Run a script as a job by using Azure Machine Learning

In the field of data science, it’s common to have long-running processes or scripts that need to be executed efficiently. Azure Machine Learning provides a way to run these scripts as jobs, allowing for better management and scalability. In this article, we’ll explore how to run a script as a job using Azure Machine Learning.

Azure Machine Learning Jobs

Azure Machine Learning provides a flexible and scalable platform for running automated machine learning workflows and training models at scale. It allows you to run scripts as jobs, which can be scheduled or run on-demand. These jobs can execute scripts written in various languages such as Python and R.

Creating a Job

To create a job in Azure Machine Learning, you start by defining a scripting environment. This environment includes the necessary dependencies and configurations required to run your script. You can create an environment using a Conda environment file, specifying the necessary packages and dependencies for your script.

Here’s an example of creating an environment using a Conda environment file:

# azureml-environment.yml

Once you have defined your environment, you can create a job by submitting a script to be run. Here’s an example of creating a job using a Python script:

# my_script.py

In the above example, we first load the Azure Machine Learning workspace and experiment. We then create a script run configuration by specifying the script to be run, the compute target to execute the job on, and the environment to use. Finally, we submit the job and wait for its completion.

Running a Job

Once the job is submitted, Azure Machine Learning takes care of provisioning the necessary resources and executing the script. You can monitor the progress of the job using the Azure Machine Learning portal or programmatically using the SDK.

Here’s an example of monitoring the progress of a job using the SDK:

# Monitor the job
from azureml.widgets import RunDetails

RunDetails(run).show()

This code snippet uses the RunDetails widget from the Azure Machine Learning SDK to display the live progress of the job. You can see information such as log output, metrics, and resource consumption.

Advanced Job Configurations

Azure Machine Learning provides additional configurations for fine-tuning your jobs. For example, you can specify the number of nodes and the number of tasks per node to parallelize the execution. You can also control the maximum number of attempts, the maximum duration, and the timeout of the job.

Here’s an example of advanced job configuration:

src = ScriptRunConfig(source_directory='./',
script='my_script.py',
compute_target='my-compute-target',
environment='my-ml-environment',
node_count=2,
distributed_job_config=DistributedJobConfig(maximum_concurrent_tasks_per_node=4),
max_retries=2,
max_run_duration_seconds=1800,
timeout_seconds=600)

In this example, we specify a job with 2 nodes executing 4 tasks per node concurrently. We also set the maximum number of retries to 2 and a maximum run duration of 30 minutes with a timeout of 10 minutes.

Scaling and Parallel Execution

Azure Machine Learning allows you to scale your jobs by leveraging multiple compute targets simultaneously. You can specify different compute targets for different jobs or create a compute cluster to handle large-scale parallel execution.

For example, you can create a compute cluster with multiple nodes and use it to run multiple jobs concurrently. This allows you to distribute the workload and speed up the execution of your scripts.

Conclusion

Running a script as a job using Azure Machine Learning provides a scalable and efficient way to execute long-running processes or scripts in the field of data science. By leveraging Azure Machine Learning jobs, you can easily manage, monitor, and scale your scripts, enabling you to focus on the development of your data science solutions. So why not give it a try and start running your scripts as jobs with Azure Machine Learning today!