diff --git a/clearml/binding/jsonargs_bind.py b/clearml/binding/jsonargs_bind.py
index eff8483b..cb916f56 100644
--- a/clearml/binding/jsonargs_bind.py
+++ b/clearml/binding/jsonargs_bind.py
@@ -1,4 +1,5 @@
import json
+import logging
try:
from jsonargparse import ArgumentParser
@@ -98,11 +99,13 @@ class PatchJsonArgParse(object):
try:
PatchJsonArgParse._load_task_params()
params = PatchJsonArgParse.__remote_task_params_dict
+ print(params)
params_namespace = Namespace()
for k, v in params.items():
params_namespace[k] = v
return params_namespace
- except Exception:
+ except Exception as e:
+ logging.getLogger(__file__).warning("Failed parsing jsonargparse arguments: {}".format(e))
return original_fn(obj, **kwargs)
parsed_args = original_fn(obj, **kwargs)
# noinspection PyBroadException
@@ -114,10 +117,14 @@ class PatchJsonArgParse(object):
PatchJsonArgParse._args_type[ns_name] = PatchJsonArgParse._command_type
subcommand = ns_val
try:
- import pytorch_lightning
+ import lightning
except ImportError:
- pytorch_lightning = None
- if subcommand and subcommand in PatchJsonArgParse._args and pytorch_lightning:
+ try:
+ import pytorch_lightning
+ lightning = pytorch_lightning
+ except ImportError:
+ lightning = None
+ if subcommand and subcommand in PatchJsonArgParse._args and lightning:
subcommand_args = flatten_dictionary(
PatchJsonArgParse._args[subcommand],
prefix=subcommand + PatchJsonArgParse._commands_sep,
@@ -127,8 +134,8 @@ class PatchJsonArgParse(object):
PatchJsonArgParse._args.update(subcommand_args)
PatchJsonArgParse._args = {k: v for k, v in PatchJsonArgParse._args.items()}
PatchJsonArgParse._update_task_args()
- except Exception:
- pass
+ except Exception as e:
+ logging.getLogger(__file__).warning("Failed parsing jsonargparse arguments: {}".format(e))
return parsed_args
@staticmethod
diff --git a/examples/frameworks/jsonargparse/pytorch_lightning_cli.py b/examples/frameworks/jsonargparse/pytorch_lightning_cli.py
index d73428e1..751a37a5 100644
--- a/examples/frameworks/jsonargparse/pytorch_lightning_cli.py
+++ b/examples/frameworks/jsonargparse/pytorch_lightning_cli.py
@@ -1,103 +1,14 @@
-# Copyright The PyTorch Lightning team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# Notice that this file has been modified to examplify the use of
-# ClearML when used with PyTorch Lightning
-
-import torch
-import torchvision.transforms as T
-from torch.nn import functional as F
-import torch.nn as nn
-from torchmetrics import Accuracy
-
-from torchvision.datasets.mnist import MNIST
-from pytorch_lightning import LightningModule
-from pytorch_lightning.utilities.cli import LightningCLI
+try:
+ from lightning.pytorch.cli import LightningCLI
+ from lightning.pytorch.demos.boring_classes import DemoModel, BoringDataModule
+except ImportError:
+ import sys
+ print("Module 'lightning' not installed (only available for Python 3.8+")
+ sys.exit(0)
from clearml import Task
-class Net(nn.Module):
- def __init__(self):
- super().__init__()
- self.conv1 = nn.Conv2d(1, 32, 3, 1)
- self.conv2 = nn.Conv2d(32, 64, 3, 1)
- self.dropout1 = nn.Dropout(0.25)
- self.dropout2 = nn.Dropout(0.5)
- self.fc1 = nn.Linear(9216, 128)
- self.fc2 = nn.Linear(128, 10)
-
- def forward(self, x):
- x = self.conv1(x)
- x = F.relu(x)
- x = self.conv2(x)
- x = F.relu(x)
- x = F.max_pool2d(x, 2)
- x = self.dropout1(x)
- x = torch.flatten(x, 1)
- x = self.fc1(x)
- x = F.relu(x)
- x = self.dropout2(x)
- x = self.fc2(x)
- output = F.log_softmax(x, dim=1)
- return output
-
-
-class ImageClassifier(LightningModule):
- def __init__(self, model=None, lr=1.0, gamma=0.7, batch_size=32):
- super().__init__()
- self.save_hyperparameters(ignore="model")
- self.model = model or Net()
- self.test_acc = Accuracy()
-
- def forward(self, x):
- return self.model(x)
-
- def training_step(self, batch, batch_idx):
- x, y = batch
- logits = self.forward(x)
- loss = F.nll_loss(logits, y.long())
- return loss
-
- def test_step(self, batch, batch_idx):
- x, y = batch
- logits = self.forward(x)
- loss = F.nll_loss(logits, y.long())
- self.test_acc(logits, y)
- self.log("test_acc", self.test_acc)
- self.log("test_loss", loss)
-
- def configure_optimizers(self):
- optimizer = torch.optim.Adadelta(self.model.parameters(), lr=self.hparams.lr)
