Update "app_integrate_bundle"

model_zoo/app_integrate_bundle/README.md

@@ -83,17 +83,18 @@ python ensemble.py --bundle_root bundle_root_path --dataset_dir data_root_path
 
 ## **How to integrate Bundle in your own application**
 ### Get component from bundle
+Check all supported properties in https://github.com/Project-MONAI/MONAI/blob/dev/monai/bundle/properties.py.
 ```
-from monai.bundle import ConfigWorkflow
-
-train_workflow = ConfigWorkflow(
-    config_file=bundle_config_path,
-    meta_file=bundle_metadata_path,
-    logging_file=bundle_logging_path,
-    workflow="train",
-)
-train_workflow.initialize()
+from monai.bundle import create_workflow
+
+train_workflow = create_workflow(config_file=bundle_config_path, workflow_type="train")
+
+# get train postprocessing
 postprocessing = train_workflow.train_postprocessing
+
+# get meta information
+version = train_workflow.version
+description = train_workflow.description
 ```
 ### Use component in your pipeline
 ```
@@ -108,9 +109,25 @@ evaluator = SupervisedEvaluator(
         )
 ```
 ### Update component with your own args
+
+- If the component you want to replace is listed [here](https://github.com/Project-MONAI/MONAI/blob/dev/monai/bundle/properties.py), you can replace it directly as below:
 ```
 # update `max_epochs` in workflow
 train_workflow.max_epochs = max_epochs
+
+# must execute 'initialize' again after changing the content
+train_workflow.initialize()
+print(train_workflow.max_epochs)
+```
+- Otherwise, you can override the components when you create the workflow.
+```
+override = {
+            "network": "$@network_def.to(@device)",
+            "dataset#_target_": "Dataset",
+            "dataset#data": [{"image": filename}],
+            "postprocessing#transforms#2#output_postfix": "seg",
+        }
+train_workflow = create_workflow(config_file=bundle_config_path, workflow_type="train", **override)
 ```
 
 ## Questions and bugs

model_zoo/app_integrate_bundle/ensemble.py

@@ -18,7 +18,7 @@
 import torch
 from monai.transforms import Compose
 from monai.transforms.post.dictionary import MeanEnsembled, VoteEnsembled
-from monai.bundle import ConfigWorkflow
+from monai.bundle import create_workflow
 from monai.engines import EnsembleEvaluator
 from monai.utils import optional_import
 
@@ -74,12 +74,7 @@ def __init__(self, path):
         self.bundle_metadata_path = os.path.join(path, "configs", Const.METADATA_JSON)
         self.bundle_logging_path = os.path.join(path, "configs", Const.LOGGING_CONFIG)
 
-        self.train_workflow = ConfigWorkflow(
-            config_file=self.bundle_config_path,
-            meta_file=self.bundle_metadata_path,
-            logging_file=self.bundle_logging_path,
-            workflow="train",
-        )
+        self.train_workflow = create_workflow(config_file=self.bundle_config_path, workflow_type="train")
 
     def _partition_datalist(self, datalist, n_splits=5, shuffle=False):
         logger.info(f"Total Records in Dataset: {len(datalist)}")
@@ -110,12 +105,7 @@ def ensemble_inference(self, device, test_datalist, ensemble="Mean"):
         logger.info(f"Total Records in Test Dataset: {len(test_datalist)}")
 
         bundle_inference_config_path = os.path.join(self.bundle_path, "configs", inference_config_paths[0])
-        inference_workflow = ConfigWorkflow(
-            config_file=bundle_inference_config_path,
-            meta_file=None,
-            logging_file=None,
-            workflow="inference",
-        )
+        inference_workflow = create_workflow(config_file=bundle_inference_config_path, workflow_type="inference")
         inference_workflow.dataset_data = test_datalist
         # this application has an additional requirement for the bundle workflow to provide the property dataloader
         inference_workflow.add_property(name="dataloader", required=True, config_id="dataloader")