import logging
import os
import pprint
import pickle
from pathlib import Path
import numpy as np
import zmq
from data_class import opunit_data
from mini_trainer import MiniTrainer
from util import logging_util
from type import OpUnit
from info import data_info
logging_util.init_logging('info')
class Callback(IntEnum):
"""
ModelServerManager <==> ModelServer callback Id.
Needs to be kept consistent with ModelServerManager.h's Callback Enum
"""
NOOP = 0
CONNECTED = 1
class Command(Enum):
"""
Command enum for actions to take from the manager.
This has to be kept consistent with the C++ ModelServerManager.
help='OLTPBench warmup period')
aparser.add_argument('--tpcc_hack',
default=False,
help='Should do feature correction for TPCC')
aparser.add_argument('--ee_sample_interval',
type=int,
default=9,
help='Sampling interval for the execution engine OUs')
aparser.add_argument('--txn_sample_interval',
type=int,
default=0,
help='Sampling interval for the transaction OUs')
aparser.add_argument('--log', default='info', help='The logging level')
args = aparser.parse_args()
logging_util.init_logging(args.log)
logging.info("Global trainer starts.")
with open(args.mini_model_file, 'rb') as pickle_file:
model_map = pickle.load(pickle_file)
trainer = GlobalTrainer(args.input_path, args.model_results_path,
args.ml_models, args.test_ratio,
args.impact_model_ratio, model_map,
args.warmup_period, args.tpcc_hack,
args.ee_sample_interval, args.txn_sample_interval)
resource_model, impact_model, direct_model = trainer.train()
with open(args.save_path + '/global_resource_model.pickle', 'wb') as file:
pickle.dump(resource_model, file)
with open(args.save_path + '/global_impact_model.pickle', 'wb') as file:
pickle.dump(impact_model, file)
from Constants import mem_per_1vCPU
from TranslationService import translate, genitive_case
from util.constants_util import get_logs_root_path, get_fn_tag
from util.file_util import read_file, read_csv_to_dataframe, write_to_csv
from util.subprocess_util import run_executable
from util.logging_util import log, init_logging
env_config = dotenv_values()
REPOSITORY_NAME = env_config["AWS_ECR_REPOSITORY_NAME"]
describe_image_exec_path = './script/aws/environment/describe-image.sh'
results_header = ["image_size_mb", "memory_size_mb", "init_duration_ms", "artificial_init_duration", "duration", "available_threads"]
init_logging()
parser = argparse.ArgumentParser(description="Results Parser")
parser.add_argument("-t", "--test", type=str)
def main():
args = parser.parse_args()
test_num = args.test
parse_logs(test_num)
def parse_logs(test_num):
log(f'Parsing logs for test: {test_num}')
