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(
