topic.replace("racecar", self._current_car_model_state.model_name)
for velocity_topic in VELOCITY_TOPICS
],
ConfigParams.STEERING_LIST.value: [
steering_topic.replace("racecar", self._current_car_model_state.model_name)
for steering_topic in STEERING_TOPICS
],
ConfigParams.CHANGE_START.value: utils.str2bool(
rospy.get_param("CHANGE_START_POSITION", False)
),
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: self._current_car_model_state.model_name,
ConfigParams.VERSION.value: version,
ConfigParams.NUMBER_OF_RESETS.value: self._number_of_resets,
ConfigParams.PENALTY_SECONDS.value: self._penalty_seconds,
ConfigParams.NUMBER_OF_TRIALS.value: self._number_of_trials,
ConfigParams.IS_CONTINUOUS.value: self._is_continuous,
ConfigParams.RACE_TYPE.value: self._race_type,
ConfigParams.COLLISION_PENALTY.value: self._collision_penalty,
ConfigParams.OFF_TRACK_PENALTY.value: self._off_track_penalty,
ConfigParams.START_POSITION.value: get_start_positions(1)[
0
], # hard-coded to the first start position
ConfigParams.DONE_CONDITION.value: self._done_condition,
ConfigParams.IS_VIRTUAL_EVENT.value: True,
ConfigParams.RACE_DURATION.value: self._race_duration,
},
}
agent_list = list()
agent_list.append(
create_rollout_agent(agent_config, self._eval_metrics, self._run_phase_subject)
)
agent_list.append(create_obstacles_agent())
agent_list.append(create_bot_cars_agent())
return agent_list
def __init__(self):
self.lock = threading.Lock()
# Read ros parameters
self.num_bot_cars = int(rospy.get_param("NUMBER_OF_BOT_CARS", 0))
self.min_bot_car_dist = float(
rospy.get_param("MIN_DISTANCE_BETWEEN_BOT_CARS", 2.0))
self.randomize = utils.str2bool(
rospy.get_param("RANDOMIZE_BOT_CAR_LOCATIONS", False))
self.bot_car_speed = float(rospy.get_param("BOT_CAR_SPEED", 0.2))
self.is_lane_change = utils.str2bool(
rospy.get_param("IS_LANE_CHANGE", False))
self.lower_lane_change_time = float(
rospy.get_param("LOWER_LANE_CHANGE_TIME", 3.0))
self.upper_lane_change_time = float(
rospy.get_param("UPPER_LANE_CHANGE_TIME", 5.0))
self.lane_change_distance = float(
rospy.get_param("LANE_CHANGE_DISTANCE", 1.0))
self.penalty_seconds = float(rospy.get_param("PENALTY_SECONDS", 2.0))
self.lane_change_duration = self.lane_change_distance / self.bot_car_speed
self.bot_car_names = [
"bot_car_{}".format(i) for i in range(self.num_bot_cars)
]
self.bot_car_poses = []
self.bot_car_progresses = {}
self.bot_car_phase = AgentPhase.RUN.value
self.bot_car_dimensions = ObstacleDimensions.BOT_CAR_DIMENSION
self.bot_car_crash_count = 0
self.pause_end_time = 0.0
# track date
self.track_data = TrackData.get_instance()
self.reverse_dir = self.track_data.reverse_dir
# Wait for ros services
rospy.wait_for_service(SET_MODEL_STATE)
rospy.wait_for_service(SPAWN_SDF_MODEL)
self.set_model_state = ServiceProxyWrapper(SET_MODEL_STATE,
SetModelState)
self.spawn_sdf_model = ServiceProxyWrapper(SPAWN_SDF_MODEL, SpawnModel)
# Build splines for inner/outer lanes
self.inner_lane = TrackSpline(lane_name=TrackLane.INNER_LANE.value)
self.outer_lane = TrackSpline(lane_name=TrackLane.OUTER_LANE.value)
# Spawn the bot cars
self._reset_sim_time()
self._spawn_bot_cars()
self._configure_randomizer()
# Subscribe to the Gazebo clock and model states
rospy.Subscriber('/clock', Clock, self._update_sim_time)
def __init__(self):
self.track_data = TrackData.get_instance()
# Read ros parameters
self.num_obstacles = int(rospy.get_param("NUMBER_OF_OBSTACLES", 0))
self.min_obstacle_dist = float(
rospy.get_param("MIN_DISTANCE_BETWEEN_OBSTACLES", 2.0))
self.randomize = utils.str2bool(
rospy.get_param("RANDOMIZE_OBSTACLE_LOCATIONS", False))
self.use_bot_car_urdf = utils.str2bool(
rospy.get_param("IS_OBSTACLE_BOT_CAR", False))
self.obstacle_names = [
"obstacle_{}".format(i) for i in range(self.num_obstacles)
]
self.obstacle_dimensions = ObstacleDimensions.BOT_CAR_DIMENSION if self.use_bot_car_urdf \
else ObstacleDimensions.BOX_OBSTACLE_DIMENSION
# Wait for ros services
rospy.wait_for_service(SET_MODEL_STATE)
rospy.wait_for_service(SPAWN_SDF_MODEL)
rospy.wait_for_service(SPAWN_URDF_MODEL)
self.set_model_state = ServiceProxyWrapper(SET_MODEL_STATE,
SetModelState)
self.spawn_sdf_model = ServiceProxyWrapper(SPAWN_SDF_MODEL, SpawnModel)
self.spawn_urdf_model = ServiceProxyWrapper(SPAWN_URDF_MODEL,
SpawnModel)
# Load the obstacle sdf/urdf
if self.use_bot_car_urdf:
self.bot_car_urdf = rospy.get_param('robot_description_bot')
else:
rospack = rospkg.RosPack()
deepracer_path = rospack.get_path(
"deepracer_simulation_environment")
obstacle_sdf_path = os.path.join(deepracer_path, "models",
"box_obstacle", "model.sdf")
with open(obstacle_sdf_path, "r") as fp:
self.obstacle_sdf = fp.read()
# Spawn the obstacles
self._spawn_obstacles()
def main():
""" Main function for tournament worker """
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--preset',
help="(string) Name of a preset to run \
(class name from the 'presets' directory.)",
type=str,
required=False)
parser.add_argument('--s3_bucket',
help='list(string) S3 bucket',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_BUCKET",
["gsaur-test"]))
parser.add_argument('--s3_prefix',
help='list(string) S3 prefix',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_PREFIX",
["sagemaker"]))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=int(rospy.get_param("NUMBER_OF_TRIALS", 10)))
parser.add_argument(
'-c',
'--local_model_directory',
help='(string) Path to a folder containing a checkpoint \
to restore the model from.',
type=str,
default='./checkpoint')
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", 2.0)))
parser.add_argument('--job_type',
help='(string) job type',
type=str,
default=rospy.get_param("JOB_TYPE", "EVALUATION"))
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",
2.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
5.0)))
args = parser.parse_args()
arg_s3_bucket = args.s3_bucket
arg_s3_prefix = args.s3_prefix
logger.info("S3 bucket: %s \n S3 prefix: %s", arg_s3_bucket, arg_s3_prefix)
