Merge pull request #196235 from xiaoharper/patch-3

update code reference

Author: Maggiemouse1

articles/machine-learning/concept-component.md

@@ -46,41 +46,8 @@ The component specification file defines the metadata and execution parameters f
 
 The following example is a component specification for a training component.
 
-```yaml
-name: Example_Train
-display_name: Example Train
-version: 20
-type: command
-description: Example of a torchvision training component
-tags: {category: Component Tutorial, contact: [email protected]}
-inputs:
-  training_data: 
-    type: path
-    description: Training data organized in torchvision structure
-  max_epochs:
-    type: integer
-    description: Maximum epochs for training
-  learning_rate: 
-    type: number
-    description: Learning rate, default is 0.01
-    default: 0.01
-  learning_rate_schedule: 
-    type: string
-    default: time-based 
-outputs:
-  model_output:
-    type: path
-code:
-  local_path: ./train_src
-environment: azureml:AzureML-Minimal:1
-command: >-
-  python train.py 
-  --training_data ${{inputs.training_data}} 
-  --max_epochs ${{inputs.max_epochs}}   
-  --learning_rate ${{inputs.learning_rate}} 
-  --learning_rate_schedule ${{inputs.learning_rate_schedule}} 
-  --model_output ${{outputs.model_output}}
-```
+:::code language="yaml" source="~/azureml-examples-main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components/train.yml":::
+
 
 The following table explains the fields in the example. For a full list of available fields, see the [YAML component specification reference page](reference-yaml-component-command.md).
 
@@ -105,55 +72,8 @@ Your Python script contains the executable logic for your component. Your script
 
 To run, you must match the arguments for your Python script with the arguments you defined in the YAML specification. The following example is a Python training script that matches the YAML specification from the previous section.
 
-```python
-## Required imports 
-import argparse
-import os
-## Import other dependencies your script needs
-from pathlib import Path
-from uuid import uuid4
-from datetime import datetime
-
-## Define an argument parser that matches the arguments from the components specification file
-parser = argparse.ArgumentParser("train")
-parser.add_argument("--training_data", type=str, help="Path to training data")
-parser.add_argument("--max_epochs", type=int, help="Max # of epochs for the training")
-parser.add_argument("--learning_rate", type=float, help="Learning rate")
-parser.add_argument("--learning_rate_schedule", type=str, help="Learning rate schedule")
-parser.add_argument("--model_output", type=str, help="Path of output model")
-
-args = parser.parse_args()
-
-## Implement your custom logic (in this case a training script)
-print ("hello training world...")
-
-lines = [
-    f'Training data path: {args.training_data}',
-    f'Max epochs: {args.max_epochs}',
-    f'Learning rate: {args.learning_rate}',
-    f'Learning rate: {args.learning_rate_schedule}',
-    f'Model output path: {args.model_output}',
-]
-
-for line in lines:
-    print(line)
-
-print("mounted_path files: ")
-arr = os.listdir(args.training_data)
-print(arr)
-
-for filename in arr:
-    print ("reading file: %s ..." % filename)
-    with open(os.path.join(args.training_data, filename), 'r') as handle:
-        print (handle.read())
-
-## Do the train and save the trained model as a file into the output folder.
-## Here only output a dummy data for example.
-curtime = datetime.now().strftime("%b-%d-%Y %H:%M:%S")
-model = f"This is a dummy model with id: {str(uuid4())} generated at: {curtime}\n"
-(Path(args.model_output) / 'model.txt').write_text(model)
+:::code language="python" source="~/azureml-examples-main/cli/jobs/pipelines-with-components/basics/1b_e2e_registered_components/train_src/train.py":::
 
-```
 
 :::image type="content" source="media/concept-component/component-introduction.png" lightbox="media/concept-component/component-introduction.png" alt-text="Conceptual doc showing mapping between source code elements and component UI." :::