Prs examples (#2064)

* Update PRS examples

* Update PRS examples

* Update prs examples

* Update PRS examples

* Update format

* Update by comments

* fix sdk default datastore issue.

* Update wording

.github/workflows/cli-jobs-parallel-1a_oj_sales_prediction-pipeline.yml

@@ -0,0 +1,51 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: cli-jobs-parallel-1a_oj_sales_prediction-pipeline
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0/8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - cli/jobs/parallel/1a_oj_sales_prediction/**
+      - infra/**
+      - .github/workflows/cli-jobs-parallel-1a_oj_sales_prediction-pipeline.yml
+      - cli/run-pipeline-jobs.sh
+      - cli/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run job
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash -x ../../../run-job.sh pipeline.yml
+      working-directory: cli/jobs/parallel/1a_oj_sales_prediction

.github/workflows/cli-jobs-parallel-2a_iris_batch_prediction-pipeline.yml

@@ -0,0 +1,51 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: cli-jobs-parallel-2a_iris_batch_prediction-pipeline
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0/8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - cli/jobs/parallel/2a_iris_batch_prediction/**
+      - infra/**
+      - .github/workflows/cli-jobs-parallel-2a_iris_batch_prediction-pipeline.yml
+      - cli/run-pipeline-jobs.sh
+      - cli/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run job
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash -x ../../../run-job.sh pipeline.yml
+      working-directory: cli/jobs/parallel/2a_iris_batch_prediction

.github/workflows/cli-jobs-parallel-3a_mnist_batch_identification-pipeline.yml

@@ -0,0 +1,51 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: cli-jobs-parallel-3a_mnist_batch_identification-pipeline
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 0/8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - cli/jobs/parallel/3a_mnist_batch_identification/**
+      - infra/**
+      - .github/workflows/cli-jobs-parallel-3a_mnist_batch_identification-pipeline.yml
+      - cli/run-pipeline-jobs.sh
+      - cli/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run job
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash -x ../../../run-job.sh pipeline.yml
+      working-directory: cli/jobs/parallel/3a_mnist_batch_identification

.github/workflows/sdk-jobs-parallel-1a_oj_sales_prediction-oj_sales_prediction.yml

@@ -0,0 +1,75 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: sdk-jobs-parallel-1a_oj_sales_prediction-oj_sales_prediction
+# This file is created by sdk/python/readme.py.
+# Please do not edit directly.
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 */8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - sdk/python/jobs/parallel/1a_oj_sales_prediction/**
+      - .github/workflows/sdk-jobs-parallel-1a_oj_sales_prediction-oj_sales_prediction.yml
+      - sdk/python/dev-requirements.txt
+      - infra/**
+      - sdk/python/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with: 
+        python-version: "3.8"
+    - name: pip install notebook reqs
+      run: pip install -r sdk/python/dev-requirements.txt
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup SDK
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: sdk/python
+      continue-on-error: true
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run jobs/parallel/1a_oj_sales_prediction/oj_sales_prediction.ipynb
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" replace_template_values "oj_sales_prediction.ipynb";
+          [ -f "../../.azureml/config" ] && cat "../../.azureml/config";
+          papermill -k python oj_sales_prediction.ipynb oj_sales_prediction.output.ipynb
+      working-directory: sdk/python/jobs/parallel/1a_oj_sales_prediction
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: 1a_oj_sales_prediction
+        path: sdk/python/jobs/parallel/1a_oj_sales_prediction

.github/workflows/sdk-jobs-parallel-2a_iris_batch_prediction-iris_batch_prediction.yml

@@ -0,0 +1,75 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: sdk-jobs-parallel-2a_iris_batch_prediction-iris_batch_prediction
+# This file is created by sdk/python/readme.py.
+# Please do not edit directly.
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 */8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - sdk/python/jobs/parallel/2a_iris_batch_prediction/**
+      - .github/workflows/sdk-jobs-parallel-2a_iris_batch_prediction-iris_batch_prediction.yml
+      - sdk/python/dev-requirements.txt
+      - infra/**
+      - sdk/python/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with: 
+        python-version: "3.8"
+    - name: pip install notebook reqs
+      run: pip install -r sdk/python/dev-requirements.txt
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup SDK
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: sdk/python
+      continue-on-error: true
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run jobs/parallel/2a_iris_batch_prediction/iris_batch_prediction.ipynb
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" replace_template_values "iris_batch_prediction.ipynb";
+          [ -f "../../.azureml/config" ] && cat "../../.azureml/config";
+          papermill -k python iris_batch_prediction.ipynb iris_batch_prediction.output.ipynb
+      working-directory: sdk/python/jobs/parallel/2a_iris_batch_prediction
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: 2a_iris_batch_prediction
+        path: sdk/python/jobs/parallel/2a_iris_batch_prediction

.github/workflows/sdk-jobs-parallel-3a_mnist_batch_identification-mnist_batch_prediction.yml

@@ -0,0 +1,75 @@
+# This code is autogenerated.
+# Code is generated by running custom script: python3 readme.py
+# Any manual changes to this file may cause incorrect behavior.
+# Any manual changes will be overwritten if the code is regenerated.
+
+name: sdk-jobs-parallel-3a_mnist_batch_identification-mnist_batch_prediction
+# This file is created by sdk/python/readme.py.
+# Please do not edit directly.
+on:
+  workflow_dispatch:
+  schedule:
+    - cron: "0 */8 * * *"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - sdk/python/jobs/parallel/3a_mnist_batch_identification/**
+      - .github/workflows/sdk-jobs-parallel-3a_mnist_batch_identification-mnist_batch_prediction.yml
+      - sdk/python/dev-requirements.txt
+      - infra/**
+      - sdk/python/setup.sh
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+    - name: check out repo
+      uses: actions/checkout@v2
+    - name: setup python
+      uses: actions/setup-python@v2
+      with: 
+        python-version: "3.8"
+    - name: pip install notebook reqs
+      run: pip install -r sdk/python/dev-requirements.txt
+    - name: azure login
+      uses: azure/login@v1
+      with:
+        creds: ${{secrets.AZUREML_CREDENTIALS}}
+    - name: bootstrap resources
+      run: |
+          echo '${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}';
+          bash bootstrap.sh
+      working-directory: infra
+      continue-on-error: false
+    - name: setup SDK
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: sdk/python
+      continue-on-error: true
+    - name: setup-cli
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash setup.sh
+      working-directory: cli
+      continue-on-error: true
+    - name: run jobs/parallel/3a_mnist_batch_identification/mnist_batch_prediction.ipynb
+      run: |
+          source "${{ github.workspace }}/infra/sdk_helpers.sh";
+          source "${{ github.workspace }}/infra/init_environment.sh";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" generate_workspace_config "../../.azureml/config.json";
+          bash "${{ github.workspace }}/infra/sdk_helpers.sh" replace_template_values "mnist_batch_prediction.ipynb";
+          [ -f "../../.azureml/config" ] && cat "../../.azureml/config";
+          papermill -k python mnist_batch_prediction.ipynb mnist_batch_prediction.output.ipynb
+      working-directory: sdk/python/jobs/parallel/3a_mnist_batch_identification
+    - name: upload notebook's working folder as an artifact
+      if: ${{ always() }}
+      uses: actions/upload-artifact@v2
+      with:
+        name: 3a_mnist_batch_identification
+        path: sdk/python/jobs/parallel/3a_mnist_batch_identification

cli/jobs/parallel/1a_oj_sales_prediction/README.md

@@ -0,0 +1,20 @@
+# Orange juice sales prediction example \[Paralle job\] \[Yaml + CLI example\]
+
+## Key notes for this example
+- How to use **parallel job** for **many model training** scenario.
+- How to use parallel job **run_function** task with predefined **entry_script**.
+- How to pre-cook data into **mltable with partition setting**.
+- How to use **mltable** with **tabular data** as the **input of parallel job**.
+- How to use **partition_keys** in parallel job to consume data with partitions. 
+- How to use **error_threshold** with **empty returns** to ignore checking failed items in mini-batch.
+- How to use parallel job settings:
+  - mini_batch_error_threshold
+  - environment_variables
+
+To run this example, please following the steps:
+- Install the Azure ML CLI: [link](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public)
+- Use Azure ML job command to run pipeline.yml.
+    - Create new job: `az ml job create --file pipeline.yml`
+    - Override settings in this job: `az ml job create --file pipeline.yml --set jobs.parallel_train.resources.instance_count=3`
+
+To get the same example with python SDK experience, please refer to: [link](../../../../sdk/python/jobs/parallel/1a_oj_sales_prediction/oj_sales_prediction.ipynb)

cli/jobs/parallel/1a_oj_sales_prediction/pipeline.yml

@@ -0,0 +1,79 @@
+$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
+type: pipeline
+display_name: partition by key parallel job
+description: The hello world pipeline job with partition by key
+experiment_name: hello-world-parallel-job
+tags:
+  tag: tagvalue
+  owner: sdkteam
+
+settings:
+  default_compute: azureml:cpu-cluster
+
+jobs:
+  partition_job:
+    type: command
+    component: ./src/partition_data/partition_data.yml
+    
+    inputs:
+      data_source:
+        type: uri_file
+        path: ./oj_sales_data/oj_sales_data.csv
+      partition_keys: Store,Brand
+    outputs:
+      tabular_output_data:
+        type: mltable
+        mode: rw_mount
+
+  parallel_train:
+    type: parallel
+    compute: azureml:cpu-cluster
+    inputs:
+      data_source: 
+        path: ${{parent.jobs.partition_job.outputs.tabular_output_data}}
+        type: mltable
+        mode: direct
+      drop_cols: "Revenue,Advert,Store,Brand"
+      target_col: "Quantity"
+      date_col: "WeekStarting"
+      lagging_orders: "1,2,3,4,5,6"
+    outputs:
+      model_folder:
+        type: uri_folder
+        mode: rw_mount
+
+    partition_keys:
+      - Store
+      - Brand
+    resources:
+      instance_count: 2
+
+    error_threshold: -1
+    mini_batch_error_threshold: 5
+    logging_level: "INFO"
+    input_data: ${{inputs.data_source}}
+    max_concurrency_per_instance: 2
+    retry_settings:
+      max_retries: 2
+      timeout: 60
+
+    environment_variables:
+      "AZUREML_PARALLEL_EXAMPLE": "1a_yaml"
+
+    task:
+      type: run_function
+      code: ./src/parallel_train
+      entry_script: parallel_train.py
+      environment:
+        image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+        conda_file: ./src/parallel_train/conda.yml
+
+      program_arguments: >-
+        --drop_cols ${{inputs.drop_cols}}
+        --target_col ${{inputs.target_col}}
+        --date_col ${{inputs.date_col}}
+        --lagging_orders ${{inputs.lagging_orders}}
+        --model_folder ${{outputs.model_folder}}
+
+
+

