def remote_run_experiment(self):
for parameter_setup in self._parameter_setups:
print(parameter_setup)
task = Task.create(
project_name=f"{self._project_name}",
task_name=self.make_task_name(parameter_setup),
repo=self._repo,
branch=self._branch,
script=self._script,
requirements_file="../requirements.txt"
)
task.set_parent(Task.current_task().id)
task.connect(parameter_setup)
Task.enqueue(task, self._queue)
def cli():
title = 'ClearML launch - launch any codebase on remote machine running clearml-agent'
print(title)
parser = ArgumentParser(description=title)
setup_parser(parser)
# get the args
args = parser.parse_args()
if args.version:
from ...version import __version__
print('Version {}'.format(__version__))
exit(0)
create_populate = CreateAndPopulate(
project_name=args.project,
task_name=args.name,
task_type=args.task_type,
repo=args.repo or args.folder,
branch=args.branch,
commit=args.commit,
script=args.script,
working_directory=args.cwd,
packages=args.packages,
requirements_file=args.requirements,
docker=args.docker,
base_task_id=args.base_task_id,
add_task_init_call=not args.skip_task_init,
raise_on_missing_entries=True,
verbose=True,
)
# verify args
create_populate.update_task_args(args.args)
print('Creating new task')
create_populate.create_task()
# update Task args
create_populate.update_task_args(args.args)
print('New task created id={}'.format(create_populate.get_id()))
if not args.queue:
print('Warning: No queue was provided, leaving task in draft-mode.')
exit(0)
Task.enqueue(create_populate.task, queue_name=args.queue)
print('Task id={} sent for execution on queue {}'.format(
create_populate.get_id(), args.queue))
print('Execution log at: {}'.format(
create_populate.task.get_output_log_web_page()))
# Select base template task
# Notice we can be more imaginative and use task_id which will eliminate the need to use project name
template_task = Task.get_task(project_name='examples',
task_name=params['experiment_template_name'])
for i in range(params['total_number_of_experiments']):
# clone the template task into a new write enabled task (where we can change parameters)
cloned_task = Task.clone(source_task=template_task,
name=template_task.name + ' {}'.format(i),
parent=template_task.id)
# get the original template parameters
cloned_task_parameters = cloned_task.get_parameters()
# override with random samples form grid
for k in space.keys():
cloned_task_parameters[k] = space[k]()
# put back into the new cloned task
cloned_task.set_parameters(cloned_task_parameters)
print('Experiment {} set with parameters {}'.format(
i, cloned_task_parameters))
# enqueue the task for execution
Task.enqueue(cloned_task.id, queue_name=params['execution_queue_name'])
print('Experiment id={} enqueue for execution'.format(cloned_task.id))
# we are done, the next step is to watch the experiments graphs
print('Done')
param["param_name_new_value"] = 3
# The queue where we want the template task (clone) to be sent to
param["execution_queue_name"] = "default"
# Simulate the work of a Task
print("Processing....")
sleep(2.0)
print("Done processing :)")
# Get a reference to the task to pipe to.
next_task = Task.get_task(project_name=task.get_project_name(),
task_name=param["next_task_name"])
# Clone the task to pipe to. This creates a task with status Draft whose parameters can be modified.
cloned_task = Task.clone(source_task=next_task,
name="Auto generated cloned task")
# Get the original parameters of the Task, modify the value of one parameter,
# and set the parameters in the next Task
cloned_task_parameters = cloned_task.get_parameters()
cloned_task_parameters[param["param_name"]] = param["param_name_new_value"]
cloned_task.set_parameters(cloned_task_parameters)
# Enqueue the Task for execution. The enqueued Task must already exist in the clearml platform
print("Enqueue next step in pipeline to queue: {}".format(
param["execution_queue_name"]))
Task.enqueue(cloned_task.id, queue_name=param["execution_queue_name"])
# We are done. The next step in the pipe line is in charge of the pipeline now.
print("Done")
def cli():
title = 'ClearML launch - launch any codebase on remote machine running clearml-agent'
print(title)
parser = ArgumentParser(description=title)
setup_parser(parser)
# get the args
args = parser.parse_args()
if len(sys.argv) < 2:
parser.print_help()
exit(0)
if args.version:
print('Version {}'.format(__version__))
exit(0)
if not args.name:
raise ValueError(
"Task name must be provided, use `--name <task-name>`")
if args.docker_bash_setup_script and Path(
args.docker_bash_setup_script).is_file():
with open(args.docker_bash_setup_script,
"r") as bash_setup_script_file:
bash_setup_script = bash_setup_script_file.readlines()
# remove Bash Shebang
if bash_setup_script and bash_setup_script[0].strip().startswith(
"#!"):
bash_setup_script = bash_setup_script[1:]
else:
bash_setup_script = args.docker_bash_setup_script or None
create_populate = CreateAndPopulate(
project_name=args.project,
task_name=args.name,
task_type=args.task_type,
repo=args.repo or args.folder,
branch=args.branch,
commit=args.commit,
script=args.script,
working_directory=args.cwd,
packages=args.packages,
requirements_file=args.requirements,
docker=args.docker,
docker_args=args.docker_args,
docker_bash_setup_script=bash_setup_script,
output_uri=args.output_uri,
base_task_id=args.base_task_id,
add_task_init_call=not args.skip_task_init,
raise_on_missing_entries=True,
verbose=True,
)
# verify args before creating the Task
create_populate.update_task_args(args.args)
print('Creating new task')
create_populate.create_task()
# update Task args
create_populate.update_task_args(args.args)
print('New task created id={}'.format(create_populate.get_id()))
if not args.queue:
print('Warning: No queue was provided, leaving task in draft-mode.')
exit(0)
Task.enqueue(create_populate.task, queue_name=args.queue)
print('Task id={} sent for execution on queue {}'.format(
create_populate.get_id(), args.queue))
print('Execution log at: {}'.format(
create_populate.task.get_output_log_web_page()))
