lightning/pl_examples/__init__.py

"""
Template model definition
-------------------------

In 99% of cases you want to just copy `one of the examples
 <https://github.com/PyTorchLightning/pytorch-lightning/tree/master/pl_examples>`_
 to start a new lightningModule and change the core of what your model is actually trying to do.

.. code-block:: bash

    # get a copy of the module template
    wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/pl_examples/new_project_templates/lightning_module_template.py  # noqa: E501


Trainer Example
---------------

**`__main__` function**

Normally, we want to let the `__main__` function start the training.
 Inside the main we parse training arguments with whatever hyperparameters we want.
 Your LightningModule will have a chance to add hyperparameters.

.. code-block:: python

    from test_tube import HyperOptArgumentParser

    if __name__ == '__main__':

        # use default args given by lightning
        root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
        parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
        add_default_args(parent_parser, root_dir)

        # allow model to overwrite or extend args
        parser = ExampleModel.add_model_specific_args(parent_parser)
        hyperparams = parser.parse_args()

        # train model
        main(hyperparams)

**Main Function**

The main function is your entry into the program. This is where you init your model, checkpoint directory,
 and launch the training. The main function should have 3 arguments:

- hparams: a configuration of hyperparameters.
- slurm_manager: Slurm cluster manager object (can be None)
- dict: for you to return any values you want (useful in meta-learning, otherwise set to)

.. code-block:: python

    def main(hparams, cluster, results_dict):
        # build model
        model = MyLightningModule(hparams)

        # configure trainer
        trainer = Trainer()

        # train model
        trainer.fit(model)


The `__main__` function will start training on your **main** function.
 If you use the HyperParameterOptimizer in hyper parameter optimization mode,
 this main function will get one set of hyperparameters. If you use it as a simple
 argument parser you get the default arguments in the argument parser.

So, calling main(hyperparams) runs the model with the default argparse arguments.::

    main(hyperparams)


CPU hyperparameter search
-------------------------

.. code-block:: python

    # run a grid search over 20 hyperparameter combinations.
    hyperparams.optimize_parallel_cpu(
        main_local,
        nb_trials=20,
        nb_workers=1
    )


Hyperparameter search on a single or multiple GPUs
--------------------------------------------------

.. code-block:: python

    # run a grid search over 20 hyperparameter combinations.
    hyperparams.optimize_parallel_gpu(
        main_local,
        nb_trials=20,
        nb_workers=1,
        gpus=[0,1,2,3]
    )


Hyperparameter search on a SLURM HPC cluster
--------------------------------------------

.. code-block:: python

    def optimize_on_cluster(hyperparams):
        # enable cluster training
        cluster = SlurmCluster(
            hyperparam_optimizer=hyperparams,
            log_path=hyperparams.tt_save_path,
            test_tube_exp_name=hyperparams.tt_name
        )

        # email for cluster coms
        cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)

        # configure cluster
        cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
        cluster.job_time = '48:00:00'
        cluster.gpu_type = '1080ti'
        cluster.memory_mb_per_node = 48000

        # any modules for code to run in env
        cluster.add_command('source activate pytorch_lightning')

        # name of exp
        job_display_name = hyperparams.tt_name.split('_')[0]
        job_display_name = job_display_name[0:3]

        # run hopt
        logging.info('submitting jobs...')
        cluster.optimize_parallel_cluster_gpu(
            main,
            nb_trials=hyperparams.nb_hopt_trials,
            job_name=job_display_name
        )

    # run cluster hyperparameter search
    optimize_on_cluster(hyperparams)

"""

from .basic_examples.lightning_module_template import LightningTemplateModel

__all__ = [
    'LightningTemplateModel'
]