cli/jobs/parallel/1a_oj_sales_prediction/src/parallel_train/conda.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - mltable==1.0.0

cli/jobs/parallel/1a_oj_sales_prediction/src/parallel_train/parallel_train.py

@@ -0,0 +1,105 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import glob
+import pandas as pd
+import numpy as np
+import pickle
+import mltable
+import argparse
+import mlflow
+
+# from azureml.opendatasets import OjSalesSimulated
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+
+
+def init():
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--drop_cols", type=str)
+    parser.add_argument("--target_col", type=str)
+    parser.add_argument("--date_col", type=str)
+    parser.add_argument("--lagging_orders", type=str)
+    parser.add_argument("--model_folder", type=str)
+    parser.add_argument("--partition_keys", type=str)
+
+    args, _ = parser.parse_known_args()
+
+    global target_col
+    target_col = args.target_col
+
+    global date_col
+    date_col = args.date_col
+
+    global lagging_orders
+    lagging_orders = [int(i) for i in args.lagging_orders.split(",")]
+
+    global model_folder
+    model_folder = args.model_folder
+
+    global drop_cols
+    drop_cols = args.drop_cols.split(",")
+
+
+def run(input_data, mini_batch_context):
+    mlflow.autolog()
+
+    if not isinstance(input_data, pd.DataFrame):
+        raise Exception("Not a valid DataFrame input.")
+
+    if target_col not in input_data.columns:
+        raise Exception("No target column found from input tabular data")
+    elif date_col not in input_data.columns:
+        raise Exception("No date column found from input tabular data")
+
+    print(f"partition_key_value = {mini_batch_context.partition_key_value}")
+
+    # data cleaning
+    input_data[date_col] = pd.to_datetime(input_data[date_col])
+    input_data = input_data.set_index(date_col).sort_index(ascending=True)
+    input_data = input_data.assign(Week_Year=input_data.index.isocalendar().week.values)
+    input_data = input_data.drop(columns=drop_cols, errors="ignore")
+
+    # data lagging
+    max_lag_order = max(lagging_orders)
+    train_tail = input_data.iloc[-max_lag_order:]
+    column_order = train_tail.columns
+    data_trans = input_data.copy()
+
+    ## Make the lag features
+    for lag_order in lagging_orders:
+        data_trans["lag_" + str(lag_order)] = data_trans[target_col].shift(lag_order)
+
+    data_trans = data_trans.loc[input_data.index]
+    data_trans = data_trans.dropna()
+
+    # traning & evaluation
+    features = data_trans.columns.drop(target_col)
+
+    X = data_trans[features].values
+    y = data_trans[target_col].values
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.05, random_state=12, shuffle=False
+    )
+
+    reg = GradientBoostingRegressor(random_state=12)
+    reg.fit(X_train, y_train)
+    reg_pred = reg.predict(X_test)
+
+    # Dump model
+    relative_path = os.path.join(
+        model_folder,
+        *list(str(i) for i in mini_batch_context.partition_key_value.values()),
+    )
+
+    if not os.path.exists(relative_path):
+        os.makedirs(relative_path)
+
+    mlflow.sklearn.save_model(reg, relative_path)
+
+    return []

cli/jobs/parallel/1a_oj_sales_prediction/src/partition_data/conda.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - mltable==1.0.0

cli/jobs/parallel/1a_oj_sales_prediction/src/partition_data/partition_data.py

@@ -0,0 +1,119 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import pandas as pd
+import numpy as np
+import argparse
+import yaml
+import json
+import re
+import mltable
+
+
+def normalize_path_value(
+    input_val: str,
+) -> str:
+    return re.sub('\.|\s|\\\\|\||\*|/|\<|\>|\?|:|"', "_", input_val)
+
+
+def dump_df_to_mltable(
+    input_df: pd.DataFrame,
+    target_path: str,
+    partition_cols=[],
+):
+    if len(partition_cols) > 0:
+        for item in partition_cols:
+            if str(item) not in input_df.columns:
+                raise Exception("No target column found from input tabular data")
+        has_partition = True
+    else:
+        has_partition = False
+
+    partitioned_file_name = "partitioned_data.csv"
+
+    # Dump df to target path
+    if has_partition:
+        for group_name, group_df in input_df.groupby(partition_cols):
+            if hasattr(group_name, "__iter__"):
+                relative_path = os.path.join(
+                    target_path, *[normalize_path_value(str(e)) for e in group_name]
+                )
+            else:
+                relative_path = os.path.join(target_path, str(group_name))
+
+            if not os.path.exists(relative_path):
+                os.makedirs(relative_path)
+
+            output_path = os.path.join(relative_path, partitioned_file_name)
+            group_df.drop(columns=partition_cols, errors="ignore").to_csv(
+                output_path, index=False
+            )
+    else:
+        if not os.path.exists(target_path):
+            os.makedirs(target_path)
+
+        output_path = os.path.join(target_path, partitioned_file_name)
+        input_df.to_csv(output_path, index=False)
+
+    # Create ML table artifact object
+    mltable_path = os.path.join(target_path, "MLTable")
+    mltable_dic = {
+        "type": "mltable",
+        "paths": [],
+        "transformations": [
+            {
+                "read_delimited": {
+                    "delimiter": ",",
+                    "encoding": "ascii",
+                    "header": "all_files_same_headers",
+                    "empty_as_string": False,
+                    "include_path_column": True,
+                },
+            },
+        ],
+    }
+
+    if has_partition:
+        partition_format_str = ""
+        partition_key_droplist = ["Path", "file_name", "extension"]
+
+        for col_name in partition_cols:
+            partition_format_str = partition_format_str + "{" + col_name + "}" + "/"
+
+        partition_format_str = partition_format_str + "{file_name}.{extension}"
+        mltable_dic["paths"].append({"pattern": "./**/*.csv"})
+        mltable_dic["transformations"].append(
+            {
+                "extract_columns_from_partition_format": {
+                    "partition_format": partition_format_str
+                },
+            }
+        )
+        mltable_dic["transformations"].append({"drop_columns": partition_key_droplist})
+    else:
+        mltable_dic["paths"].append({"pattern": "./*.csv"})
+
+    # Dump ML table artifact object to file
+    with open(mltable_path, "w") as yaml_file:
+        yaml.dump(mltable_dic, yaml_file, default_flow_style=False)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_source", type=str)
+parser.add_argument("--partition_keys", type=str)
+parser.add_argument("--tabular_output_data", type=str)
+
+args, _ = parser.parse_known_args()
+
+try:
+    input_df = pd.read_csv(args.data_source)
+except:
+    raise Exception("Can not load input data as csv tabular data.")
+
+partition_cols = args.partition_keys.split(",")
+dump_df_to_mltable(
+    input_df=input_df,
+    target_path=args.tabular_output_data,
+    partition_cols=partition_cols,
+)

cli/jobs/parallel/1a_oj_sales_prediction/src/partition_data/partition_data.yml

@@ -0,0 +1,29 @@
+$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
+
+type: command
+
+name: partition_data
+display_name: Partition input data by keys
+version: 1
+
+inputs:
+  data_source:
+    type: uri_file
+  partition_keys:
+    type: string
+
+outputs:
+  tabular_output_data:
+    type: mltable
+
+code: ./
+
+environment: 
+  image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+  conda_file: ./conda.yml
+
+command: >-
+  python partition_data.py
+  --data_source ${{inputs.data_source}}
+  --partition_keys ${{inputs.partition_keys}}
+  --tabular_output_data ${{outputs.tabular_output_data}}

cli/jobs/parallel/2a_iris_batch_prediction/README.md

@@ -0,0 +1,22 @@
+# iris batch prediction example \[Paralle job\] \[Yaml + CLI example\]
+
+## Key notes for this example
+- How to use **parallel job** for **batch inferencing** scenario.
+- How to use parallel job **run_function** task with predefined **entry_script**.
+- How to use **mltable** with **tabular data** as the **input of parallel job**.
+- How to use **mini_batch_size** in parallel job to split input data by size. 
+- How to use **append_row_to** to aggregate returns to **uri_file** output.
+- How to use parallel job advanced args:
+  - allowed_failed_percent
+  - task_overhead_timeout
+  - progress_update_timeout
+  - first_task_creation_timeout
+  - resource_monitor_interval
+
+To run this example, please following the steps:
+- Install the Azure ML CLI: [link](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public)
+- Use Azure ML job command to run pipeline.yml.
+    - Create new job: `az ml job create --file pipeline.yml`
+    - Override settings in this job: `az ml job create --file pipeline.yml --set jobs.batch_prediction.resources.instance_count=3`
+
+To get the same example with python SDK experience, please refer to: [link](../../../../sdk/python/jobs/parallel/2a_iris_batch_prediction/iris_batch_prediction.ipynb)

cli/jobs/parallel/2a_iris_batch_prediction/environment/environment_parallel.yml

@@ -0,0 +1,16 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2

cli/jobs/parallel/2a_iris_batch_prediction/iris-model/MLmodel

@@ -0,0 +1,11 @@
+flavors:
+  python_function:
+    env: conda.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    python_version: 3.6.8
+  sklearn:
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 0.20.3
+utc_time_created: '2021-02-05 10:59:19.184646'

cli/jobs/parallel/2a_iris_batch_prediction/neural-iris-mltable/MLTable

@@ -0,0 +1,7 @@
+paths:
+  - file: ./iris.csv
+transformations:
+  - read_delimited:
+      delimiter: ","
+      encoding: utf8
+      header: all_files_same_headers

cli/jobs/parallel/2a_iris_batch_prediction/neural-iris-mltable/iris.csv

@@ -0,0 +1,75 @@
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cli/jobs/parallel/2a_iris_batch_prediction/pipeline.yml

