def test_leaky_policy(self):
"""Tests, whether our diagnostics tools can detect leaks in a policy."""
config = dqn.DEFAULT_CONFIG.copy()
# Make sure we have an env to test on the local worker.
# Otherwise, `check_memory_leaks` will complain.
config["create_env_on_driver"] = True
config["env"] = "CartPole-v0"
config["multiagent"]["policies"] = {
"default_policy": PolicySpec(policy_class=MemoryLeakingPolicy),
}
trainer = dqn.DQN(config=config)
results = check_memory_leaks(trainer, to_check={"policy"}, repeats=300)
assert results["policy"]
trainer.stop()
def test_leaky_env(self):
"""Tests, whether our diagnostics tools can detect leaks in an env."""
config = ppo.DEFAULT_CONFIG.copy()
# Make sure we have an env to test on the local worker.
# Otherwise, `check_memory_leaks` will complain.
config["create_env_on_driver"] = True
config["env"] = MemoryLeakingEnv
config["env_config"] = {
"static_samples": True,
}
trainer = ppo.PPO(config=config)
results = check_memory_leaks(trainer, to_check={"env"}, repeats=150)
assert results["env"]
trainer.stop()
# Move "env" specifier into config.
experiment["config"]["env"] = experiment["env"]
experiment.pop("env", None)
# Print out the actual config.
print("== Test config ==")
print(yaml.dump(experiment))
# Construct the trainer instance based on the given config.
leaking = True
try:
ray.init(num_cpus=5, local_mode=args.local_mode)
trainer = get_trainer_class(experiment["run"])(
experiment["config"])
results = check_memory_leaks(
trainer,
to_check=set(args.to_check),
)
if not results:
leaking = False
finally:
ray.shutdown()
_register_all()
if not leaking:
print("Memory leak test PASSED")
else:
print("Memory leak test FAILED. Exiting with Error.")
sys.exit(1)
