Concepts
If you are preparing for the exam “Designing and Implementing a Data Science Solution on Azure,” understanding how to configure job run settings for a script using Azure Machine Learning is essential. In this article, we will walk through the process of configuring job run settings step-by-step.
Step 1: Create an Azure Machine Learning Workspace
The first step is to create an Azure Machine Learning workspace, which will serve as the central hub for managing your machine learning resources. Follow the instructions in the Azure documentation to create your workspace.
Step 2: Create a Compute Target
Azure Machine Learning requires a compute target to execute your script. You can choose from various compute targets such as Azure Machine Learning Compute, Azure Databricks, or a remote VM. Refer to the relevant Azure documentation to create the compute target of your choice.
Step 3: Upload Your Script
Once you have a compute target, upload your script to the Azure Machine Learning workspace. You can upload a single script or multiple scripts, along with any necessary dependencies or data files.
Step 4: Create a Run Configuration
To run your script, you need to create a run configuration. The run configuration defines the settings for executing your script, including the script file, compute target, and any additional packages or environment settings required by the script. Use the code snippet below as a template to create your run configuration:
from azureml.core import Workspace, ScriptRunConfig, Environment
# Load the workspace
ws = Workspace.from_config()
# Create a run configuration
script_run_config = ScriptRunConfig(
source_directory='',
script='',
compute_target='',
environment=''
)
# Submit the script for execution
run = ws.submit(script_run_config)
In the above code snippet, replace
Step 5: Specify Environment Settings
You have the option to specify environment settings using an Environment object. This allows you to define dependencies, environment variables, and Docker-based environments. Refer to the Azure Machine Learning documentation for more details on configuring environments.
Step 6: Submit the Script for Execution
Finally, use the submit method of your workspace to submit the script for execution. This will initiate the job run based on the specified run configuration. The script will run on the chosen compute target, utilizing the specified environment settings.
By following these steps, you can easily configure job run settings for a script using Azure Machine Learning. This gives you control over the execution environment, allows you to manage dependencies, and enables efficient scaling of your machine learning workflows. Leveraging Azure Machine Learning for job execution provides a powerful framework for building and deploying data science solutions on Azure.
Remember to refer to the official Microsoft documentation for more detailed information on the Azure Machine Learning services and features mentioned in this article.
Answer the Questions in Comment Section
Which task enable you to define the dataset for a data science job in Azure Machine Learning?
a) CreateComputeTargetTask
b) CreateOrUpdateMachineLearningJobTask
c) CreateWorkspaceRunTask
d) CreateDatasetTask
Correct answer: d) CreateDatasetTask
True or False: Azure Machine Learning supports running data science jobs on Linux environments only.
Correct answer: False
When defining job run settings for a script, which option enables you to specify the Docker image for the job?
a) job_run_config.environment.docker.enabled
b) job_run_config.environment.docker.image
c) job_run_config.environment.docker.utilize
d) job_run_config.environment.docker.version
Correct answer: b) job_run_config.environment.docker.image
Which of the following compute targets is NOT supported for running data science jobs in Azure Machine Learning?
a) Azure Container Instances
b) Azure Kubernetes Service
c) Azure Batch AI
d) Azure App Service
Correct answer: d) Azure App Service
True or False: When configuring job run settings for a script, you can specify a timeout duration to control how long the job can run.
Correct answer: True
Which option allows you to define a Python conda environment for a data science job in Azure Machine Learning?
a) job_run_config.environment.python_version
b) job_run_config.environment.conda_dependencies
c) job_run_config.environment.docker.enabled
d) job_run_config.environment.environment_variables
Correct answer: b) job_run_config.environment.conda_dependencies
When configuring job run settings, which option allows you to specify if the job should use a remote or local compute target?
a) experiment.submit_remote
b) experiment.job_run_behavior
c) experiment.submit_local
d) experiment.submit_config
Correct answer: c) experiment.submit_local
True or False: You can configure job run settings to specify the maximum number of nodes that can run concurrently for a data science job.
Correct answer: True
Which method in the Azure Machine Learning SDK enables you to configure job run settings for a data science job?
a) run_configuration()
b) compute_target.config()
c) experiment.run_config()
d) job_run_config.environment()
Correct answer: c) experiment.run_config()
When configuring job run settings for a script, which option allows you to specify the entry script file?
a) job_run_config.source_directory
b) job_run_config.script
c) job_run_config.entry_script
d) job_run_config.environment.script
Correct answer: c) job_run_config.entry_script
Thanks for this insightful post on configuring job run settings for a script in Azure!
Could you elaborate on how to optimize the job run settings for a large dataset?
This helped me a lot. I was stuck for hours. Thank you!
Can anyone provide best practices for setting retry policies for Azure ML jobs?
Great article, really clarified a lot of my doubts.
I encountered an issue where my script hangs indefinitely. Any ideas?
This is very useful, thanks for sharing.
How do you set up logging for an Azure ML job run?