@@ -0,0 +1,65 @@
+$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
+type: pipeline
+
+display_name: iris-batch-prediction-using-parallel
+description: The hello world pipeline job with inline parallel job
+experiment_name: hello-world-parallel-job
+tags:
+  tag: tagvalue
+  owner: sdkteam
+
+settings:
+  default_compute: azureml:cpu-cluster
+
+jobs:
+  batch_prediction:
+    type: parallel
+    compute: azureml:cpu-cluster
+    inputs:
+      input_data: 
+        type: mltable
+        path: ./neural-iris-mltable
+        mode: direct
+      score_model: 
+        type: uri_folder
+        path: ./iris-model
+        mode: download
+    outputs:
+      job_output_file:
+        type: uri_file
+        path: azureml://datastores/${{default_datastore}}/paths/${{name}}/aggregated_returns.csv
+        mode: rw_mount
+
+    input_data: ${{inputs.input_data}}
+    mini_batch_size: "10kb"
+    resources:
+        instance_count: 2
+    max_concurrency_per_instance: 2
+
+    logging_level: "DEBUG"
+    error_threshold: 5
+    mini_batch_error_threshold: 5
+    retry_settings:
+      max_retries: 2
+      timeout: 60
+
+    environment_variables:
+      "AZUREML_PARALLEL_EXAMPLE": "2a_yaml"
+    
+    task:
+      type: run_function
+      code: "./script"
+      entry_script: iris_prediction.py
+      environment:
+        name: "prs-env"
+        version: 1
+        image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
+        conda_file: ./environment/environment_parallel.yml
+      program_arguments: >-
+        --model ${{inputs.score_model}}
+        --allowed_failed_percent 30
+        --task_overhead_timeout 1200
+        --progress_update_timeout 600
+        --first_task_creation_timeout 600
+        --resource_monitor_interval 20
+      append_row_to: ${{outputs.job_output_file}}

cli/jobs/parallel/2a_iris_batch_prediction/script/iris_prediction.py

@@ -0,0 +1,39 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+"""This module will load mlflow model and do prediction."""
+
+import argparse
+import os
+import pickle
+from pathlib import Path
+
+from mlflow.sklearn import load_model
+
+
+def init():
+    print("Environment variables start ****")
+    for key, val in os.environ.items():
+        print(key, val)
+    print("Environment variables end ****")
+
+    parser = argparse.ArgumentParser(
+        allow_abbrev=False, description="ParallelRunStep Agent"
+    )
+    parser.add_argument("--model", type=str, default=0)
+    args, _ = parser.parse_known_args()
+
+    global iris_model
+
+    iris_model = load_model(args.model)
+
+
+def run(input_data):
+    num_rows, num_cols = input_data.shape
+    pred = iris_model.predict(input_data).reshape((num_rows, 1))
+
+    # cleanup output
+    result = input_data.drop(input_data.columns[4:], axis=1)
+    result["variety"] = pred
+
+    return result

cli/jobs/parallel/3a_mnist_batch_identification/README.md

@@ -0,0 +1,16 @@
+# mnist batch prediction example \[Paralle job\] \[Yaml + CLI example\]
+
+## Key notes for this example
+- How to use **parallel job** for **batch inferencing** scenario.
+- How to use parallel job **run_function** task with predefined **entry_script**.
+- How to use **url_folder** with **files data** as the **input of parallel job**.
+- How to use **mini_batch_size** in parallel job to split input data by size. 
+- How to use **append_row_to** to aggregate returns to **uri_file** output.
+
+To run this example, please following the steps:
+- Install the Azure ML CLI: [link](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public)
+- Use Azure ML job command to run pipeline.yml.
+    - Create new job: `az ml job create --file pipeline.yml`
+    - Override settings in this job: `az ml job create --file pipeline.yml --set jobs.predict_digits_mnist.resources.instance_count=3`
+
+To get the same example with python SDK experience, please refer to: [link](../../../../sdk/python/jobs/parallel/3a_mnist_batch_identification/mnist_batch_prediction.ipynb)

cli/jobs/parallel/3a_mnist_batch_identification/pipeline.yml

@@ -0,0 +1,58 @@
+$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
+type: pipeline
+display_name: mnist-batch-identification-using-parallel
+description: The hello world pipeline job with inline parallel job
+experiment_name: hello-world-parallel-job
+tags:
+  tag: tagvalue
+  owner: sdkteam
+
+jobs:
+  prepare_data:
+    type: command
+    compute: azureml:cpu-cluster
+    component: ./script/prepare_data.yml
+
+  predict_digits_mnist:
+    type: parallel
+    compute: azureml:cpu-cluster
+    inputs:
+      job_data_path:
+        type: uri_folder
+        path: ${{parent.jobs.prepare_data.outputs.mnist_png}}
+        mode: ro_mount
+      score_model:
+        type: uri_folder
+        path: ./mnist_models
+        mode: download
+    outputs:
+      job_output_file:
+        type: uri_file
+        path: azureml://datastores/${{default_datastore}}/paths/${{name}}/aggregated_returns.csv
+        mode: rw_mount
+
+    mini_batch_size: "5"
+    resources:
+      instance_count: 2
+    mini_batch_error_threshold: 5
+    logging_level: "DEBUG"
+    input_data: ${{inputs.job_data_path}}
+    max_concurrency_per_instance: 2
+    retry_settings:
+      max_retries: 2
+      timeout: 60
+    
+    environment_variables:
+      "AZUREML_PARALLEL_EXAMPLE": "3a_yaml"
+    
+    task:
+      type: run_function
+      code: ./script
+      entry_script: digit_identification.py
+      environment:
+        image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
+        conda_file: ./script/environment_parallel.yml
+      program_arguments: >-
+        --model ${{inputs.score_model}}
+      append_row_to: ${{outputs.job_output_file}}
+

cli/jobs/parallel/3a_mnist_batch_identification/script/digit_identification.py

@@ -0,0 +1,46 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import numpy as np
+import argparse
+import tensorflow as tf
+from PIL import Image
+
+
+def init():
+    global g_tf_sess
+
+    parser = argparse.ArgumentParser(
+        allow_abbrev=False, description="ParallelRunStep Agent"
+    )
+    parser.add_argument("--model", type=str, default=0)
+    args, _ = parser.parse_known_args()
+
+    # pull down model from workspace
+    model_path = args.model
+
+    # contruct graph to execute
+    tf.reset_default_graph()
+    saver = tf.train.import_meta_graph(os.path.join(model_path, "mnist-tf.model.meta"))
+    g_tf_sess = tf.Session(config=tf.ConfigProto(device_count={"GPU": 0}))
+    saver.restore(g_tf_sess, os.path.join(model_path, "mnist-tf.model"))
+
+
+def run(mini_batch):
+    print(f"run method start: {__file__}, run({mini_batch})")
+    resultList = []
+    in_tensor = g_tf_sess.graph.get_tensor_by_name("network/X:0")
+    output = g_tf_sess.graph.get_tensor_by_name("network/output/MatMul:0")
+
+    for image in mini_batch:
+        # prepare each image
+        data = Image.open(image)
+        np_im = np.array(data).reshape((1, 784))
+        # perform inference
+        inference_result = output.eval(feed_dict={in_tensor: np_im}, session=g_tf_sess)
+        # find best probability, and add to result list
+        best_result = np.argmax(inference_result)
+        resultList.append("{}: {}".format(os.path.basename(image), best_result))
+
+    return resultList

cli/jobs/parallel/3a_mnist_batch_identification/script/dump_mltable.py

@@ -0,0 +1,14 @@
+import yaml
+import argparse
+import os
+
+d = {"paths": [{"file": "./mnist"}]}
+
+parser = argparse.ArgumentParser(allow_abbrev=False, description="dump mltable")
+
+parser.add_argument("--output_folder", type=str, default=0)
+args, _ = parser.parse_known_args()
+dump_path = os.path.join(args.output_folder, "MLTable")
+with open(dump_path, "w") as yaml_file:
+    yaml.dump(d, yaml_file, default_flow_style=False)
+print("Saved MLTable file")

cli/jobs/parallel/3a_mnist_batch_identification/script/environment_parallel.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - protobuf==3.20.0

cli/jobs/parallel/3a_mnist_batch_identification/script/environment_prepare.yml

@@ -0,0 +1,10 @@
+name: prepare-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - idx2numpy
+      - pillow
+      - PyYAML

cli/jobs/parallel/3a_mnist_batch_identification/script/prepare_data.py

@@ -0,0 +1,28 @@
+import os
+import argparse
+import gzip
+import idx2numpy
+import urllib.request
+from PIL import Image
+
+parser = argparse.ArgumentParser(allow_abbrev=False, description="parse user arguments")
+parser.add_argument("--output_folder", type=str, default=0)
+
+args, _ = parser.parse_known_args()
+
+data_folder = os.path.join(args.output_folder, "mnist")
+os.makedirs(data_folder, exist_ok=True)
+urllib.request.urlretrieve(
+    "https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz",
+    filename=os.path.join(os.getcwd(), "test-images.gz"),
+)
+
+file_handler = gzip.open("test-images.gz", "r")
+imagearray = idx2numpy.convert_from_file(file_handler)
+
+# Choose the first 1000 images and save to the output folder.
+for i in range(1000):
+    im = Image.fromarray(imagearray[i])
+    im.save(os.path.join(data_folder, f"{i}.png"))
+
+print("Saved 1000 images to the output folder")

cli/jobs/parallel/3a_mnist_batch_identification/script/prepare_data.yml

@@ -0,0 +1,18 @@
+$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
+type: command
+
+name: prepare_data
+display_name: Prepare mnist data
+version: 1
+
+command: >-
+      python prepare_data.py --output_folder ${{outputs.mnist_png}}
+code: ./
+outputs:
+  mnist_png:
+    type: uri_folder
+    mode: rw_mount
+
+environment:
+  image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
+  conda_file: ./environment_prepare.yml

