Concepts
When it comes to configuring compute for a job run in Azure as part of the “Designing and Implementing a Data Science Solution on Azure” exam, there are several options available to suit different requirements and workloads. Microsoft Azure offers a wide range of services and tools to meet the computational needs of data science solutions and enable efficient job execution. In this article, we will explore some of the compute options you can leverage in Azure to effectively run your data science jobs.
Azure Machine Learning Compute
Azure Machine Learning Compute is a managed compute resource that simplifies the provisioning and management of compute targets for training machine learning models. This service offers scalable compute options and supports distributed training across multiple nodes. By utilizing Azure Machine Learning Compute, you can dynamically allocate resources based on workload requirements, optimizing both cost and performance.
To configure Azure Machine Learning Compute for a job run, you can use the following code snippet:
from azureml.core import Workspace
from azureml.core.compute import AmlCompute, ComputeTarget
workspace = Workspace.get(name='your_workspace_name', subscription_id='your_subscription_id', resource_group='your_resource_group')
compute_name = 'your_compute_name'
try:
compute_target = ComputeTarget(workspace=workspace, name=compute_name)
print('Compute target already exists.')
except:
compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_DS2_v2', max_nodes=4)
compute_target = ComputeTarget.create(workspace=workspace, name=compute_name, provisioning_configuration=compute_config)
compute_target.wait_for_completion(show_output=True)
In the above code, first import the necessary libraries and retrieve the workspace by specifying the workspace name, subscription ID, and resource group. Then, define the desired compute name and check if the compute target already exists. If it does exist, proceed with the existing compute target. Otherwise, define the provisioning configuration and create the compute target.
Azure Batch AI
Azure Batch AI is a service designed for running large-scale, parallel, and high-performance computing workloads. It provides a distributed infrastructure and scheduling capabilities to efficiently run data science jobs that require significant computational power. Azure Batch AI supports popular deep learning frameworks like TensorFlow and PyTorch, making it easy to integrate into your data science workflow.
To configure Azure Batch AI for a job run, you can use the following code snippet:
from azure.common.credentials import ServicePrincipalCredentials
from azure.mgmt.batchai import BatchAIManagementClient
from azure.storage.blob import BlockBlobService
credentials = ServicePrincipalCredentials(client_id='your_client_id', client_secret='your_client_secret', tenant_id='your_tenant_id')
batchai_client = BatchAIManagementClient(credentials, subscription_id='your_subscription_id')
resource_group = 'your_resource_group'
workspace = 'your_workspace'
cluster_name = 'your_cluster_name'
storage_account = 'your_storage_account'
container_name = 'your_container_name'
# Configure execution environment
mount_volumes = [{
'host_path': 'your_host_path',
'container_path': 'your_container_path',
'read_only': False
}]
# Define job and job parameters
job_name = 'your_job_name'
job_parameters = {
'container_settings': {
'image_source_registry': {
'image': {
'registry': 'your_registry',
'repository': 'your_repository',
'tag': 'your_tag'
}
},
'container_resource_requirements': {
'volumes': mount_volumes
}
},
'node_count': 4
}
# Create job
batchai_client.jobs.create(resource_group, workspace, experiment_name, job_name, job_parameters)
In the above code, begin by importing the required libraries. Then, create a Batch AI management client using the service principal credentials. Define the necessary Batch AI resources such as the resource group, workspace, cluster name, storage account, and container name. Configure the execution environment by specifying the volume mount settings. Finally, define the job and job parameters, including the container settings, node count, and other relevant information. Create the job using the Batch AI client.
Azure Databricks
Azure Databricks provides an Apache Spark-based analytics platform for collaborative environments and large-scale data processing. It offers a scalable and highly available compute resource for running data science jobs efficiently. Azure Databricks integrates with popular data science libraries and tools, making it a powerful choice for compute-intensive workloads.
