def run(args, parser):
if args.config_file:
with open(args.config_file) as f:
experiments = yaml.load(f)
else:
# Note: keep this in sync with tune/config_parser.py
experiments = {
args.experiment_name: { # i.e. log to ~/ray_results/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
"local_dir": args.local_dir,
"trial_resources": (
args.trial_resources and
resources_to_json(args.trial_resources)),
"stop": args.stop,
"config": dict(args.config, env=args.env),
"restore": args.restore,
"repeat": args.repeat,
"upload_dir": args.upload_dir,
}
}
for exp in experiments.values():
if not exp.get("run"):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
ray.init(redis_address=args.redis_address,
num_cpus=args.ray_num_cpus,
num_gpus=args.ray_num_gpus)
run_experiments(experiments,
scheduler=_make_scheduler(args),
queue_trials=args.queue_trials)
def run(args, parser):
if args.config_file:
with open(args.config_file) as f:
experiments = yaml.load(f)
else:
# Note: keep this in sync with tune/config_parser.py
experiments = {
args.experiment_name: { # i.e. log to ~/ray_results/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
"local_dir": args.local_dir,
"resources_per_trial": (
args.resources_per_trial and
resources_to_json(args.resources_per_trial)),
"stop": args.stop,
"config": dict(args.config, env=args.env),
"restore": args.restore,
"num_samples": args.num_samples,
"upload_dir": args.upload_dir,
}
}
for exp in experiments.values():
if not exp.get("run"):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
if args.ray_num_local_schedulers:
cluster = Cluster()
for _ in range(args.ray_num_local_schedulers):
cluster.add_node(resources={
"num_cpus": args.ray_num_cpus or 1,
"num_gpus": args.ray_num_gpus or 0,
},
object_store_memory=args.ray_object_store_memory,
redis_max_memory=args.ray_redis_max_memory)
ray.init(redis_address=cluster.redis_address)
else:
ray.init(redis_address=args.redis_address,
object_store_memory=args.ray_object_store_memory,
redis_max_memory=args.ray_redis_max_memory,
num_cpus=args.ray_num_cpus,
num_gpus=args.ray_num_gpus)
run_experiments(experiments,
scheduler=_make_scheduler(args),
queue_trials=args.queue_trials)
help="If specified, use config options from this file. Note that this "
"overrides any trial-specific options set via flags above.")
if __name__ == "__main__":
args = parser.parse_args(sys.argv[1:])
if args.config_file:
with open(args.config_file) as f:
experiments = yaml.load(f)
else:
# Note: keep this in sync with tune/config_parser.py
experiments = {
args.experiment_name: { # i.e. log to /tmp/ray/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
"local_dir": args.local_dir,
"resources": resources_to_json(args.resources),
"stop": args.stop,
"config": dict(args.config, env=args.env),
"restore": args.restore,
"repeat": args.repeat,
"upload_dir": args.upload_dir,
}
}
for exp in experiments.values():
if not exp.get("run"):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
run_experiments(experiments,
"--num-cpus", default=2, type=int,
help="Number of CPUs to allocate to Ray.")
parser.add_argument(
"--num-gpus", default=1, type=int,
help="Number of GPUs to allocate to Ray.")
parser.add_argument(
"--experiment-name", default="default", type=str,
help="Name of the subdirectory under `local_dir` to put results in.")
parser.add_argument(
"--env", default=None, type=str, help="The gym environment to use.")
ModelCatalog.register_custom_preprocessor("sc_prep", StarCraftPreprocessor)
register_env("sc2", lambda config: StarCraft(config))
register_trainable("SC_A3C", A3CAgent)
if __name__ == "__main__":
args = parser.parse_args(sys.argv[1:])
experiments = {
'experiment_name': {
"run" : 'SC_A3C',
"env" : 'sc2',
"trial_resources" : resources_to_json(args.trial_resources),
"config": dict(args.config, env=args.env),
}
}
ray.init(redis_address=args.redis_address, num_gpus=1, num_cpus=args.num_cpus)
run_experiments(experiments)
"overrides any trial-specific options set via flags above.")
if __name__ == "__main__":
args = parser.parse_args(sys.argv[1:])
if args.config_file:
with open(args.config_file) as f:
experiments = yaml.load(f)
else:
# Note: keep this in sync with tune/config_parser.py
experiments = {
args.experiment_name: { # i.e. log to ~/ray_results/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
"local_dir": args.local_dir,
"resources": resources_to_json(args.resources),
"stop": args.stop,
"config": dict(args.config, env=args.env),
"restore": args.restore,
"repeat": args.repeat,
"upload_dir": args.upload_dir,
}
}
for exp in experiments.values():
if not exp.get("run"):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
ray.init(
if __name__ == "__main__":
args = parser.parse_args(sys.argv[1:])
if args.config_file:
with open(args.config_file) as f:
experiments = yaml.load(f)
else:
# Note: keep this in sync with tune/config_parser.py
experiments = {
args.experiment_name: { # i.e. log to ~/ray_results/default
"run": args.run,
"checkpoint_freq": args.checkpoint_freq,
"local_dir": args.local_dir,
"trial_resources": (
args.trial_resources and
resources_to_json(args.trial_resources)),
"stop": args.stop,
"config": dict(args.config, env=args.env),
"restore": args.restore,
"repeat": args.repeat,
"upload_dir": args.upload_dir,
}
}
for exp in experiments.values():
if not exp.get("run"):
parser.error("the following arguments are required: --run")
if not exp.get("env") and not exp.get("config", {}).get("env"):
parser.error("the following arguments are required: --env")
ray.init(redis_address=args.redis_address,