def init_db():
connection = SQLEngine.connect()
context = MigrationContext.configure(connection)
current_revision = context.get_current_revision()
logger.boot('Database revision: %s', current_revision)
if current_revision is None:
DataBase.metadata.create_all(SQLEngine)
config = Config(ALEMBIC_CONFIG)
script = ScriptDirectory.from_config(config)
head_revision = script.get_current_head()
if current_revision is None or current_revision != head_revision:
logger.boot('Upgrading database to version %s.', head_revision)
command.upgrade(config, 'head')
from config import Option
session = Session()
options = session.query(Option).first()
if options is None:
options = Option()
options.version = head_revision
session.add(options)
from pulse import Pulse
pulse = session.query(Pulse).first()
if pulse is None:
pulse = Pulse()
session.add(pulse)
session.commit()
class CMConfig(BaseConfig):
log_level = Option('INFO')
listen_addr = Option('')
listen_port = IntOption(51235)
cadir = Option('/etc/pki/certmaster/ca')
cert_dir = Option('/etc/pki/certmaster')
certroot = Option('/var/lib/certmaster/certmaster/certs')
csrroot = Option('/var/lib/certmaster/certmaster/csrs')
cert_extension = Option('cert')
autosign = BoolOption(False)
sync_certs = BoolOption(False)
peering = BoolOption(True)
peerroot = Option('/var/lib/certmaster/peers')
clustering_loss = criterion.compute(pairwise_distances, labels, opt,
global_step)
return clustering_loss
if __name__ == "__main__":
import warnings
import time
warnings.filterwarnings("ignore")
print("hello")
torch.save(torch.rand(3, 3), "/home/wyt/1_lrz/8-3/observe/step.pt")
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
ml = metric_loss()
opt = Option()
# f = tl.cudafy(Variable(torch.randn(128, 50),requires_grad=True))[0]
# l=tl.cudafy(torch.cat((torch.ones([100]),torch.zeros([28])),0))[0]
# torch.save(f,"/home/wyt/1_lrz/test/f.pt")
# torch.save(l, "/home/wyt/1_lrz/test/l.pt")
f = torch.load("/home/wyt/1_lrz/test/f.pt")
l = torch.load("/home/wyt/1_lrz/test/l.pt")
# ans = ml.pairwise_distance()
# t1=time.time()
loss, num_class = ml.cluster_loss(opt, f, l)
# t2=time.time()
print("loss:", loss)
print("num_class:", num_class)
# print("ttime:",t2-t1," seconds")
# print(ans)
# centroid_ids = torch.tensor([0, 2, 3])
from config import Option
from arguments_configurator import ArgsConfigurator
opts = []
opts.append(Option('host', 'host', 'h', False, 'localhost', 'help...'))
opts.append(Option('vcip', 'vcip', 'v', True, True, 'help...'))
config_manager = ArgsConfigurator(opts)
res = config_manager.config
print '====='
class FuncdConfig(BaseConfig):
log_level = Option('INFO')
acl_dir = Option('/etc/func/minion-acl.d')
certmaster_overrides_acls = BoolOption(True)
class MinionConfig(BaseConfig):
log_level = Option('INFO')
certmaster = Option('certmaster')
certmaster_port = IntOption(51235)
cert_dir = Option('/etc/pki/certmaster')
from config import Option
from environment_configurator import EnvironmentConfigurator
opts = []
opts.append(Option('Virgo Directory', 'virgoDir', 'h', False, 'localhost', 'help...'))
opts.append(Option('Virgo Dir 1', 'virgoDir1', 'h', False, 'localhost', 'help...'))
opts.append(Option('NVM bin', 'NVM_BIN', 'h', False, 'localhost', 'help...'))
config_manager = EnvironmentConfigurator(opts)
res = config_manager.config
print '====='
print res