Fix jsonargparse support (#403)

examples/frameworks/jsonargparse/jsonargparse_command.py

@@ -11,5 +11,5 @@ class Main:
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="jsonargparse command", auto_connect_frameworks={"pytorch_lightning": False})
+    Task.init(project_name="examples", task_name="jsonargparse command")
     print(CLI(Main))

examples/frameworks/jsonargparse/jsonargparse_nested_namespaces.py

@@ -10,7 +10,7 @@ class Arg2:
 
 
 if __name__ == "__main__":
-    Task.init(project_name="examples", task_name="jsonargparse nested namespaces", auto_connect_frameworks={"pytorch-lightning": False})
+    Task.init(project_name="examples", task_name="jsonargparse nested namespaces")
     parser = ArgumentParser()
     parser.add_argument("--arg1.opt1", default="from default 1")
     parser.add_argument("--arg1.opt2", default="from default 2")

examples/frameworks/jsonargparse/pytorch_lightning_cli.py

@@ -0,0 +1,103 @@
+# 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
+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)

examples/frameworks/jsonargparse/pytorch_lightning_cli.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

examples/frameworks/jsonargparse/requirements.txt

@@ -1,2 +1,7 @@
 clearml
-jsonargparse
+jsonargparse
+pytorch_lightning
+torch
+torchmetrics
+torchvision
+docstring_parser