Azure ML SDK V2 sample for forecasting a time series | by Balamurugan Balakreshnan | Sep, 2022

• Azure Account
• Azure Machine learning resource
• Default AML storage
• Create Training, Test and Validation folder
• MLTable Config for above folders
• Install preview features
• Remember print version for preview is only available using _version.VERSION

import azure.ai.ml
print(azure.ai.ml._version.VERSION)

# import required libraries
from azure.ai.ml import MLClient
from azure.ai.ml.entities import Workspace
from azure.identity import DefaultAzureCredential

• now specificy the workspace name and subscription id

# Enter details of your AML workspace
subscription_id = "xxxxxxxxxxxxxxxxxxxxxxxxx"
resource_group = "rgname"
workspace = "workspacename"

# get a handle to the workspace
ml_client = MLClient(
DefaultAzureCredential(), subscription_id, resource_group, workspace
)

• Import the necessary libraries

# Import required libraries
from azure.ai.ml import MLClient

• Get Default Authentication

from azure.identity import DefaultAzureCredential, InteractiveBrowserCredentialtry:
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
# This will open a browser page for
credential = InteractiveBrowserCredential()

try:
ml_client = MLClient.from_config(credential=credential)
except Exception as ex:
# NOTE: Update following workspace information if not correctly configure before
client_config = {
"subscription_id": "xxxxxxxxxxxxxxxxxxxxxxxxxxx",
"resource_group": "rgname",
"workspace_name": "workspacename",
}    if client_config["subscription_id"].startswith("<"):
print(
"please update your    in notebook cell"
)
raise ex
else:  # write and reload from config file
import json, os        config_path = "../.azureml/config.json"
os.makedirs(os.path.dirname(config_path), exist_ok=True)
with open(config_path, "w") as fo:
fo.write(json.dumps(client_config))
ml_client = MLClient.from_config(credential=credential, path=config_path)
print(ml_client)

• Now create the compute target

from azure.ai.ml.entities import AmlCompute# specify aml compute name.
cpu_compute_target = "cpu-cluster"try:
ml_client.compute.get(cpu_compute_target)
except Exception:
print("Creating a new cpu compute target...")
compute = AmlCompute(
name=cpu_compute_target, size="STANDARD_D2_V2", min_instances=0, max_instances=4
)
ml_client.compute.begin_create_or_update(compute)

• lets include other libraries

# import required libraries
from azure.identity import DefaultAzureCredential, InteractiveBrowserCredentialfrom azure.ai.ml import MLClient, Input, command, Output
from azure.ai.ml.dsl import pipeline
from azure.ai.ml.automl import forecasting
from azure.ai.ml.entities._job.automl.tabular.forecasting_settings import ForecastingSettings# add environment variable to enable private preview feature
import os
os.environ["AZURE_ML_CLI_PRIVATE_FEATURES_ENABLED"] = "true"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 workspace
ml_client = MLClient.from_config(credential=credential)# Retrieve an already attached Azure Machine Learning Compute.
cluster_name = "cpu-cluster"
print(ml_client.compute.get(cluster_name))

# Define pipeline
@pipeline(
description="AutoML Forecasting Pipeline",
)
def automl_forecasting(
forecasting_train_data,
):
# define forecasting settings
forecasting_settings = ForecastingSettings(time_column_name="timeStamp", forecast_horizon=12, frequency="H", target_lags=[ 12 ], target_rolling_window_size=4)
# define the automl forecasting task with automl function
forecasting_node = forecasting(
training_data=forecasting_train_data,
target_column_name="demand",
primary_metric="normalized_root_mean_squared_error",
n_cross_validations=2,
forecasting_settings=forecasting_settings,
# currently need to specify outputs "mlflow_model" explictly to reference it in following nodes 
outputs={"best_model": Output(type="mlflow_model")},
)
forecasting_node.set_limits(timeout_minutes=180)    command_func = command(
inputs=dict(
automl_output=Input(type="mlflow_model")
),
command="ls ${{inputs.automl_output}}",
environment="AzureML-sklearn-0.24-ubuntu18.04-py37-cpu:1"
)
show_output = command_func(automl_output=forecasting_node.outputs.best_model)
data_folder = "./data"
pipeline = automl_forecasting(
forecasting_train_data=Input(path=f"{data_folder}/training-mltable-folder/", type="mltable"),
)# set pipeline level compute
pipeline.settings.default_compute="cpu-cluster"

# submit the pipeline job
pipeline_job = ml_client.jobs.create_or_update(
pipeline, experiment_name="pipeline_samples"
)
pipeline_job

• Now get the URL to watch the pipeline job execute

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

• Wait for it to complete
• Monitor the job status