def initialize(self):
""" Initialize the environment when an episode starts
Returns:
state: np.array, current state
"""
self.score = 0.0
self.last_external_metrics = []
self.steps = 0
self.terminate = False
flag = self._apply_knobs(self.default_knobs)
i = 0
while not flag:
flag = self._apply_knobs(self.default_knobs)
i += 1
if i >= 5:
print("Initialize: {} times ....".format(i))
external_metrics, internal_metrics = self._get_state(
knob=self.default_knobs, method=self.method)
self.last_external_metrics = external_metrics
self.default_externam_metrics = external_metrics
state = internal_metrics
knobs.save_knobs(
self.default_knobs,
metrics=external_metrics,
knob_file='%sAutoTuner/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return state, external_metrics
def step(self, knob):
"""step
"""
restart_time = utils.time_start()
flag = self._apply_knobs(knob)
restart_time = utils.time_end(restart_time)
# ?
if not flag:
return -10000000.0, np.array([0] * self.num_metric), True, self.score - 10000000, [0, 0, 0], restart_time
s = self._get_state(knob, method=self.method)
# crash
if s is None:
return -10000000.0, np.array([0] * self.num_metric), True, self.score - 10000000, [0, 0, 0], restart_time
external_metrics, internal_metrics = s
reward = self._get_reward(external_metrics)
flag = self.record_best(external_metrics)
if flag == True:
logger.info('Better performance changed!')
else:
logger.info('Performance remained!')
#get the best performance so far to calculate the reward
best_now_performance = self._get_best_now(self.best_result)
self.last_external_metrics = best_now_performance
next_state = internal_metrics
terminate = self._terminate()
knobs.save_knobs(
knob = knob,
metrics = external_metrics,
instance=self.db_info,
task_id=self.task_id
)
return reward, next_state, terminate, self.score, external_metrics, restart_time
def step(self, knob):
"""step
"""
restart_time = utils.time_start()
flag = self._apply_knobs(knob)
restart_time = utils.time_end(restart_time)
if not flag:
return -100.0, np.array([0] *
self.num_metric), True, self.score - 100, [
0, 0, 0
], restart_time
s = self._get_state(knob, method=self.method)
if s is None:
return -100.0, np.array([0] *
self.num_metric), True, self.score - 100, [
0, 0, 0
], restart_time
external_metrics, internal_metrics = s
reward = self._get_reward(external_metrics)
self.last_external_metrics = external_metrics
next_state = internal_metrics
terminate = self._terminate()
knobs.save_knobs(
knob=knob,
metrics=external_metrics,
knob_file='%sAutoTuner/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return reward, next_state, terminate, self.score, external_metrics, restart_time
def initialize(self):
""" Initialize the environment when an episode starts
Returns:
state: np.array, current state
"""
self.score = 0.0
self.last_external_metrics = []
self.steps = 0
self.terminate = False
flag = self._apply_knobs(self.default_knobs)
i = 0
while not flag:
if i >= 2:
logger.info("Initialize: {} times ....".format(i))
os_quit(Err.SET_MYSQL_PARAM_FAILED)
flag = self._apply_knobs(self.default_knobs)
i += 1
external_metrics, internal_metrics = self._get_state(
knob=self.default_knobs, method=self.method)
if os.path.exists(self.best_result):
if os.path.getsize(self.best_result):
with open(self.best_result) as f:
lines = f.readlines()
best_now = lines[0].split(',')
self.last_external_metrics = [
float(best_now[0]),
float(best_now[1]),
float(best_now[0])
]
else:
self.last_external_metrics = external_metrics
self.default_externam_metrics = external_metrics
state = internal_metrics
knobs.save_knobs(self.default_knobs,
metrics=external_metrics,
instance=self.db_info,
task_id=self.task_id)
return state, external_metrics
def initialize(self):
""" Initialize the environment when an episode starts
Returns:
state: np.array, current state
"""
self.score = 0.0
self.last_external_metrics = []
self.steps = 0
self.terminate = False
external_metrics, internal_metrics = self._get_state(
knob=self.default_knobs, method=self.method)
self.last_external_metrics = external_metrics
self.default_externam_metrics = external_metrics
state = internal_metrics
knobs.save_knobs(
self.default_knobs,
metrics=external_metrics,
knob_file='%sAutoTuner1/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return state, external_metrics
def step(self, knob):
"""step
"""
filename = 'bestnow.log'
restart_time = utils.time_start()
# time delay
restart_time = utils.time_end(restart_time)
# s conclude external_metrics and internal_metrics
s = self._get_state(knob, method=self.method)
if s is None:
return -10000000.0, np.array(
[0] * self.num_metric), True, self.score - 10000000, [
0, 0, 0
], restart_time
external_metrics, internal_metrics = s
# 在这里改变了score的记录
reward = self._get_reward(external_metrics)
# 记录最佳的reward
flag = self.record_best(external_metrics)
if flag == True:
print('Better performance changed!')
else:
print('Performance remained!')
# get the best performance so far to calculate the reward
best_now_performance = self._get_best_now(filename)
self.last_external_metrics = best_now_performance
next_state = internal_metrics
terminate = self._terminate()
knobs.save_knobs(
knob=knob,
metrics=external_metrics,
knob_file='%sAutoTuner1/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return reward, next_state, terminate, self.score, external_metrics, restart_time
def step(self, knob):
#apply_knobs
#Yiming?
restart_time = 123 # random value
filename = 'bestnow.log'
flag = self._apply_knobs(knob)
if flag != 0:
return -10000000.0, np.array(
[0] * self.num_metric), True, self.score - 10000000, [
0, 0, 0
], restart_time
s = self._get_state()
if s is None:
return -10000000.0, np.array(
[0] * self.num_metric), True, self.score - 10000000, [
0, 0, 0
], restart_time
external_metrics, internal_metrics = s
reward = self._get_reward(external_metrics)
flag = self.record_best(external_metrics)
if flag == True:
print('Better performance changed!')
else:
print('Performance remained!')
best_now_performance = self._get_best_now(filename)
self.last_external_metrics = best_now_performance
next_state = internal_metrics
self.terminate = self._encode_complete()
terminate = self._terminate()
knobs.save_knobs(knob=knob,
metrics=external_metrics,
knob_file='%s/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return reward, next_state, terminate, self.score, external_metrics, restart_time
def initialize(self):
"""Initialize the encoder instance environment
"""
self.score = 0.0
self.steps = 0
self.terminate = False
self.last_external_metrics = []
push_frame_thread(self.video)
flag = self._apply_knobs(self.default_knobs)
i = 0
while not flag:
flag = self._apply_knobs(self.default_knobs)
i += 1
if i >= 5:
print("Initialize: {} times ....".format(i))
'''
def_knobs = {}
for item in KNOB_DETAILS:
flag = self._apply_knobs([(str(item),str(KNOB_DETAILS[item][1][2]))])
def_knobs[str(item)] = str(KNOB_DETAILS[item][1][2])
if flag != 0:
print("Knob Application of knob " + str(item) + " with value " +str(KNOB_DETAILS[item][1][2]) + " resulted in error " + str(flag) + " in my encoder initialize")
'''
external_metrics, internal_metrics = self._get_state()
state = internal_metrics
self.default_externam_metrics = external_metrics
self.last_external_metrics = external_metrics
knobs.save_knobs(self.default_knobs,
metrics=external_metrics,
knob_file='%s/tuner/save_knobs/knob_metric.txt' %
PROJECT_DIR)
return state, external_metrics