def _multiprocess_upload_s3(self, s3_bucket, s3_prefix, aws_region,
local_file):
if os.path.exists(local_file):
s3_client = SageS3Client(bucket=s3_bucket,
s3_prefix=s3_prefix,
aws_region=aws_region,
s3_endpoint_url=self.s3_endpoint_url)
s3_keys = "{}/{}/{}-{}".format(
s3_prefix,
os.path.dirname(re.sub(self.agent_pattern, '', local_file)),
self.upload_num, os.path.basename(local_file))
s3_client.upload_file(s3_keys, local_file)
def test_load_model_metadata(s3_bucket, s3_prefix, aws_region, model_metadata_s3_key):
"""This function checks the functionality of load_model_metadata function
in markov/utils.py
The function checks if model_metadata.json file is downloaded into the required directory.
If the function fails, it will generate an exception which will call log_and_exit internally.
Hence the test will fail.
Args:
s3_bucket (String): S3_BUCKET
s3_prefix (String): S3_PREFIX
aws_region (String): AWS_REGION
model_metadata_s3_key (String): MODEL_METADATA_S3_KEY
"""
s3_client = SageS3Client(bucket=s3_bucket, s3_prefix=s3_prefix, aws_region=aws_region)
model_metadata_local_path = 'test_model_metadata.json'
utils.load_model_metadata(s3_client, model_metadata_s3_key, model_metadata_local_path)
assert os.path.isfile(model_metadata_local_path)
# Remove file downloaded
if os.path.isfile(model_metadata_local_path):
os.remove(model_metadata_local_path)
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"]
)
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=os.environ.get("MODEL_S3_BUCKET",
"gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=os.environ.get("MODEL_S3_PREFIX", "sagemaker"))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("APP_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=os.environ.get("NUMBER_OF_TRIALS", 15))
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('--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)
register(id=defaults.ENV_ID,
entry_point=defaults.ENTRY_POINT,
max_episode_steps=defaults.MAX_STEPS,
reward_threshold=defaults.THRESHOLD)
# 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 the model
# s3_client.download_model(args.local_model_directory)
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:
logger.info("Preset file could not be downloaded. Exiting!")
sys.exit(1)
ds_params_instance = S3BotoDataStoreParameters(
bucket_name=args.s3_bucket,
checkpoint_dir=args.local_model_directory,
aws_region=args.aws_region,
s3_folder=args.s3_prefix)
data_store = S3BotoDataStore(ds_params_instance)
data_store.get_a_particular_model(checkpoint_number=1)
graph_manager.data_store = data_store
graph_manager.env_params.seed = 0
evaluation_worker(graph_manager=graph_manager,
number_of_trials=args.number_of_trials,
local_model_directory=args.local_model_directory)
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 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():
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=os.environ.get("MODEL_S3_BUCKET", "gsaur-test"))
parser.add_argument('--s3_prefix',
help='(string) S3 prefix',
type=str,
default=os.environ.get("MODEL_S3_PREFIX", "sagemaker"))
parser.add_argument('--aws_region',
help='(string) AWS region',
type=str,
default=os.environ.get("APP_REGION", "us-east-1"))
parser.add_argument('--number_of_trials',
help='(integer) Number of trials',
type=int,
default=os.environ.get("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()
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, os.path.normpath("%s/model/model_metadata.json" % args.s3_prefix), model_metadata_local_path)
# Download the model
s3_client.download_model(args.local_model_directory)
# 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.")
from markov.sagemaker_graph_manager import get_graph_manager
graph_manager, _ = get_graph_manager(**sm_hyperparams_dict)
ds_params_instance = S3BotoDataStoreParameters(bucket_name=args.s3_bucket,
checkpoint_dir=args.local_model_directory,
aws_region=args.aws_region,
s3_folder=args.s3_prefix)
data_store = S3BotoDataStore(ds_params_instance)
graph_manager.data_store = data_store
evaluation_worker(
graph_manager=graph_manager,
number_of_trials=args.number_of_trials,
local_model_directory=args.local_model_directory
)
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():
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 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 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 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)
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)