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132 lines
3.9 KiB
Markdown
132 lines
3.9 KiB
Markdown
### Template model definition
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In 99% of cases you want to just copy [one of the examples](https://github.com/williamFalcon/pytorch-lightning/tree/master/examples) to start a new lightningModule and change the core of what your model is actually trying to do.
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```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/examples/new_project_templates/lightning_module_template.py
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```
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---
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### Trainer Example
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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. Your LightningModule will have a
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chance to add hyperparameters.
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```{.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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```
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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, and launch the training.
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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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```python
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def main(hparams, cluster, results_dict):
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"""
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Main training routine specific for this project
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:param hparams:
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:return:
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"""
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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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```
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The __main__ function will start training on your **main** function. If you use the HyperParameterOptimizer
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in hyper parameter optimization mode, 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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```{.python}
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main(hyperparams)
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```
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---
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#### CPU hyperparameter search
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```{.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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```
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---
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#### Hyperparameter search on a single or multiple GPUs
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```{.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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```
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---
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#### Hyperparameter search on a SLURM HPC cluster
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```{.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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print('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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