cleaned up demos

examples/__init__.py

@@ -1,4 +1,4 @@
-from .new_project_templates.lightning_module_template import LightningTemplateModel
+from .basic_examples.lightning_module_template import LightningTemplateModel
 
 __all__ = [
     'LightningTemplateModel'

examples/basic_examples/cpu_template.py

@@ -1,15 +1,13 @@
 """
-16-bit single node, CPU example
+Runs a model on a single node across N-gpus.
 """
 import os
 import numpy as np
 import torch
 
-from test_tube import HyperOptArgumentParser, Experiment
+from argparse import ArgumentParser
 from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from examples.basic_examples.lightning_module_template import LightningTemplateModel
 
 SEED = 2334
 torch.manual_seed(SEED)
@@ -20,7 +18,6 @@ def main(hparams):
     """
     Main training routine specific for this project
     :param hparams:
-    :return:
     """
     # ------------------------
     # 1 INIT LIGHTNING MODEL
@@ -30,11 +27,7 @@ def main(hparams):
     # ------------------------
     # 2 INIT TRAINER
     # ------------------------
-    trainer = Trainer(
-        gpus=hparams.gpus,
-        use_amp=True,
-        distributed_backend='dp'
-    )
+    trainer = Trainer()
 
     # ------------------------
     # 3 START TRAINING
@@ -43,19 +36,14 @@ def main(hparams):
 
 
 if __name__ == '__main__':
-
-    # dirs
+    # ------------------------
+    # TRAINING ARGUMENTS
+    # ------------------------
+    # these are project-wide arguments
     root_dir = os.path.dirname(os.path.realpath(__file__))
+    parent_parser = ArgumentParser(add_help=False)
 
-    # although we user hyperOptParser, we are using it only as argparse right now
-    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
-
-    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    'value -1 uses all the gpus on the node')
-
-    # allow model to overwrite or extend args
+    # each LightningModule defines arguments relevant to it
     parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
     hyperparams = parser.parse_args()
 

examples/basic_examples/gpu_template.py

@@ -0,0 +1,78 @@
+"""
+Runs a model on a single node across N-gpus.
+"""
+import os
+import numpy as np
+import torch
+
+from argparse import ArgumentParser
+from pytorch_lightning import Trainer
+from examples.basic_examples.lightning_module_template import LightningTemplateModel
+
+SEED = 2334
+torch.manual_seed(SEED)
+np.random.seed(SEED)
+
+
+def main(hparams):
+    """
+    Main training routine specific for this project
+    :param hparams:
+    """
+    # ------------------------
+    # 1 INIT LIGHTNING MODEL
+    # ------------------------
+    model = LightningTemplateModel(hparams)
+
+    # ------------------------
+    # 2 INIT TRAINER
+    # ------------------------
+    trainer = Trainer(
+        gpus=hparams.gpus,
+        distributed_backend=hparams.distributed_backend,
+        use_amp=hparams.use_16bit
+    )
+
+    # ------------------------
+    # 3 START TRAINING
+    # ------------------------
+    trainer.fit(model)
+
+
+if __name__ == '__main__':
+    # ------------------------
+    # TRAINING ARGUMENTS
+    # ------------------------
+    # these are project-wide arguments
+
+    root_dir = os.path.dirname(os.path.realpath(__file__))
+    parent_parser = ArgumentParser(add_help=False)
+
+    # gpu args
+    parent_parser.add_argument(
+        '--gpus',
+        type=str,
+        default='-1',
+        help='any integer (number of GPUs to use) or -1 for all'
+    )
+    parent_parser.add_argument(
+        '--distributed_backend',
+        type=str,
+        default=None,
+        help='supports three options dp, ddp, ddp2'
+    )
+    parent_parser.add_argument(
+        '--use_16bit',
+        dest='use_16bit',
+        action='store_true',
+        help='if true uses 16 bit precision'
+    )
+
+    # each LightningModule defines arguments relevant to it
+    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
+    hyperparams = parser.parse_args()
+
+    # ---------------------
+    # RUN TRAINING
+    # ---------------------
+    main(hyperparams)

examples/multi_node_examples/multi_node_cluster_auto_slurm.py

@@ -10,7 +10,7 @@ from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
 
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from examples.basic_examples.lightning_module_template import LightningTemplateModel
 
