def run(self):
for _n in range(self.batch_number):
clean_logs(self.lambda_path)
if (not self.celery_async and not self.lambda_async):
start_time = time.time()
for _num in range(self.invoke_time):
# print("Lambda is invoked %d time" %(num + 1))
invoke_lambda(function_name=self.lambda_name,
sync=True,
payload=self.sync_payload,
decoder=self.decoder)
host_execu_time = 1000 * (time.time() - start_time)
elif (not self.celery_async and self.lambda_async):
# Add counter to make sure identifier is unique
start_time = time.time()
for _num in range(self.invoke_time):
# print("Lambda is invoked %d time" %(num + 1))
invoke_lambda(function_name=self.lambda_name,
sync=False,
payload=self.async_payload)
host_execu_time = 1000 * (time.time() - start_time)
elif (self.celery_async and not self.lambda_async):
start_time = time.time()
job = group(
invoke_lambda.s(function_name=self.lambda_name,
sync=True,
payload=self.sync_payload,
decoder=self.decoder)
for i in range(self.invoke_time))
print("===Async Tasks start===")
job.apply_async()
# result.join()
host_execu_time = 1000 * (time.time() - start_time)
print("===Async Tasks end===")
elif (self.celery_async and self.lambda_async):
start_time = time.time()
job = group(
invoke_lambda.s(function_name=self.lambda_name,
sync=False,
payload=self.async_payload)
for i in range(self.invoke_time))
print("===Async Tasks start===")
result = job.apply_async()
result.join()
host_execu_time = 1000 * (time.time() - start_time)
print("===Async Tasks end===")
time.sleep(25)
show_result(self.lambda_path, self.celery_async, self.lambda_async,
host_execu_time)
def sqs_trigger(self):
sqs = boto3.client('sqs')
invokeType = "Event" if self.lambda_async else "RequestResponse"
for _i in range(self.batch_number):
self.identifiers = []
clean_logs(self.lambda_path)
start_time = time.time()
for _j in range(self.invoke_time):
body = {
"messageType": "refreshConfig",
"invokeType": invokeType
}
_response = sqs.send_message(
QueueUrl=
'https://sqs.us-west-2.amazonaws.com/603495292017/container-test-queue',
MessageBody=json.dumps(body))
host_execu_time = 1000 * (time.time() - start_time)
time.sleep(25)
_response_path, _metrics_path = show_result(
self.lambda_path, self.celery_async, self.lambda_async,
host_execu_time)
args.experiment_time,
"max_k":
1,
"covars": [
"full-tied", "full-untied", "diag-tied", "diag-untied", "spher-tied",
"spher-untied"
],
"columns": ["Dimension 1", "Dimension 2"],
"scale":
True,
"s3_file_key":
"normal.csv"
}
lambda_name = "/aws/lambda/worker"
for _ in range(args.batch_number):
print("===Centaurus jobs start===")
clean_logs(lambda_name)
clean_dynamodb('kmeansservice', 'job_id', 'task_id')
start_time = time.time()
invoke_lambda(function_name='create_job',
sync=True,
payload=payload,
decoder='utf-8')
host_execu_time = 1000 * (time.time() - start_time)
time.sleep(25)
show_result(lambda_name, True, True, host_execu_time)
print("===Centaurus jobs end===")
def grid_search_controller(config_path):
# start = time.time()
# Dynamic importing config file from config_path
config = load(config_path)
# Dynamic loading lambda name
LAMBDA_NAME = getattr(config.Hyperparameter, "LAMBDA_NAME")
# Clean the log of specified lambda function
clean_logs('/aws/lambda/' + LAMBDA_NAME)
# Dynamic load parameters
DATASETS = []
TARGETS = getattr(config.Hyperparameter, 'TARGETS')
for key in getattr(config.Hyperparameter, 'DATASETS'):
DATASETS.append(key)
# Tune forecast horizon of the chosen model
payload_list = create_event(config, DATASETS, TARGETS)
min_metric = float('inf')
chosen_model_event = None
metrics = []
# from src.lambda_func.multi_regression.multi_regression import lambda_handler
# for payload in payload_list:
# print ("======Payload========")
# print (payload)
# map_item = lambda_handler(payload)
# metrics.append(map_item['metric'])
# if map_item['metric'] < min_metric:
# print ("======Update chosen model event==========")
# chosen_model_event = map_item['event']
# min_metric = map_item['metric']
# print ("======Metric=======")
# print (min_metric)
# print ("======Event=======")
# print (chosen_model_event)
# print ("======Metrics========")
# print (metrics)
# print ("====Execution time====")
# print (time.time() - start)
start = time.time()
print ("=====Time Stamp======")
print (start)
job = group(invoke_lambda.s(
function_name = LAMBDA_NAME,
sync = True,
payload = payload
) for payload in payload_list)
print("===Async Tasks start===")
result = job.apply_async()
result.save()
from celery.result import GroupResult
saved_result = GroupResult.restore(result.id)
while not saved_result.ready():
time.sleep(0.1)
model_list = saved_result.get(timeout=None)
print("===Async Tasks end===")
print (time.time() - start)
for item in model_list:
payload = item['Payload']
