Microsoft Updated DP-100 Exam Questions and Answers by taran

Step 1: Install Docker on the workstation

Prerequisites include having a working Docker installation on your local system.

Build or download the dockerfile to the compute node.

Step 2: Create an AksWebservice deployment configuration and deploy the model to it

To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.

# If deploying to a cluster configured for dev/test, ensure that it was created with enough

# cores and memory to handle this deployment configuration. Note that memory is also used by

# things such as dependencies and AML components.

deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)

service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target)

service.wait_for_deployment(show_output = True)

print(service.state)

print(service.get_logs())

Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it

To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.

Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.

During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.

Box 1: No

Environment is a required parameter. The environment to use for the run. If no environment is specified, azureml.core.runconfig.DEFAULT_CPU_IMAGE will be used as the Docker image for the run.

The following example shows how to instantiate a new environment.

from azureml.core import Environment

myenv = Environment(name="myenv")

Box 2: Yes

Parameter compute_target: The compute target where training will happen. This can either be a ComputeTarget object, the name of an existing ComputeTarget, or the string "local". If no compute target is specified, your local machine will be used.

Box 3: Yes

Parameter source_directory. A local directory containing code files needed for a run.

Parameter script. The file path relative to the source_directory of the script to be run.

A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.

References:

https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning