def onStartBtn(self, event): self.onSaveSettingsBtn(event) basepath = self.gallery_path.GetValue() if basepath == None or basepath == "": wx.MessageDialog(self, "No gallery path given, cannot proceed without one.", "", wx.OK ).ShowModal() else: # self.exitBtn.Enable(enable=False) launch.main(basepath=basepath)
#!/usr/bin/python import os, sys path = "None" sys.path.append(path) import launch os.chdir(path) launch.main()
init initialize a MicroStack node
add-compute generate a connection string for a node to join the cluster
launch launch a virtual machine
''')
parser.add_argument('command',
help='A subcommand to run:\n'
' {init, launch, add-compute}')
args = parser.parse_args(sys.argv[1:2])
COMMANDS = {
'init': init.main.init,
'add-compute': cluster.add_compute.main,
'launch': launch.main.main
}
cmd = COMMANDS.get(args.command, None)
if cmd is None:
parser.print_help()
raise Exception('Unrecognized command')
# TODO: Implement this properly via subparsers and get rid of
# extra modules.
sys.argv[0] = sys.argv[1]
# Get rid of the command name in the args and call the actual command.
del (sys.argv[1])
cmd()
if __name__ == '__main__':
main()
def run(workout):
workdir = workout
name = os.path.basename(workdir)
chartdir = os.path.join(os.path.dirname(os.path.realpath(sys.argv[0])), 'chart')
try:
if not os.path.exists(os.path.join(workdir, '('+name+')'+'performace.xls')):
wb = open_workbook(os.path.join(chartdir, 'performance.xls'), formatting_info=True)
rb = copy(wb)
rb.save(os.path.join(workdir, '('+name+')'+'performance.xls'))
else:
print 'file already exists'
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'scenes')):
scenesgpuchart.main(workdir)
gfxinfo.main(workdir, name)
skinfo.main(workdir, name)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'memory')):
memory.main(workdir, name)
memchart.getData(workdir)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'monkey')):
if os.path.exists(os.path.join(os.path.join(workdir, 'monkey'), 'monkey.csv')):
allreader.main(workdir, name)
else:
monkeygpuchart.main(workdir)
monkeymemchart.getData(workdir)
alonemonkey.main(workdir, name)
monkeygfx.main(workdir, name)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'stress.csv')):
stress.main(workdir, name)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'compat.csv')):
compat.main(workdir, name)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'launch.csv')):
launch.main(workdir, name)
except KeyboardInterrupt:
pass
try:
if os.path.exists(os.path.join(workdir, 'uptime.csv')):
uptime.main(workdir, name)
except KeyboardInterrupt:
pass
try:
hypelink.main(workdir, name)
print 'ok'
except KeyboardInterrupt:
pass
print 'alltest finish'
raise ValueError(f"Task {sm_args.nlp_problem} is not supported.")
return task_path, transformer_args
if __name__ == "__main__":
# Get initial configuration to select appropriate HuggingFace task and its configuration
print('Starting training...')
parser = ArgumentParser()
parser.add_argument('--nlp-problem', type=str, default="language-modeling", help="Define NLP problem to run from HuggingFace example library. See for options: \
https://github.com/huggingface/transformers/tree/master/examples#the-big-table-of-tasks.")
parser.add_argument('--dataset', type=str, default=None, help="Define which dataset to use.")
sm_args, transformer_args = parser.parse_known_args()
# Get task script and its cofiguration
task_script, transformer_args = task_selector(sm_args, transformer_args)
# Derive parameters of distributed training cluster in Sagemaker
world = get_training_world()
# Creates launch configuration according to PyTorch Distributed Launch utility requirements:
# https://github.com/pytorch/pytorch/blob/master/torch/distributed/launch.py
launch_config = ["--nnodes", str(world['number_of_machines']), "--node_rank", str(world['machine_rank']),
"--nproc_per_node", str(world['number_of_processes']), "--master_addr", world['master_addr'],
"--master_port", world['master_port']]
# Launch distributed training. Note, that launch script configuration is passed as script arguments
sys.argv = [""] + launch_config + [task_script] + transformer_args
launch.main()