def validate_config(config):
"""Validates the Trainer's config dict.
Args:
config (TrainerConfigDict): The Trainer's config to check.
Raises:
ValueError: In case something is wrong with the config.
"""
# Auto-train_batch_size: Calculate from rollout len and envs-per-worker.
if config["train_batch_size"] == -1:
config["train_batch_size"] = (
config["rollout_fragment_length"] * config["num_envs_per_worker"])
# Users should not define `train_batch_size` directly (always -1).
else:
raise ValueError(
"Set rollout_fragment_length instead of train_batch_size "
"for DDPPO.")
# Only supported for PyTorch so far.
if config["framework"] != "torch":
raise ValueError(
"Distributed data parallel is only supported for PyTorch")
# `num_gpus` must be 0/None, since all optimization happens on Workers.
if config["num_gpus"]:
raise ValueError(
"When using distributed data parallel, you should set "
"num_gpus=0 since all optimization "
"is happening on workers. Enable GPUs for workers by setting "
"num_gpus_per_worker=1.")
# `batch_mode` must be "truncate_episodes".
if config["batch_mode"] != "truncate_episodes":
raise ValueError(
"Distributed data parallel requires truncate_episodes "
"batch mode.")
# Call (base) PPO's config validation function.
ppo.validate_config(config)
def validate_config(config):
if config["train_batch_size"] == -1:
# Auto set.
config["train_batch_size"] = (
config["rollout_fragment_length"] * config["num_envs_per_worker"])
else:
raise ValueError(
"Set rollout_fragment_length instead of train_batch_size "
"for DDPPO.")
if not config["use_pytorch"]:
raise ValueError(
"Distributed data parallel is only supported for PyTorch")
if config["num_gpus"]:
raise ValueError(
"When using distributed data parallel, you should set "
"num_gpus=0 since all optimization "
"is happening on workers. Enable GPUs for workers by setting "
"num_gpus_per_worker=1.")
if config["batch_mode"] != "truncate_episodes":
raise ValueError(
"Distributed data parallel requires truncate_episodes "
"batch mode.")
ppo.validate_config(config)
def validate_config_basic(config):
assert "joint_dataset_sample_batch_size" in config
assert "use_joint_dataset" in config
assert "novelty_mode" in config
assert config["novelty_mode"] in ["mean", "min", "max"]
validate_config(config)