Create and run machine learning pipelines using components with the Azure Machine Learning SDK v2

APPLIES TO: Python SDK azure-ai-ml v2 (current)

In this article, you learn how to build an Azure Machine Learning pipeline using Python SDK v2 to complete an image classification task containing three steps: prepare data, train an image classification model, and score the model. Machine learning pipelines optimize your workflow with speed, portability, and reuse, so you can focus on machine learning instead of infrastructure and automation.

The example trains a small Keras convolutional neural network to classify images in the Fashion MNIST dataset. The pipeline looks like following.

Screenshot showing pipeline graph of the image classification Keras example.

In this article, you complete the following tasks:

  • Prepare input data for the pipeline job
  • Create three components to prepare the data, train and score
  • Compose a Pipeline from the components
  • Get access to workspace with compute
  • Submit the pipeline job
  • Review the output of the components and the trained neural network
  • (Optional) Register the component for further reuse and sharing within workspace

If you don't have an Azure subscription, create a free account before you begin. Try the free or paid version of Azure Machine Learning today.

Prerequisites

  • Complete the Quickstart: Get started with Azure Machine Learning if you don't already have an Azure Machine Learning workspace.

  • A Python environment in which you've installed Azure Machine Learning Python SDK v2 - install instructions - check the getting started section. This environment is for defining and controlling your Azure Machine Learning resources and is separate from the environment used at runtime for training.

  • Clone examples repository

    To run the training examples, first clone the examples repository and change into the sdk directory:

    git clone --depth 1 https://github.com/Azure/azureml-examples --branch sdk-preview
    cd azureml-examples/sdk
    

Start an interactive Python session

This article uses the Python SDK for Azure ML to create and control an Azure Machine Learning pipeline. The article assumes that you'll be running the code snippets interactively in either a Python REPL environment or a Jupyter notebook.

This article is based on the image_classification_keras_minist_convnet.ipynb notebook found in the sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet directory of the AzureML Examples repository.

Import required libraries

Import all the Azure Machine Learning required libraries that you'll need for this article:

# import required libraries
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredential

from azure.ai.ml import MLClient
from azure.ai.ml.dsl import pipeline
from azure.ai.ml import load_component

Prepare input data for your pipeline job

You need to prepare the input data for this image classification pipeline.

Fashion-MNIST is a dataset of fashion images divided into 10 classes. Each image is a 28x28 grayscale image and there are 60,000 training and 10,000 test images. As an image classification problem, Fashion-MNIST is harder than the classic MNIST handwritten digit database. It's distributed in the same compressed binary form as the original handwritten digit database.

To define the input data of a job that references the Web-based data, run:

from azure.ai.ml import Input

fashion_ds = Input(
    path="wasbs://demo@data4mldemo6150520719.blob.core.windows.net/mnist-fashion/"
)

By defining an Input, you create a reference to the data source location. The data remains in its existing location, so no extra storage cost is incurred.

Create components for building pipeline

The image classification task can be split into three steps: prepare data, train model and score model.

Azure Machine Learning component is a self-contained piece of code that does one step in a machine learning pipeline. In this article, you'll create three components for the image classification task:

  • Prepare data for training and test
  • Train a neural networking for image classification using training data
  • Score the model using test data

For each component, you need to prepare the following staff:

  1. Prepare the Python script containing the execution logic

  2. Define the interface of the component,

  3. Add other metadata of the component, including run-time environment, command to run the component, and etc.

The next section will show create components in two different ways: the first two components using Python function and the third component using yaml definition.

Create the data-preparation component

The first component in this pipeline will convert the compressed data files of fashion_ds into two csv files, one for training and the other for scoring. You'll use Python function to define this component.

If you're following along with the example in the AzureML Examples repo, the source files are already available in prep/ folder. This folder contains two files to construct the component: prep_component.py, which defines the component and conda.yaml, which defines the run-time environment of the component.

Define component using Python function

By using command_component() function as a decorator, you can easily define the component's interface, metadata and code to execute from a Python function. Each decorated Python function will be transformed into a single static specification (YAML) that the pipeline service can process.

