def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--hidden_dim', type=int, default=128)
parser.add_argument('--learning_rate', type=float, default=0.0001)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# init neptune experiment
import os
from utils import init_neptune
api_key = os.environ['NEPTUNE_API_TOKEN']
project_name = 'zzragida/examples-mnist'
experiment_name = 'pytorch-lightning-mnist'
experiment_tags = ['lightning', 'mnist']
neptune_logger = init_neptune(args, api_key, project_name, experiment_name, experiment_tags)
# ------------
# data
# ------------
dataset = MNIST('', train=True, download=True, transform=transforms.ToTensor())
mnist_test = MNIST('', train=False, download=True, transform=transforms.ToTensor())
mnist_train, mnist_val = random_split(dataset, [55000, 5000])
train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
# ------------
# model
# ------------
model = LitClassifier(args.hidden_dim, args.learning_rate)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args, max_epochs=10, logger=neptune_logger)
trainer.fit(model, train_loader, val_loader)
# ------------
# testing
# ------------
trainer.test(test_dataloaders=test_loader)
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
parser.add_argument('--lr', default=0.0002, type=float)
parser.add_argument('--b1', default=0.5, type=float)
parser.add_argument('--b2', default=0.999, type=float)
parser.add_argument('--latent_dim', default=100, type=int)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# init neptune experiment
import os
from utils import init_neptune
api_key = os.environ['NEPTUNE_API_TOKEN']
project_name = 'zzragida/examples-lit-gan'
experiment_name = 'pytorch-lightning-gan'
experiment_tags = ['lightning', 'gan']
neptune_logger = init_neptune(args, api_key, project_name, experiment_name,
experiment_tags)
# ------------
# model
# ------------
model = GAN(args)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args, logger=neptune_logger)
trainer.fit(model)
# ------------
# testing
# ------------
trainer.test()
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# init neptune experiment
import os
from utils import init_neptune
api_key = os.environ['NEPTUNE_API_TOKEN']
project_name = 'zzragida/examples-lit-bert'
experiment_name = 'pytorch-lightning-bert'
experiment_tags = ['lightning', 'bert']
neptune_logger = init_neptune(args, api_key, project_name, experiment_name,
experiment_tags)
# ------------
# model
# ------------
model = BertMNLIFinetuner()
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args, logger=neptune_logger)
trainer.fit(model)
# ------------
# testing
# ------------
trainer.test()
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# init neptune experiment
import os
from utils import init_neptune
api_key = os.environ['NEPTUNE_API_TOKEN']
project_name = 'zzragida/examples-lit-sklearn'
experiment_name = 'pytorch-lightning-sklearn'
experiment_tags = ['lightning', 'sklearn']
neptune_logger = init_neptune(args, api_key, project_name, experiment_name,
experiment_tags)
# ------------
# data
# ------------
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target
# train/test split
Y_col = ['target']
X_col = [col for col in df.columns if not col in Y_col]
X_train, X_test, Y_train, Y_test = train_test_split(df[X_col],
df[Y_col],
test_size=0.2,
random_state=0)
X_train, X_val, Y_train, Y_val = train_test_split(X_train[X_col],
Y_train[Y_col],
test_size=0.2,
random_state=0)
train_ds = DataFrameDataset(X_train, Y_train)
val_ds = DataFrameDataset(X_val, Y_val)
test_ds = DataFrameDataset(X_test, Y_test)
train_loader = DataLoader(train_ds, batch_size=args.batch_size)
val_loader = DataLoader(val_ds, batch_size=args.batch_size)
test_loader = DataLoader(test_ds, batch_size=args.batch_size)
# ------------
# model
# ------------
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=100, random_state=0)
clf.fit(X_train, Y_train)
print(accuracy_score(Y_test, clf.predict(X_test).ravel()))
model = SklearnClassifier(clf)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args,
max_epochs=1,
logger=neptune_logger)
trainer.fit(model, train_loader, val_loader)
# ------------
# testing
# ------------
trainer.test(test_dataloaders=test_loader)
def cli_main():
pl.seed_everything(1234)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument("--batch_size",
type=int,
default=16,
help="size of the batches")
parser.add_argument("--lr", type=float, default=1e-2, help="learning rate")
parser.add_argument("--env",
type=str,
default="CartPole-v0",
help="gym environment tag")
parser.add_argument("--gamma",
type=float,
default=0.99,
help="discount factor")
parser.add_argument("--sync_rate",
type=int,
default=10,
help="how many frames do we update the target network")
parser.add_argument("--replay_size",
type=int,
default=1000,
help="capacity of the replay buffer")
parser.add_argument(
"--warm_start_size",
type=int,
default=1000,
help=
"how many samples do we use to fill our buffer at the start of training"
)
parser.add_argument("--eps_last_frame",
type=int,
default=1000,
help="what frame should epsilon stop decaying")
parser.add_argument("--eps_start",
type=float,
default=1.0,
help="starting value of epsilon")
parser.add_argument("--eps_end",
type=float,
default=0.01,
help="final value of epsilon")
parser.add_argument("--episode_length",
type=int,
default=200,
help="max length of an episode")
parser.add_argument("--max_episode_reward",
type=int,
default=200,
help="max episode reward in the environment")
parser.add_argument("--warm_start_steps",
type=int,
default=1000,
help="max episode reward in the environment")
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# init neptune experiment
import os
from utils import init_neptune
api_key = os.environ['NEPTUNE_API_TOKEN']
project_name = 'zzragida/examples-lit-dqn'
experiment_name = 'pytorch-lightning-dqn'
experiment_tags = ['lightning', 'dqn']
neptune_logger = init_neptune(args, api_key, project_name, experiment_name,
experiment_tags)
# ------------
# model
# ------------
model = DQNLightning(args)
# ------------
# training
# ------------
trainer = pl.Trainer.from_argparse_args(args,
max_epochs=10,
logger=neptune_logger)
trainer.fit(model)