215 lines
6.3 KiB
Python
215 lines
6.3 KiB
Python
import os
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import sys
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import torch
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import numpy as np
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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 time import sleep
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from pytorch_lightning.callbacks.pt_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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# ---------------------
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# DEFINE MODEL HERE
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# ---------------------
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from pytorch_lightning.models.sample_model_template.model_template import ExampleModel1
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# ---------------------
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AVAILABLE_MODELS = {
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'model_1': ExampleModel1
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}
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"""
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Allows training by using command line arguments
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Run by:
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# TYPE YOUR RUN COMMAND HERE
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"""
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def main_local(hparams):
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main(hparams, None, None)
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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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on_gpu = torch.cuda.is_available()
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if hparams.disable_cuda:
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on_gpu = False
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device = 'cuda' if on_gpu else 'cpu'
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hparams.__setattr__('device', device)
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hparams.__setattr__('on_gpu', on_gpu)
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hparams.__setattr__('nb_gpus', torch.cuda.device_count())
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hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
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# delay each training start to not overwrite logs
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process_position, current_gpu = TRAINING_MODEL.get_process_position(hparams.gpus)
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sleep(process_position + 1)
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# init experiment
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exp = Experiment(
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name=hparams.tt_name,
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debug=hparams.debug,
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save_dir=hparams.tt_save_path,
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version=hparams.hpc_exp_number,
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autosave=False,
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description=hparams.tt_description
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)
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exp.argparse(hparams)
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exp.save()
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# build model
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print('loading model...')
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model = TRAINING_MODEL(hparams)
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print('model built')
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# callbacks
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early_stop = EarlyStopping(
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monitor=hparams.early_stop_metric,
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patience=hparams.early_stop_patience,
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verbose=True,
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mode=hparams.early_stop_mode
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)
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_function=None,
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save_best_only=True,
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verbose=True,
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monitor=hparams.model_save_monitor_value,
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mode=hparams.model_save_monitor_mode
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)
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# configure trainer
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trainer = Trainer(
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experiment=exp,
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on_gpu=on_gpu,
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cluster=cluster,
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enable_tqdm=hparams.enable_tqdm,
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overfit_pct=hparams.overfit,
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track_grad_norm=hparams.track_grad_norm,
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fast_dev_run=hparams.fast_dev_run,
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check_val_every_n_epoch=hparams.check_val_every_n_epoch,
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accumulate_grad_batches=hparams.accumulate_grad_batches,
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process_position=process_position,
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current_gpu_name=current_gpu,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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enable_early_stop=hparams.enable_early_stop,
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max_nb_epochs=hparams.max_nb_epochs,
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min_nb_epochs=hparams.min_nb_epochs,
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train_percent_check=hparams.train_percent_check,
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val_percent_check=hparams.val_percent_check,
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test_percent_check=hparams.test_percent_check,
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val_check_interval=hparams.val_check_interval,
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log_save_interval=hparams.log_save_interval,
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add_log_row_interval=hparams.add_log_row_interval,
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lr_scheduler_milestones=hparams.lr_scheduler_milestones
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)
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# train model
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trainer.fit(model)
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def get_default_parser(strategy, root_dir):
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possible_model_names = list(AVAILABLE_MODELS.keys())
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parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
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add_default_args(parser, root_dir, possible_model_names, SEED)
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return parser
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def get_model_name(args):
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for i, arg in enumerate(args):
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if 'model_name' in arg:
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return args[i+1]
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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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if __name__ == '__main__':
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model_name = get_model_name(sys.argv)
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# use default args
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root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
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parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
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# allow model to overwrite or extend args
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TRAINING_MODEL = AVAILABLE_MODELS[model_name]
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parser = TRAINING_MODEL.add_model_specific_args(parent_parser)
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parser.json_config('-c', '--config', default=root_dir + '/run_configs/local.json')
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hyperparams = parser.parse_args()
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# format GPU layout
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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gpu_ids = hyperparams.gpus.split(';')
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# RUN TRAINING
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if hyperparams.on_cluster:
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print('RUNNING ON SLURM CLUSTER')
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os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
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optimize_on_cluster(hyperparams)
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elif hyperparams.single_run_gpu:
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print(f'RUNNING 1 TRIAL ON GPU. gpu: {gpu_ids[0]}')
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os.environ["CUDA_VISIBLE_DEVICES"] = gpu_ids[0]
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main(hyperparams, None, None)
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elif hyperparams.local or hyperparams.single_run:
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os.environ["CUDA_VISIBLE_DEVICES"] = '0'
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print('RUNNING LOCALLY')
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main(hyperparams, None, None)
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else:
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print(f'RUNNING MULTI GPU. GPU ids: {gpu_ids}')
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hyperparams.optimize_parallel_gpu(
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main_local,
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gpu_ids=gpu_ids,
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nb_trials=hyperparams.nb_hopt_trials,
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nb_workers=len(gpu_ids)
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)
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