# Converts MNIST-formatted files at the passed-in input path to training data output path and test data output path
import os
from pathlib import Path
from mldesigner import command_component, Input, Output


@command_component(
    name="prep_data",
    version="1",
    display_name="Prep Data",
    description="Convert data to CSV file, and split to training and test data",
    environment=dict(
        conda_file=Path(__file__).parent / "conda.yaml",
        image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04",
    ),
)
def prepare_data_component(
    input_data: Input(type="uri_folder"),
    training_data: Output(type="uri_folder"),
    test_data: Output(type="uri_folder"),
):
    convert(
        os.path.join(input_data, "train-images-idx3-ubyte"),
        os.path.join(input_data, "train-labels-idx1-ubyte"),
        os.path.join(training_data, "mnist_train.csv"),
        60000,
    )
    convert(
        os.path.join(input_data, "t10k-images-idx3-ubyte"),
        os.path.join(input_data, "t10k-labels-idx1-ubyte"),
        os.path.join(test_data, "mnist_test.csv"),
        10000,
    )


def convert(imgf, labelf, outf, n):
    f = open(imgf, "rb")
    l = open(labelf, "rb")
    o = open(outf, "w")

    f.read(16)
    l.read(8)
    images = []

    for i in range(n):
        image = [ord(l.read(1))]
        for j in range(28 * 28):
            image.append(ord(f.read(1)))
        images.append(image)

    for image in images:
        o.write(",".join(str(pix) for pix in image) + "\n")
    f.close()
    o.close()
    l.close()

The code above define a component with display name Prep Data using @command_component decorator:

  • name is the unique identifier of the component.

  • version is the current version of the component. A component can have multiple versions.

  • display_name is a friendly display name of the component in UI, which isn't unique.

  • description usually describes what task this component can complete.

  • environment specifies the run-time environment for this component. The environment of this component specifies a docker image and refers to the conda.yaml file.

    The conda.yaml file contains all packages used for the component like following:

    name: imagekeras_prep_conda_env
    channels:
      - defaults
    dependencies:
      - python=3.7.11
      - pip=20.0
      - pip:
        - mldesigner==0.1.0b4
    
  • The prepare_data_component function defines one input for input_data and two outputs for training_data and test_data. input_data is input data path. training_data and test_data are output data paths for training data and test data.

  • This component converts the data from input_data into a training data csv to training_data and a test data csv to test_data.

Following is what a component looks like in the studio UI.

  • A component is a block in a pipeline graph.
  • The input_data, training_data and test_data are ports of the component, which connects to other components for data streaming.

Screenshot of the Prep Data component in the UI and code.

Now, you've prepared all source files for the Prep Data component.

Create the train-model component

In this section, you'll create a component for training the image classification model in the Python function like the Prep Data component.

The difference is that since the training logic is more complicated, you can put the original training code in a separate Python file.

The source files of this component are under train/ folder in the AzureML Examples repo. This folder contains three files to construct the component:

  • train.py: contains the actual logic to train model.
  • train_component.py: defines the interface of the component and imports the function in train.py.
  • conda.yaml: defines the run-time environment of the component.

Get a script containing execution logic

The train.py file contains a normal Python function, which performs the training model logic to train a Keras neural network for image classification. You can find the code here.

Define component using Python function

After defining the training function successfully, you can use @command_component in Azure Machine Learning SDK v2 to wrap your function as a component, which can be used in AzureML pipelines.

import os
from pathlib import Path
from mldesigner import command_component, Input, Output


@command_component(
    name="train_image_classification_keras",
    version="1",
    display_name="Train Image Classification Keras",
    description="train image classification with keras",
    environment=dict(
        conda_file=Path(__file__).parent / "conda.yaml",
        image="mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04",
    ),
)
def keras_train_component(
    input_data: Input(type="uri_folder"),
    output_model: Output(type="uri_folder"),
    epochs=10,
):
    # avoid dependency issue, execution logic is in train() func in train.py file
    from train import train

    train(input_data, output_model, epochs)

The code above define a component with display name Train Image Classification Keras using @command_component:

  • The keras_train_component function defines one input input_data where training data comes from, one input epochs specifying epochs during training, and one output output_model where outputs the model file. The default value of epochs is 10. The execution logic of this component is from train() function in train.py above.

The train-model component has a slightly more complex configuration than the prep-data component. The conda.yaml is like following:

name: imagekeras_train_conda_env
channels:
  - defaults
dependencies:
  - python=3.7.11
  - pip=20.0
  - pip:
    - mldesigner==0.1.0b4
    - azureml-mlflow==1.42.0
    - tensorflow==2.7.0
    - numpy==1.21.4
    - scikit-learn==1.0.1
    - pandas==1.3.4
    - matplotlib==3.2.2
    - protobuf==3.20.0

Now, you've prepared all source files for the Train Image Classification Keras component.

Create the score-model component

In this section, other than the previous components, you'll create a component to score the trained model via Yaml specification and script.

If you're following along with the example in the AzureML Examples repo, the source files are already available in score/ folder. This folder contains three files to construct the component:

  • score.py: contains the source code of the component.
  • score.yaml: defines the interface and other details of the component.
  • conda.yaml: defines the run-time environment of the component.

