114 lines
3.3 KiB
Python
114 lines
3.3 KiB
Python
"""
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Runs a model on a single node across N-gpus.
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"""
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import os
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import sys
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import numpy as np
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from time import sleep
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import torch
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from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
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from pytorch_lightning.models.trainer import Trainer
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from pytorch_lightning.utils.arg_parse import add_default_args
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from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
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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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from lightning_module_template import LightningTemplateModel
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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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print('loading model...')
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model = LightningTemplateModel(hparams)
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print('model built')
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# ------------------------
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# 2 INIT TEST TUBE 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 DEFINE CALLBACKS
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# ------------------------
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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early_stop = EarlyStopping(
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monitor='val_acc',
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patience=3,
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verbose=True,
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mode='max'
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)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_best_only=True,
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verbose=True,
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monitor='val_loss',
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mode='min'
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)
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# ------------------------
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# 4 INIT TRAINER
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# ------------------------
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trainer = Trainer(
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experiment=exp,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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gpus=hparams.gpus,
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use_amp=True
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)
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# ------------------------
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# 5 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('--gpus', type=str, default='-1', help='how many gpus to use in the node. -1 uses all the gpus on the node')
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parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
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parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
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parent_parser.add_argument('--experiment_name', type=str, 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(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
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main(hyperparams)
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