Batch deployment is a crucial process when dealing with data science solutions on Microsoft’s Azure platform, especially when preparing for the Design and Implementation of a Data Science Solution on Azure (Exam DP-100). A proper compute configuration is pivotal to ensure that batch deployments are optimized for speed, efficiency, and accuracy.
In this context, the term “compute” alludes to the process of leveraging Azure’s cloud computing resources to execute a batch deployment. We’ll be looking at how to properly configure compute for optimal batch deployment results.
What is Batch Deployment?
Before we tread any further, it’s crucial to understand the concept of batch deployment. In the context of an Azure data science solution, batch deployment is an operational strategy where multiple instances of a machine learning model are sent to a server at the same time. This strategy allows for parallel processing and improves the throughput of a machine learning solution. With batch deployment, entire sets of data can be processed concurrently, which drastically reduces the time taken to analyze vast amounts of data.
Setting up a Batch Deployment
Azure provides multiple ways to configure your compute resources for a batch deployment. One of the most common approaches is through Azure Machine Learning studio. Other ways include Azure Machine Learning SDK for Python which gives you more control over the resources and configurations.
Azure Workspaces
In Azure Machine Learning, a workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object in the service.
You create a workspace in the portal and access it through the Azure ML SDK for Python as shown below:
<code>
from azureml.core import Workspace
ws = Workspace.create(name=’myworkspace’,
subscription_id='<azure-subscription-id>’,
resource_group=’myresourcegroup’,
create_resource_group=True,
location=’eastus2′
)
</code>
Compute Targets
Compute targets are the compute resources used to run your training script or host your service deployment. There are two types of compute targets:
- Training compute target: To execute your training script, you can use local compute or cloud resources.
- Inference/deployment compute target: To host your scoring, aka prediction or inferencing, you can use local compute or cloud resources.
The Azure ML Compute is a service for managing clusters of Azure virtual machines for running your tasks. A key benefit of Azure ML Compute is that it allows you to ramp up or shrink the number of VMs automatically based on the workload.
You can create an Azure ML Compute instance using the Azure Machine Learning SDK for Python as shown:
<code>
from azureml.core.compute import AmlCompute, ComputeTarget
# choose a name for your cluster
cluster_name = “cpu-cluster”
try:
# check if the cluster exists
compute_target = ComputeTarget(workspace=ws, name=cluster_name)
print(f”{cluster_name} exists already”)
except:
# if not, create it
compute_config = AmlCompute.provisioning_configuration(vm_size=’STANDARD_D2_V2′,
max_nodes=4)
compute_target = ComputeTarget.create(ws, cluster_name, compute_config)
</code>
Batch Inference Pipelines
A batch inference pipeline is a sequence of data processing and model inference steps. The Azure Machine Learning has in-built support to create pipelines. A pipeline is a workflow of machine learning tasks. Azure ML pipelines provide an independent and reusable way to build, evaluate, deploy, and run ML workflows.
The Python script below demonstrates how to create a parallel run step for batch prediction.
<code>
from azureml.pipeline.steps import ParallelRunConfig, ParallelRunStep
from azureml.pipeline.core import PipelineData
output_dir = PipelineData(name=”inferences”,
datastore=ws.get_default_datastore())
parallel_run_config = ParallelRunConfig(
source_directory=”.”,
entry_script=”batch_scoring_script.py”, # Your Python script to do batch prediction
mini_batch_size=”5″,
error_threshold=10,
output_action=”append_row”,
environment=batch_env,
compute_target=compute_target,
process_count_per_node=2,
node_count=2
)
parallel_step_name = “batchscoring”
parallel_run_step = ParallelRunStep(
name=parallel_step_name,
parallel_run_config=parallel_run_config,
inputs=[ named_input ],
output=output_dir,
allow_reuse=False
)
</code>
In conclusion, configuring compute for a batch deployment requires the consideration of several factors, including the workspace, compute targets, and inference pipelines. By configuring compute optimally, data scientists can leverage Azure’s vast resources to process vast volumes of data with unparalleled speed and efficiency. This is an essential skill to demonstrate when sitting for the DP-100 Azure exam.
Practice Test
True or False: In Azure, is it possible to execute a batch of operations in parallel?
- True
- False
Answer: True
Explanation: By creating and configuring compute resources for a batch deployment in Azure, you can execute a batch of operations simultaneously.
Which Azure service can be used to manage batch jobs?
- A) Azure Batch
- B) Azure Functions
- C) Azure Cosmos DB
- D) Azure SQL Database
Answer: A) Azure Batch
Explanation: Azure Batch is used to manage and execute batch jobs, effectively parallelizing the execution process and streamlining the work.
In Azure Batch, what is the purpose of a Job?
- A) A Job defines the data to be processed.
- B) A Job defines a collection of Tasks.
