def dry_run(sub_problem, train_mode, dev_mode):
config, cloud_mode = setup_env(SOLUTION_CONFIG, sub_problem)
check_inputs(train_mode, config, solution_pipeline)
trainer = Trainer(solution_pipeline, config, dev_mode)
if train_mode:
trainer.train()
_evaluate(trainer)
K.clear_session()
def dry_run(sub_problem, train_mode, dev_mode):
config, cloud_mode = setup_env(SOLUTION_CONFIG, sub_problem)
pipeline = pipeline_dict[sub_problem]
check_inputs(train_mode, config, pipeline)
trainer = Trainer(pipeline, config, dev_mode, cloud_mode, sub_problem)
if train_mode:
trainer.train()
_evaluate(trainer, sub_problem)
def submit_task(sub_problem, task_nr, filepath, dev_mode):
config, _ = setup_env(SOLUTION_CONFIG, sub_problem)
check_inputs(train_mode=False, config=config, pipeline=solution_pipeline)
submit_config = submit_setup(config)
trainer = Trainer(solution_pipeline, submit_config, dev_mode)
user_task_solution, user_config = _fetch_task_solution(filepath)
task_handler = registered_tasks[task_nr](trainer)
new_trainer = task_handler.substitute(user_task_solution, user_config)
new_trainer.train()
_evaluate(new_trainer)
K.clear_session()
submit_teardown(submit_config)
def submit_task(sub_problem, task_nr, filepath, dev_mode):
with TaskSolutionParser(filepath) as task_solution:
config, cloud_mode = setup_env(SOLUTION_CONFIG, sub_problem)
pipeline = pipeline_dict[sub_problem]
check_inputs(train_mode=False, config=config, pipeline=pipeline)
submit_config = submit_setup(config)
trainer = Trainer(pipeline, submit_config, dev_mode, cloud_mode,
sub_problem)
user_task_solution = task_solution.get('solution')
user_config = task_solution.get('CONFIG')
task_handler = registered_tasks[task_nr](trainer)
new_trainer = task_handler.substitute(user_task_solution, user_config)
new_trainer.train()
_evaluate(new_trainer, sub_problem)
submit_teardown(submit_config)