def publish_progress(self, specification, result):
if isinstance(result, tuple):
if self.diff_namer is not None:
name = self.diff_namer.get_name(specification)
else:
name = specification_hash(specification)
put_in_event_queue(self.eventQueue, ProgressEvent(name, result[0], result[1]))
def run(self, name: typing.AnyStr, specifications: typing.List[Specification], experiment: ExperimentBase,
continue_from_last_run=True, propagate_exceptions=False,
force_pickle=False, specification_runner: SimpleAbstractRunner = MultiprocessingRunner(),
use_dashboard=True, context_type="fork", multiprocessing_lib=None,save_every_k=None) -> typing.NoReturn:
"""
The method called to run an experiment
:param propagate_exceptions: If True, exceptions won't be caught and logged as failed experiments but will cause the program to crash (like normal), useful for debugging exeperiments
:param name: The name of this experiment batch
:param specifications: The list of specifications to run. Should be a list of dictionaries. Each dictionary is passed to the experiment run method
:param experiment: The experiment object to run
:param continue_from_last_run: If true, will not redo already completed experiments. Defaults to true
:param show_progress: Whether or not to show a progress bar for experiment completion
:param force_pickle: If true, don't attempt to json serialze results and default to pickling
:param specification_runner: An instance of ```AbstractRunner``` that will be used to run the specification
:param use_dashboard: If true, use the terminal monitoring dashboard. If false, just stream logs to stdout.
:return: No return
"""
if multiprocessing_lib is None:
import multiprocessing as mp
else:
mp = multiprocessing_lib
ctx = mp.get_context(context_type)
specification_runner.set_multiprocessing_context(ctx)
if specification_runner is None:
specification_runner = JoblibRunner(None)
dashboard_process = None
try:
manager = ctx.Manager()
eventQueue = manager.Queue(maxsize=2000)
put_in_event_queue(eventQueue, StartExperimentEvent(name))
# Set up root smallab logger
folder_loc = os.path.join("experiment_runs", name, "logs", str(datetime.datetime.now()))
file_loc = os.path.join(folder_loc, "main.log")
if not os.path.exists(folder_loc):
os.makedirs(folder_loc)
logger = logging.getLogger("smallab")
logger.setLevel(logging.DEBUG)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
# Can't do this with non-fork multiprocessing
if context_type == "fork":
fh = logging.FileHandler(file_loc)
fh.setFormatter(formatter)
logger.addHandler(fh)
if not use_dashboard:
sh = logging.StreamHandler()
sh.setFormatter(formatter)
logger.addHandler(sh)
else:
# fq = LogToEventQueue(eventQueue)
# sh = logging.StreamHandler(fq)
# sh.setFormatter(formatter)
# Add to root so all logging appears in dashboard not just smallab.
# logging.getLogger().addHandler(sh)
dashboard_process = ctx.Process(target=start_dashboard, args=(eventQueue,))
dashboard_process.start()
experiment.set_logging_folder(folder_loc)
self.force_pickle = force_pickle
if not os.path.exists(get_save_directory(name)):
os.makedirs(get_save_directory(name))
if continue_from_last_run:
need_to_run_specifications = self._find_uncompleted_specifications(name, specifications)
else:
need_to_run_specifications = specifications
for callback in self.callbacks:
callback.set_experiment_name(name)
for specification in need_to_run_specifications:
put_in_event_queue(eventQueue, RegisterEvent(specification_hash(specification), specification))
if isinstance(specification_runner, SimpleAbstractRunner):
specification_runner.run(need_to_run_specifications,
lambda specification: run_and_save(name, experiment, specification,
propagate_exceptions, self.callbacks,
self.force_pickle, eventQueue))
elif isinstance(specification_runner, ComplexAbstractRunner):
specification_runner.run(need_to_run_specifications, name, experiment, propagate_exceptions,
self.callbacks, self.force_pickle, eventQueue)
self._write_to_completed_json(name, specification_runner.get_completed(),
specification_runner.get_failed_specifications())
# Call batch complete functions
if specification_runner.get_exceptions() != []:
for callback in self.callbacks:
callback.on_batch_failure(specification_runner.get_exceptions(),
specification_runner.get_failed_specifications())
if specification_runner.get_completed() != []:
for callback in self.callbacks:
callback.on_batch_complete(specification_runner.get_completed())
finally:
if dashboard_process is not None:
dashboard_process.terminate()
def run_and_save(name, experiment, specification, propagate_exceptions,
callbacks, force_pickle, eventQueue, diff_namer):
experiment = deepcopy(experiment)
if diff_namer is None:
specification_id = specification_hash(specification)
else:
specification_id = diff_namer.get_name(specification)
logger_name = "smallab.{specification_id}".format(
specification_id=specification_id)
logger = logging.getLogger(logger_name)
logger.setLevel(logging.DEBUG)
file_handler = logging.FileHandler(
get_log_file(experiment, specification_id))
#formatter = logging.Formatter("%(asctime)s [%(levelname)-5.5s] %(message)s")
formatter = logging.Formatter(
'%(asctime)s - %(name)s - %(levelname)s - %(message)s')
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
fq = LogToEventQueue(eventQueue)
sh = logging.StreamHandler(fq)
sh.setFormatter(formatter)
logger.addHandler(sh)
#TODO need to attach eventqueue logger handler here and not at base logger
experiment.set_logger_name(logger_name)
experiment.set_experiment_local_storage(get_experiment_local_storage(name))
experiment.set_specification_local_storage(
get_specification_local_storage(name, specification, diff_namer))
put_in_event_queue(eventQueue, BeginEvent(specification_id))
def _interior_fn():
result = run_with_correct_handler(experiment,
name,
specification,
eventQueue,
diff_namer=diff_namer)
if isinstance(result, types.GeneratorType):
for cur_result in result:
if diff_namer is not None:
diff_namer.extend_name(cur_result["specification"])
save_run(name,
experiment,
cur_result["specification"],
cur_result["result"],
force_pickle,
diff_namer=diff_namer,
extended_keys=True)
else:
save_run(name,
experiment,
specification,
result,
force_pickle,
diff_namer=diff_namer)
for callback in callbacks:
callback.on_specification_complete(specification, result)
return None
if not propagate_exceptions:
try:
_interior_fn()
put_in_event_queue(eventQueue, CompleteEvent(specification_id))
except Exception as e:
logger.error("Specification Failure", exc_info=True)
put_in_event_queue(eventQueue, FailedEvent(specification_id))
for callback in callbacks:
callback.on_specification_failure(e, specification)
return e
else:
_interior_fn()
put_in_event_queue(eventQueue, CompleteEvent(specification_id))
return None
def publish_progress(self, specification, result):
if isinstance(result, tuple):
put_in_event_queue(
self.eventQueue,
ProgressEvent(specification_hash(specification), result[0],
result[1]))