def __init__(self,
training_func: Callable,
world_rank: int,
local_rank: int,
dataset_shard: Optional[RayDataset] = None,
checkpoint: Optional[Dict] = None,
detailed_autofilled_metrics: bool = False):
self.dataset_shard = dataset_shard
# The Thread object that is running the training function.
self.training_thread = PropagatingThread(target=training_func,
daemon=True)
self.world_rank = world_rank
self.local_rank = local_rank
self.loaded_checkpoint = checkpoint
# This lock is used to control the execution of the training thread.
self.continue_lock = threading.Semaphore(0)
# Queue for sending results across threads.
self.result_queue = queue.Queue(1)
# Autofilled metrics attributes.
self.detailed_autofilled_metrics = detailed_autofilled_metrics
self.last_report_time = time.time()
self.iteration = 0
self.time_total = 0.0
self.local_ip = self.get_current_ip()
self.ignore_report = False
self.training_started = False
class Session:
"""Holds information for training on each worker."""
def __init__(self,
training_func: Callable,
world_rank: int,
local_rank: int,
dataset_shard: Optional[RayDataset] = None,
checkpoint: Optional[Dict] = None,
detailed_autofilled_metrics: bool = False):
self.dataset_shard = dataset_shard
# The Thread object that is running the training function.
self.training_thread = PropagatingThread(target=training_func,
daemon=True)
self.world_rank = world_rank
self.local_rank = local_rank
self.loaded_checkpoint = checkpoint
# This lock is used to control the execution of the training thread.
self.continue_lock = threading.Semaphore(0)
# Queue for sending results across threads.
self.result_queue = queue.Queue(1)
# Autofilled metrics attributes.
self.detailed_autofilled_metrics = detailed_autofilled_metrics
self.last_report_time = time.time()
self.iteration = 0
self.time_total = 0.0
self.local_ip = self.get_current_ip()
self.ignore_report = False
self.training_started = False
def get_current_ip(self):
self.local_ip = ray.util.get_node_ip_address()
return self.local_ip
def start(self):
"""Starts the training thread."""
self.training_started = True
self.training_thread.start()
def pause_reporting(self):
"""Ignore all future ``sgd.report()`` calls."""
self.ignore_report = True
def finish(self):
"""Finishes the training thread.
Either returns the output from training or raises any Exception from
training.
"""
# Wait for training to finish.
# This will raise any errors that occur during training, including
# SystemError
func_output = self.training_thread.join()
# If training finished successfully, then return results.
return func_output
def get_next(self) -> Optional[TrainingResult]:
"""Gets next result from the queue."""
if not self.training_started:
raise RuntimeError("Please call start before calling get_next.")
result = None
# While training is still ongoing, attempt to get the result.
while result is None and self.training_thread.is_alive():
try:
result = self.result_queue.get(block=True,
timeout=RESULT_FETCH_TIMEOUT)
except queue.Empty:
pass
# If no result was found, then the runner must no longer be alive.
if result is None:
# Try one last time to fetch results in case results were
# reported in between the time of the last check and the
# termination of the thread runner.
try:
result = self.result_queue.get(block=False,
timeout=RESULT_FETCH_TIMEOUT)
except queue.Empty:
pass
# Release the lock to trigger training to continue.
self.continue_lock.release()
# Return None if there are no more results to fetch.
return result
def _auto_fill_metrics(self, result: dict) -> dict:
"""Add autofilled metrics and update attributes."""
current_time = time.time()
current_datetime = datetime.now()
if TIME_THIS_ITER_S in result:
time_this_iter = result[TIME_THIS_ITER_S]
else:
time_this_iter = current_time - self.last_report_time
self.iteration += 1
self.time_total += time_this_iter
self.last_report_time = current_time
auto_filled_metrics = {
DATE: current_datetime.strftime("%Y-%m-%d_%H-%M-%S"),
TIMESTAMP: int(time.mktime(current_datetime.timetuple())),
TIME_THIS_ITER_S: time_this_iter,
TIME_TOTAL_S: self.time_total,
PID: os.getpid(),
HOSTNAME: platform.node(),
NODE_IP: self.local_ip,
TRAINING_ITERATION: self.iteration
}
if not self.detailed_autofilled_metrics:
auto_filled_metrics = {
k: v
for k, v in auto_filled_metrics.items()
if k not in DETAILED_AUTOFILLED_KEYS
}
result = result.copy()
result.update(auto_filled_metrics)
return result
def report(self, **kwargs):
"""Adds kwargs to the queue to be consumed by main thread."""
if self.ignore_report:
return
kwargs = self._auto_fill_metrics(kwargs)
result = TrainingResult(TrainingResultType.REPORT, kwargs.copy())
# Add result to a thread-safe queue.
self.result_queue.put(result, block=True)
# Acquire lock to stop the training thread until main thread
# triggers resume.
self.continue_lock.acquire()
def checkpoint(self, **kwargs):
"""Adds kwargs to the queue to be consumed by main thread.
Also stores the checkpoint in ``self.loaded_checkpoint``.
"""
# Update session checkpoint to latest checkpoint.
self.loaded_checkpoint = kwargs
# Only store checkpoints on worker with rank 0.
if self.world_rank != 0:
kwargs = {}
result = TrainingResult(TrainingResultType.CHECKPOINT, kwargs)
# Add result to a thread-safe queue.
self.result_queue.put(result, block=True)
# Acquire lock to stop the training thread until
# checkpoint has been processed.
self.continue_lock.acquire()