 PORT = np.random.randint(12000, 20000, 1)[0]
 SEED = 2334

examples/multi_node_examples/multi_node_own_slurm_script.py

@@ -7,7 +7,7 @@ import torch
 
 from test_tube import HyperOptArgumentParser, Experiment
 from pytorch_lightning import Trainer
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from examples.basic_examples.lightning_module_template import LightningTemplateModel
 
 SEED = 2334
 torch.manual_seed(SEED)

examples/new_project_templates/single_cpu_template.py

@@ -1,42 +0,0 @@
-"""
-Runs a model on a single node on CPU only..
-"""
-import os
-import numpy as np
-import torch
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer()
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-    main(hyperparams)

examples/new_project_templates/single_gpu_node_ddp_template.py

@@ -1,69 +0,0 @@
-"""
-Runs a model on a single node across N-gpus.
-"""
-import os
-import numpy as np
-import torch
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    model = LightningTemplateModel(hparams)
-
-    # ------------------------
-    # 2 INIT TRAINER
-    # ------------------------
-    trainer = Trainer(
-        gpus=hparams.gpus,
-        distributed_backend=hparams.dist_backend
-    )
-
-    # ------------------------
-    # 3 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # dirs
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
-    checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
-    test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
-
-    # although we user hyperOptParser, we are using it only as argparse right now
-    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
-
-    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    ' value -1 uses all the gpus on the node')
-    parent_parser.add_argument('--dist_backend', type=str, default='ddp',
-                                help='When using multiple GPUs set Trainer(distributed_backend=dp) (or ddp)')     
-
-    # allow model to overwrite or extend args
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    main(hyperparams)

examples/new_project_templates/single_gpu_node_dp_template.py

@@ -1,94 +0,0 @@
-"""
-Runs a model on a single node across N-gpus using dataParallel
-"""
-import os
-import numpy as np
-import torch
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning import Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # ------------------------
-    # 1 INIT LIGHTNING MODEL
-    # ------------------------
-    print('loading model...')
-    model = LightningTemplateModel(hparams)
-    print('model built')
-
-    # ------------------------
-    # 2 INIT Logger
-    # ------------------------
-    # init experiment
-    exp = Experiment(
-        name=hyperparams.experiment_name,
-        save_dir=hyperparams.test_tube_save_path,
-        autosave=False,
-        description='test demo'
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # ------------------------
-    # 3 INIT TRAINER
-    # ------------------------
-    trainer = Trainer(
-        experiment=exp,
-        gpus=hparams.gpus,
-        distributed_backend=hparams.dist_backend,
-    )
-
-    # ------------------------
-    # 4 START TRAINING
-    # ------------------------
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # dirs
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
-    checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
-    test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')
-
-    # although we user hyperOptParser, we are using it only as argparse right now
-    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)
-
-    # gpu args
-    parent_parser.add_argument('--gpus', type=str, default='-1',
-                               help='how many gpus to use in the node.'
-                                    ' value -1 uses all the gpus on the node')
-    parent_parser.add_argument('--dist_backend', type=str, default='dp',
-                               help='When using multiple GPUs set Trainer(distributed_backend=dp) (or ddp)')
-    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir,
-                               help='where to save logs')
-    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir,
-                               help='where to save model')
-    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a',
-                               help='test tube exp name')
-
-    # allow model to overwrite or extend args
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-    # run on HPC cluster
-    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
-    main(hyperparams)

examples/new_project_templates/trainer_cpu_template.py

@@ -1,53 +0,0 @@
-import os
-import sys
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning import Trainer
-from pytorch_lightning.utilities.arg_parse import add_default_args
-from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
-
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # init experiment
-    exp = Experiment(
-        name=hparams.tt_name,
-        debug=hparams.debug,
-        save_dir=hparams.tt_save_path,
-        version=hparams.hpc_exp_number,
-        autosave=False,
-        description=hparams.tt_description
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    model = LightningTemplateModel(hparams)
-
-    # configure trainer
-    trainer = Trainer(experiment=exp)
-
-    # train model
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # use default args given by lightning
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
-    add_default_args(parent_parser, root_dir)
-
-    # allow model to overwrite or extend args
-    parser = LightningTemplateModel.add_model_specific_args(parent_parser)
-    hyperparams = parser.parse_args()
-
-    # train model
-    main(hyperparams)