# tournament_worker: names to be displayed in MP4.
# This is racer alias in tournament worker case.
display_names = rospy.get_param('DISPLAY_NAME', "")
metrics_s3_buckets = rospy.get_param('METRICS_S3_BUCKET')
metrics_s3_object_keys = rospy.get_param('METRICS_S3_OBJECT_KEY')
arg_s3_bucket, arg_s3_prefix = utils.force_list(
arg_s3_bucket), utils.force_list(arg_s3_prefix)
metrics_s3_buckets = utils.force_list(metrics_s3_buckets)
metrics_s3_object_keys = utils.force_list(metrics_s3_object_keys)
validate_list = [
arg_s3_bucket, arg_s3_prefix, metrics_s3_buckets,
metrics_s3_object_keys
]
simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None)
mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET', None)
if simtrace_s3_bucket:
simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX')
simtrace_s3_bucket = utils.force_list(simtrace_s3_bucket)
simtrace_s3_object_prefix = utils.force_list(simtrace_s3_object_prefix)
validate_list.extend([simtrace_s3_bucket, simtrace_s3_object_prefix])
if mp4_s3_bucket:
mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX')
mp4_s3_bucket = utils.force_list(mp4_s3_bucket)
mp4_s3_object_prefix = utils.force_list(mp4_s3_object_prefix)
validate_list.extend([mp4_s3_bucket, mp4_s3_object_prefix])
if not all([lambda x: len(x) == len(validate_list[0]), validate_list]):
utils.log_and_exit(
"Eval worker error: Incorrect arguments passed: {}".format(
validate_list), utils.SIMAPP_SIMULATION_WORKER_EXCEPTION,
utils.SIMAPP_EVENT_ERROR_CODE_500)
if args.number_of_resets != 0 and args.number_of_resets < MIN_RESET_COUNT:
raise GenericRolloutException(
"number of resets is less than {}".format(MIN_RESET_COUNT))
# Instantiate Cameras
if len(arg_s3_bucket) == 1:
configure_camera(namespaces=['racecar'])
else:
configure_camera(namespaces=[
'racecar_{}'.format(str(agent_index))
for agent_index in range(len(arg_s3_bucket))
])
agent_list = list()
s3_bucket_dict = dict()
s3_prefix_dict = dict()
s3_writers = list()
# tournament_worker: list of required S3 locations
simtrace_s3_bucket_dict = dict()
simtrace_s3_prefix_dict = dict()
metrics_s3_bucket_dict = dict()
metrics_s3_obect_key_dict = dict()
mp4_s3_bucket_dict = dict()
mp4_s3_object_prefix_dict = dict()
for agent_index, s3_bucket_val in enumerate(arg_s3_bucket):
agent_name = 'agent' if len(arg_s3_bucket) == 1 else 'agent_{}'.format(
str(agent_index))
racecar_name = 'racecar' if len(
arg_s3_bucket) == 1 else 'racecar_{}'.format(str(agent_index))
s3_bucket_dict[agent_name] = arg_s3_bucket[agent_index]
s3_prefix_dict[agent_name] = arg_s3_prefix[agent_index]
# tournament_worker: remap key with agent_name instead of agent_index for list of S3 locations.
simtrace_s3_bucket_dict[agent_name] = simtrace_s3_bucket[agent_index]
simtrace_s3_prefix_dict[agent_name] = simtrace_s3_object_prefix[
agent_index]
metrics_s3_bucket_dict[agent_name] = metrics_s3_buckets[agent_index]
metrics_s3_obect_key_dict[agent_name] = metrics_s3_object_keys[
agent_index]
mp4_s3_bucket_dict[agent_name] = mp4_s3_bucket[agent_index]
mp4_s3_object_prefix_dict[agent_name] = mp4_s3_object_prefix[
agent_index]
s3_client = SageS3Client(bucket=arg_s3_bucket[agent_index],
s3_prefix=arg_s3_prefix[agent_index],
aws_region=args.aws_region)
# Load the model metadata
if not os.path.exists(os.path.join(CUSTOM_FILES_PATH, agent_name)):
os.makedirs(os.path.join(CUSTOM_FILES_PATH, agent_name))
model_metadata_local_path = os.path.join(
os.path.join(CUSTOM_FILES_PATH, agent_name), 'model_metadata.json')
utils.load_model_metadata(
s3_client,
os.path.normpath("%s/model/model_metadata.json" %
arg_s3_prefix[agent_index]),
model_metadata_local_path)
# 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(arg_s3_bucket[agent_index], arg_s3_prefix[agent_index], args.aws_region):
utils.make_compatible(arg_s3_bucket[agent_index],
arg_s3_prefix[agent_index], args.aws_region,
SyncFiles.TRAINER_READY.value)
# Select the optimal model
utils.do_model_selection(s3_bucket=arg_s3_bucket[agent_index],
s3_prefix=arg_s3_prefix[agent_index],
region=args.aws_region)
# Download hyperparameters from SageMaker
if not os.path.exists(agent_name):
os.makedirs(agent_name)
hyperparameters_file_success = False
hyperparams_s3_key = os.path.normpath(arg_s3_prefix[agent_index] +
"/ip/hyperparameters.json")
hyperparameters_file_success = s3_client.download_file(
s3_key=hyperparams_s3_key,
local_path=os.path.join(agent_name, "hyperparameters.json"))
sm_hyperparams_dict = {}
if hyperparameters_file_success:
logger.info("Received Sagemaker hyperparameters successfully!")
