Merge pull request #232737 from santiagxf/santiagxf/azureml-batch-sample-fix

Update how-to-deploy-model-custom-output.md

Author: Stacyrch140

articles/machine-learning/how-to-deploy-model-custom-output.md

@@ -29,20 +29,20 @@ In any of those cases, Batch Deployments allow you to take control of the output
 
 ## About this sample
 
-This example shows how you can deploy a model to perform batch inference and customize how your predictions are written in the output. This example uses an MLflow model based on the [UCI Heart Disease Data Set](https://archive.ics.uci.edu/ml/datasets/Heart+Disease). The database contains 76 attributes, but we are using a subset of 14 of them. The model tries to predict the presence of heart disease in a patient. It is integer valued from 0 (no presence) to 1 (presence).
+This example shows how you can deploy a model to perform batch inference and customize how your predictions are written in the output. This example uses a model based on the [UCI Heart Disease Data Set](https://archive.ics.uci.edu/ml/datasets/Heart+Disease). The database contains 76 attributes, but we are using a subset of 14 of them. The model tries to predict the presence of heart disease in a patient. It is integer valued from 0 (no presence) to 1 (presence).
 
 The model has been trained using an `XGBBoost` classifier and all the required preprocessing has been packaged as a `scikit-learn` pipeline, making this model an end-to-end pipeline that goes from raw data to predictions.
 
-The information in this article is based on code samples contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste YAML and other files, clone the repo and then change directories to the `cli/endpoints/batch` if you are using the Azure CLI or `sdk/endpoints/batch` if you are using our SDK for Python.
+The information in this article is based on code samples contained in the [azureml-examples](https://github.com/azure/azureml-examples) repository. To run the commands locally without having to copy/paste YAML and other files, clone the repo and then change directories to the `cli/endpoints/batch/deploy-models/custom-outputs-parquet` if you are using the Azure CLI or `sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet` if you are using our SDK for Python.
 
 ```azurecli
 git clone https://github.com/Azure/azureml-examples --depth 1
-cd azureml-examples/cli/endpoints/batch
+cd azureml-examples/cli/endpoints/batch/deploy-models/custom-outputs-parquet
 ```
 
 ### Follow along in Jupyter Notebooks
 
-You can follow along this sample in a Jupyter Notebook. In the cloned repository, open the notebook: [custom-output-batch.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/batch/custom-output-batch.ipynb).
+You can follow along this sample in a Jupyter Notebook. In the cloned repository, open the notebook: [custom-output-batch.ipynb](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/batch/deploy-models/custom-outputs-parquet/custom-output-batch.ipynb).
 
 ## Prerequisites
 
@@ -63,23 +63,20 @@ Batch Endpoint can only deploy registered models. In this case, we already have
 # [Azure CLI](#tab/cli)
 
 ```azurecli
-MODEL_NAME='heart-classifier'
-az ml model create --name $MODEL_NAME --type "mlflow_model" --path "heart-classifier-mlflow/model"
+MODEL_NAME='heart-classifier-sklpipe'
+az ml model create --name $MODEL_NAME --type "custom_model" --path "model"
 ```
 
 # [Python](#tab/sdk)
 
 ```python
 model_name = 'heart-classifier'
 model = ml_client.models.create_or_update(
-     Model(name=model_name, path='heart-classifier-mlflow/model', type=AssetTypes.MLFLOW_MODEL)
+     Model(name=model_name, path='model', type=AssetTypes.CUSTOM_MODEL)
 )
 ```
 ---
 
-> [!NOTE]
-> The model used in this tutorial is an MLflow model. However, the steps apply for both MLflow models and custom models.
-
 ### Creating a scoring script
 
 We need to create a scoring script that can read the input data provided by the batch deployment and return the scores of the model. We are also going to write directly to the output folder of the job. In summary, the proposed scoring script does as follows:
@@ -89,38 +86,9 @@ We need to create a scoring script that can read the input data provided by the
 3. Appends the predictions to a `pandas.DataFrame` along with the input data.
 4. Writes the data in a file named as the input file, but in `parquet` format.
 
