update

controller.py

@@ -37,19 +37,6 @@ pipe.add_step(name='stage_train',
                     "General/dataset_project": "${stage_data.parameters.General/dataset_project}"}
                 )
 
-    model = step_two(
-                    dataset_name_training=dataset_name_training,
-                    dataset_name_test=dataset_name_test,
-                    dataset_project=dataset_project,
-                    epochs=10,
-                    train_batch_size=256,
-                    validation_batch_size=256,
-                    train_num_workers=0,
-                    validation_num_workers=0,
-                    resize=28,
-                    lr=1e-3
-                )
-
 # Starting the pipeline
 # pipe.start_locally()
 pipe.start(queue='test')

stage_one.py

 from clearml import Task, Dataset
 
-Task.add_requirements("./requirements.txt")
 task = Task.init(project_name="pipeline",
                 task_name="pipeline step 1 dataset artifact")
+task.add_requirements("./requirements.txt")
 
 # only create the task, it will be executed remotely later
 task.execute_remotely()

stage_two.py

@@ -75,9 +75,9 @@ def validate(model, testloader, criterion):
     epoch_acc = 100. * (valid_running_correct / len(testloader.dataset))
     return epoch_loss, epoch_acc
 
-Task.add_requirements("./requirements.txt")
 task = Task.init(project_name="pipeline",
                 task_name="pipeline step 2 train model")
+task.add_requirements("./requirements.txt")
 
 # only create the task, we will actually execute it later
 task.execute_remotely()
@@ -95,6 +95,8 @@ args = {
     'lr': 1e-3
 }
 
+task.connect(args)
+
 mnist_train = Dataset.get(
     dataset_name=args['train_dataset_name'], dataset_project=args['dataset_project']
 ).get_local_copy()