Get a script containing execution logic

The score.py file contains a normal Python function, which performs the training model logic.

from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.utils import to_categorical
from keras.callbacks import Callback
from keras.models import load_model

import argparse
from pathlib import Path
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import mlflow


def get_file(f):

    f = Path(f)
    if f.is_file():
        return f
    else:
        files = list(f.iterdir())
        if len(files) == 1:
            return files[0]
        else:
            raise Exception("********This path contains more than one file*******")


def parse_args():
    # setup argparse
    parser = argparse.ArgumentParser()

    # add arguments
    parser.add_argument(
        "--input_data", type=str, help="path containing data for scoring"
    )
    parser.add_argument(
        "--input_model", type=str, default="./", help="input path for model"
    )

    parser.add_argument(
        "--output_result", type=str, default="./", help="output path for model"
    )

    # parse args
    args = parser.parse_args()

    # return args
    return args


def score(input_data, input_model, output_result):

    test_file = get_file(input_data)
    data_test = pd.read_csv(test_file, header=None)

    img_rows, img_cols = 28, 28
    input_shape = (img_rows, img_cols, 1)

    # Read test data
    X_test = np.array(data_test.iloc[:, 1:])
    y_test = to_categorical(np.array(data_test.iloc[:, 0]))
    X_test = (
        X_test.reshape(X_test.shape[0], img_rows, img_cols, 1).astype("float32") / 255
    )

    # Load model
    files = [f for f in os.listdir(input_model) if f.endswith(".h5")]
    model = load_model(input_model + "/" + files[0])

    # Log metrics of the model
    eval = model.evaluate(X_test, y_test, verbose=0)

    mlflow.log_metric("Final test loss", eval[0])
    print("Test loss:", eval[0])

    mlflow.log_metric("Final test accuracy", eval[1])
    print("Test accuracy:", eval[1])

    # Score model using test data
    y_predict = model.predict(X_test)
    y_result = np.argmax(y_predict, axis=1)

    # Output result
    np.savetxt(output_result + "/predict_result.csv", y_result, delimiter=",")


def main(args):
    score(args.input_data, args.input_model, args.output_result)


# run script
if __name__ == "__main__":
    # parse args
    args = parse_args()

    # call main function
    main(args)

The code in score.py takes three command-line arguments: input_data, input_model and output_result. The program score the input model using input data and then output the scoring result.

Define component via Yaml

In this section, you'll learn to create a component specification in the valid YAML component specification format. This file specifies the following information:

  • Metadata: name, display_name, version, type, and so on.
  • Interface: inputs and outputs
  • Command, code, & environment: The command, code, and environment used to run the component
$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
type: command

name: score_image_classification_keras
display_name: Score Image Classification Keras
inputs:
  input_data: 
    type: uri_folder
  input_model:
    type: uri_folder
outputs:
  output_result:
    type: uri_folder
code: ./
command: python score.py --input_data ${{inputs.input_data}} --input_model ${{inputs.input_model}} --output_result ${{outputs.output_result}}
environment:
  conda_file: ./conda.yaml
  image: mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04
  • name is the unique identifier of the component. Its display name is Score Image Classification Keras.
  • This component has two inputs and one output.
  • The source code path of it's defined in the code section and when the component is run in cloud, all files from that path will be uploaded as the snapshot of this component.
  • The command section specifies the command to execute while running this component.
  • The environment section contains a docker image and a conda yaml file. The source file is in the sample repository.

Now, you've got all source files for score-model component.

Load components to build pipeline

For prep-data component and train-model component defined by Python function, you can import the components just like normal Python functions.

In the following code, you import prepare_data_component() and keras_train_component() function from the prep_component.py file under prep folder and train_component file under train folder respectively.

%load_ext autoreload
%autoreload 2

# load component function from component python file
from prep.prep_component import prepare_data_component
from train.train_component import keras_train_component

# print hint of components
help(prepare_data_component)
help(keras_train_component)

For score component defined by yaml, you can use load_component() function to load.

# load component function from yaml
keras_score_component = load_component(source="./score/score.yaml")

Build your pipeline

Now that you've created and loaded all components and input data to build the pipeline. You can compose them into a pipeline:

# define a pipeline containing 3 nodes: Prepare data node, train node, and score node
@pipeline(
    default_compute=cpu_compute_target,
)
def image_classification_keras_minist_convnet(pipeline_input_data):
    """E2E image classification pipeline with keras using python sdk."""
    prepare_data_node = prepare_data_component(input_data=pipeline_input_data)

    train_node = keras_train_component(
        input_data=prepare_data_node.outputs.training_data
    )
    train_node.compute = gpu_compute_target

    score_node = keras_score_component(
        input_data=prepare_data_node.outputs.test_data,
        input_model=train_node.outputs.output_model,
    )


# create a pipeline
pipeline_job = image_classification_keras_minist_convnet(pipeline_input_data=fashion_ds)

The pipeline has a default compute cpu_compute_target, which means if you don't specify compute for a specific node, that node will run on the default compute.

