When the machine learning (ML) Engine executes your application, it passes arguments that provide information about the operating environment. The table lists the possible arguments.
Machine Learning Arguments
| Argument | Operation |
--job-dir |
Location of the application’s data |
--train_batch_size |
Batch size for training |
--train_steps |
Number of steps for each training epoch |
--eval_batch_size |
Batch size for evaluation |
--eval_steps |
Number of steps to run evaluation at each checkpoint |
--eval_delay_secs |
Time to wait before first evaluation |
--min_eval_frequency |
Minimum number of training steps between evaluations |
--job-dir is particularly important because it tells the application where it should store its output files. The following code demonstrates how you can access this using an ArgumentParser:
if __name__ == '__main__':In addition to the built-in arguments, you can provide arguments of your own. When you submit a job, the ML Engine will pass your arguments to the application. But keep two points in mind:parser = argparse.ArgumentParser()
parser.add_argument(
'--job-dir',
help='Checkpoint/output location',
required=True
)
args = parser.parse_args()
- User-defined flags must follow all of the built-in flags.
- Two dashes (
--) must separate the built-in flags from the user-defined flags.
data_dir and num_epochs. When you execute a command, you need to set the --data_dir and --num_epochs flags at the end of the command and separate them from the command's normal flags with --.