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.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.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.svc.svc import lambda_handler
# for payload in payload_list:
# map_item = lambda_handler(payload)
# # Metric is Accuracy Score => Large than
# metrics.append(map_item['metric'])
# if map_item['metric'] > max_metric:
# print ("======Update chosen model event==========")
# chosen_model_event = map_item['event']
# max_metric = map_item['metric']
# print ("====Max Accuracy Score=====")
# print (max_metric)
# print ("====Event============")
# print (chosen_model_event)
# print ("====Metrics 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/svc_worker")