with open(os.path.join(agent_name,
"hyperparameters.json")) as file:
sm_hyperparams_dict = json.load(file)
else:
logger.info("SageMaker hyperparameters not found.")
agent_config = {
'model_metadata': model_metadata_local_path,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [
link_name.replace('racecar', racecar_name)
for link_name in LINK_NAMES
],
ConfigParams.VELOCITY_LIST.value: [
velocity_topic.replace('racecar', racecar_name)
for velocity_topic in VELOCITY_TOPICS
],
ConfigParams.STEERING_LIST.value: [
steering_topic.replace('racecar', racecar_name)
for steering_topic in STEERING_TOPICS
],
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION',
False)),
ConfigParams.ALT_DIR.value:
utils.str2bool(
rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)),
ConfigParams.ACTION_SPACE_PATH.value:
'custom_files/' + agent_name + '/model_metadata.json',
ConfigParams.REWARD.value:
reward_function,
ConfigParams.AGENT_NAME.value:
racecar_name,
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:
args.number_of_trials,
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
}
}
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
metrics_s3_buckets[agent_index],
MetricsS3Keys.METRICS_KEY.value:
metrics_s3_object_keys[agent_index],
# Replaced rospy.get_param('AWS_REGION') to be equal to the argument being passed
# or default argument set
MetricsS3Keys.REGION.value:
args.aws_region,
# Replaced rospy.get_param('MODEL_S3_BUCKET') to be equal to the argument being passed
# or default argument set
MetricsS3Keys.STEP_BUCKET.value:
arg_s3_bucket[agent_index],
# Replaced rospy.get_param('MODEL_S3_PREFIX') to be equal to the argument being passed
# or default argument set
MetricsS3Keys.STEP_KEY.value:
os.path.join(arg_s3_prefix[agent_index],
EVALUATION_SIMTRACE_DATA_S3_OBJECT_KEY)
}
aws_region = rospy.get_param('AWS_REGION', args.aws_region)
s3_writer_job_info = []
if simtrace_s3_bucket:
s3_writer_job_info.append(
IterationData(
'simtrace', simtrace_s3_bucket[agent_index],
simtrace_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
SIM_TRACE_EVALUATION_LOCAL_FILE.value)))
if mp4_s3_bucket:
s3_writer_job_info.extend([
IterationData(
'pip', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_PIP_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'45degree', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_45DEGREE_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'topview', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_TOPVIEW_MP4_VALIDATION_LOCAL_PATH.value))
])
s3_writers.append(S3Writer(job_info=s3_writer_job_info))
run_phase_subject = RunPhaseSubject()
agent_list.append(
create_rollout_agent(agent_config,
EvalMetrics(agent_name, metrics_s3_config),
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)
graph_manager, _ = get_graph_manager(hp_dict=sm_hyperparams_dict,
agent_list=agent_list,
run_phase_subject=run_phase_subject)
ds_params_instance = S3BotoDataStoreParameters(
aws_region=args.aws_region,
bucket_names=s3_bucket_dict,
base_checkpoint_dir=args.local_model_directory,
s3_folders=s3_prefix_dict)
graph_manager.data_store = S3BotoDataStore(params=ds_params_instance,
graph_manager=graph_manager,
ignore_lock=True)
graph_manager.env_params.seed = 0
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.local_model_directory
tournament_worker(graph_manager=graph_manager,
number_of_trials=args.number_of_trials,
task_parameters=task_parameters,
s3_writers=s3_writers,
is_continuous=args.is_continuous)
# tournament_worker: write race report to local file.
write_race_report(graph_manager,
model_s3_bucket_map=s3_bucket_dict,
model_s3_prefix_map=s3_prefix_dict,
metrics_s3_bucket_map=metrics_s3_bucket_dict,
metrics_s3_key_map=metrics_s3_obect_key_dict,
simtrace_s3_bucket_map=simtrace_s3_bucket_dict,
simtrace_s3_prefix_map=simtrace_s3_prefix_dict,
mp4_s3_bucket_map=mp4_s3_bucket_dict,
mp4_s3_prefix_map=mp4_s3_object_prefix_dict,
display_names=display_names)
