def setExperiment(self, experiment):
parameters = Parameter.parametersToDict(experiment.parameters)
parameters_linearly_spaced_vals = []
# parameter start from the lower bound to higher bound
for idx, parameter in enumerate(experiment.parameters):
ncpp = self.num_configs_per_param[idx]
# step_size = (parameter.maximum - parameter.minimum) / (ncpp - 1)
if ncpp == 1:
step_size = 0
else:
step_size = (parameter.maximum - parameter.minimum) / (ncpp -
1)
parameter_linearly_spaced_vals = [
parameter.minimum + (i * step_size) for i in range(ncpp)
]
parameter_linearly_spaced_vals = reversed(
parameter_linearly_spaced_vals)
parameters_linearly_spaced_vals.append(
parameter_linearly_spaced_vals)
# get cartesian product of configs
parameter_configs_product = itertools.product(
*parameters_linearly_spaced_vals)
# create collections of ParameterConfigs from config values
for parameter_config_collection in parameter_configs_product:
parameter_configs = []
for parameter, value in zip(experiment.parameters,
parameter_config_collection):
parameter_configs.append(
ParameterConfig(parameter=parameter, value=value))
self.grid_parameter_configs.append(parameter_configs)
def _configDictToParameterConfigs(self, config_dict):
"""
Given a dictionary of parameters configurations, keyed by parameter name, value is value,
return an array of ParameterConfigs
"""
parameter_configs = []
for name, value in config_dict.items():
param = self.parameters_by_name.get(name, None)
if param == None:
raise Exception('Parameter with name "{}" not found in optimizer'.format(name))
# TODO: This should return ParameterConfig values with the proper type (e.g. int, float)
parameter_configs.append(ParameterConfig(parameter=param, value=value))
return parameter_configs
def setExperiment(self, experiment):
parameters = Parameter.parametersToDict(experiment.parameters)
ncpp = self.num_configs_per_param
parameters_linearly_spaced_vals = []
for parameter in experiment.parameters:
step_size = (parameter.maximum - parameter.minimum) / (ncpp - 1)
parameter_linearly_spaced_vals = [
parameter.minimum + (i * step_size) for i in range(ncpp)
]
parameters_linearly_spaced_vals.append(
parameter_linearly_spaced_vals)
# get cartesian product of configs
parameter_configs_product = itertools.product(
*parameters_linearly_spaced_vals)
# create collections of ParameterConfigs from config values
for parameter_config_collection in parameter_configs_product:
parameter_configs = []
for parameter, value in zip(experiment.parameters,
parameter_config_collection):
parameter_configs.append(
ParameterConfig(parameter=parameter, value=value))
self.grid_parameter_configs.append(parameter_configs)
def run(self, debug=False):
"""
Run trials provided by the optimizer while saving results.
"""
if debug:
parsl.set_stream_logger()
self._dfk = parsl.load(self.parsl_config)
logger.info(f'Starting ParslRunner with config\n{self}')
flag = True
initialize_flag = True
result = None
for idx, parameter_configs in enumerate(self.optimizer):
try:
logger.info(
f'Writing script with configs {parameter_configs}\n')
# command_script_path, command_script_content = self._writeScript(self.command, parameter_configs, 'command')
# if self.experiment.setup_template_string != None:
# _, setup_script_content = self._writeScript(self.experiment.setup_template_string, parameter_configs, 'setup')
# else:
# setup_script_content = None
# if self.experiment.finish_template_string != None:
# _, finish_script_content = self._writeScript(self.experiment.finish_template_string, parameter_configs, 'finish')
# else:
# finish_script_content = None
setup_script_content, command_script_path, command_script_content, finish_script_content = self._createScript(
self.experiment.setup_template_string, self.command,
self.experiment.finish_template_string, parameter_configs)
# set warm-up experiments
if initialize_flag:
initialize_flag = False
logger.info(
f'[Initial trial for warm-up] Starting trial with script at {command_script_path}\n'
)
runConfig = paropt.runner.RunConfig(
command_script_content=command_script_content,
experiment_dict=self.experiment.asdict(),
setup_script_content=setup_script_content,
finish_script_content=finish_script_content,
)
initializing_func_param = {}
for key, val in self.obj_func_params.items():
initializing_func_param[key] = val
initializing_func_param['timeout'] = 300
# result = self.obj_func(runConfig, **self.obj_func_params).result()
result = self.obj_func(runConfig,
**initializing_func_param).result()
# run baseline experiment
if (self.baseline) and (self.get_baseline_output is False):
self.baseline = False
logger.info(f'Creating baseline trial')
baseline_parameter_configs = []
for parameter in self.baseline_experiment.parameters:
baseline_parameter_configs.append(
ParameterConfig(parameter=parameter,
value=parameter.minimum))
baseline_setup_script_content, baseline_command_script_path, baseline_command_script_content, baseline_finish_script_content = self._createScript(
self.experiment.setup_template_string,
self.baseline_command,
self.experiment.finish_template_string,
baseline_parameter_configs)
logger.info(
f'Starting baseline trial with script at {baseline_command_script_path}\n'
)
runConfig = paropt.runner.RunConfig(
command_script_content=baseline_command_script_content,
experiment_dict=self.baseline_experiment.asdict(),
setup_script_content=baseline_setup_script_content,
finish_script_content=baseline_finish_script_content,
)
result = None
result = self.obj_func(runConfig,
**self.obj_func_params).result()
self._validateResult(baseline_parameter_configs, result)
result['obj_parameters']['wrt_baseline'] = 1
self.baseline_obj_output = result['obj_output']
trial = Trial(
outcome=result['obj_output'],
parameter_configs=baseline_parameter_configs,
run_number=self.run_number,
experiment_id=self.experiment.id,
obj_parameters=result['obj_parameters'],
)
self.storage.saveResult(self.session, trial)
self.baseline_time = result['obj_parameters'][
'caller_time']
self.get_baseline_output = True
if 'baseline_time' in self.obj_func_params.keys(
) and self.obj_func_params[
'baseline_time'] is None and self.baseline_time is not None:
self.obj_func_params['baseline_time'] = self.baseline_time
# start normal trials
logger.info(
f'Starting trial with script at {command_script_path}\n')
runConfig = paropt.runner.RunConfig(
command_script_content=command_script_content,
experiment_dict=self.experiment.asdict(),
setup_script_content=setup_script_content,
finish_script_content=finish_script_content,
)
result = None
result = self.obj_func(runConfig,
**self.obj_func_params).result()
print(result)
self._validateResult(parameter_configs, result)
if self.get_baseline_output:
result['obj_parameters']['wrt_baseline'] = result[
'obj_output'] / self.baseline_obj_output
trial = Trial(
outcome=result['obj_output'],
parameter_configs=parameter_configs,
run_number=self.run_number,
experiment_id=self.experiment.id,
obj_parameters=result['obj_parameters'],
)
self.storage.saveResult(self.session, trial)
self.optimizer.register(trial)
self.run_result[
'success'] = True and self.run_result['success']
flag = flag and self.run_result['success']
self.run_result['message'][
f'experiment {self.experiment.id} run {self.run_number}, config is {ParameterConfig.configsToDict(parameter_configs)}'] = (
f'Successfully completed trials {idx} for experiment, output is {result}'
)
except Exception as e:
err_traceback = traceback.format_exc()
print(result)
if result is not None and result[
'stdout'] == 'Timeout': # for timeCommandLimitTime in lib, timeout
if self.get_baseline_output:
result['obj_parameters']['wrt_baseline'] = result[
'obj_output'] / self.baseline_obj_output
trial = Trial(
outcome=result['obj_output'],
parameter_configs=parameter_configs,
run_number=self.run_number,
experiment_id=self.experiment.id,
obj_parameters=result['obj_parameters'],
)
self.optimizer.register(trial)
logger.exception(f'time out\n')
self.storage.saveResult(self.session, trial)
self.run_result['success'] = False
self.run_result['message'][
f'experiment {self.experiment.id} run {self.run_number}, config is {parameter_configs}'] = (
f'Failed to complete trials {idx} due to timeout:\nError: {e};\t{err_traceback};\toutput is {result}'
)
else: # do have error
trial = Trial(
outcome=10000000,
parameter_configs=parameter_configs,
run_number=self.run_number,
experiment_id=self.experiment.id,
obj_parameters={},
)
if self.save_fail_trial:
self.storage.saveResult(self.session, trial)
self.run_result['success'] = False
self.run_result['message'][
f'experiment {self.experiment.id} run {self.run_number}, config is {parameter_configs}'] = (
f'Failed to complete trials {idx}:\nError: {e};\t{err_traceback};\toutput is {result}'
)
print(err_traceback)
print(result)
logger.info(f'Finished; Run result: {self.run_result}\n')
# plot part
if self.plot_info['draw_plot']:
try:
trials = self.storage.getTrials(self.session,
self.experiment.id)
trials_dicts = [trial.asdict() for trial in trials]
except:
self.session.rollback()
raise
logger.info(f'res: {trials_dicts}\n')
if isinstance(self.optimizer, GridSearch):
ret = GridSearch_plot(trials_dicts, self.plot_info)
else:
logger.info(f'Unsupport type of optimizer for plot\n')
if ret['success'] == False:
logger.info(f'Error when generating plot: {ret["error"]}\n')
else:
logger.info(f'Successfully generating plot {ret["error"]}\n')
else:
logger.info(f'Skip generating plot\n')