Concepts
To configure an environment for a job run related to the exam “Designing and Implementing a Data Science Solution on Azure,” you can follow these steps using Azure services:
1. Set up an Azure Machine Learning Workspace
- Create a new Azure Machine Learning workspace using the Azure portal or Azure Machine Learning SDK.
from azureml.core import Workspace
# Provide your subscription ID, resource group, and workspace name
subscription_id = ''
resource_group = ''
workspace_name = ''
# Create the workspace
ws = Workspace.create(name=workspace_name,
subscription_id=subscription_id,
resource_group=resource_group,
create_resource_group=True,
location='eastus2')
2. Create a Compute Target
- Azure Machine Learning compute targets are used to run your machine learning pipelines and experiments.
from azureml.core.compute import AmlCompute
from azureml.core.compute import ComputeTarget
# Set the compute cluster details
compute_name = ''
compute_vm_size = 'Standard_DS2_v2'
max_nodes = 4
# Define the compute configuration
compute_config = AmlCompute.provisioning_configuration(vm_size=compute_vm_size,
max_nodes=max_nodes)
# Create the compute target
compute_target = ComputeTarget.create(ws, compute_name, compute_config)
compute_target.wait_for_completion(show_output=True)
3. Set up Data Storage
- Azure Blob Storage can be used to store datasets, training data, and intermediate outputs.
from azureml.core import Datastore
# Provide your storage account name and key
storage_account_name = ''
storage_account_key = ''
# Register the datastore
blob_datastore = Datastore.register_azure_blob_container(workspace=ws,
datastore_name='',
container_name='',
account_name=storage_account_name,
account_key=storage_account_key)
# Set the default datastore
ws.set_default_datastore('blob_datastore')
4. Prepare and Upload Data
- Before running a data science job, you need to upload your data to the Azure Blob Storage container.
# Upload data to the datastore
blob_datastore.upload(files=['
target_path='
overwrite=True,
show_progress=True)
5. Create and Configure a Compute Environment
- A compute environment specifies the configuration for executing jobs, such as the Python packages required.
from azureml.core import Environment
# Create a new environment
myenv = Environment(name="")
# Specify the Python version and packages required
myenv.python.conda_dependencies = ''
myenv.docker.enabled = True
# Register the environment
myenv.register(workspace=ws)
6. Create and Submit a Job
- To run your data science job, create a job configuration and submit it to the experiment.
from azureml.core import Experiment, ScriptRunConfig
# Set up the experiment
experiment_name = ''
experiment = Experiment(workspace=ws, name=experiment_name)
# Create a script run configuration
src = ScriptRunConfig(source_directory='',
script='',
compute_target=compute_target,
environment=myenv)
# Submit the job
run = experiment.submit(src)
7. Monitor and Access Job Results
- You can monitor the progress of your job and access the job logs and outputs.
# Wait for the job to complete
run.wait_for_completion(show_output=True)
# View job logs
print(run.get_portal_url())
# Get job outputs
run.download_files(output_directory='
By following these steps, you can configure an environment for a job run related to the “Designing and Implementing a Data Science Solution on Azure” exam. Remember to replace the placeholders in the code with your specific Azure resources and configurations.
Please note that the code snippets provided are just examples, and you should refer to the official Microsoft documentation for detailed guidance on using Azure Machine Learning and other Azure services.
Answer the Questions in Comment Section
Which Azure service should you use to configure an environment for a job run in a data science solution?
– A) Azure Functions
– B) Azure Logic Apps
– C) Azure Event Grid
– D) Azure Machine Learning
Answer: D) Azure Machine Learning
When configuring an environment for a job run in Azure Machine Learning, what is required for running a script?
– A) Docker image
– B) Virtual machine
– C) Managed compute target
– D) Batch AI cluster
Answer: C) Managed compute target
True or False: Azure Machine Learning supports running Python scripts only.
– A) True
– B) False
Answer: B) False
When configuring an environment in Azure Machine Learning, what is a benefit of using Docker images?
– A) Simplifies package dependencies
– B) Enables running multiple experiments simultaneously
– C) Reduces storage costs
– D) Provides built-in machine learning algorithms
Answer: A) Simplifies package dependencies
Which Azure service can be used to track and monitor the status of a job run in Azure Machine Learning?
– A) Azure Monitor
– B) Azure Application Insights
– C) Azure Data Factory
– D) Azure Machine Learning Studio
Answer: D) Azure Machine Learning Studio
True or False: Azure Machine Learning provides built-in support for popular deep learning frameworks such as TensorFlow and PyTorch.
– A) True
– B) False
Answer: A) True
Which statement best describes the purpose of Azure Machine Learning compute targets?
– A) They provide data storage for machine learning experiments.
– B) They allow scaling of resources for machine learning workloads.
– C) They enable integration with external data sources.
– D) They automatically create virtual networks for secure communication.
Answer: B) They allow scaling of resources for machine learning workloads.
Which Azure service can be used to create a network of connected compute resources for distributed machine learning tasks?
– A) Azure Virtual Machines
– B) Azure Kubernetes Service
– C) Azure Container Instances
– D) Azure Virtual Networks
Answer: B) Azure Kubernetes Service
True or False: Azure Machine Learning provides native integration with popular IDEs such as Visual Studio Code and PyCharm.
– A) True
– B) False
Answer: A) True
What is the purpose of specifying a conda dependencies file when configuring a job run environment in Azure Machine Learning?
– A) To specify the entry script for the job run.
– B) To define the packages and their versions required for the job run.
– C) To configure the security settings for the job run.
– D) To set up environment variables for the job run.
Answer: B) To define the packages and their versions required for the job run.
Great post on configuring an environment for job runs in Azure for the DP-100 exam!
This was really helpful, thanks!
I have a question about setting up GPU clusters. Any advice on the best practices?
Can someone explain the optimal configuration for data caching in Azure Machine Learning?
The step-by-step guide on setting up the environment was spot on. Thanks!
I’m unsure about the networking configurations. Any pointers?
How do you manage dependencies for different experiments?
This article saved me a lot of time. Much appreciated!