cli/jobs/parallel/README.md

@@ -0,0 +1,32 @@
+---
+page_type: sample
+languages:
+- azurecli
+products:
+- azure-machine-learning
+description: Directory for AzureML parallel job examples.
+---
+
+# AzureML parallel job CLI (v2) examples
+
+[![cleanup](https://github.com/Azure/azureml-examples/workflows/cleanup-cli/badge.svg)](https://github.com/Azure/azureml-examples/actions/workflows/cleanup-cli.yml)
+[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![license: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](../LICENSE)
+
+Welcome to the AzureML parallel job examples repository! The following parallel job examples will provide detailed guidance and best practices for how to parallelize your machine learn tasks to accelerate the execution and save more cost. Each example uses different input data types, data division method, and parallel settings to help you onboard parallel job in different scenarios. 
+
+Please refer to parallel job introduction doc to learn more before reading these examples. [How to use parallel job in pipeline](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?tabs=cliv2)
+
+**Prerequisite**
+- A basic understanding of Machine Learning
+- An Azure account with an active subscription - [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)
+- An Azure ML workspace - [Configure workspace](https://learn.microsoft.com/en-us/azure/machine-learning/concept-workspace#create-a-workspace)
+- A CPU compute cluster with name "cpu-cluster" and 4 max instances - [Create compute cluster](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster?tabs=azure-cli#create)
+- Installed Azure Machine Learning CLI v2 - [Install instructions](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-cli?tabs=public#installation)
+# Parallel job example list
+| Example name | Description | Scenario | Parallel task type | Parallel job input type | Data division for mini-batches | Output action |
+| ------------ | ----------- | -------- | ------------------ | ----------------------- | ------------------------------ | ------------- |
+| [1a - Orange juice sales prediction](./1a_oj_sales_prediction/README.md) | A pipeline job to train orange juice sales prediction model. Each store and brand need a dedicated model for prediction.<br><br>This pipeline contains 2 steps:<br>1) A command job which read full size of data and partition it to output mltable.<br>2) A parallel job which train model for each partition from mltable. | Many models training | run_function | MLTable with tabular data | by partition_keys | ignore mini-batch returns |
+| [2a - Iris batch prediction](./2a_iris_batch_prediction/README.md)  | A pipeline job with a single parallel step to classify iris. Iris data is stored in csv format and a MLTable artifact file helps the job to load iris data into dataframe. | Batch inferencing | run_function | MLTable with tabular data | by mini_batch_size | aggregate mini-batch returns to single file |
+| [3a - mnist batch prediction](./3a_mnist_batch_identification/README.md)  | A pipeline job to predict mnist images. <br><br>This pipeline contains 2 steps:<br>1) A command job which download mnist images from internet into a folder on data store. <br>2) A parallel job read images from the output folder of previous step then process images in parallel. | Batch inferencing | run_function | uri_folder with image files | by mini_batch_size | aggregate mini-batch returns to single file |
+

cli/readme.py

@@ -69,6 +69,7 @@ def main(args):
         glob.glob("jobs/automl-standalone-jobs/**/*cli-automl*.yml", recursive=True)
     )
     jobs += sorted(glob.glob("responsible-ai/**/cli-*.yml", recursive=True))
+    jobs += sorted(glob.glob("jobs/parallel/**/*pipeline*.yml", recursive=True))
     jobs = [
         job.replace(".yml", "")
         for job in jobs

sdk/python/endpoints/online/kubernetes/kubernetes-online-endpoints-safe-rollout.ipynb

@@ -66,8 +66,12 @@
     "    CodeConfiguration,\n",
     ")\n",
     "from azure.identity import DefaultAzureCredential\n",
-    "from azure.ai.ml.entities._deployment.resource_requirements_settings import ResourceRequirementsSettings\n",
-    "from azure.ai.ml.entities._deployment.container_resource_settings import ResourceSettings"
+    "from azure.ai.ml.entities._deployment.resource_requirements_settings import (\n",
+    "    ResourceRequirementsSettings,\n",
+    ")\n",
+    "from azure.ai.ml.entities._deployment.container_resource_settings import (\n",
+    "    ResourceSettings,\n",
+    ")"
    ]
   },
   {
@@ -266,7 +270,7 @@
     "            cpu=\"100m\",\n",
     "            memory=\"0.5Gi\",\n",
     "        ),\n",
-    "    )\n",
+    "    ),\n",
     ")"
    ]
   },

sdk/python/endpoints/online/kubernetes/kubernetes-online-endpoints-simple-deployment.ipynb

@@ -55,8 +55,12 @@
     "    CodeConfiguration,\n",
     ")\n",
     "from azure.identity import DefaultAzureCredential\n",
-    "from azure.ai.ml.entities._deployment.resource_requirements_settings import ResourceRequirementsSettings\n",
-    "from azure.ai.ml.entities._deployment.container_resource_settings import ResourceSettings"
+    "from azure.ai.ml.entities._deployment.resource_requirements_settings import (\n",
+    "    ResourceRequirementsSettings,\n",
+    ")\n",
+    "from azure.ai.ml.entities._deployment.container_resource_settings import (\n",
+    "    ResourceSettings,\n",
+    ")"
    ]
   },
   {
@@ -215,7 +219,7 @@
     "            cpu=\"100m\",\n",
     "            memory=\"0.5Gi\",\n",
     "        ),\n",
-    "    )    \n",
+    "    ),\n",
     ")"
    ]
   },
@@ -420,7 +424,7 @@
     "            cpu=\"100m\",\n",
     "            memory=\"0.5Gi\",\n",
     "        ),\n",
-    "    )      \n",
+    "    ),\n",
     ")"
    ]
   },