# tournament_worker: terminate tournament_race_node.
terminate_tournament_race()
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 __init__(self):
"""Instantiates the class and creates clients for the relevant ROS services"""
self._park_positions_ = deque()
self._park_location = ParkLocation(
rospy.get_param("PARK_LOCATION",
ParkLocation.BOTTOM.value).lower())
self._reverse_dir_ = utils.str2bool(
rospy.get_param("REVERSE_DIR", False))
if TrackData._instance_ is not None:
raise GenericRolloutException(
"Attempting to construct multiple TrackData objects")
try:
rospack = rospkg.RosPack()
deepracer_path = rospack.get_path(
"deepracer_simulation_environment")
waypoints_path = os.path.join(
deepracer_path, "routes",
"{}.npy".format(rospy.get_param("WORLD_NAME")))
self._is_bot_car_ = int(rospy.get_param("NUMBER_OF_BOT_CARS",
0)) > 0
self._bot_car_speed_ = float(rospy.get_param("BOT_CAR_SPEED", 0.0))
waypoints = np.load(waypoints_path)
self.is_loop = np.all(waypoints[0, :] == waypoints[-1, :])
poly_func = LinearRing if self.is_loop else LineString
# forward direction
self._center_line_forward_ = TrackLine(poly_func(waypoints[:,
0:2]))
self._inner_border_forward_ = TrackLine(
poly_func(waypoints[:, 2:4]))
self._outer_border_forward_ = TrackLine(
poly_func(waypoints[:, 4:6]))
self._inner_lane_forward_ = TrackLine(
poly_func((waypoints[:, 2:4] + waypoints[:, 0:2]) / 2))
self._outer_lane_forward_ = TrackLine(
poly_func((waypoints[:, 4:6] + waypoints[:, 0:2]) / 2))
# reversed direction
self._center_line_reverse_ = TrackLine(
poly_func(waypoints[:, 0:2][::-1]))
self._inner_border_reverse_ = TrackLine(
poly_func(waypoints[:, 2:4][::-1]))
self._outer_border_reverse_ = TrackLine(
poly_func(waypoints[:, 4:6][::-1]))
self._inner_lane_reverse_ = TrackLine(
poly_func(
(waypoints[:, 2:4][::-1] + waypoints[:, 0:2][::-1]) / 2))
self._outer_lane_reverse_ = TrackLine(
poly_func(
(waypoints[:, 4:6][::-1] + waypoints[:, 0:2][::-1]) / 2))
if self.is_loop:
self._inner_poly_ = Polygon(self.center_line,
[self.inner_border])
self._road_poly_ = Polygon(self.outer_border,
[self.inner_border])
self._is_ccw_ = self._center_line_forward_.is_ccw
else:
self._inner_poly_ = Polygon(
np.vstack(
(self.center_line.line, np.flipud(self.inner_border))))
self._road_poly_ = Polygon(
np.vstack(
(self.outer_border, np.flipud(self.inner_border))))
self._is_ccw_ = True
self.object_poses = OrderedDict()
self.object_dims = OrderedDict()
self.noncollidable_objects = set()
self.noncollidable_object_lock = threading.Lock()
# There should only be one track data object
TrackData._instance_ = self
# declare a lock to prevent read and write at the same time
self._lock_ = threading.Lock()
except Exception as ex:
raise GenericRolloutException(
"Failed to create track data: {}".format(ex))
def get_robomaker_profiler_env():
"""Read robomaker profiler environment"""
is_profiler_on = str2bool(rospy.get_param(ROBOMAKER_IS_PROFILER_ON, False))
profiler_s3_bucker = rospy.get_param(ROBOMAKER_PROFILER_S3_BUCKET, None)
profiler_s3_prefix = rospy.get_param(ROBOMAKER_PROFILER_S3_PREFIX, None)
return is_profiler_on, profiler_s3_bucker, profiler_s3_prefix
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():
""" Main function for evaluation worker """
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--preset',
help="(string) Name of a preset to run \
(class name from the 'presets' directory.)",
type=str,
required=False)
parser.add_argument('--s3_bucket',
help='list(string) S3 bucket',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_BUCKET",
["gsaur-test"]))
parser.add_argument('--s3_prefix',
help='list(string) S3 prefix',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_PREFIX",
["sagemaker"]))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=int(rospy.get_param("NUMBER_OF_TRIALS", 10)))
parser.add_argument(
'-c',
'--local_model_directory',
help='(string) Path to a folder containing a checkpoint \
to restore the model from.',
type=str,
default='./checkpoint')
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", 2.0)))
parser.add_argument('--job_type',
help='(string) job type',
type=str,
default=rospy.get_param("JOB_TYPE", "EVALUATION"))
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",
2.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
5.0)))
args = parser.parse_args()
arg_s3_bucket = args.s3_bucket
arg_s3_prefix = args.s3_prefix
logger.info("S3 bucket: %s \n S3 prefix: %s", arg_s3_bucket, arg_s3_prefix)
metrics_s3_buckets = rospy.get_param('METRICS_S3_BUCKET')
metrics_s3_object_keys = rospy.get_param('METRICS_S3_OBJECT_KEY')
arg_s3_bucket, arg_s3_prefix = utils.force_list(
arg_s3_bucket), utils.force_list(arg_s3_prefix)
metrics_s3_buckets = utils.force_list(metrics_s3_buckets)
metrics_s3_object_keys = utils.force_list(metrics_s3_object_keys)
validate_list = [
arg_s3_bucket, arg_s3_prefix, metrics_s3_buckets,
metrics_s3_object_keys
]
simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None)
mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET', None)
if simtrace_s3_bucket:
simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX')
simtrace_s3_bucket = utils.force_list(simtrace_s3_bucket)
simtrace_s3_object_prefix = utils.force_list(simtrace_s3_object_prefix)
validate_list.extend([simtrace_s3_bucket, simtrace_s3_object_prefix])
if mp4_s3_bucket:
mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX')
mp4_s3_bucket = utils.force_list(mp4_s3_bucket)
mp4_s3_object_prefix = utils.force_list(mp4_s3_object_prefix)
validate_list.extend([mp4_s3_bucket, mp4_s3_object_prefix])
if not all([lambda x: len(x) == len(validate_list[0]), validate_list]):
log_and_exit(
"Eval worker error: Incorrect arguments passed: {}".format(
validate_list), SIMAPP_SIMULATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_500)
if args.number_of_resets != 0 and args.number_of_resets < MIN_RESET_COUNT:
raise GenericRolloutException(
"number of resets is less than {}".format(MIN_RESET_COUNT))
# Instantiate Cameras
if len(arg_s3_bucket) == 1:
configure_camera(namespaces=['racecar'])
else:
configure_camera(namespaces=[
'racecar_{}'.format(str(agent_index))
for agent_index in range(len(arg_s3_bucket))
])
agent_list = list()
s3_bucket_dict = dict()
s3_prefix_dict = dict()
checkpoint_dict = dict()
simtrace_video_s3_writers = []
start_positions = get_start_positions(len(arg_s3_bucket))
