mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-10-29 13:30:57 +08:00
William Falcon
bf09060fef
* early stopping callback is not default * added a default logger * added default checkpoint callback * added default checkpoint/loggers * added default checkpoint/loggers * updated docs * cleaned demos * cleaned demos * cleaned demos * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers * clean up docs around loggers
84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
"""
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Runs a model on a single node on CPU only..
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"""
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import os
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import numpy as np
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import torch
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from test_tube import HyperOptArgumentParser, Experiment
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from pytorch_lightning import Trainer
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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from examples.new_project_templates.lightning_module_template import LightningTemplateModel
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SEED = 2334
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torch.manual_seed(SEED)
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np.random.seed(SEED)
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def main(hparams):
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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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# ------------------------
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# 1 INIT LIGHTNING MODEL
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# ------------------------
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model = LightningTemplateModel(hparams)
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# ------------------------
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# 2 INIT EXP
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# ------------------------
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# init experiment
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exp = Experiment(
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name=hyperparams.experiment_name,
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save_dir=hyperparams.test_tube_save_path,
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autosave=False,
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description='test demo'
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)
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exp.argparse(hparams)
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exp.save()
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# ------------------------
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# 3 INIT TRAINER
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# ------------------------
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trainer = Trainer(experiment=exp)
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# ------------------------
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# 4 START TRAINING
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# ------------------------
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trainer.fit(model)
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if __name__ == '__main__':
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# dirs
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root_dir = os.path.dirname(os.path.realpath(__file__))
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demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
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checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
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test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
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# although we user hyperOptParser, we are using it only as argparse right now
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parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
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# gpu args
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parent_parser.add_argument('--test_tube_save_path', type=str,
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default=test_tube_dir, help='where to save logs')
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parent_parser.add_argument('--model_save_path', type=str,
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default=checkpoint_dir, help='where to save model')
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parent_parser.add_argument('--experiment_name', type=str,
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default='pt_lightning_exp_a', help='test tube exp name')
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# allow model to overwrite or extend args
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parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
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hyperparams = parser.parse_args()
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# ---------------------
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# RUN TRAINING
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# ---------------------
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# run on HPC cluster
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print('RUNNING ON CPU')
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main(hyperparams)
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