sdk/python/jobs/parallel/1a_oj_sales_prediction/oj_sales_prediction.ipynb

@@ -0,0 +1,376 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Orange juice sales prediction example \\[Parallel job\\] \\[SDK example\\]\n",
+    "## Key notes for this example\n",
+    "- How to use **parallel job** for **many model training** scenario.\n",
+    "- How to use parallel job **run_function** task with predefined **entry_script**.\n",
+    "- How to pre-cook data into **mltable with partition setting**.\n",
+    "- How to use **mltable** with **tabular data** as the **input of parallel job**.\n",
+    "- How to use **partition_keys** in parallel job to consume data with partitions. \n",
+    "- How to use **error_threshold** with **empty returns** to ignore checking failed items in mini-batch.\n",
+    "- How to use other parallel job settings:\n",
+    "  - mini_batch_error_threshold\n",
+    "  - environment_variables\n",
+    "\n",
+    "To get the same example with CLI + Yaml experience, please refer to: [link](../../../../../cli/jobs/parallel/1a_oj_sales_prediction/README.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Connect to Azure Machine Learning Workspace\n",
+    "## 1.1 Import the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673838207609
+    },
+    "name": "required-library"
+   },
+   "outputs": [],
+   "source": [
+    "# import required libraries\n",
+    "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n",
+    "from azure.ai.ml import MLClient, Input, Output, load_component\n",
+    "from azure.ai.ml.dsl import pipeline\n",
+    "from azure.ai.ml.entities import Environment, ResourceConfiguration\n",
+    "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n",
+    "from azure.ai.ml.parallel import parallel_run_function, RunFunction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Configure credential\n",
+    "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. \n",
+    "\n",
+    "Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673838211196
+    },
+    "name": "credential"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    credential = DefaultAzureCredential()\n",
+    "    # Check if given credential can get token successfully.\n",
+    "    credential.get_token(\"https://management.azure.com/.default\")\n",
+    "except Exception as ex:\n",
+    "    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n",
+    "    credential = InteractiveBrowserCredential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Get a handle to the workspace\n",
+    "\n",
+    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673858642606
+    },
+    "name": "workspace"
+   },
+   "outputs": [],
+   "source": [
+    "# Get a handle to workspace\n",
+    "ml_client = MLClient.from_config(credential=credential)\n",
+    "\n",
+    "# Retrieve an already attached Azure Machine Learning Compute.\n",
+    "cpu_compute_target = \"cpu-cluster\"\n",
+    "print(ml_client.compute.get(cpu_compute_target))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Define components and jobs in pipeline\n",
+    "\n",
+    "## 2.1 Load existing command component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673860079582
+    },
+    "name": "load-from-yaml"
+   },
+   "outputs": [],
+   "source": [
+    "# load existing command component to partition the single csv data to mltable.\n",
+    "partition_data = load_component(source=\"./src/partition_data/partition_data.yml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Declare parallel job by `parallel_run_function`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673860081288
+    },
+    "name": "parallel-job-for-file-data"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare parallel job with run_function task\n",
+    "many_model_training_with_partition_keys = parallel_run_function(\n",
+    "    name=\"train_many_models_with_partition_keys\",\n",
+    "    display_name=\"Train Many Models With Partition Keys\",\n",
+    "    description=\"parallel job to train many models with partition_keys on mltable input\",\n",
+    "    tags={\n",
+    "        \"azureml_parallel_example\": \"oj_many-model_sdk\",\n",
+    "    },\n",
+    "    inputs=dict(\n",
+    "        data_source=Input(\n",
+    "            type=AssetTypes.MLTABLE,\n",
+    "            description=\"Input mltable with predefined partition format.\",\n",
+    "            mode=InputOutputModes.DIRECT,  # [Important] To use 'partition_keys', input MLTable is required to use 'direct' mode.\n",
+    "        ),\n",
+    "        drop_cols=Input(\n",
+    "            type=\"string\",\n",
+    "            description=\"Columns need to be dropped before training. Split by comma.\",\n",
+    "        ),\n",
+    "        target_col=Input(\n",
+    "            type=\"string\",\n",
+    "            description=\"The column name for label of the input data.\",\n",
+    "        ),\n",
+    "        date_col=Input(\n",
+    "            type=\"string\",\n",
+    "            description=\"The column name for datatime. This will be used for generating time-series lagging data.\",\n",
+    "        ),\n",
+    "        lagging_orders=Input(\n",
+    "            type=\"string\",\n",
+    "            description=\"List of int which indicate how to generate lagging data for time-series input. Split by comma.\",\n",
+    "        ),\n",
+    "    ),\n",
+    "    outputs=dict(\n",
+    "        model_folder=Output(\n",
+    "            type=AssetTypes.URI_FOLDER,\n",
+    "            mode=InputOutputModes.RW_MOUNT,\n",
+    "        ),\n",
+    "    ),\n",
+    "    input_data=\"${{inputs.data_source}}\",  # Define which input data will be splitted into mini-batches\n",
+    "    partition_keys=[\n",
+    "        \"Store\",\n",
+    "        \"Brand\",\n",
+    "    ],  # Use 'partition_keys' as the data division method. This method requires MLTable input with partition setting pre-defined in MLTable artifact.\n",
+    "    instance_count=2,  # Use 2 nodes from compute cluster to run this parallel job.\n",
+    "    max_concurrency_per_instance=1,  # Create 2 worker processors in each compute node to execute mini-batches.\n",
+    "    error_threshold=-1,  # Monitor the failures of item processed by the gap between mini-batch input count and returns. 'Many model training' scenario doesn't fit this setting and '-1' means ignore counting failure items by mini-batch returns.\n",
+    "    mini_batch_error_threshold=5,  # Monitor the failed mini-batch by exception, time out, or null return. When failed mini-batch count is higher than this setting, the parallel job will be marked as 'failed'.\n",
+    "    retry_settings=dict(\n",
+    "        max_retries=2,  # Define how many retries when mini-batch execution is failed by exception, time out, or null return.\n",
+    "        timeout=60,  # Define the timeout in second for each mini-batch execution.\n",
+    "    ),\n",
+    "    logging_level=\"DEBUG\",\n",
+    "    environment_variables={\n",
+    "        \"AZUREML_PARALLEL_EXAMPLE\": \"1a_sdk\",\n",
+    "    },\n",
+    "    task=RunFunction(\n",
+    "        code=\"./src/parallel_train/\",\n",
+    "        entry_script=\"parallel_train.py\",\n",
+    "        environment=Environment(\n",
+    "            image=\"mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04\",\n",
+    "            conda_file=\"./src/parallel_train/conda.yml\",\n",
+    "        ),\n",
+    "        program_arguments=\"--drop_cols ${{inputs.drop_cols}} \"  # Passthrough input parameters into parallel_train script.\n",
+    "        \"--target_col ${{inputs.target_col}} \"\n",
+    "        \"--date_col ${{inputs.date_col}} \"\n",
+    "        \"--lagging_orders ${{inputs.lagging_orders}} \"\n",
+    "        \"--model_folder ${{outputs.model_folder}} \",\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Build pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673860087207
+    },
+    "name": "build-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare the overall input of the job.\n",
+    "input_oj_data = Input(\n",
+    "    path=\"./oj_sales_data/oj_sales_data.csv\",\n",
+    "    type=AssetTypes.URI_FILE,\n",
+    "    mode=InputOutputModes.RO_MOUNT,\n",
+    ")\n",
+    "\n",
+    "# Declare pipeline structure.\n",
+    "@pipeline(\n",
+    "    display_name=\"parallel job for oj many model training\",\n",
+    ")\n",
+    "def partition_job_in_pipeline(\n",
+    "    pipeline_input_data,\n",
+    "):\n",
+    "    # Declare 1st data partition command job.\n",
+    "    partition_job = partition_data(\n",
+    "        data_source=pipeline_input_data,\n",
+    "        partition_keys=\"Store,Brand\",\n",
+    "    )\n",
+    "\n",
+    "    # Declare 2nd parallel model training job.\n",
+    "    parallel_train = many_model_training_with_partition_keys(\n",
+    "        data_source=partition_job.outputs.tabular_output_data,\n",
+    "        drop_cols=\"Revenue,Advert,Store,Brand\",\n",
+    "        target_col=\"Quantity\",\n",
+    "        date_col=\"WeekStarting\",\n",
+    "        lagging_orders=\"1,2,3,4,5,6\",\n",
+    "    )\n",
+    "\n",
+    "    # User could override parallel job run-level property when invoke that parallel job/component in pipeline.\n",
+    "    parallel_train.resources.instance_count = 3\n",
+    "    parallel_train.max_concurrency_per_instance = 2\n",
+    "    parallel_train.mini_batch_error_threshold = 10\n",
+    "\n",
+    "\n",
+    "# Create pipeline instance\n",
+    "my_job = partition_job_in_pipeline(\n",
+    "    pipeline_input_data=input_oj_data,\n",
+    ")\n",
+    "\n",
+    "# Set pipeline level compute\n",
+    "my_job.settings.default_compute = \"cpu-cluster\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673860094391
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(my_job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Submit pipeline job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1673858666260
+    },
+    "name": "submit-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "pipeline_job = ml_client.jobs.create_or_update(\n",
+    "    my_job,\n",
+    "    experiment_name=\"hello-world-parallel-job\",\n",
+    ")\n",
+    "pipeline_job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "name": "stream-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# wait until the job completes\n",
+    "ml_client.jobs.stream(pipeline_job.name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": {
+   "description": "Create pipeline with parallel node to do batch inference"
+  },
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "vnext",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  },
+  "microsoft": {
+   "host": {
+    "AzureML": {
+     "notebookHasBeenCompleted": true
+    }
+   }
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f1648a01f415a15976454e88ab551f1eeb39d06522c1fdad5697f49923f4699e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

sdk/python/jobs/parallel/1a_oj_sales_prediction/src/parallel_train/conda.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - mltable==1.0.0

sdk/python/jobs/parallel/1a_oj_sales_prediction/src/parallel_train/parallel_train.py

@@ -0,0 +1,105 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import glob
+import pandas as pd
+import numpy as np
+import pickle
+import mltable
+import argparse
+import mlflow
+
+# from azureml.opendatasets import OjSalesSimulated
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import GradientBoostingRegressor
+
+
+def init():
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--drop_cols", type=str)
+    parser.add_argument("--target_col", type=str)
+    parser.add_argument("--date_col", type=str)
+    parser.add_argument("--lagging_orders", type=str)
+    parser.add_argument("--model_folder", type=str)
+    parser.add_argument("--partition_keys", type=str)
+
+    args, _ = parser.parse_known_args()
+
+    global target_col
+    target_col = args.target_col
+
+    global date_col
+    date_col = args.date_col
+
+    global lagging_orders
+    lagging_orders = [int(i) for i in args.lagging_orders.split(",")]
+
+    global model_folder
+    model_folder = args.model_folder
+
+    global drop_cols
+    drop_cols = args.drop_cols.split(",")
+
+
+def run(input_data, mini_batch_context):
+    mlflow.autolog()
+
+    if not isinstance(input_data, pd.DataFrame):
+        raise Exception("Not a valid DataFrame input.")
+
+    if target_col not in input_data.columns:
+        raise Exception("No target column found from input tabular data")
+    elif date_col not in input_data.columns:
+        raise Exception("No date column found from input tabular data")
+
+    print(f"partition_key_value = {mini_batch_context.partition_key_value}")
+
+    # data cleaning
+    input_data[date_col] = pd.to_datetime(input_data[date_col])
+    input_data = input_data.set_index(date_col).sort_index(ascending=True)
+    input_data = input_data.assign(Week_Year=input_data.index.isocalendar().week.values)
+    input_data = input_data.drop(columns=drop_cols, errors="ignore")
+
+    # data lagging
+    max_lag_order = max(lagging_orders)
+    train_tail = input_data.iloc[-max_lag_order:]
+    column_order = train_tail.columns
+    data_trans = input_data.copy()
+
+    ## Make the lag features
+    for lag_order in lagging_orders:
+        data_trans["lag_" + str(lag_order)] = data_trans[target_col].shift(lag_order)
+
+    data_trans = data_trans.loc[input_data.index]
+    data_trans = data_trans.dropna()
+
+    # traning & evaluation
+    features = data_trans.columns.drop(target_col)
+
+    X = data_trans[features].values
+    y = data_trans[target_col].values
+
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.05, random_state=12, shuffle=False
+    )
+
+    reg = GradientBoostingRegressor(random_state=12)
+    reg.fit(X_train, y_train)
+    reg_pred = reg.predict(X_test)
+
+    # Dump model
+    relative_path = os.path.join(
+        model_folder,
+        *list(str(i) for i in mini_batch_context.partition_key_value.values()),
+    )
+
+    if not os.path.exists(relative_path):
+        os.makedirs(relative_path)
+
+    mlflow.sklearn.save_model(reg, relative_path)
+
+    return []

sdk/python/jobs/parallel/1a_oj_sales_prediction/src/partition_data/conda.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - mltable==1.0.0

sdk/python/jobs/parallel/1a_oj_sales_prediction/src/partition_data/partition_data.py

