def dry_run(problem, sub_problem, eval_mode, dev_mode, cloud_mode):
if problem == 'whales':
setup_torch_multiprocessing()
pm = importlib.import_module('minerva.{}.problem_manager'.format(problem))
logging.info('running: {0}'.format(sub_problem))
pm.dry_run(sub_problem, eval_mode, dev_mode, cloud_mode)
def dry_run(problem, train_mode, dev_mode):
if problem == 'whales':
setup_torch_multiprocessing()
pm = importlib.import_module('minerva.{}.problem_manager'.format(problem))
sub_problems = SUBPROBLEM_INFERENCE.get(problem, {0: None})
for sub_problem in list(set(sub_problems.values())):
if sub_problem:
logging.info('running: {0}'.format(sub_problem))
pm.dry_run(sub_problem, train_mode, dev_mode)
def submit(problem, task_nr, file_path, dev_mode):
if file_path is None:
file_path = 'resources/{}/tasks/task{}.ipynb'.format(problem, task_nr)
if problem == 'whales':
setup_torch_multiprocessing()
sub_problems = SUBPROBLEM_INFERENCE.get(problem, {})
task_sub_problem = sub_problems.get(task_nr, None)
pm = importlib.import_module('minerva.{}.problem_manager'.format(problem))
pm.submit_task(task_sub_problem, task_nr, file_path, dev_mode)
def submit(problem, sub_problem, task_nr, filepath, dev_mode, cloud_mode):
if problem == 'whales':
setup_torch_multiprocessing()
pm = importlib.import_module('minerva.{}.problem_manager'.format(problem))
pm.submit_task(sub_problem, task_nr, filepath, dev_mode, cloud_mode)