def reset_trial(
self,
trial: Trial,
new_config: Dict,
new_experiment_tag: str,
logger_creator: Optional[Callable[[Dict], "ray.tune.Logger"]] = None,
) -> bool:
"""Tries to invoke `Trainable.reset()` to reset trial.
Args:
trial: Trial to be reset.
new_config: New configuration for Trial trainable.
new_experiment_tag: New experiment name for trial.
logger_creator: Function that instantiates a logger on the
actor process.
Returns:
True if `reset_config` is successful else False.
"""
trial.set_experiment_tag(new_experiment_tag)
trial.set_config(new_config)
trainable = trial.runner
# Pass magic variables
extra_config = copy.deepcopy(new_config)
extra_config[TRIAL_INFO] = _TrialInfo(trial)
stdout_file, stderr_file = trial.log_to_file
extra_config[STDOUT_FILE] = stdout_file
extra_config[STDERR_FILE] = stderr_file
with self._change_working_directory(trial):
with warn_if_slow("reset"):
try:
reset_val = ray.get(
trainable.reset.remote(extra_config, logger_creator),
timeout=DEFAULT_GET_TIMEOUT,
)
except GetTimeoutError:
logger.exception("Trial %s: reset timed out.", trial)
return False
return reset_val
def _setup_remote_runner(self, trial):
trial.init_logdir()
# We checkpoint metadata here to try mitigating logdir duplication
self._trials_to_cache.add(trial)
logger_creator = partial(noop_logger_creator, logdir=trial.logdir)
if len(self._cached_actor_pg) > 0:
assert self._reuse_actors
existing_runner, pg = self._cached_actor_pg.popleft()
logger.debug(f"Trial {trial}: Reusing cached runner "
f"{existing_runner}")
trial.set_runner(existing_runner)
if pg:
self._pg_manager.assign_cached_pg(pg, trial)
if not self.reset_trial(trial, trial.config, trial.experiment_tag,
logger_creator):
raise _AbortTrialExecution(
"Trainable runner reuse requires reset_config() to be "
"implemented and return True.")
return existing_runner
trainable_cls = trial.get_trainable_cls()
if not trainable_cls:
raise _AbortTrialExecution(
f"Invalid trainable: {trial.trainable_name}. If you passed "
f"a string, make sure the trainable was registered before.")
_actor_cls = _class_cache.get(trainable_cls)
if not self._pg_manager.has_ready(trial):
return None
full_actor_class = self._pg_manager.get_full_actor_cls(
trial, _actor_cls)
# Clear the Trial's location (to be updated later on result)
# since we don't know where the remote runner is placed.
trial.set_location(_Location())
logger.debug("Trial %s: Setting up new remote runner.", trial)
# Logging for trials is handled centrally by TrialRunner, so
# configure the remote runner to use a noop-logger.
trial_config = copy.deepcopy(trial.config)
trial_config[TRIAL_INFO] = _TrialInfo(trial)
stdout_file, stderr_file = trial.log_to_file
trial_config[STDOUT_FILE] = stdout_file
trial_config[STDERR_FILE] = stderr_file
kwargs = {
"config": trial_config,
"logger_creator": logger_creator,
}
if trial.uses_cloud_checkpointing:
# We keep these kwargs separate for backwards compatibility
# with trainables that don't provide these keyword arguments
kwargs["remote_checkpoint_dir"] = trial.remote_checkpoint_dir
kwargs["sync_function_tpl"] = trial.sync_function_tpl
# Throw a meaningful error if trainable does not use the
# new API
sig = inspect.signature(trial.get_trainable_cls())
try:
sig.bind_partial(**kwargs)
except Exception as e:
raise RuntimeError(
"Your trainable class does not accept a "
"`remote_checkpoint_dir` or `sync_function_tpl` argument "
"in its constructor, but you've passed a "
"`upload_dir` to your SyncConfig. Without accepting "
"these parameters and passing them to the base trainable "
"constructor in the init call, cloud checkpointing is "
"effectively disabled. To resolve this issue, add the "
"parameters to your trainable class constructor or "
"disable cloud checkpointing by setting `upload_dir=None`."
) from e
with self._change_working_directory(trial):
return full_actor_class.remote(**kwargs)
def testWandbDecoratorConfig(self):
config = {"par1": 4, "par2": 9.12345678}
trial = Trial(
config,
0,
"trial_0",
"trainable",
PlacementGroupFactory([{"CPU": 1}]),
"/tmp",
)
trial_info = _TrialInfo(trial)
@wandb_mixin
def train_fn(config):
return 1
train_fn.__mixins__ = (_MockWandbTrainableMixin,)
config[TRIAL_INFO] = trial_info
if WANDB_ENV_VAR in os.environ:
del os.environ[WANDB_ENV_VAR]
# Needs at least a project
with self.assertRaises(ValueError):
wrapped = wrap_function(train_fn)(config)
# No API key
config["wandb"] = {"project": "test_project"}
with self.assertRaises(ValueError):
wrapped = wrap_function(train_fn)(config)
# API Key in config
config["wandb"] = {"project": "test_project", "api_key": "1234"}
wrapped = wrap_function(train_fn)(config)
self.assertEqual(os.environ[WANDB_ENV_VAR], "1234")
del os.environ[WANDB_ENV_VAR]
# API Key file
with tempfile.NamedTemporaryFile("wt") as fp:
fp.write("5678")
fp.flush()
config["wandb"] = {"project": "test_project", "api_key_file": fp.name}
wrapped = wrap_function(train_fn)(config)
self.assertEqual(os.environ[WANDB_ENV_VAR], "5678")
del os.environ[WANDB_ENV_VAR]
# API Key in env
os.environ[WANDB_ENV_VAR] = "9012"
config["wandb"] = {"project": "test_project"}
wrapped = wrap_function(train_fn)(config)
# From now on, the API key is in the env variable.
# Default configuration
config["wandb"] = {"project": "test_project"}
config[TRIAL_INFO] = trial_info
wrapped = wrap_function(train_fn)(config)
self.assertEqual(wrapped.wandb.kwargs["project"], "test_project")
self.assertEqual(wrapped.wandb.kwargs["id"], trial.trial_id)
self.assertEqual(wrapped.wandb.kwargs["name"], trial.trial_name)