@@ -0,0 +1,119 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import pandas as pd
+import numpy as np
+import argparse
+import yaml
+import json
+import re
+import mltable
+
+
+def normalize_path_value(
+    input_val: str,
+) -> str:
+    return re.sub('\.|\s|\\\\|\||\*|/|\<|\>|\?|:|"', "_", input_val)
+
+
+def dump_df_to_mltable(
+    input_df: pd.DataFrame,
+    target_path: str,
+    partition_cols=[],
+):
+    if len(partition_cols) > 0:
+        for item in partition_cols:
+            if str(item) not in input_df.columns:
+                raise Exception("No target column found from input tabular data")
+        has_partition = True
+    else:
+        has_partition = False
+
+    partitioned_file_name = "partitioned_data.csv"
+
+    # Dump df to target path
+    if has_partition:
+        for group_name, group_df in input_df.groupby(partition_cols):
+            if hasattr(group_name, "__iter__"):
+                relative_path = os.path.join(
+                    target_path, *[normalize_path_value(str(e)) for e in group_name]
+                )
+            else:
+                relative_path = os.path.join(target_path, str(group_name))
+
+            if not os.path.exists(relative_path):
+                os.makedirs(relative_path)
+
+            output_path = os.path.join(relative_path, partitioned_file_name)
+            group_df.drop(columns=partition_cols, errors="ignore").to_csv(
+                output_path, index=False
+            )
+    else:
+        if not os.path.exists(target_path):
+            os.makedirs(target_path)
+
+        output_path = os.path.join(target_path, partitioned_file_name)
+        input_df.to_csv(output_path, index=False)
+
+    # Create ML table artifact object
+    mltable_path = os.path.join(target_path, "MLTable")
+    mltable_dic = {
+        "type": "mltable",
+        "paths": [],
+        "transformations": [
+            {
+                "read_delimited": {
+                    "delimiter": ",",
+                    "encoding": "ascii",
+                    "header": "all_files_same_headers",
+                    "empty_as_string": False,
+                    "include_path_column": True,
+                },
+            },
+        ],
+    }
+
+    if has_partition:
+        partition_format_str = ""
+        partition_key_droplist = ["Path", "file_name", "extension"]
+
+        for col_name in partition_cols:
+            partition_format_str = partition_format_str + "{" + col_name + "}" + "/"
+
+        partition_format_str = partition_format_str + "{file_name}.{extension}"
+        mltable_dic["paths"].append({"pattern": "./**/*.csv"})
+        mltable_dic["transformations"].append(
+            {
+                "extract_columns_from_partition_format": {
+                    "partition_format": partition_format_str
+                },
+            }
+        )
+        mltable_dic["transformations"].append({"drop_columns": partition_key_droplist})
+    else:
+        mltable_dic["paths"].append({"pattern": "./*.csv"})
+
+    # Dump ML table artifact object to file
+    with open(mltable_path, "w") as yaml_file:
+        yaml.dump(mltable_dic, yaml_file, default_flow_style=False)
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--data_source", type=str)
+parser.add_argument("--partition_keys", type=str)
+parser.add_argument("--tabular_output_data", type=str)
+
+args, _ = parser.parse_known_args()
+
+try:
+    input_df = pd.read_csv(args.data_source)
+except:
+    raise Exception("Can not load input data as csv tabular data.")
+
+partition_cols = args.partition_keys.split(",")
+dump_df_to_mltable(
+    input_df=input_df,
+    target_path=args.tabular_output_data,
+    partition_cols=partition_cols,
+)

sdk/python/jobs/parallel/1a_oj_sales_prediction/src/partition_data/partition_data.yml

@@ -0,0 +1,29 @@
+$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
+
+type: command
+
+name: partition_data
+display_name: Partition input data by keys
+version: 1
+
+inputs:
+  data_source:
+    type: uri_file
+  partition_keys:
+    type: string
+
+outputs:
+  tabular_output_data:
+    type: mltable
+
+code: ./
+
+environment: 
+  image: mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04
+  conda_file: ./conda.yml
+
+command: >-
+  python partition_data.py
+  --data_source ${{inputs.data_source}}
+  --partition_keys ${{inputs.partition_keys}}
+  --tabular_output_data ${{outputs.tabular_output_data}}

sdk/python/jobs/parallel/2a_iris_batch_prediction/environment/environment_parallel.yml

@@ -0,0 +1,16 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2

sdk/python/jobs/parallel/2a_iris_batch_prediction/iris-model/MLmodel

@@ -0,0 +1,11 @@
+flavors:
+  python_function:
+    env: conda.yaml
+    loader_module: mlflow.sklearn
+    model_path: model.pkl
+    python_version: 3.6.8
+  sklearn:
+    pickled_model: model.pkl
+    serialization_format: cloudpickle
+    sklearn_version: 0.20.3
+utc_time_created: '2021-02-05 10:59:19.184646'

sdk/python/jobs/parallel/2a_iris_batch_prediction/iris_batch_prediction.ipynb

@@ -0,0 +1,331 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# iris batch prediction example \\[Parallel job\\] \\[SDK example\\]\n",
+    "## Key notes for this example\n",
+    "- How to use **parallel job** for **batch inferencing** scenario.\n",
+    "- How to use parallel job **run_function** task with predefined **entry_script**.\n",
+    "- How to use **mltable** with **tabular data** as the **input of parallel job**.\n",
+    "- How to use **mini_batch_size** in parallel job to split input data by size. \n",
+    "- How to use **append_row_to** to aggregate returns to **uri_file** output.\n",
+    "- How to use parallel job advanced args:\n",
+    "  - allowed_failed_percent\n",
+    "  - task_overhead_timeout\n",
+    "  - progress_update_timeout\n",
+    "  - first_task_creation_timeout\n",
+    "  - resource_monitor_interval\n",
+    "\n",
+    "To get the same example with CLI + Yaml experience, please refer to: [link](../../../../../cli/jobs/parallel/2a_iris_batch_prediction/README.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Connect to Azure Machine Learning Workspace\n",
+    "## 1.1 Import the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675409840552
+    },
+    "name": "required-library"
+   },
+   "outputs": [],
+   "source": [
+    "# import required libraries\n",
+    "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n",
+    "from azure.ai.ml import MLClient, Input, Output, load_component\n",
+    "from azure.ai.ml.dsl import pipeline\n",
+    "from azure.ai.ml.entities import Environment, ResourceConfiguration\n",
+    "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n",
+    "from azure.ai.ml.parallel import parallel_run_function, RunFunction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Configure credential\n",
+    "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. \n",
+    "\n",
+    "Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675409848584
+    },
+    "name": "credential"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    credential = DefaultAzureCredential()\n",
+    "    # Check if given credential can get token successfully.\n",
+    "    credential.get_token(\"https://management.azure.com/.default\")\n",
+    "except Exception as ex:\n",
+    "    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n",
+    "    credential = InteractiveBrowserCredential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Get a handle to the workspace\n",
+    "\n",
+    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675409855519
+    },
+    "name": "workspace"
+   },
+   "outputs": [],
+   "source": [
+    "# Get a handle to workspace\n",
+    "ml_client = MLClient.from_config(credential=credential)\n",
+    "\n",
+    "# Retrieve an already attached Azure Machine Learning Compute.\n",
+    "cpu_compute_target = \"cpu-cluster\"\n",
+    "print(ml_client.compute.get(cpu_compute_target))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Define parallel job in pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675411996528
+    },
+    "name": "parallel-job-for-file-data"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare parallel job with run_function task\n",
+    "batch_inferencing_with_mini_batch_size = parallel_run_function(\n",
+    "    name=\"batch_inferencing_with_mini_batch_size\",\n",
+    "    display_name=\"Batch Inferencing with mini_batch_size\",\n",
+    "    description=\"parallel job to do batch inferencing with mini_batch_size on mltable tabular input\",\n",
+    "    tags={\n",
+    "        \"azureml_parallel_example\": \"2a_sdk\",\n",
+    "    },\n",
+    "    inputs=dict(\n",
+    "        input_data=Input(\n",
+    "            type=AssetTypes.MLTABLE,\n",
+    "            description=\"Input tabular mltable data.\",\n",
+    "            mode=InputOutputModes.DIRECT,  # [Important] To use mltable tabular data, it is required to use 'direct' mode.\n",
+    "        ),\n",
+    "        score_model=Input(\n",
+    "            type=AssetTypes.URI_FOLDER,\n",
+    "            description=\"Folder contains the model file.\",\n",
+    "            mode=InputOutputModes.DOWNLOAD,\n",
+    "        ),\n",
+    "    ),\n",
+    "    outputs=dict(\n",
+    "        job_output_file=Output(\n",
+    "            type=AssetTypes.URI_FILE,\n",
+    "            mode=InputOutputModes.RW_MOUNT,\n",
+    "        ),\n",
+    "    ),\n",
+    "    input_data=\"${{inputs.input_data}}\",  # Define which input data will be splitted into mini-batches\n",
+    "    mini_batch_size=\"10kb\",  # Use 'mini_batch_size' as the data division method. For tabular input data, it split data by physical size.\n",
+    "    instance_count=2,  # Use 2 nodes from compute cluster to run this parallel job.\n",
+    "    max_concurrency_per_instance=1,  # Create 2 worker processors in each compute node to execute mini-batches.\n",
+    "    error_threshold=5,  # Monitor the failures of item processed by the gap between mini-batch input count and returns. 'Batch inferencing' scenario should return a list, dataframe, or tuple with the successful items to try to meet this threshold.\n",
+    "    mini_batch_error_threshold=5,  # Monitor the failed mini-batch by exception, time out, or null return. When failed mini-batch count is higher than this setting, the parallel job will be marked as 'failed'.\n",
+    "    retry_settings=dict(\n",
+    "        max_retries=2,  # Define how many retries when mini-batch execution is failed by exception, time out, or null return.\n",
+    "        timeout=60,  # Define the timeout in second for each mini-batch execution.\n",
+    "    ),\n",
+    "    logging_level=\"DEBUG\",\n",
+    "    environment_variables={\n",
+    "        \"AZUREML_PARALLEL_EXAMPLE\": \"2a_sdk\",\n",
+    "    },\n",
+    "    task=RunFunction(\n",
+    "        code=\"./script\",\n",
+    "        entry_script=\"iris_prediction.py\",\n",
+    "        environment=Environment(\n",
+    "            image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\",\n",
+    "            conda_file=\"./environment/environment_parallel.yml\",\n",
+    "        ),\n",
+    "        program_arguments=\"--model ${{inputs.score_model}} \"  # Passthrough input model folder path into script.\n",
+    "        \"--allowed_failed_percent 30 \"  # Advanced parallel setting in arguments. Visit https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?tabs=cliv2#consider-automation-settings for more details.\n",
+    "        \"--task_overhead_timeout 1200 \"\n",
+    "        \"--progress_update_timeout 600 \"\n",
+    "        \"--resource_monitor_interval 20 \",\n",
+    "        append_row_to=\"${{outputs.job_output_file}}\",  # Define where to output the aggregated returns from each mini-batches.\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Build pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675412000673
+    },
+    "name": "build-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare the inputs of the job.\n",
+    "input_iris_data = Input(\n",
+    "    path=\"./neural-iris-mltable\", type=AssetTypes.MLTABLE, mode=InputOutputModes.DIRECT\n",
+    ")\n",
+    "input_model_folder = Input(\n",
+    "    path=\"./iris-model\", type=AssetTypes.URI_FOLDER, mode=InputOutputModes.DOWNLOAD\n",
+    ")\n",
+    "\n",
+    "# Declare pipeline structure.\n",
+    "@pipeline(\n",
+    "    display_name=\"parallel job for iris batch inferencing\",\n",
+    ")\n",
+    "def parallel_job_in_pipeline():\n",
+    "    # Declare parallel inferencing job.\n",
+    "    parallel_train = batch_inferencing_with_mini_batch_size(\n",
+    "        input_data=input_iris_data,\n",
+    "        score_model=input_model_folder,\n",
+    "    )\n",
+    "\n",
+    "    # User could override parallel job run-level property when invoke that parallel job/component in pipeline.\n",
+    "    parallel_train.resources.instance_count = 2\n",
+    "    parallel_train.max_concurrency_per_instance = 2\n",
+    "    parallel_train.mini_batch_error_threshold = 10\n",
+    "    parallel_train.outputs.job_output_file.path = \"azureml://datastores/${{default_datastore}}/paths/${{name}}/aggregated_returns.csv\"\n",
+    "\n",
+    "\n",
+    "# Create pipeline instance\n",
+    "my_job = parallel_job_in_pipeline()\n",
+    "\n",
+    "# Set pipeline level compute\n",
+    "my_job.tags.update\n",
+    "my_job.settings.default_compute = \"cpu-cluster\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675412007635
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(my_job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Submit pipeline job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675412018584
+    },
+    "name": "submit-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "pipeline_job = ml_client.jobs.create_or_update(\n",
+    "    my_job,\n",
+    "    experiment_name=\"hello-world-parallel-job\",\n",
+    ")\n",
+    "pipeline_job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "name": "stream-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# wait until the job completes\n",
+    "ml_client.jobs.stream(pipeline_job.name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": {
+   "description": "Create pipeline with parallel node to do batch inference"
+  },
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "vnext",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  },
+  "microsoft": {
+   "host": {
+    "AzureML": {
+     "notebookHasBeenCompleted": true
+    }
+   }
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f1648a01f415a15976454e88ab551f1eeb39d06522c1fdad5697f49923f4699e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