- C) A Job manages the virtual machines.
- D) A Job defines the network setup.
Answer: B) A Job defines a collection of Tasks.
Explanation: In Azure batch, a Job is a logical grouping of one or more Tasks. It aids in the management, scheduling and execution of these Tasks.
True or False: Azure Batch always requires manual scaling.
- True
- False
Answer: False
Explanation: Azure Batch supports both manual and automatic scaling. With automatic scaling, Azure Batch can adjust the number of compute nodes based on customizable rules and criteria.
Which of the following statements is correct regarding Azure Databricks?
- A) Azure Databricks cannot be used for batch processing.
- B) Azure Databricks is used for real-time processing only.
- C) Azure Databricks supports both batch and real-time processing.
- D) Azure Databricks supports neither batch nor real-time processing.
Answer: C) Azure Databricks supports both batch and real-time processing.
Explanation: Azure Databricks, an Apache Spark-based analytics platform optimized for Azure, supports both batch and real-time processing.
Will an Azure Batch Job continue to run if one of its Tasks fails?
- Yes
- No
Answer: Yes
Explanation: An Azure Batch Job will continue to execute other Tasks even if one Task fails. It allows for resilience in the batch processing pipeline.
When creating an Azure Batch account, which of these components would you need to configure?
- A) Jobs
- B) Tasks
- C) Pools
- D) All of the above
Answer: D) All of the above
Explanation: All of these components must be configured while creating an Azure Batch account. Pools are needed to host compute nodes, Jobs are needed to manage Tasks, and Tasks are the actual work items.
Which component in Azure Batch represents a unit of compute resources in the system?
- A) Job
- B) Task
- C) Pool
- D) Pipeline
Answer: C) Pool
Explanation: In Azure Batch, a Pool is a collection of compute nodes (virtual machines), representing the allocated compute resources in the system.
True or False: Azure Batch supports only Windows-based compute nodes.
- True
- False
Answer: False
Explanation: Azure Batch supports both Windows and Linux-based compute nodes, providing flexibility around running different types of Tasks.
Once an Azure Batch Job is finished, does it automatically delete the data produced by its Tasks?
- Yes
- No
Answer: No
Explanation: After a Batch Job has completed, data produced by its Tasks are not automatically deleted. This data has to be cleaned up manually or through a cleanup Task.
Interview Questions
What is Azure Batch?
Azure Batch enables large-scale parallel and high-performance computing applications to efficiently run in Azure. It provides job scheduling, allowing users to provision hundreds to thousands of applications for processing.
What are the key components of the Azure batch service?
The main components of Azure batch service are: the batch account, pool of compute nodes, Job to be run on the nodes, and tasks within the job.
How can you control access to the Batch service?
Access to the Batch service can be controlled using Azure Active Directory and Role-Based Access Control (RBAC).
In the Azure batch service, what do you mean by ‘pool’?
In Azure batch service, a pool is a set of compute nodes (virtual machines) on which tasks of batch jobs are run.
How are tasks scheduled on nodes in Azure Batch Service?
The batch service automatically schedules tasks on the compute nodes in a pool according to the choice of scheduling algorithm, which can either be Spread or Pack scheduling.
What type of workloads is Azure Batch best suited for?
Azure Batch is best suited for running large-scale parallel and high-performance computing applications in the cloud. This includes tasks such as media processing, rendering, drug discovery, and data analytics.
How can you specify the number of CPU cores to allocate for each node in an Azure Batch pool?
You can specify the VM sizes that should be allocated when creating the pool.
How do you choose a VM size for your Azure batch pool?
The VM size should be chosen based on the resource requirements of the workload that the pool is intended to run.
What is a job in the context of Azure Batch Service?
A job in Azure Batch is a collection of tasks that are processed on the compute nodes in a pool.
How can you manage task dependencies in Azure Batch?
Task dependencies are managed in Azure Batch using task dependency actions – these allow you to manage when a task should run based on the completion status of other tasks.
How do you monitor the progress of jobs and tasks in Azure Batch?
You can monitor the progress of jobs and tasks using Azure Monitor, Application Insights, or Batch Service API.
What are some of the ways to optimize performance in Azure Batch?
Some ways to optimize performance in Azure Batch include selecting the right VM size, adjusting the maximum number of tasks per node, using InfiniBand for network-intensive workloads, and using a premium storage account for IO-intensive workloads.
Can Azure Batch automatically resize a pool based on workload?
Yes, Azure Batch has an autoscale feature where it can automatically resize a pool based on the workload.
What happens when a task fails in Azure Batch?
If a task fails in Azure Batch, the batch service will try to re-run the task up to 3 times by default.
Can Azure Batch run tasks that require GPU resources?
Yes, Azure Batch supports the use of GPU resources. Specific GPU-focused VM sizes can be selected when creating a pool.
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