from args import parser, get_config_module import hashlib import hmac import sys import requests import urllib parser.add_argument("path", metavar="P", type=str, help="Path to query metadata of") vals = parser.parse_args() config = get_config_module(vals.config) path = vals.path try: from config import app_config key = app_config["PUBLISHER_SECRET_KEY"] root = app_config.get("APPLICATION_ROOT", "") except ImportError, KeyError: print "Unable to retrieve secret key for metadata request" sys.exit(1) try: from config import host_config if "host" in host_config: host = host_config["host"] else: host = "127.0.0.1" if "port" in host_config: port = host_config["port"] else: port = 8000
from args import parser, get_config_module import hashlib import hmac import sys import requests import urllib parser.add_argument("group", metavar="G", type=str, help="Group to associate file with") parser.add_argument("path", metavar="P", type=str, help="Path to expose") parser.add_argument("key", metavar="K", type=str, help="Key for path") vals = parser.parse_args() config = get_config_module(vals.config) path = vals.path try: from config import app_config key = app_config["PUBLISHER_SECRET_KEY"] root = app_config.get("APPLICATION_ROOT", "") except ImportError, KeyError: print "Unable to retrieve secret key for metadata request" sys.exit(1) try: from config import host_config if "host" in host_config: host = host_config["host"] else: host = "127.0.0.1" if "port" in host_config:
except Exception as e: log.error('Error while running query', exc_info=e) if runner.query_process is not None: kill_family(runner.query_process.pid, signal.SIGKILL) if runner.server_process is not None: kill_family(runner.server_process.pid, signal.SIGKILL) return None finally: runner.clean() if db is not None: db.close() if __name__ == '__main__': parser.add_argument('-v', '--verbose', action='store_true', default=False) args = parser.parse_args() if args.fault == 'flip' and args.single and args.mean_runtime is None: parser.error( '--mean-runtime is required when --fault is set to flip and --single is given' ) if args.fault == 'flip' and args.flip_rate is None and not args.single: parser.error( '--flip-rate is required when --fault is set to flip and --single is not given' ) check_injector(args.debug) init_pool(args.threads)
import os import subprocess from collect import collect from args import parser from util import get_dir_name if __name__ == '__main__': parser.add_argument('-n', '--nodes', type=str, required=True) parser.add_argument('-o', '--output-dir', type=str, required=True) parser.add_argument('-v', '--values', nargs='+', required=True) args = parser.parse_args() for db in args.database: for val in args.values: val = float(val) exp_name = get_dir_name( database=db, query=args.query, fault=args.fault, inject_to_heap=args.heap, inject_to_anon=args.anon, inject_to_stack=args.stack, flip_rate=val, random_flip_rate=args.random_flip_rate, suffix=args.suffix ) clush_command = ['clush', '-v', '-w', args.nodes, 'cd', 'chaos_db', '&&', 'PYTHONPATH=./orchestrator', 'python3', 'orchestrator/orchestrator.py', '-d', db, '-q', args.query, '-w', args.working_directory, '-i', str(args.iterations), '-t', str(args.threads), '-fr', str(val)]
from torch import nn import torch.nn.functional as F from model import get_model_optimizer from loops import train_loop, evaluate, infer from dataset import cross_validation_split, get_test_dataset, BucketingSampler, make_collate_fn from transformers import BertTokenizer, AlbertTokenizer from torch.utils.data import DataLoader, Dataset from evaluation import target_metric from misc import target_columns, input_columns from args import parser # parser = argparse.ArgumentParser() parser.add_argument("--checkpoint", type=str, required=True) parser.add_argument("--dataframe", type=str, required=True) parser.add_argument("--output_dir", type=str, required=True) args = parser.parse_args() logging.getLogger("transformers").setLevel(logging.ERROR) test_df = pd.read_csv(args.dataframe) tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=("uncased" in args.bert_model)) test_set = get_test_dataset(args, test_df, tokenizer) test_loader = DataLoader(