-__batch_driver_parquet.py__
+__code/batch_driver.py__
 
-```python
-import os
-import mlflow
-import pandas as pd
-from pathlib import Path
-
-def init():
-    global model
-    global output_path
-
-    # AZUREML_MODEL_DIR is an environment variable created during deployment
-    # It is the path to the model folder
-    # Please provide your model's folder name if there's one:
-    model_path = os.path.join(os.environ["AZUREML_MODEL_DIR"], "model")
-    output_path = os.environ['AZUREML_BI_OUTPUT_PATH']
-    model = mlflow.pyfunc.load_model(model_path)
-
-def run(mini_batch):
-    for file_path in mini_batch:        
-        data = pd.read_csv(file_path)
-        pred = model.predict(data)
-        
-        data['prediction'] = pred
-        
-        output_file_name = Path(file_path).stem
-        output_file_path = os.path.join(output_path, output_file_name + '.parquet')
-        data.to_parquet(output_file_path)
-        
-     return mini_batch
-```
+:::code language="python" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/custom-outputs-parquet/code/batch_driver.py" :::
 
 __Remarks:__
 * Notice how the environment variable `AZUREML_BI_OUTPUT_PATH` is used to get access to the output path of the deployment job. 
@@ -140,18 +108,21 @@ Follow the next steps to create a deployment using the previous scoring script:
 
    No extra step is required for the Azure Machine Learning CLI. The environment definition will be included in the deployment file.
 
+   :::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/custom-outputs-parquet/deployment.yml" range="8-11":::
+   
    # [Python](#tab/sdk)
 
    Let's get a reference to the environment:
 
    ```python
    environment = Environment(
-       conda_file="./heart-classifier-mlflow/environment/conda.yaml",
-       image="mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest",
+       name="batch-mlflow-xgboost",
+       conda_file="environment/conda.yaml",
+       image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04:latest",
    )
    ```
 
-2. MLflow models don't require you to indicate an environment or a scoring script when creating the deployments as it is created for you. However, in this case we are going to indicate a scoring script and environment since we want to customize how inference is executed.
+2. Create the deployment
 
    > [!NOTE]
    > This example assumes you have an endpoint created with the name `heart-classifier-batch` and a compute cluster with name `cpu-cluster`. If you don't, please follow the steps in the doc [Use batch endpoints for batch scoring](how-to-use-batch-endpoint.md).
@@ -160,37 +131,11 @@ Follow the next steps to create a deployment using the previous scoring script:
 
    To create a new deployment under the created endpoint, create a `YAML` configuration like the following:
 
-   ```yaml
-   $schema: https://azuremlschemas.azureedge.net/latest/batchDeployment.schema.json
-   endpoint_name: heart-classifier-batch
-   name: classifier-xgboost-parquet
-   description: A heart condition classifier based on XGBoost
-   model: azureml:heart-classifier@latest
-   environment:
-      image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:latest
-      conda_file: ./heart-classifier-mlflow/environment/conda.yaml
-   code_configuration:
-     code: ./heart-classifier-custom/code/
-     scoring_script: batch_driver_parquet.py
-   compute: azureml:cpu-cluster
-   resources:
-     instance_count: 2
-   max_concurrency_per_instance: 2
-   mini_batch_size: 2
-   output_action: summary_only
-   retry_settings:
-     max_retries: 3
-     timeout: 300
-   error_threshold: -1
-   logging_level: info
-   ```
+   :::code language="yaml" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/custom-outputs-parquet/deployment.yml":::
 
    Then, create the deployment with the following command:
 
-   ```azurecli
-   DEPLOYMENT_NAME="classifier-xgboost-parquet"
-   az ml batch-deployment create -f endpoint.yml
-   ```
+   :::code language="azurecli" source="~/azureml-examples-main/cli/endpoints/batch/deploy-models/custom-outputs-parquet/deploy-and-run.sh" ID="create_batch_deployment_set_default" :::
 
    # [Python](#tab/sdk)
 
@@ -204,8 +149,8 @@ Follow the next steps to create a deployment using the previous scoring script:
        model=model,
        environment=environment,
        code_configuration=CodeConfiguration(
-           code="./heart-classifier-mlflow/code/",
-           scoring_script="batch_driver_parquet.py",
+           code="code/",
+           scoring_script="batch_driver.py",
        ),
        compute=compute_name,
        instance_count=2,