- return [optimizer], [torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=self.hparams.gamma)]
-
- @property
- def transform(self):
- return T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
-
- def prepare_data(self) -> None:
- MNIST("./data", download=True)
-
- def train_dataloader(self):
- train_dataset = MNIST("./data", train=True, download=False, transform=self.transform)
- return torch.utils.data.DataLoader(train_dataset, batch_size=self.hparams.batch_size)
-
- def test_dataloader(self):
- test_dataset = MNIST("./data", train=False, download=False, transform=self.transform)
- return torch.utils.data.DataLoader(test_dataset, batch_size=self.hparams.batch_size)
-
-
if __name__ == "__main__":
- Task.add_requirements('requirements.txt')
- task = Task.init(project_name="example", task_name="pytorch_lightning_jsonargparse")
- LightningCLI(ImageClassifier, seed_everything_default=42, save_config_overwrite=True, run=True)
+ Task.add_requirements("requirements.txt")
+ Task.init(project_name="example", task_name="pytorch_lightning_jsonargparse")
+ LightningCLI(DemoModel, BoringDataModule)
diff --git a/examples/frameworks/jsonargparse/pytorch_lightning_cli.yml b/examples/frameworks/jsonargparse/pytorch_lightning_cli.yml
index fe8c31a1..ab2de927 100644
--- a/examples/frameworks/jsonargparse/pytorch_lightning_cli.yml
+++ b/examples/frameworks/jsonargparse/pytorch_lightning_cli.yml
@@ -1,12 +1,13 @@
trainer:
callbacks:
- - class_path: pytorch_lightning.callbacks.LearningRateMonitor
+ - class_path: lightning.pytorch.callbacks.LearningRateMonitor
init_args:
logging_interval: epoch
- - class_path: pytorch_lightning.callbacks.ModelCheckpoint
+ - class_path: lightning.pytorch.callbacks.ModelCheckpoint
init_args:
filename: best
save_last: False
save_top_k: 1
monitor: loss
mode: min
+ max_epochs: 10
diff --git a/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.py b/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.py
new file mode 100644
index 00000000..38cd91c6
--- /dev/null
+++ b/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.py
@@ -0,0 +1,114 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Notice that this file has been modified to examplify the use of
+# ClearML when used with PyTorch Lightning
+import sys
+
+import torch
+import torchvision.transforms as T
+from torch.nn import functional as F
+import torch.nn as nn
+from torchmetrics import Accuracy
+
+from torchvision.datasets.mnist import MNIST
+from pytorch_lightning import LightningModule
+from clearml import Task
+try:
+ from pytorch_lightning.cli import LightningCLI
+except ImportError:
+ try:
+ from pytorch_lightning.utilities.cli import LightningCLI
+ except ImportError:
+ print("Looks like you are using pytorch_lightning>=2.0. This example only works with older versions")
+ sys.exit(0)
+
+
+class Net(nn.Module):
+ def __init__(self):
+ super().__init__()
+ self.conv1 = nn.Conv2d(1, 32, 3, 1)
+ self.conv2 = nn.Conv2d(32, 64, 3, 1)
+ self.dropout1 = nn.Dropout(0.25)
+ self.dropout2 = nn.Dropout(0.5)
+ self.fc1 = nn.Linear(9216, 128)
+ self.fc2 = nn.Linear(128, 10)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = F.relu(x)
+ x = self.conv2(x)
+ x = F.relu(x)
+ x = F.max_pool2d(x, 2)
+ x = self.dropout1(x)
+ x = torch.flatten(x, 1)
+ x = self.fc1(x)
+ x = F.relu(x)
+ x = self.dropout2(x)
+ x = self.fc2(x)
+ output = F.log_softmax(x, dim=1)
+ return output
+
+
+class ImageClassifier(LightningModule):
+ def __init__(self, model=None, lr=1.0, gamma=0.7, batch_size=32):
+ super().__init__()
+ self.save_hyperparameters(ignore="model")
+ self.model = model or Net()
+ try:
+ self.test_acc = Accuracy()
+ except TypeError:
+ self.test_acc = Accuracy("binary")
+
+ def forward(self, x):
+ return self.model(x)
+
+ def training_step(self, batch, batch_idx):
+ x, y = batch
+ logits = self.forward(x)
+ loss = F.nll_loss(logits, y.long())
+ return loss
+
+ def test_step(self, batch, batch_idx):
+ x, y = batch
+ logits = self.forward(x)
+ loss = F.nll_loss(logits, y.long())
+ self.test_acc(logits, y)
+ self.log("test_acc", self.test_acc)
+ self.log("test_loss", loss)
+
+ def configure_optimizers(self):
+ optimizer = torch.optim.Adadelta(self.model.parameters(), lr=self.hparams.lr)
+ return [optimizer], [torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=self.hparams.gamma)]