done_condition = utils.str_to_done_condition(
rospy.get_param("DONE_CONDITION", any))
park_positions = utils.pos_2d_str_to_list(
rospy.get_param("PARK_POSITIONS", []))
# if not pass in park positions for all done condition case, use default
if not park_positions:
park_positions = [DEFAULT_PARK_POSITION for _ in arg_s3_bucket]
for agent_index, _ in enumerate(arg_s3_bucket):
agent_name = 'agent' if len(arg_s3_bucket) == 1 else 'agent_{}'.format(
str(agent_index))
racecar_name = 'racecar' if len(
arg_s3_bucket) == 1 else 'racecar_{}'.format(str(agent_index))
s3_bucket_dict[agent_name] = arg_s3_bucket[agent_index]
s3_prefix_dict[agent_name] = arg_s3_prefix[agent_index]
# download model metadata
model_metadata = ModelMetadata(
bucket=arg_s3_bucket[agent_index],
s3_key=get_s3_key(arg_s3_prefix[agent_index],
MODEL_METADATA_S3_POSTFIX),
region_name=args.aws_region,
local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format(agent_name))
model_metadata_info = model_metadata.get_model_metadata_info()
version = model_metadata_info[ModelMetadataKeys.VERSION.value]
# checkpoint s3 instance
checkpoint = Checkpoint(bucket=arg_s3_bucket[agent_index],
s3_prefix=arg_s3_prefix[agent_index],
region_name=args.aws_region,
agent_name=agent_name,
checkpoint_dir=args.local_model_directory)
# 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())
checkpoint_dict[agent_name] = checkpoint
agent_config = {
'model_metadata': model_metadata,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [
link_name.replace('racecar', racecar_name)
for link_name in LINK_NAMES
],
ConfigParams.VELOCITY_LIST.value: [
velocity_topic.replace('racecar', racecar_name)
for velocity_topic in VELOCITY_TOPICS
],
ConfigParams.STEERING_LIST.value: [
steering_topic.replace('racecar', racecar_name)
for steering_topic in STEERING_TOPICS
],
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION',
False)),
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_name,
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:
args.number_of_trials,
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,
ConfigParams.START_POSITION.value:
start_positions[agent_index],
ConfigParams.DONE_CONDITION.value:
done_condition
}
}
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
metrics_s3_buckets[agent_index],
MetricsS3Keys.METRICS_KEY.value:
metrics_s3_object_keys[agent_index],
# Replaced rospy.get_param('AWS_REGION') to be equal to the argument being passed
# or default argument set
MetricsS3Keys.REGION.value:
args.aws_region
}
aws_region = rospy.get_param('AWS_REGION', args.aws_region)
if simtrace_s3_bucket:
simtrace_video_s3_writers.append(
SimtraceVideo(
upload_type=SimtraceVideoNames.SIMTRACE_EVAL.value,
bucket=simtrace_s3_bucket[agent_index],
s3_prefix=simtrace_s3_object_prefix[agent_index],
region_name=aws_region,
local_path=SIMTRACE_EVAL_LOCAL_PATH_FORMAT.format(
agent_name)))
if mp4_s3_bucket:
simtrace_video_s3_writers.extend([
SimtraceVideo(
upload_type=SimtraceVideoNames.PIP.value,
bucket=mp4_s3_bucket[agent_index],
s3_prefix=mp4_s3_object_prefix[agent_index],
region_name=aws_region,
local_path=CAMERA_PIP_MP4_LOCAL_PATH_FORMAT.format(
agent_name)),
SimtraceVideo(
upload_type=SimtraceVideoNames.DEGREE45.value,
bucket=mp4_s3_bucket[agent_index],
s3_prefix=mp4_s3_object_prefix[agent_index],
region_name=aws_region,
local_path=CAMERA_45DEGREE_LOCAL_PATH_FORMAT.format(
agent_name)),
SimtraceVideo(
upload_type=SimtraceVideoNames.TOPVIEW.value,
bucket=mp4_s3_bucket[agent_index],
s3_prefix=mp4_s3_object_prefix[agent_index],
region_name=aws_region,
local_path=CAMERA_TOPVIEW_LOCAL_PATH_FORMAT.format(
agent_name))
])
run_phase_subject = RunPhaseSubject()
agent_list.append(
create_rollout_agent(
agent_config,
EvalMetrics(agent_name, metrics_s3_config, args.is_continuous),
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)
enable_domain_randomization = utils.str2bool(
rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False))
sm_hyperparams_dict = {}
# 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)
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,
done_condition=done_condition,
pause_physics=pause_physics,
unpause_physics=unpause_physics)
ds_params_instance = S3BotoDataStoreParameters(
checkpoint_dict=checkpoint_dict)
graph_manager.data_store = S3BotoDataStore(params=ds_params_instance,
graph_manager=graph_manager,
ignore_lock=True)
graph_manager.env_params.seed = 0
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.local_model_directory
evaluation_worker(graph_manager=graph_manager,
number_of_trials=args.number_of_trials,
task_parameters=task_parameters,
simtrace_video_s3_writers=simtrace_video_s3_writers,
is_continuous=args.is_continuous,
park_positions=park_positions,
race_type=args.race_type,
pause_physics=pause_physics,
unpause_physics=unpause_physics)
def main():
""" Main function for virutal event manager """
parser = argparse.ArgumentParser()
parser.add_argument(
'--queue_url',
help='the sqs queue url to receive next racer information',
type=str,
default=str(rospy.get_param("SQS_QUEUE_URL", "sqs_queue_url")))
parser.add_argument('--race_duration',
help='the length of the race in seconds.',
type=int,
default=int(
rospy.get_param("RACE_DURATION",
DEFAULT_RACE_DURATION)))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=int(rospy.get_param("NUMBER_OF_TRIALS", 3)))
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", 2.0)))
parser.add_argument('--off_track_penalty',
help='(float) off track penalty second',
type=float,
default=float(rospy.get_param("OFF_TRACK_PENALTY",
2.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
5.0)))
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('--body_shell_type',
help='(string) body shell type',
type=str,
default=rospy.get_param("BODY_SHELL_TYPE",
"deepracer"))
args = parser.parse_args()
manager = VirtualEventManager(queue_url=args.queue_url,
aws_region=args.aws_region,
race_duration=args.race_duration,
number_of_trials=args.number_of_trials,
number_of_resets=args.number_of_resets,
penalty_seconds=args.penalty_seconds,
off_track_penalty=args.off_track_penalty,
collision_penalty=args.collision_penalty,
is_continuous=args.is_continuous,
race_type=args.race_type,
body_shell_type=args.body_shell_type)
while True:
# poll for next racer
if not manager.is_event_end and manager.current_racer is None:
LOG.info("[virtual event worker] polling for next racer.")
