mirror of
https://github.com/wassname/pytorch-lightning.git
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Jirka Borovec
d71556e7a1
* upgrade req. * move MkDocs * create Sphinx * init Sphinx * move md from MkDocs to Sphinx * CI: build docs * build Sphinx formatting move docs from MD to docstring in particular package/modules formatting add Sphinx ext. rename root_module to core drop implicit name "_logger" drop duplicate name "overwrite" fix imports use pytorch theme add sample link mapping try fix RTD build use forked template fix some docs warnings fix paths add deprecation warnings fix flake8 fix paths revert refactor revert MLFlowLogger * revert example import * update link * Update lightning_module_template.py
147 lines
4.3 KiB
Python
147 lines
4.3 KiB
Python
"""
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Template model definition
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-------------------------
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In 99% of cases you want to just copy `one of the examples
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<https://github.com/williamFalcon/pytorch-lightning/tree/master/pl_examples>`_
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to start a new lightningModule and change the core of what your model is actually trying to do.
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.. code-block:: bash
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# get a copy of the module template
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wget https://raw.githubusercontent.com/williamFalcon/pytorch-lightning/master/pl_examples/new_project_templates/lightning_module_template.py # noqa: E501
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Trainer Example
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---------------
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**`__main__` function**
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Normally, we want to let the `__main__` function start the training.
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Inside the main we parse training arguments with whatever hyperparameters we want.
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Your LightningModule will have a chance to add hyperparameters.
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.. code-block:: python
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from test_tube import HyperOptArgumentParser
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if __name__ == '__main__':
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# use default args given by lightning
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root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
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parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
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add_default_args(parent_parser, root_dir)
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# allow model to overwrite or extend args
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parser = ExampleModel.add_model_specific_args(parent_parser)
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hyperparams = parser.parse_args()
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# train model
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main(hyperparams)
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**Main Function**
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The main function is your entry into the program. This is where you init your model, checkpoint directory,
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and launch the training. The main function should have 3 arguments:
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- hparams: a configuration of hyperparameters.
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- slurm_manager: Slurm cluster manager object (can be None)
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- dict: for you to return any values you want (useful in meta-learning, otherwise set to)
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.. code-block:: python
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def main(hparams, cluster, results_dict):
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# build model
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model = MyLightningModule(hparams)
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# configure trainer
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trainer = Trainer()
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# train model
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trainer.fit(model)
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The `__main__` function will start training on your **main** function.
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If you use the HyperParameterOptimizer in hyper parameter optimization mode,
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this main function will get one set of hyperparameters. If you use it as a simple
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argument parser you get the default arguments in the argument parser.
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So, calling main(hyperparams) runs the model with the default argparse arguments.::
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main(hyperparams)
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CPU hyperparameter search
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-------------------------
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.. code-block:: python
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# run a grid search over 20 hyperparameter combinations.
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hyperparams.optimize_parallel_cpu(
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main_local,
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nb_trials=20,
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nb_workers=1
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)
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Hyperparameter search on a single or multiple GPUs
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--------------------------------------------------
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.. code-block:: python
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# run a grid search over 20 hyperparameter combinations.
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hyperparams.optimize_parallel_gpu(
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main_local,
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nb_trials=20,
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nb_workers=1,
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gpus=[0,1,2,3]
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)
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Hyperparameter search on a SLURM HPC cluster
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--------------------------------------------
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.. code-block:: python
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def optimize_on_cluster(hyperparams):
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# enable cluster training
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cluster = SlurmCluster(
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hyperparam_optimizer=hyperparams,
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log_path=hyperparams.tt_save_path,
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test_tube_exp_name=hyperparams.tt_name
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)
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# email for cluster coms
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cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
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# configure cluster
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cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
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cluster.job_time = '48:00:00'
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cluster.gpu_type = '1080ti'
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cluster.memory_mb_per_node = 48000
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# any modules for code to run in env
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cluster.add_command('source activate pytorch_lightning')
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# name of exp
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job_display_name = hyperparams.tt_name.split('_')[0]
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job_display_name = job_display_name[0:3]
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# run hopt
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logging.info('submitting jobs...')
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cluster.optimize_parallel_cluster_gpu(
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main,
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nb_trials=hyperparams.nb_hopt_trials,
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job_name=job_display_name
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)
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# run cluster hyperparameter search
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optimize_on_cluster(hyperparams)
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"""
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from .basic_examples.lightning_module_template import LightningTemplateModel
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__all__ = [
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'LightningTemplateModel'
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]
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