The pipeline has a pipeline level input pipeline_input_data. You can assign value to pipeline input when you submit a pipeline job.

The pipeline contains three nodes, prepare_data_node, train_node and score_node.

  • The input_data of prepare_data_node uses the value of pipeline_input_data.

  • The input_data of train_node is from the training_data output of the prepare_data_node.

  • The input_data of score_node is from the test_data output of prepare_data_node, and the input_model is from the output_model of train_node.

  • Since train_node will train a CNN model, you can specify its compute as the gpu_compute_target, which can improve the training performance.

Submit your pipeline job

Now you've constructed the pipeline, you can submit to your workspace. To submit a job, you need to firstly connect to a workspace.

Get access to your workspace

Configure credential

We'll use DefaultAzureCredential to get access to workspace. DefaultAzureCredential to get access to workspace.

DefaultAzureCredential should be capable of handling most Azure SDK authentication scenarios.

Reference for more available credentials if it doesn't work for you: configure credential example, azure-identity reference doc.

try:
    credential = DefaultAzureCredential()
    # Check if given credential can get token successfully.
    credential.get_token("https://management.azure.com/.default")
except Exception as ex:
    # Fall back to InteractiveBrowserCredential in case DefaultAzureCredential not work
    credential = InteractiveBrowserCredential()

Get a handle to a workspace with compute

Create a MLClient object to manage Azure Machine Learning services.

# Get a handle to workspace
ml_client = MLClient.from_config(credential=credential)

# Retrieve an already attached Azure Machine Learning Compute.
cpu_compute_target = "cpu-cluster"
print(ml_client.compute.get(cpu_compute_target))
gpu_compute_target = "gpu-cluster"
print(ml_client.compute.get(gpu_compute_target))

Important

This code snippet expects the workspace configuration json file to be saved in the current directory or its parent. For more information on creating a workspace, see Create workspace resources. For more information on saving the configuration to file, see Create a workspace configuration file.

Submit pipeline job to workspace

Now you've get a handle to your workspace, you can submit your pipeline job.

pipeline_job = ml_client.jobs.create_or_update(
    pipeline_job, experiment_name="pipeline_samples"
)
pipeline_job

The code above submit this image classification pipeline job to experiment called pipeline_samples. It will auto create the experiment if not exists. The pipeline_input_data uses fashion_ds.

The call to pipeline_jobproduces output similar to:

The call to submit the Experiment completes quickly, and produces output similar to:

Experiment Name Type Status Details Page
pipeline_samples sharp_pipe_4gvqx6h1fb pipeline Preparing Link to Azure Machine Learning studio.

You can monitor the pipeline run by opening the link or you can block until it completes by running:

# wait until the job completes
ml_client.jobs.stream(pipeline_job.name)

Important

The first pipeline run takes roughly 15 minutes. All dependencies must be downloaded, a Docker image is created, and the Python environment is provisioned and created. Running the pipeline again takes significantly less time because those resources are reused instead of created. However, total run time for the pipeline depends on the workload of your scripts and the processes that are running in each pipeline step.

Checkout outputs and debug your pipeline in UI

You can open the Link to Azure Machine Learning studio, which is the job detail page of your pipeline. You'll see the pipeline graph like following.

Screenshot of the pipeline job detail page.

You can check the logs and outputs of each component by right clicking the component, or select the component to open its detail pane. To learn more about how to debug your pipeline in UI, see How to use studio UI to build and debug Azure ML pipelines.

(Optional) Register components to workspace

In the previous section, you have built a pipeline using three components to E2E complete an image classification task. You can also register components to your workspace so that they can be shared and resued within the workspace. Following is an example to register prep-data component.

try:
    # try get back the component
    prep = ml_client.components.get(name="prep_data", version="1")
except:
    # if not exists, register component using following code
    prep = ml_client.components.create_or_update(prepare_data_component)

# list all components registered in workspace
for c in ml_client.components.list():
    print(c)

Using ml_client.components.get(), you can get a registered component by name and version. Using ml_client.compoennts.create_or_update(), you can register a component previously loaded from Python function or yaml.

Next steps