def start_forever_strategy(wf):
""" executes forever - changes must come from definition file """
info("> ExecStrategy | Forever ", Fore.CYAN)
wf.totalExperiments = -1
while True:
experimentFunction(wf, {
"knobs": {"forever": True},
"ignore_first_n_results": wf.execution_strategy["ignore_first_n_results"],
"sample_size": wf.execution_strategy["sample_size"],
})
def start_sequential_strategy(wf):
""" executes all experiments from the definition file """
info("> ExecStrategy | Sequential", Fore.CYAN)
wf.totalExperiments = len(wf.execution_strategy["knobs"])
for kn in wf.execution_strategy["knobs"]:
experimentFunction(
wf, {
"knobs":
kn,
"ignore_first_n_results":
wf.execution_strategy["ignore_first_n_results"],
"sample_size":
wf.execution_strategy["sample_size"],
})
def start_sequential_strategy(wf):
applyInitKnobs(wf)
""" executes all experiments from the definition file """
info("> ExecStrategy | Sequential", Fore.CYAN)
wf.totalExperiments = len(wf.execution_strategy["knobs"])
for knob in wf.execution_strategy["knobs"]:
knob = parseKnobs(knob)
wf.setup_stage(wf, knob)
experimentFunction(wf, {
"knobs": knob,
"ignore_first_n_samples": wf.primary_data_provider["ignore_first_n_samples"],
"sample_size": wf.execution_strategy["sample_size"],
})
# finished
info(">")
applyDefaultKnobs(wf)
def self_optimizer_execution(wf, opti_values, variables):
""" this is the function we call and that returns a value for optimization """
knob_object = recreate_knob_from_optimizer_values(variables, opti_values)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
return experimentFunction(wf, exp)
def self_optimizer_execution(wf, opti_values, variables):
""" this is the function we call and that returns a value for optimization """
knob_object = recreate_knob_from_optimizer_values(variables, opti_values)
print("knob_object in self_opt_execution", knob_object)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_samples"] = wf.primary_data_provider[
"ignore_first_n_samples"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
wf.setup_stage(wf, exp["knobs"])
return experimentFunction(wf, exp)
def step_execution(wf, knob):
""" runs a single step_execution experiment """
knob = parseKnobs(knob)
debug("knob in step_execution" + str(knob), Fore.GREEN)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_samples"] = wf.primary_data_provider[
"ignore_first_n_samples"]
# anova process will call this part, so set sample_size accordingly
exp["sample_size"] = wf.analysis["sample_size"]
exp["knobs"] = knob
wf.setup_stage(wf, exp["knobs"])
return experimentFunction(wf, exp)
def step_execution(wf, configuration, variables):
""" runs a single step_execution experiment """
knob_object = recreate_knob_from_step_explorer_values(
variables, configuration)
debug("knob_object in step_execution" + str(knob_object), Fore.GREEN)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
wf.setup_stage(wf, exp["knobs"])
return experimentFunction(wf, exp)
def random_execution(wf, optimum_values, variables):
""" this is the function we call and that returns a value for optimization """
knob_object = recreate_knob_from_optimizer_values(variables,
optimum_values)
# create a new experiment to run in execution
exp = dict()
exp["ignore_first_n_results"] = wf.execution_strategy[
"ignore_first_n_results"]
exp["sample_size"] = wf.execution_strategy["sample_size"]
exp["knobs"] = knob_object
wf.setup_stage(wf, exp["knobs"])
execution_result = experimentFunction(wf, exp)
info("> | execution_result: " + str(execution_result))
return execution_result