manager.poll_next_racer()
# if event end signal received, break out loop and finish the job
if manager.is_event_end:
LOG.info("[virtual event worker] received event end.")
break
# Setting up the race environment
if manager.setup_race():
# proceed with start and finish race only if setup is successful.
# Start race
manager.start_race()
# Finish race
manager.finish_race()
utils.cancel_simulation_job(
os.environ.get('AWS_ROBOMAKER_SIMULATION_JOB_ARN'),
rospy.get_param('AWS_REGION'))
def __init__(self):
'''Instantiates the class and creates clients for the relevant ROS services'''
self._reverse_dir_ = utils.str2bool(
rospy.get_param("REVERSE_DIR", False))
if TrackData._instance_ is not None:
raise GenericRolloutException(
"Attempting to construct multiple TrackData objects")
rospy.wait_for_service(GET_LINK_STATE)
rospy.wait_for_service(GET_MODEL_STATE)
self._get_link_state_ = ServiceProxyWrapper(GET_LINK_STATE,
GetLinkState)
self._get_model_state_ = ServiceProxyWrapper(GET_MODEL_STATE,
GetModelState)
try:
rospack = rospkg.RosPack()
deepracer_path = rospack.get_path(
"deepracer_simulation_environment")
waypoints_path = os.path.join(
deepracer_path, "routes",
"{}.npy".format(rospy.get_param("WORLD_NAME")))
self._is_bot_car_ = int(rospy.get_param("NUMBER_OF_BOT_CARS",
0)) > 0
self._bot_car_speed_ = float(rospy.get_param("BOT_CAR_SPEED", 0.0))
waypoints = np.load(waypoints_path)
self.is_loop = np.all(waypoints[0, :] == waypoints[-1, :])
poly_func = LinearRing if self.is_loop else LineString
# forward direction
self._center_line_forward_ = TrackLine(poly_func(waypoints[:,
0:2]))
self._inner_border_forward_ = TrackLine(
poly_func(waypoints[:, 2:4]))
self._outer_border_forward_ = TrackLine(
poly_func(waypoints[:, 4:6]))
self._inner_lane_forward_ = TrackLine(poly_func((waypoints[:, 2:4] + \
waypoints[:, 0:2])/2))
self._outer_lane_forward_ = TrackLine(poly_func((waypoints[:, 4:6] + \
waypoints[:, 0:2])/2))
# reversed direction
self._center_line_reverse_ = TrackLine(
poly_func(waypoints[:, 0:2][::-1]))
self._inner_border_reverse_ = TrackLine(
poly_func(waypoints[:, 2:4][::-1]))
self._outer_border_reverse_ = TrackLine(
poly_func(waypoints[:, 4:6][::-1]))
self._inner_lane_reverse_ = TrackLine(poly_func((waypoints[:, 2:4][::-1] + \
waypoints[:, 0:2][::-1]) / 2))
self._outer_lane_reverse_ = TrackLine(poly_func((waypoints[:, 4:6][::-1] + \
waypoints[:, 0:2][::-1]) / 2))
if self.is_loop:
self._left_poly_ = Polygon(self.center_line,
[self.inner_border])
self._road_poly_ = Polygon(self.outer_border,
[self.inner_border])
else:
self._left_poly_ = Polygon(
np.vstack(
(self.center_line.line, np.flipud(self.inner_border))))
self._road_poly_ = Polygon(
np.vstack(
(self.outer_border, np.flipud(self.inner_border))))
self.car_ndist = 0.0 # TEMPORARY -- REMOVE THIS
self.object_poses = OrderedDict()
self.object_dims = OrderedDict()
rospy.Subscriber('/gazebo/model_states', ModelStates,
self._update_objects)
# There should only be one track data object
TrackData._instance_ = self
# declare a lock to prevent read and write at the same time
self._lock_ = threading.Lock()
except Exception as ex:
raise GenericRolloutException(
'Failed to create track data: {}'.format(ex))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--preset',
help="(string) Name of a preset to run \
(class name from the 'presets' directory.)",
type=str,
required=False)
parser.add_argument('--s3_bucket',
help='(string) S3 bucket',
type=str,
default=rospy.get_param("MODEL_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=rospy.get_param("MODEL_S3_PREFIX",
"sagemaker"))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=int(rospy.get_param("NUMBER_OF_TRIALS", 10)))
parser.add_argument(
'-c',
'--local_model_directory',
help='(string) Path to a folder containing a checkpoint \
to restore the model from.',
type=str,
default='./checkpoint')
args = parser.parse_args()
logger.info("S3 bucket: %s \n S3 prefix: %s", args.s3_bucket,
args.s3_prefix)
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,
os.path.normpath("%s/model/model_metadata.json" % args.s3_prefix),
model_metadata_local_path)
# 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.s3_bucket, args.s3_prefix, args.aws_region):
utils.make_compatible(args.s3_bucket, args.s3_prefix, args.aws_region,
SyncFiles.TRAINER_READY.value)
# 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 file:
sm_hyperparams_dict = json.load(file)
else:
logger.info("SageMaker hyperparameters not found.")