if payload['metric'] < min_metric:
chosen_model_event = payload['event']
min_metric = payload['metric']
print (chosen_model_event)
from src.celery_lambda import measurement
measurement.parse_log("/aws/lambda/multi_regression_worker")
def grid_search_controller(config_path):
# start = time.time()
# Dynamic importing config file from config_path
config = load(config_path)
# Dynamic loading lambda name
LAMBDA_NAME = getattr(config.Cross_Validation, "LAMBDA_NAME")
# Clean the log of specified lambda function
clean_logs('/aws/lambda/' + LAMBDA_NAME)
# Dynamic load parameters
PARAMETERS = []
CV_SETTINGS = []
for key in dir(config.Hyperparameter):
if key.isupper():
PARAMETERS.append(key)
for key in dir(config.Cross_Validation):
if key.isupper():
CV_SETTINGS.append(key)
# Tune forecast horizon of the chosen model
payload_list = create_event(config, PARAMETERS, CV_SETTINGS)
min_metric = float('inf')
chosen_model_event = None
metrics = []
# from src.lambda_func.prophet.prophet import grid_search_worker
# for payload in payload_list:
# map_item = grid_search_worker(payload)
# metrics.append(map_item['average_metric'])
# if map_item['average_metric'] < min_metric:
# print ("======Update chosen model event==========")
# chosen_model_event = map_item['event']
# min_metric = map_item['average_metric']
# print ("=======Metric=======")
# print (min_metric)
# print ("======Event=======")
# print (chosen_model_event)
# print ("======Metrics=======")
# print (metrics)
# print ("====Execution time====")
# print (time.time() - start)
start = time.time()
print ("=====Time Stamp======")
print (start)
job = group(invoke_lambda.s(
function_name = LAMBDA_NAME,
sync = True,
payload = payload
) for payload in payload_list)
print("===Async Tasks start===")
result = job.apply_async()
result.save()
from celery.result import GroupResult
saved_result = GroupResult.restore(result.id)
while not saved_result.ready():
time.sleep(0.1)
model_list = saved_result.get(timeout=None)
print("===Async Tasks end===")
print (time.time() - start)
for item in model_list:
payload = item['Payload']
if payload['average_metric'] < min_metric:
chosen_model_event = payload['event']
min_metric = payload['average_metric']
from src.celery_lambda import measurement
measurement.parse_log("/aws/lambda/prophet_worker")
# Non-zero forecast period makes lambda upload graphs to s3
chosen_model_event['forecast'] = getattr(config.Cross_Validation, "FORECAST")
# Invoke Lambda with forecast
response = invoke_lambda(function_name = LAMBDA_NAME,
sync=True,
payload=chosen_model_event)
print ("=======The Execution Time===========")
print (time.time() - start)
print (response)
def grid_search_controller(config_path):
# start = time.time()
# Dynamic importing config file from config_path
config = load(config_path)
# Dynamic loading lambda name
LAMBDA_NAME = getattr(config.Config, "LAMBDA_NAME")
# Clean the log of specified lambda function
clean_logs('/aws/lambda/' + LAMBDA_NAME)
# Dynamic load parameters
PARAMETERS = []
CONFIG = []
for key in dir(config.Hyperparameter):
if key.isupper():
PARAMETERS.append(key)
for key in dir(config.Config):
if key.isupper():
CONFIG.append(key)
# Tune forecast horizon of the chosen model
payload_list = create_event(config, PARAMETERS, CONFIG)
max_metric = float('-inf')
chosen_model_event = None
metrics = []
# from src.lambda_func.neural_network.neural_network import lambda_handler
# for payload in payload_list:
# map_item = lambda_handler(payload)
# metrics.append(map_item['metric'])
# # Metric is Accuracy Score => Large than
# if map_item['metric'] > max_metric:
# print ("======Update chosen model event==========")
# chosen_model_event = map_item['event']
# max_metric = map_item['metric']
# print ("===Event===")
# print (chosen_model_event)
# print ("===Max Metric===")
# print (max_metric)
# print ("===Metric List===")
# print (metrics)
# print ("====Execution time====")
# print (time.time() - start)
start = time.time()
print("=====Time Stamp======")
print(start)
job = group(
invoke_lambda.s(function_name=LAMBDA_NAME, sync=True, payload=payload)
for payload in payload_list)
print("===Async Tasks start===")
result = job.apply_async()
result.save()
from celery.result import GroupResult
saved_result = GroupResult.restore(result.id)
while not saved_result.ready():
time.sleep(0.1)
model_list = saved_result.get(timeout=None)
print("===Async Tasks end===")
print(time.time() - start)
for item in model_list:
payload = item['Payload']
# Metric is Accuracy Score => Large than
if payload['metric'] > max_metric:
chosen_model_event = payload['event']
max_metric = payload['metric']
print(max_metric)
print(chosen_model_event)
from src.celery_lambda import measurement
measurement.parse_log("/aws/lambda/neural_network_worker")