Source code for ray.tune.function_runner

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import logging
import time
import inspect
import threading
import traceback
from six.moves import queue

from ray.tune import track
from ray.tune import TuneError
from ray.tune.trainable import Trainable
from ray.tune.result import TIME_THIS_ITER_S, RESULT_DUPLICATE

logger = logging.getLogger(__name__)

# Time between FunctionRunner checks when fetching
# new results after signaling the reporter to continue


[docs]class StatusReporter(object): """Object passed into your function that you can report status through. Example: >>> def trainable_function(config, reporter): >>> assert isinstance(reporter, StatusReporter) >>> reporter(timesteps_this_iter=1) """ def __init__(self, result_queue, continue_semaphore, logdir=None): self._queue = result_queue self._last_report_time = None self._continue_semaphore = continue_semaphore self._logdir = logdir
[docs] def __call__(self, **kwargs): """Report updated training status. Pass in `done=True` when the training job is completed. Args: kwargs: Latest training result status. Example: >>> reporter(mean_accuracy=1, training_iteration=4) >>> reporter(mean_accuracy=1, training_iteration=4, done=True) Raises: StopIteration: A StopIteration exception is raised if the trial has been signaled to stop. """ assert self._last_report_time is not None, ( "StatusReporter._start() must be called before the first " "report __call__ is made to ensure correct runtime metrics.") # time per iteration is recorded directly in the reporter to ensure # any delays in logging results aren't counted report_time = time.time() if TIME_THIS_ITER_S not in kwargs: kwargs[TIME_THIS_ITER_S] = report_time - self._last_report_time self._last_report_time = report_time # add results to a thread-safe queue self._queue.put(kwargs.copy(), block=True) # This blocks until notification from the FunctionRunner that the last # result has been returned to Tune and that the function is safe to # resume training. self._continue_semaphore.acquire()
def _start(self): self._last_report_time = time.time() @property def logdir(self): return self._logdir
class _RunnerThread(threading.Thread): """Supervisor thread that runs your script.""" def __init__(self, entrypoint, error_queue): threading.Thread.__init__(self) self._entrypoint = entrypoint self._error_queue = error_queue self.daemon = True def run(self): try: self._entrypoint() except StopIteration: logger.debug( ("Thread runner raised StopIteration. Interperting it as a " "signal to terminate the thread without error.")) except Exception as e: logger.exception("Runner Thread raised error.") try: # report the error but avoid indefinite blocking which would # prevent the exception from being propagated in the unlikely # case that something went terribly wrong err_tb_str = traceback.format_exc() self._error_queue.put( err_tb_str, block=True, timeout=ERROR_REPORT_TIMEOUT) except queue.Full: logger.critical( ("Runner Thread was unable to report error to main " "function runner thread. This means a previous error " "was not processed. This should never happen.")) raise e class FunctionRunner(Trainable): """Trainable that runs a user function reporting results. This mode of execution does not support checkpoint/restore.""" _name = "func" def _setup(self, config): # Semaphore for notifying the reporter to continue with the computation # and to generate the next result. self._continue_semaphore = threading.Semaphore(0) # Queue for passing results between threads self._results_queue = queue.Queue(1) # Queue for passing errors back from the thread runner. The error queue # has a max size of one to prevent stacking error and force error # reporting to block until finished. self._error_queue = queue.Queue(1) self._status_reporter = StatusReporter( self._results_queue, self._continue_semaphore, self.logdir) self._last_result = {} config = config.copy() def entrypoint(): return self._trainable_func(config, self._status_reporter) # the runner thread is not started until the first call to _train self._runner = _RunnerThread(entrypoint, self._error_queue) def _trainable_func(self): """Subclasses can override this to set the trainable func.""" raise NotImplementedError def _train(self): """Implements train() for a Function API. If the RunnerThread finishes without reporting "done", Tune will automatically provide a magic keyword __duplicate__ along with a result with "done=True". The TrialRunner will handle the result accordingly (see tune/ """ if self._runner.is_alive(): # if started and alive, inform the reporter to continue and # generate the next result self._continue_semaphore.release() else: # if not alive, try to start self._status_reporter._start() try: self._runner.start() except RuntimeError: # If this is reached, it means the thread was started and is # now done or has raised an exception. pass result = None while result is None and self._runner.is_alive(): # fetch the next produced result try: result = self._results_queue.get( block=True, timeout=RESULT_FETCH_TIMEOUT) except queue.Empty: pass # if no result were 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._results_queue.get(block=False) except queue.Empty: pass # check if error occured inside the thread runner if result is None: # only raise an error from the runner if all results are consumed self._report_thread_runner_error(block=True) # Under normal conditions, this code should never be reached since # this branch should only be visited if the runner thread raised # an exception. If no exception were raised, it means that the # runner thread never reported any results which should not be # possible when wrapping functions with `wrap_function`. raise TuneError( ("Wrapped function ran until completion without reporting " "results or raising an exception.")) else: if not self._error_queue.empty(): logger.warning( ("Runner error waiting to be raised in main thread. " "Logging all available results first.")) # This keyword appears if the train_func using the Function API # finishes without "done=True". This duplicates the last result, but # the TrialRunner will not log this result again. if "__duplicate__" in result: new_result = self._last_result.copy() new_result.update(result) result = new_result self._last_result = result return result def _stop(self): # If everything stayed in synch properly, this should never happen. if not self._results_queue.empty(): logger.warning( ("Some results were added after the trial stop condition. " "These results won't be logged.")) # Check for any errors that might have been missed. self._report_thread_runner_error() def _report_thread_runner_error(self, block=False): try: err_tb_str = self._error_queue.get( block=block, timeout=ERROR_FETCH_TIMEOUT) raise TuneError(("Trial raised an exception. Traceback:\n{}" .format(err_tb_str))) except queue.Empty: pass def wrap_function(train_func): use_track = False try: func_args = inspect.getargspec(train_func).args use_track = ("reporter" not in func_args and len(func_args) == 1) if use_track:"tune.track signature detected.") except Exception: "Function inspection failed - assuming reporter signature.") class WrappedFunc(FunctionRunner): def _trainable_func(self, config, reporter): output = train_func(config, reporter) # If train_func returns, we need to notify the main event loop # of the last result while avoiding double logging. This is done # with the keyword RESULT_DUPLICATE -- see tune/ reporter(**{RESULT_DUPLICATE: True}) return output class WrappedTrackFunc(FunctionRunner): def _trainable_func(self, config, reporter): track.init(_tune_reporter=reporter) output = train_func(config) reporter(**{RESULT_DUPLICATE: True}) track.shutdown() return output return WrappedTrackFunc if use_track else WrappedFunc