#! 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', False)),
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('MODEL_S3_BUCKET'),
MetricsS3Keys.STEP_KEY.value:
os.path.join(rospy.get_param('MODEL_S3_PREFIX'),
EVALUATION_SIMTRACE_DATA_S3_OBJECT_KEY)
}
agent_list = list()
agent_list.append(
create_rollout_agent(agent_config, EvalMetrics(metrics_s3_config)))
agent_list.append(create_obstacles_agent())
agent_list.append(create_bot_cars_agent())
graph_manager, _ = get_graph_manager(sm_hyperparams_dict, agent_list)
ds_params_instance = S3BotoDataStoreParameters(
aws_region=args.aws_region,
bucket_name=args.s3_bucket,
checkpoint_dir=args.local_model_directory,
s3_folder=args.s3_prefix)
data_store = S3BotoDataStore(ds_params_instance)
data_store.graph_manager = graph_manager
graph_manager.data_store = data_store
graph_manager.env_params.seed = 0
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.local_model_directory
evaluation_worker(
graph_manager=graph_manager,
data_store=data_store,
number_of_trials=args.number_of_trials,
task_parameters=task_parameters,
)
def main():
""" Main function for evaluation worker """
parser = argparse.ArgumentParser()
parser.add_argument('-p',
'--preset',
help="(string) Name of a preset to run \
(class name from the 'presets' directory.)",
type=str,
required=False)
parser.add_argument('--s3_bucket',
help='list(string) S3 bucket',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_BUCKET",
["gsaur-test"]))
parser.add_argument('--s3_prefix',
help='list(string) S3 prefix',
type=str,
nargs='+',
default=rospy.get_param("MODEL_S3_PREFIX",
["sagemaker"]))
parser.add_argument('--s3_endpoint_url',
help='(string) S3 endpoint URL',
type=str,
default=rospy.get_param("S3_ENDPOINT_URL", None))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=rospy.get_param("AWS_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=int(rospy.get_param("NUMBER_OF_TRIALS", 10)))
parser.add_argument(
'-c',
'--local_model_directory',
help='(string) Path to a folder containing a checkpoint \
to restore the model from.',
type=str,
default='./checkpoint')
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", 2.0)))
parser.add_argument('--job_type',
help='(string) job type',
type=str,
default=rospy.get_param("JOB_TYPE", "EVALUATION"))
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",
2.0)))
parser.add_argument('--collision_penalty',
help='(float) collision penalty second',
type=float,
default=float(rospy.get_param("COLLISION_PENALTY",
5.0)))
parser.add_argument('--round_robin_advance_dist',
help='(float) round robin distance 0-1',
type=float,
default=float(
rospy.get_param("ROUND_ROBIN_ADVANCE_DIST", 0.05)))
parser.add_argument('--start_position_offset',
help='(float) offset start 0-1',
type=float,
default=float(
rospy.get_param("START_POSITION_OFFSET", 0.0)))
args = parser.parse_args()
arg_s3_bucket = args.s3_bucket
arg_s3_prefix = args.s3_prefix
logger.info("S3 bucket: %s \n S3 prefix: %s \n S3 endpoint URL: %s",
args.s3_bucket, args.s3_prefix, args.s3_endpoint_url)
metrics_s3_buckets = rospy.get_param('METRICS_S3_BUCKET')
metrics_s3_object_keys = rospy.get_param('METRICS_S3_OBJECT_KEY')
arg_s3_bucket, arg_s3_prefix = utils.force_list(
arg_s3_bucket), utils.force_list(arg_s3_prefix)
metrics_s3_buckets = utils.force_list(metrics_s3_buckets)
metrics_s3_object_keys = utils.force_list(metrics_s3_object_keys)
validate_list = [
arg_s3_bucket, arg_s3_prefix, metrics_s3_buckets,
metrics_s3_object_keys
]
simtrace_s3_bucket = rospy.get_param('SIMTRACE_S3_BUCKET', None)
mp4_s3_bucket = rospy.get_param('MP4_S3_BUCKET', None)
if simtrace_s3_bucket:
simtrace_s3_object_prefix = rospy.get_param('SIMTRACE_S3_PREFIX')
simtrace_s3_bucket = utils.force_list(simtrace_s3_bucket)
simtrace_s3_object_prefix = utils.force_list(simtrace_s3_object_prefix)
validate_list.extend([simtrace_s3_bucket, simtrace_s3_object_prefix])
if mp4_s3_bucket:
mp4_s3_object_prefix = rospy.get_param('MP4_S3_OBJECT_PREFIX')
mp4_s3_bucket = utils.force_list(mp4_s3_bucket)
mp4_s3_object_prefix = utils.force_list(mp4_s3_object_prefix)
validate_list.extend([mp4_s3_bucket, mp4_s3_object_prefix])
if not all([lambda x: len(x) == len(validate_list[0]), validate_list]):
log_and_exit(
"Eval worker error: Incorrect arguments passed: {}".format(
validate_list), SIMAPP_SIMULATION_WORKER_EXCEPTION,
SIMAPP_EVENT_ERROR_CODE_500)
if args.number_of_resets != 0 and args.number_of_resets < MIN_RESET_COUNT:
raise GenericRolloutException(
"number of resets is less than {}".format(MIN_RESET_COUNT))
# Instantiate Cameras
if len(arg_s3_bucket) == 1:
configure_camera(namespaces=['racecar'])
else:
configure_camera(namespaces=[
'racecar_{}'.format(str(agent_index))
for agent_index in range(len(arg_s3_bucket))
])
agent_list = list()
s3_bucket_dict = dict()
s3_prefix_dict = dict()
s3_writers = list()
start_positions = get_start_positions(len(arg_s3_bucket))
