def run_cli(options: RunOptions) -> None:
try:
print("""
▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓
""")
except Exception:
print("\n\n\tUnity Technologies\n")
print(get_version_string())
if options.debug:
log_level = logging_util.DEBUG
else:
log_level = logging_util.INFO
# disable noisy warnings from tensorflow
tf_utils.set_warnings_enabled(False)
logging_util.set_log_level(log_level)
logger.debug("Configuration for this run:")
logger.debug(json.dumps(options._asdict(), indent=4))
# Options deprecation warnings
if options.load_model:
logger.warning(
"The --load option has been deprecated. Please use the --resume option instead."
)
if options.train_model:
logger.warning(
"The --train option has been deprecated. Train mode is now the default. Use "
"--inference to run in inference mode.")
run_seed = options.seed
if options.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
# Add some timer metadata
add_timer_metadata("mlagents_version", mlagents.trainers.__version__)
add_timer_metadata("mlagents_envs_version", mlagents_envs.__version__)
add_timer_metadata("communication_protocol_version",
UnityEnvironment.API_VERSION)
add_timer_metadata("tensorflow_version", tf_utils.tf.__version__)
if options.seed == -1:
run_seed = np.random.randint(0, 10000)
run_training(run_seed, options)
def run_cli(options: RunOptions) -> None:
try:
print(
"""
▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓
"""
)
except Exception:
print("\n\n\tUnity Technologies\n")
print(get_version_string())
trainer_logger = logging.getLogger("mlagents.trainers")
env_logger = logging.getLogger("mlagents_envs")
if options.debug:
trainer_logger.setLevel("DEBUG")
env_logger.setLevel("DEBUG")
else:
trainer_logger.setLevel("INFO")
env_logger.setLevel("INFO")
# disable noisy warnings from tensorflow.
tf_utils.set_warnings_enabled(False)
trainer_logger.debug("Configuration for this run:")
trainer_logger.debug(json.dumps(options._asdict(), indent=4))
run_seed = options.seed
if options.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
if options.seed == -1:
run_seed = np.random.randint(0, 10000)
run_training(run_seed, options)
def main():
try:
print(
"""
▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓
"""
)
except Exception:
print("\n\n\tUnity Technologies\n")
print(get_version_string())
options = parse_command_line()
trainer_logger = logging.getLogger("mlagents.trainers")
env_logger = logging.getLogger("mlagents_envs")
trainer_logger.info(options)
if options.debug:
trainer_logger.setLevel("DEBUG")
env_logger.setLevel("DEBUG")
else:
# disable noisy warnings from tensorflow.
tf_utils.set_warnings_enabled(False)
run_seed = options.seed
if options.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
if options.seed == -1:
run_seed = np.random.randint(0, 10000)
run_training(run_seed, options)
def run_cli(options: RunOptions) -> None:
# print(logo)
trainer_logger = logging.getLogger("mlagents.trainers")
env_logger = logging.getLogger("mlagents_envs")
if options.debug:
trainer_logger.setLevel("DEBUG")
env_logger.setLevel("DEBUG")
else:
# disable noisy warnings from tensorflow.
tf_utils.set_warnings_enabled(False)
trainer_logger.debug("Configuration for this run:")
trainer_logger.debug(json.dumps(options._asdict(), indent=4))
run_seed = options.seed
if options.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
if options.seed == -1:
run_seed = randint(0, 2)
run_training(run_seed, options)
def main():
try:
print(
"""
▄▄▄▓▓▓▓
╓▓▓▓▓▓▓█▓▓▓▓▓
,▄▄▄m▀▀▀' ,▓▓▓▀▓▓▄ ▓▓▓ ▓▓▌
▄▓▓▓▀' ▄▓▓▀ ▓▓▓ ▄▄ ▄▄ ,▄▄ ▄▄▄▄ ,▄▄ ▄▓▓▌▄ ▄▄▄ ,▄▄
▄▓▓▓▀ ▄▓▓▀ ▐▓▓▌ ▓▓▌ ▐▓▓ ▐▓▓▓▀▀▀▓▓▌ ▓▓▓ ▀▓▓▌▀ ^▓▓▌ ╒▓▓▌
▄▓▓▓▓▓▄▄▄▄▄▄▄▄▓▓▓ ▓▀ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▄ ▓▓▌
▀▓▓▓▓▀▀▀▀▀▀▀▀▀▀▓▓▄ ▓▓ ▓▓▌ ▐▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▌ ▐▓▓▐▓▓
^█▓▓▓ ▀▓▓▄ ▐▓▓▌ ▓▓▓▓▄▓▓▓▓ ▐▓▓ ▓▓▓ ▓▓▓ ▓▓▓▄ ▓▓▓▓`
'▀▓▓▓▄ ^▓▓▓ ▓▓▓ └▀▀▀▀ ▀▀ ^▀▀ `▀▀ `▀▀ '▀▀ ▐▓▓▌
▀▀▀▀▓▄▄▄ ▓▓▓▓▓▓, ▓▓▓▓▀
`▀█▓▓▓▓▓▓▓▓▓▌
¬`▀▀▀█▓
"""
)
except Exception:
print("\n\n\tUnity Technologies\n")
print(get_version_string())
options = parse_command_line()
trainer_logger = logging.getLogger("mlagents.trainers")
env_logger = logging.getLogger("mlagents.envs")
trainer_logger.info(options)
if options.debug:
trainer_logger.setLevel("DEBUG")
env_logger.setLevel("DEBUG")
else:
# disable noisy warnings from tensorflow.
tf_utils.set_warnings_enabled(False)
if options.env_path is None and options.num_runs > 1:
raise TrainerError(
"It is not possible to launch more than one concurrent training session "
"when training from the editor."
)
jobs = []
run_seed = options.seed
if options.cpu:
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
if options.num_runs == 1:
if options.seed == -1:
run_seed = np.random.randint(0, 10000)
run_training(0, run_seed, options, Queue())
else:
for i in range(options.num_runs):
if options.seed == -1:
run_seed = np.random.randint(0, 10000)
process_queue = Queue()
p = Process(target=run_training, args=(i, run_seed, options, process_queue))
jobs.append(p)
p.start()
# Wait for signal that environment has successfully launched
while process_queue.get() is not True:
continue
# Wait for jobs to complete. Otherwise we'll have an extra
# unhandled KeyboardInterrupt if we end early.
try:
for job in jobs:
job.join()
except KeyboardInterrupt:
pass