+
+ @property
+ def transform(self):
+ return T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
+
+ def prepare_data(self) -> None:
+ MNIST("./data", download=True)
+
+ def train_dataloader(self):
+ train_dataset = MNIST("./data", train=True, download=False, transform=self.transform)
+ return torch.utils.data.DataLoader(train_dataset, batch_size=self.hparams.batch_size)
+
+ def test_dataloader(self):
+ test_dataset = MNIST("./data", train=False, download=False, transform=self.transform)
+ return torch.utils.data.DataLoader(test_dataset, batch_size=self.hparams.batch_size)
+
+
+if __name__ == "__main__":
+ Task.add_requirements("requirements.txt")
+ Task.init(project_name="example", task_name="pytorch_lightning_jsonargparse")
+ LightningCLI(ImageClassifier, seed_everything_default=42, run=True)
diff --git a/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.yml b/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.yml
new file mode 100644
index 00000000..d5010ac4
--- /dev/null
+++ b/examples/frameworks/jsonargparse/pytorch_lightning_cli_old.yml
@@ -0,0 +1,12 @@
+trainer:
+ callbacks:
+ - class_path: pytorch_lightning.callbacks.LearningRateMonitor
+ init_args:
+ logging_interval: epoch
+ - class_path: pytorch_lightning.callbacks.ModelCheckpoint
+ init_args:
+ filename: best
+ save_last: False
+ save_top_k: 1
+ monitor: loss
+ mode: min
\ No newline at end of file
diff --git a/examples/frameworks/jsonargparse/requirements.txt b/examples/frameworks/jsonargparse/requirements.txt
index ee8b5de0..6b54e31b 100644
--- a/examples/frameworks/jsonargparse/requirements.txt
+++ b/examples/frameworks/jsonargparse/requirements.txt
@@ -1,7 +1,8 @@
-clearml
jsonargparse
pytorch_lightning
torch
torchmetrics
torchvision
docstring_parser
+pytorch-lightning[extra]
+lightning; python_version >= '3.8'
diff --git a/examples/frameworks/pytorch-lightning/pytorch_lightning_example.py b/examples/frameworks/pytorch-lightning/pytorch_lightning_example.py
index 91f2d085..337d829e 100644
--- a/examples/frameworks/pytorch-lightning/pytorch_lightning_example.py
+++ b/examples/frameworks/pytorch-lightning/pytorch_lightning_example.py
@@ -1,13 +1,14 @@
-import os
+import sys
from argparse import ArgumentParser
-import torch
+
import pytorch_lightning as pl
+import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader, random_split
-from clearml import Task
-
-from torchvision.datasets.mnist import MNIST
from torchvision import transforms
+from torchvision.datasets.mnist import MNIST
+
+from clearml import Task
class LitClassifier(pl.LightningModule):
@@ -35,12 +36,13 @@ class LitClassifier(pl.LightningModule):
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
self.log('valid_loss', loss)
+ return loss
def test_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
- self.log('test_loss', loss)
+ return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
@@ -54,19 +56,17 @@ class LitClassifier(pl.LightningModule):
if __name__ == '__main__':
- # Connecting ClearML with the current process,
- # from here on everything is logged automatically
- task = Task.init(project_name="examples", task_name="PyTorch lightning MNIST example")
-
pl.seed_everything(0)
parser = ArgumentParser()
parser.add_argument('--batch_size', default=32, type=int)
- parser = pl.Trainer.add_argparse_args(parser)
- parser.set_defaults(max_epochs=3)
+ parser.add_argument('--max_epochs', default=3, type=int)
+ sys.argv.extend(['--max_epochs', '2'])
parser = LitClassifier.add_model_specific_args(parser)
args = parser.parse_args()
+ Task.init(project_name="examples-internal", task_name="lightning checkpoint issue and argparser")
+
# ------------
# data
# ------------
@@ -74,9 +74,9 @@ if __name__ == '__main__':
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, num_workers=os.cpu_count())
- val_loader = DataLoader(mnist_val, batch_size=args.batch_size, num_workers=os.cpu_count())
- test_loader = DataLoader(mnist_test, batch_size=args.batch_size, num_workers=os.cpu_count())
+ 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
@@ -86,7 +86,7 @@ if __name__ == '__main__':
# ------------
# training
# ------------
- trainer = pl.Trainer.from_argparse_args(args)
+ trainer = pl.Trainer(max_epochs=args.max_epochs)
trainer.fit(model, train_loader, val_loader)
# ------------