done_condition = utils.str_to_done_condition(
rospy.get_param("DONE_CONDITION", any))
park_positions = utils.pos_2d_str_to_list(
rospy.get_param("PARK_POSITIONS", []))
# if not pass in park positions for all done condition case, use default
if not park_positions:
park_positions = [DEFAULT_PARK_POSITION for _ in arg_s3_bucket]
for agent_index, _ in enumerate(arg_s3_bucket):
agent_name = 'agent' if len(arg_s3_bucket) == 1 else 'agent_{}'.format(
str(agent_index))
racecar_name = 'racecar' if len(
arg_s3_bucket) == 1 else 'racecar_{}'.format(str(agent_index))
s3_bucket_dict[agent_name] = arg_s3_bucket[agent_index]
s3_prefix_dict[agent_name] = arg_s3_prefix[agent_index]
# download model metadata
model_metadata = ModelMetadata(
bucket=arg_s3_bucket[agent_index],
s3_key=get_s3_key(arg_s3_prefix[agent_index],
MODEL_METADATA_S3_POSTFIX),
region_name=args.aws_region,
s3_endpoint_url=args.s3_endpoint_url,
local_path=MODEL_METADATA_LOCAL_PATH_FORMAT.format(agent_name))
_, _, version = model_metadata.get_model_metadata_info()
# Select the optimal model
utils.do_model_selection(s3_bucket=arg_s3_bucket[agent_index],
s3_prefix=arg_s3_prefix[agent_index],
region=args.aws_region,
s3_endpoint_url=args.s3_endpoint_url)
agent_config = {
'model_metadata': model_metadata,
ConfigParams.CAR_CTRL_CONFIG.value: {
ConfigParams.LINK_NAME_LIST.value: [
link_name.replace('racecar', racecar_name)
for link_name in LINK_NAMES
],
ConfigParams.VELOCITY_LIST.value: [
velocity_topic.replace('racecar', racecar_name)
for velocity_topic in VELOCITY_TOPICS
],
ConfigParams.STEERING_LIST.value: [
steering_topic.replace('racecar', racecar_name)
for steering_topic in STEERING_TOPICS
],
ConfigParams.CHANGE_START.value:
utils.str2bool(rospy.get_param('CHANGE_START_POSITION',
False)),
ConfigParams.ALT_DIR.value:
utils.str2bool(
rospy.get_param('ALTERNATE_DRIVING_DIRECTION', False)),
ConfigParams.ACTION_SPACE_PATH.value:
model_metadata.local_path,
ConfigParams.REWARD.value:
reward_function,
ConfigParams.AGENT_NAME.value:
racecar_name,
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:
args.number_of_trials,
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,
ConfigParams.START_POSITION.value:
start_positions[agent_index],
ConfigParams.DONE_CONDITION.value:
done_condition,
ConfigParams.ROUND_ROBIN_ADVANCE_DIST.value:
args.round_robin_advance_dist,
ConfigParams.START_POSITION_OFFSET.value:
args.start_position_offset
}
}
metrics_s3_config = {
MetricsS3Keys.METRICS_BUCKET.value:
metrics_s3_buckets[agent_index],
MetricsS3Keys.METRICS_KEY.value:
metrics_s3_object_keys[agent_index],
MetricsS3Keys.ENDPOINT_URL.value:
rospy.get_param('S3_ENDPOINT_URL', None),
# Replaced rospy.get_param('AWS_REGION') to be equal to the argument being passed
# or default argument set
MetricsS3Keys.REGION.value:
args.aws_region
}
aws_region = rospy.get_param('AWS_REGION', args.aws_region)
s3_writer_job_info = []
if simtrace_s3_bucket:
s3_writer_job_info.append(
IterationData(
'simtrace', simtrace_s3_bucket[agent_index],
simtrace_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
SIM_TRACE_EVALUATION_LOCAL_FILE.value)))
if mp4_s3_bucket:
s3_writer_job_info.extend([
IterationData(
'pip', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_PIP_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'45degree', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_45DEGREE_MP4_VALIDATION_LOCAL_PATH.value)),
IterationData(
'topview', mp4_s3_bucket[agent_index],
mp4_s3_object_prefix[agent_index], aws_region,
os.path.join(
ITERATION_DATA_LOCAL_FILE_PATH, agent_name,
IterationDataLocalFileNames.
CAMERA_TOPVIEW_MP4_VALIDATION_LOCAL_PATH.value))
])
s3_writers.append(
S3Writer(job_info=s3_writer_job_info,
s3_endpoint_url=args.s3_endpoint_url))
run_phase_subject = RunPhaseSubject()
agent_list.append(
create_rollout_agent(
agent_config,
EvalMetrics(agent_name, metrics_s3_config, args.is_continuous),
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)
enable_domain_randomization = utils.str2bool(
rospy.get_param('ENABLE_DOMAIN_RANDOMIZATION', False))
sm_hyperparams_dict = {}
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,
done_condition=done_condition)
ds_params_instance = S3BotoDataStoreParameters(
aws_region=args.aws_region,
bucket_names=s3_bucket_dict,
base_checkpoint_dir=args.local_model_directory,
s3_folders=s3_prefix_dict,
s3_endpoint_url=args.s3_endpoint_url)
graph_manager.data_store = S3BotoDataStore(params=ds_params_instance,
graph_manager=graph_manager,
ignore_lock=True)
graph_manager.env_params.seed = 0
task_parameters = TaskParameters()
task_parameters.checkpoint_restore_path = args.local_model_directory
evaluation_worker(graph_manager=graph_manager,
number_of_trials=args.number_of_trials,
task_parameters=task_parameters,
s3_writers=s3_writers,
is_continuous=args.is_continuous,
park_positions=park_positions)
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)
