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_nodes:
cluster = Cluster()
for _ in range(args.ray_num_nodes):
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,
resume=args.resume)
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_nodes:
cluster = Cluster()
for _ in range(args.ray_num_nodes):
cluster.add_node(
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,
resume=args.resume)
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,
}
}
# The default maximum number of bytes to allocate to the object store unless
# overridden by the user.
DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES = 20 * 10**9
# The smallest cap on the memory used by the object store that we allow.
OBJECT_STORE_MINIMUM_MEMORY_BYTES = 10**7
# The default maximum number of bytes that the non-primary Redis shards are
# allowed to use unless overridden by the user.
DEFAULT_REDIS_MAX_MEMORY_BYTES = 10**10
# The smallest cap on the memory used by Redis that we allow.
REDIS_MINIMUM_MEMORY_BYTES = 10**7
def on_episode_end(info):
episode = info["episode"]
env = info['env'].get_unwrapped()[0]
if hasattr(env, 'capital'):
capital_return = (env.capital -
env.initial_funds) / env.initial_funds
episode.custom_metrics['capital_return'] = capital_return
key = list(experiments.keys())[0]
experiments[key]["config"]["callbacks"] = {
"on_episode_end": tune.function(on_episode_end)
}
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_nodes:
cluster = Cluster()
for _ in range(args.ray_num_nodes):
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:
print('init')
ray.init(redis_address=args.redis_address,
object_store_memory=int(0.5 * 10**9),
redis_max_memory=int(0.5 * 10**9),
num_cpus=args.ray_num_cpus,
num_gpus=args.ray_num_gpus)
run_experiments(experiments,
scheduler=_make_scheduler(args),
queue_trials=args.queue_trials,
resume=args.resume)