How to use the setproctitle.setproctitle function in setproctitle

import caffe
import surgery, score

import numpy as np
import os
import sys

try:
    import setproctitle
    setproctitle.setproctitle(os.path.basename(os.getcwd()))
except:
    pass

weights = '../ilsvrc-nets/vgg16-fcn.caffemodel'

# init
caffe.set_device(int(sys.argv[1]))
caffe.set_mode_gpu()

solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)

# surgeries
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
surgery.interp(solver.net, interp_layers)

def set_process_title(config):
    setproctitle("%s" % (get_process_name()))

def main_loop():
    setproctitle('webshelld')

    dispatcher = SimpleXMLRPCDispatcher()
    SOCKFILE = '/var/run/webshell.sock'
    if os.path.exists(SOCKFILE):
        os.unlink(SOCKFILE)
    server = socketserver.UnixStreamServer(SOCKFILE, XMLRPCHandler)
    os.chmod(SOCKFILE, 0o700)
    dispatcher.register_instance(
        Multiplex("/usr/local/bin/bash", "xterm-color"))
    server.dispatcher = dispatcher
    server.serve_forever()

def _run_plugins_parallel_helper(timeout):
            setproctitle.setproctitle('Plugin-Runner')
            processes = []
            queue_lock = mp.Lock()

            for Plugin in plugins:
                process = mp.Process(target=self.run_plugin, name=Plugin.get_name(), args=(Plugin, output_queue, queue_lock))
                process.start()
                processes.append(process)

            start_time = time.time()
            should_end = False
            while len(processes) > 0:

                time_spent = time.time() - start_time
                should_end = time_spent > timeout

                try:

def main():
    import argparse
    import logging
    import setproctitle
    from kolejka.client.client import config_parser as client_parser

    setproctitle.setproctitle('kolejka-client')
    parser = argparse.ArgumentParser(description='KOLEJKA client')
    parser.add_argument('-v', '--verbose', action='store_true', default=False, help='show more info')
    parser.add_argument('-d', '--debug', action='store_true', default=False, help='show debug info')
    parser.add_argument('--config-file', help='config file')
    parser.add_argument('--config', help='config')
    client_parser(parser)
    args = parser.parse_args()
    level=logging.WARNING
    if args.verbose:
        level = logging.INFO
    if args.debug:
        level = logging.DEBUG
    logging.basicConfig(level = level)
    args.execute(args)

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--save', type=str, default='work/mse')
    parser.add_argument('--nEpoch', type=float, default=50)
    # parser.add_argument('--trainBatchSz', type=int, default=25)
    parser.add_argument('--trainBatchSz', type=int, default=70)
    # parser.add_argument('--testBatchSz', type=int, default=2048)
    parser.add_argument('--nGdIter', type=int, default=30)
    parser.add_argument('--noncvx', action='store_true')
    parser.add_argument('--seed', type=int, default=42)
    # parser.add_argument('--valSplit', type=float, default=0)

    args = parser.parse_args()

    setproctitle.setproctitle('bamos.icnn.comp.mse')

    npr.seed(args.seed)
    tf.set_random_seed(args.seed)

    save = os.path.expanduser(args.save)
    if os.path.isdir(save):
        shutil.rmtree(save)
    os.makedirs(save)
    ckptDir = os.path.join(save, 'ckpt')
    args.ckptDir = ckptDir
    if not os.path.exists(ckptDir):
        os.makedirs(ckptDir)

    data = olivetti.load("data/olivetti")

    nTrain = data['trainX'].shape[0]

def main():
    (opts, args) = parse_arguments(sys.argv[1:])

    # Rename this process so 'ps' output looks like this is a native
    # executable.  Can not seperate command-line arguments from actual name of
    # the executable by NUL bytes, so only show the name of the executable
    # instead.  setproctitle.setproctitle("\x00".join(sys.argv))
    setproctitle.setproctitle(sys.argv[0])
    setup_logging(opts)

    # Special case for the clean up queues action
    if opts.purge_queues:
        purge_queues()
        return

    # Special case for the clean up exch action
    if opts.purge_exchanges:
        purge_exchanges()
        return

    if opts.drain_queue:
        drain_queue(opts.drain_queue)
        return

parser.add_argument('--batch_size', type=int, default=50)
    parser.add_argument('--epochs', type=int, default=20)
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--epsilon", type=float, default=0.05)
    parser.add_argument("--starting_epsilon", type=float, default=None)
    parser.add_argument('--prefix')
    parser.add_argument('--baseline', action='store_true')
    parser.add_argument('--verbose', type=int, default=1)
    parser.add_argument('--alpha_grad', action='store_true')
    parser.add_argument('--scatter_grad', action='store_true')
    parser.add_argument('--old_weights', action='store_true')
    parser.add_argument('--l1_proj', type=int, default=None)
    args = parser.parse_args()
    args.prefix = args.prefix or 'har_conv_{:.4f}_{:.4f}_0'.format(args.epsilon, args.lr).replace(".","_")
    setproctitle.setproctitle(args.prefix)

    train_log = open(args.prefix + "_train.log", "w")
    test_log = open(args.prefix + "_test.log", "w")

    train_loader, test_loader = pblm.har_loaders(args.batch_size)

    torch.manual_seed(args.seed)
    torch.cuda.manual_seed(args.seed)

    model = pblm.har_500_model().cuda()
    # model = pblm.har_resnet_model().cuda()

    opt = optim.Adam(model.parameters(), lr=args.lr)
    for t in range(args.epochs):
        if args.baseline: 
            train_baseline(train_loader, model, opt, t, train_log, args.verbose)

import caffe
import surgery, score

import numpy as np
import os

import setproctitle
setproctitle.setproctitle(os.path.basename(os.getcwd()))


proto = 'path-to-VGG_ILSVRC_16_layers_deploy.prototxt' 
weights = 'path-to-VGG_ILSVRC_16_layers.caffemodel'
caffe.set_device(0)
caffe.set_mode_gpu()

solver = caffe.SGDSolver('Dilated_FCN-2s_VGG16/solver.prototxt')
# surgeries
custom_net = caffe.Net(proto, weights, caffe.TEST) 
surgery.transplant(solver.net, custom_net)
del custom_net


interp_layers = [k for k in solver.net.params.keys() if 'up' in k] 
surgery.interp(solver.net, interp_layers)

import caffe
import surgery, score

import numpy as np
import os
import sys

try:
    import setproctitle
    setproctitle.setproctitle(os.path.basename(os.getcwd()))
except:
    pass

weights = '../pascalcontext-fcn16s/pascalcontext-fcn16s.caffemodel'

# init
caffe.set_device(int(sys.argv[1]))
caffe.set_mode_gpu()

solver = caffe.SGDSolver('solver.prototxt')
solver.net.copy_from(weights)

# surgeries
interp_layers = [k for k in solver.net.params.keys() if 'up' in k]
surgery.interp(solver.net, interp_layers)