sdk/python/jobs/parallel/2a_iris_batch_prediction/neural-iris-mltable/MLTable

@@ -0,0 +1,7 @@
+paths:
+  - file: ./iris.csv
+transformations:
+  - read_delimited:
+      delimiter: ","
+      encoding: utf8
+      header: all_files_same_headers

sdk/python/jobs/parallel/2a_iris_batch_prediction/neural-iris-mltable/iris.csv

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sdk/python/jobs/parallel/2a_iris_batch_prediction/script/iris_prediction.py

@@ -0,0 +1,39 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+"""This module will load mlflow model and do prediction."""
+
+import argparse
+import os
+import pickle
+from pathlib import Path
+
+from mlflow.sklearn import load_model
+
+
+def init():
+    print("Environment variables start ****")
+    for key, val in os.environ.items():
+        print(key, val)
+    print("Environment variables end ****")
+
+    parser = argparse.ArgumentParser(
+        allow_abbrev=False, description="ParallelRunStep Agent"
+    )
+    parser.add_argument("--model", type=str, default=0)
+    args, _ = parser.parse_known_args()
+
+    global iris_model
+
+    iris_model = load_model(args.model)
+
+
+def run(input_data):
+    num_rows, num_cols = input_data.shape
+    pred = iris_model.predict(input_data).reshape((num_rows, 1))
+
+    # cleanup output
+    result = input_data.drop(input_data.columns[4:], axis=1)
+    result["variety"] = pred
+
+    return result

sdk/python/jobs/parallel/3a_mnist_batch_identification/mnist_batch_prediction.ipynb

@@ -0,0 +1,357 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# mnist batch prediction example \\[Parallel job\\] \\[SDK example\\]\n",
+    "## Key notes for this example\n",
+    "- How to use **parallel job** for **batch inferencing** scenario.\n",
+    "- How to use parallel job **run_function** task with predefined **entry_script**.\n",
+    "- How to use **url_folder** with **files data** as the **input of parallel job**.\n",
+    "- How to use **mini_batch_size** in parallel job to split input data by size. \n",
+    "- How to use **append_row_to** to aggregate returns to **uri_file** output.\n",
+    "\n",
+    "To get the same example with CLI + Yaml experience, please refer to: [link](../../../../../cli/jobs/parallel/3a_mnist_batch_identification/README.md)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Connect to Azure Machine Learning Workspace\n",
+    "## 1.1 Import the required libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675653935697
+    },
+    "name": "required-library"
+   },
+   "outputs": [],
+   "source": [
+    "# import required libraries\n",
+    "from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential\n",
+    "from azure.ai.ml import MLClient, Input, Output, load_component\n",
+    "from azure.ai.ml.dsl import pipeline\n",
+    "from azure.ai.ml.entities import Environment, ResourceConfiguration\n",
+    "from azure.ai.ml.constants import AssetTypes, InputOutputModes\n",
+    "from azure.ai.ml.parallel import parallel_run_function, RunFunction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Configure credential\n",
+    "`DefaultAzureCredential` should be capable of handling most Azure SDK authentication scenarios. \n",
+    "\n",
+    "Reference for more available credentials if it does not work for you: [configure credential example](../../configuration.ipynb), [azure-identity reference doc](https://docs.microsoft.com/en-us/python/api/azure-identity/azure.identity?view=azure-python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675653940684
+    },
+    "name": "credential"
+   },
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    credential = DefaultAzureCredential()\n",
+    "    # Check if given credential can get token successfully.\n",
+    "    credential.get_token(\"https://management.azure.com/.default\")\n",
+    "except Exception as ex:\n",
+    "    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work\n",
+    "    credential = InteractiveBrowserCredential()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 Get a handle to the workspace\n",
+    "\n",
+    "We use config file to connect to a workspace. The Azure ML workspace should be configured with computer cluster. [Check this notebook for configure a workspace](../../configuration.ipynb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675653943687
+    },
+    "name": "workspace"
+   },
+   "outputs": [],
+   "source": [
+    "# Get a handle to workspace\n",
+    "ml_client = MLClient.from_config(credential=credential)\n",
+    "\n",
+    "# Retrieve an already attached Azure Machine Learning Compute.\n",
+    "cpu_compute_target = \"cpu-cluster\"\n",
+    "print(ml_client.compute.get(cpu_compute_target))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Define components and jobs in pipeline\n",
+    "## 2.1 Load existing component"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675654881745
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "prepare_data_component = load_component(source=\"./script/prepare_data.yml\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "## 2.2 Declare parallel job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675654195739
+    },
+    "name": "parallel-job-for-file-data"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare parallel job with run_function task\n",
+    "batch_inferencing_with_mini_batch_size = parallel_run_function(\n",
+    "    name=\"batch_inferencing_with_mini_batch_size\",\n",
+    "    display_name=\"Batch Inferencing with mini_batch_size\",\n",
+    "    description=\"parallel job to do batch inferencing with mini_batch_size on url folder with files input\",\n",
+    "    tags={\n",
+    "        \"azureml_parallel_example\": \"3a_sdk\",\n",
+    "    },\n",
+    "    inputs=dict(\n",
+    "        job_data_path=Input(\n",
+    "            type=AssetTypes.URI_FOLDER,\n",
+    "            description=\"Input tabular mltable data.\",\n",
+    "            mode=InputOutputModes.RO_MOUNT,\n",
+    "        ),\n",
+    "        score_model=Input(\n",
+    "            type=AssetTypes.URI_FOLDER,\n",
+    "            description=\"Folder contains the model file.\",\n",
+    "            mode=InputOutputModes.DOWNLOAD,\n",
+    "        ),\n",
+    "    ),\n",
+    "    outputs=dict(\n",
+    "        job_output_file=Output(\n",
+    "            type=AssetTypes.URI_FILE,\n",
+    "            mode=InputOutputModes.RW_MOUNT,\n",
+    "        ),\n",
+    "    ),\n",
+    "    input_data=\"${{inputs.job_data_path}}\",  # Define which input data will be splitted into mini-batches\n",
+    "    mini_batch_size=\"5\",  # Use 'mini_batch_size' as the data division method. For files input data, this number define the file count for each mini-batch.\n",
+    "    instance_count=2,  # Use 2 nodes from compute cluster to run this parallel job.\n",
+    "    max_concurrency_per_instance=2,  # Create 2 worker processors in each compute node to execute mini-batches.\n",
+    "    error_threshold=5,  # Monitor the failures of item processed by the gap between mini-batch input count and returns. 'Batch inferencing' scenario should return a list, dataframe, or tuple with the successful items to try to meet this threshold.\n",
+    "    mini_batch_error_threshold=5,  # Monitor the failed mini-batch by exception, time out, or null return. When failed mini-batch count is higher than this setting, the parallel job will be marked as 'failed'.\n",
+    "    retry_settings=dict(\n",
+    "        max_retries=2,  # Define how many retries when mini-batch execution is failed by exception, time out, or null return.\n",
+    "        timeout=60,  # Define the timeout in second for each mini-batch execution.\n",
+    "    ),\n",
+    "    logging_level=\"DEBUG\",\n",
+    "    environment_variables={\n",
+    "        \"AZUREML_PARALLEL_EXAMPLE\": \"3a_sdk\",\n",
+    "    },\n",
+    "    task=RunFunction(\n",
+    "        code=\"./script\",\n",
+    "        entry_script=\"digit_identification.py\",\n",
+    "        environment=Environment(\n",
+    "            image=\"mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\",\n",
+    "            conda_file=\"./script/environment_parallel.yml\",\n",
+    "        ),\n",
+    "        program_arguments=\"--model ${{inputs.score_model}} \",\n",
+    "        append_row_to=\"${{outputs.job_output_file}}\",  # Define where to output the aggregated returns from each mini-batches.\n",
+    "    ),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Build pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675654935751
+    },
+    "name": "build-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# Declare the inputs of the job.\n",
+    "input_model_folder = Input(\n",
+    "    path=\"./mnist_models\", type=AssetTypes.URI_FOLDER, mode=InputOutputModes.DOWNLOAD\n",
+    ")\n",
+    "\n",
+    "# Declare pipeline structure.\n",
+    "@pipeline(\n",
+    "    display_name=\"parallel job for iris batch inferencing\",\n",
+    ")\n",
+    "def parallel_job_in_pipeline():\n",
+    "    # Declare command job to prepare mnist data\n",
+    "    prepare_data = prepare_data_component()\n",
+    "\n",
+    "    # Declare parallel inferencing job.\n",
+    "    predict_digits_mnist = batch_inferencing_with_mini_batch_size(\n",
+    "        job_data_path=prepare_data.outputs.mnist_png,\n",
+    "        score_model=input_model_folder,\n",
+    "    )\n",
+    "\n",
+    "    # User could override parallel job run-level property when invoke that parallel job/component in pipeline.\n",
+    "    predict_digits_mnist.resources.instance_count = 2\n",
+    "    predict_digits_mnist.max_concurrency_per_instance = 2\n",
+    "    predict_digits_mnist.mini_batch_error_threshold = 10\n",
+    "    predict_digits_mnist.outputs.job_output_file.path = \"azureml://datastores/${{default_datastore}}/paths/${{name}}/aggregated_returns.csv\"\n",
+    "\n",
+    "\n",
+    "# Create pipeline instance\n",
+    "my_job = parallel_job_in_pipeline()\n",
+    "\n",
+    "# Set pipeline level compute\n",
+    "my_job.tags.update\n",
+    "my_job.settings.default_compute = \"cpu-cluster\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675412007635
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print(my_job)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4. Submit pipeline job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1675654944788
+    },
+    "name": "submit-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "pipeline_job = ml_client.jobs.create_or_update(\n",
+    "    my_job,\n",
+    "    experiment_name=\"hello-world-parallel-job\",\n",
+    ")\n",
+    "pipeline_job"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "name": "stream-pipeline"
+   },
+   "outputs": [],
+   "source": [
+    "# wait until the job completes\n",
+    "ml_client.jobs.stream(pipeline_job.name)"
+   ]
+  }
+ ],
+ "metadata": {
+  "description": {
+   "description": "Create pipeline with parallel node to do batch inference"
+  },
+  "kernel_info": {
+   "name": "python3"
+  },
+  "kernelspec": {
+   "display_name": "vnext",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.15"
+  },
+  "microsoft": {
+   "host": {
+    "AzureML": {
+     "notebookHasBeenCompleted": true
+    }
+   }
+  },
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "f1648a01f415a15976454e88ab551f1eeb39d06522c1fdad5697f49923f4699e"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/digit_identification.py

