173 lines
5.0 KiB
Python
173 lines
5.0 KiB
Python
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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# ---------------------
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# DEFINE MODEL HERE
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# ---------------------
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from lightning_module_template import LightningTemplateModel
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# ---------------------
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AVAILABLE_MODELS = {
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'model_template': LightningTemplateModel
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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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path = 'emv_' + os.environ['SLURM_CLUSTER_NAME']
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os.makedirs(os.path.join(hparams.test_tube_save_path, path), exist_ok=True)
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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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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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version=cluster.hpc_exp_number
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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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cluster=cluster,
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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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nb_gpu_nodes=hyperparams.nb_gpu_nodes
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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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def optimize_on_cluster(hyperparams):
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# enable cluster training
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# log all scripts to the test tube folder
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cluster = SlurmCluster(
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hyperparam_optimizer=hyperparams,
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log_path=hyperparams.slurm_log_path,
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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.per_experiment_nb_nodes = hyperparams.nb_gpu_nodes
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cluster.job_time = '2:00:00'
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cluster.gpu_type = 'volta'
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cluster.memory_mb_per_node = 0
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# any modules for code to run in env
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cluster.add_command('source activate lightning')
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# run only on 32GB voltas
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cluster.add_slurm_cmd(cmd='constraint', value='volta32gb', comment='use 32gb gpus')
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cluster.add_slurm_cmd(cmd='partition', value=hyperparams.gpu_partition, comment='use 32gb gpus')
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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=hyperparams.experiment_name
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)
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if __name__ == '__main__':
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# use default args
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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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slurm_out_dir = os.path.join(demo_log_dir, 'slurm_scripts')
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parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
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# cluster args not defined inside the model
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parent_parser.add_argument('--gpu_partition', type=str)
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parent_parser.add_argument('--per_experiment_nb_gpus', type=int)
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parent_parser.add_argument('--nb_gpu_nodes', type=int, default=1)
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parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir)
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parent_parser.add_argument('--slurm_log_path', type=str, default=slurm_out_dir)
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parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir)
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parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a')
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parent_parser.add_argument('--gpus', type=str, default='-1')
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parent_parser.add_argument('--nb_hopt_trials', type=int, default=1)
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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 SLURM CLUSTER')
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optimize_on_cluster(hyperparams)
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