To configure Azure Databricks for a job run, you can use the following code snippet:
from azure.identity import ClientSecretCredential
from azure.mgmt.databricks import DatabricksManagementClient
credentials = ClientSecretCredential(client_id='your_client_id', client_secret='your_client_secret', tenant_id='your_tenant_id')
databricks_client = DatabricksManagementClient(credentials, subscription_id='your_subscription_id')
resource_group = 'your_resource_group'
workspace_name = 'your_workspace'
# Create a new job
new_job = {
'name': 'your_job_name',
'existing_cluster_id': 'your_existing_cluster_id',
'spark_jar_task': {
'main_class_name': 'your_main_class_name',
'parameters': [
'--input', 'your_input_file',
'--output', 'your_output_file'
]
}
}
# Submit job
databricks_client.jobs.create(resource_group, workspace_name, new_job)
In the above code, start by importing the necessary libraries. Then, create a client using the client secret credential. Define the resource group and workspace name. Create a new job by specifying the job name, existing cluster ID, and spark jar task details, including the main class name and parameters. Finally, submit the job using the Databricks client.
Conclusion
Configuring compute for a job run in the context of the “Designing and Implementing a Data Science Solution on Azure” exam requires knowledge of the different compute options available in Azure. Azure Machine Learning Compute, Azure Batch AI, and Azure Databricks are powerful platforms that cater to various data science workload requirements. Leveraging these compute options will enable you to effectively run your data science jobs and achieve optimal performance and scalability in your Azure-based data science solutions.
Answer the Questions in Comment Section
Which component in Azure Machine Learning service allows you to configure the compute target for a job run?
a) Azure Machine Learning Studio
b) Azure Machine Learning SDK
c) Azure Machine Learning designer
d) Azure Machine Learning compute
Correct answer: b) Azure Machine Learning SDK
True or False: Azure Machine Learning compute provides scalable and managed infrastructure for running machine learning workloads.
Correct answer: True
Which types of compute targets are supported by Azure Machine Learning service? (Select all that apply)
a) Azure virtual machine
b) Azure Databricks cluster
c) Azure Kubernetes Service (AKS)
d) Azure Functions
e) Azure Batch
Correct answers: a), b), c), d), e)
What is the purpose of a compute target in Azure Machine Learning service?
a) It determines the location of the machine learning models.
b) It defines the types of virtual machines to be used for training.
c) It provides the environment and resources for running jobs and experiments.
d) It automates the deployment of machine learning pipelines.
Correct answer: c) It provides the environment and resources for running jobs and experiments.
True or False: You can configure a compute target for both training and inference in Azure Machine Learning service.
Correct answer: True
Which compute target is best suited for running distributed training jobs with high performance and scalability?
a) Azure Functions
b) Azure Machine Learning compute
c) Azure Databricks cluster
d) Azure Batch AI
Correct answer: c) Azure Databricks cluster
True or False: Azure Machine Learning compute supports auto-scaling, which automatically adjusts the compute resources based on the workload.
Correct answer: True
Which property of a compute target determines the maximum number of nodes that can be provisioned for running jobs?
a) Minimum nodes
b) Maximum nodes
c) Idle seconds before scale down
d) Idle seconds before scale up
Correct answer: b) Maximum nodes
True or False: Azure Machine Learning compute can be provisioned in multiple regions to ensure high availability.
Correct answer: True
Which compute target is best suited for running jobs that require high frequency, low latency, and stateless compute?
a) Azure Functions
b) Azure Machine Learning compute
c) Azure Databricks cluster
d) Azure Batch AI
Correct answer: a) Azure Functions
Great article! It really helped me understand how to configure compute for my job runs on Azure.
Can anyone explain the key differences between using Azure ML Compute Instances vs Azure Databricks for job runs?
Configuring the vCPU and memory settings can be tricky. Does anyone have tips on what would be an optimal setup for deep learning models?
Thanks! This was exactly what I needed.
The blog post didn’t mention much about cost considerations. How do you manage costs when scaling up compute resources?
I appreciate this detailed guide. It’s crucial for my upcoming DP-100 exam prep.
I found the topic of configuring compute for a job run very interesting. Can anyone share their experience with this process?
Thanks for the detailed explanation on compute configuration. It helped me understand the concepts better.