@@ -0,0 +1,46 @@
+# Copyright (c) Microsoft. All rights reserved.
+# Licensed under the MIT license.
+
+import os
+import numpy as np
+import argparse
+import tensorflow as tf
+from PIL import Image
+
+
+def init():
+    global g_tf_sess
+
+    parser = argparse.ArgumentParser(
+        allow_abbrev=False, description="ParallelRunStep Agent"
+    )
+    parser.add_argument("--model", type=str, default=0)
+    args, _ = parser.parse_known_args()
+
+    # pull down model from workspace
+    model_path = args.model
+
+    # contruct graph to execute
+    tf.reset_default_graph()
+    saver = tf.train.import_meta_graph(os.path.join(model_path, "mnist-tf.model.meta"))
+    g_tf_sess = tf.Session(config=tf.ConfigProto(device_count={"GPU": 0}))
+    saver.restore(g_tf_sess, os.path.join(model_path, "mnist-tf.model"))
+
+
+def run(mini_batch):
+    print(f"run method start: {__file__}, run({mini_batch})")
+    resultList = []
+    in_tensor = g_tf_sess.graph.get_tensor_by_name("network/X:0")
+    output = g_tf_sess.graph.get_tensor_by_name("network/output/MatMul:0")
+
+    for image in mini_batch:
+        # prepare each image
+        data = Image.open(image)
+        np_im = np.array(data).reshape((1, 784))
+        # perform inference
+        inference_result = output.eval(feed_dict={in_tensor: np_im}, session=g_tf_sess)
+        # find best probability, and add to result list
+        best_result = np.argmax(inference_result)
+        resultList.append("{}: {}".format(os.path.basename(image), best_result))
+
+    return resultList

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/dump_mltable.py

@@ -0,0 +1,14 @@
+import yaml
+import argparse
+import os
+
+d = {"paths": [{"file": "./mnist"}]}
+
+parser = argparse.ArgumentParser(allow_abbrev=False, description="dump mltable")
+
+parser.add_argument("--output_folder", type=str, default=0)
+args, _ = parser.parse_known_args()
+dump_path = os.path.join(args.output_folder, "MLTable")
+with open(dump_path, "w") as yaml_file:
+    yaml.dump(d, yaml_file, default_flow_style=False)
+print("Saved MLTable file")

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/environment_parallel.yml

@@ -0,0 +1,17 @@
+name: prs-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - mlflow
+      - azureml-dataset-runtime[pandas,fuse]
+      - azureml-telemetry
+      - pandas
+      - pillow
+      - azureml-core
+      - scikit-learn~=0.20.0
+      - cloudpickle==1.1.1
+      - tensorflow==1.15.2
+      - protobuf==3.20.0

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/environment_prepare.yml

@@ -0,0 +1,10 @@
+name: prepare-env
+channels:
+  - conda-forge
+dependencies:
+  - python=3.7.6
+  - pip
+  - pip:
+      - idx2numpy
+      - pillow
+      - PyYAML

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/prepare_data.py

@@ -0,0 +1,28 @@
+import os
+import argparse
+import gzip
+import idx2numpy
+import urllib.request
+from PIL import Image
+
+parser = argparse.ArgumentParser(allow_abbrev=False, description="parse user arguments")
+parser.add_argument("--output_folder", type=str, default=0)
+
+args, _ = parser.parse_known_args()
+
+data_folder = os.path.join(args.output_folder, "mnist")
+os.makedirs(data_folder, exist_ok=True)
+urllib.request.urlretrieve(
+    "https://azureopendatastorage.blob.core.windows.net/mnist/t10k-images-idx3-ubyte.gz",
+    filename=os.path.join(os.getcwd(), "test-images.gz"),
+)
+
+file_handler = gzip.open("test-images.gz", "r")
+imagearray = idx2numpy.convert_from_file(file_handler)
+
+# Choose the first 1000 images and save to the output folder.
+for i in range(1000):
+    im = Image.fromarray(imagearray[i])
+    im.save(os.path.join(data_folder, f"{i}.png"))
+
+print("Saved 1000 images to the output folder")

sdk/python/jobs/parallel/3a_mnist_batch_identification/script/prepare_data.yml

@@ -0,0 +1,18 @@
+$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
+type: command
+
+name: prepare_data
+display_name: Prepare mnist data
+version: 1
+
+command: >-
+      python prepare_data.py --output_folder ${{outputs.mnist_png}}
+code: ./
+outputs:
+  mnist_png:
+    type: uri_folder
+    mode: rw_mount
+
+environment:
+  image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
+  conda_file: ./environment_prepare.yml

sdk/python/jobs/parallel/README.md

@@ -0,0 +1,30 @@
+---
+page_type: sample
+languages:
+- python
+products:
+- azure-machine-learning
+description: Directory for AzureML parallel job examples.
+---
+
+# AzureML parallel job python SDK (v2) examples
+
+[![code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![license: MIT](https://img.shields.io/badge/License-MIT-purple.svg)](../LICENSE)
+
+Welcome to the AzureML parallel job examples repository! The following parallel job examples will provide detailed guidance and best practices for how to parallelize your machine learn tasks to accelerate the execution and save more cost. Each example uses different input data types, data division method, and parallel settings to help you onboard parallel job in different scenarios. 
+
+**Prerequisite**
+- A basic understanding of Machine Learning
+- A basic understanding for how AzureML parallel job works - [How to use parallel job in pipeline](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-job-in-pipeline?tabs=cliv2)
+- An Azure account with an active subscription - [Create an account for free](https://azure.microsoft.com/free/?WT.mc_id=A261C142F)
+- An Azure ML workspace with computer cluster - [Configure workspace](../configuration.ipynb)
+- A python environment
+- Installed Azure Machine Learning Python SDK v2 - [install instructions](../../README.md#getting-started)
+
+# Parallel job example list
+| Example name | Description | Scenario | Parallel task type | Parallel job input type | Data division for mini-batches | Output action |
+| ------------ | ----------- | -------- | ------------------ | ----------------------- | ------------------------------ | ------------- |
+| [1a - Orange juice sales prediction](./1a_oj_sales_prediction/README.md) | A pipeline job to train orange juice sales prediction model. Each store and brand need a dedicated model for prediction.<br><br>This pipeline contains 2 steps:<br>1) A command job which read full size of data and partition it to output mltable.<br>2) A parallel job which train model for each partition from mltable. | Many models training | run_function | MLTable with tabular data | by partition_keys | ignore mini-batch returns |
+| [2a - Iris batch prediction](./2a_iris_batch_prediction/README.md)  | A pipeline job with a single parallel step to classify iris. Iris data is stored in csv format and a MLTable artifact file helps the job to load iris data into dataframe. | Batch inferencing | run_function | MLTable with tabular data | by mini_batch_size | aggregate mini-batch returns to single file |
+| [3a - mnist batch prediction](./3a_mnist_batch_identification/README.md)  | A pipeline job to predict mnist images. <br><br>This pipeline contains 2 steps:<br>1) A command job which download mnist images from internet into a folder on data store. <br>2) A parallel job read images from the output folder of previous step then process images in parallel. | Batch inferencing | run_function | uri_folder with image files | by mini_batch_size | aggregate mini-batch returns to single file |

tutorials/azureml-in-a-day/azureml-in-a-day.ipynb

@@ -177,14 +177,11 @@
     "        # Dedicated or LowPriority. The latter is cheaper but there is a chance of job termination\n",
     "        tier=\"Dedicated\",\n",
     "    )\n",
-  
     "    print(\n",
-    "         f\"AMLCompute with name {cpu_cluster.name} will be created, with compute size {cpu_cluster.size}\"\n",
-    "          )",
-    "\n",
+    "        f\"AMLCompute with name {cpu_cluster.name} will be created, with compute size {cpu_cluster.size}\"\n",
+    "    )\n",
     "    # Now, we pass the object to MLClient's create_or_update method\n",
-    "    cpu_cluster = ml_client.compute.begin_create_or_update(cpu_cluster)\n",
-    "\n"
+    "    cpu_cluster = ml_client.compute.begin_create_or_update(cpu_cluster)"
    ]
   },
   {