Create compute targets for experiments and training

1. Introduction to Compute Targets:

Compute targets in Azure serve as the infrastructure for running data science workloads such as training models and executing experiments. By creating dedicated compute targets, you can effectively manage and scale your computational resources based on workload requirements. Azure offers several options for compute targets that cater to different workload types and scenarios.

2. Azure Machine Learning Compute:

Azure Machine Learning Compute is a managed service that simplifies the process of setting up and managing compute targets for machine learning workloads. It enables you to dynamically provision and scale compute resources based on workload demands.

To create an Azure Machine Learning Compute target, follow these steps:

python
from azureml.core import Workspace
from azureml.core.compute import AmlCompute, ComputeTarget

# Wait for the operation to complete
compute_target.wait_for_completion(show_output=True)

3. Azure Databricks:

Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform. It provides a cloud-based environment for running data engineering and data science workloads at scale.

To create an Azure Databricks compute target, follow these steps:

python
from azureml.core import Workspace
from azureml.core.compute import DatabricksCompute, ComputeTarget

# Wait for the operation to complete
compute_target.wait_for_completion(show_output=True)


4. Azure HDInsight:

Azure HDInsight is a fully-managed cloud service that makes it easy to process big data using popular open-source frameworks such as Hadoop, Spark, and Hive.

To create an Azure HDInsight compute target, follow these steps:

python
from azureml.core import Workspace
from azureml.core.compute import HdInsightCompute, ComputeTarget

# Wait for the operation to complete
compute_target.wait_for_completion(show_output=True)


5. Custom Compute Targets:

Azure also allows you to create custom compute targets if you have specialized infrastructure requirements. This can be achieved by using Azure Kubernetes Service (AKS) or Azure Batch AI.

Here’s an example of creating a custom AKS compute target:

python
from azureml.core import Workspace
from azureml.core.compute import AksCompute, ComputeTarget

# Wait for the operation to complete
compute_target.wait_for_completion(show_output=True)

6. Scaling Compute Targets:

One of the key advantages of Azure is the ability to scale compute targets based on workload requirements. For Azure Machine Learning Compute and Azure Databricks, you can dynamically scale the compute resources up or down by changing the min_nodes and max_nodes configuration. For Azure HDInsight, you can adjust the number of worker nodes based on the workload demands.

7. Deleting Compute Targets:

When a compute target is no longer needed, it is important to delete it to avoid unnecessary costs. To delete a compute target, you can use the following code snippet:

python
compute_target.delete()

By creating compute targets for experiments and training on Azure, you can effectively manage and optimize your data science workloads. Whether it’s using Azure Machine Learning Compute, Azure Databricks, Azure HDInsight, or custom compute targets, Azure provides a range of options to cater to your specific requirements. Remember to scale or delete compute targets as needed to optimize costs and resource utilization. Start creating your compute targets on Azure today and unlock the full potential of your data science solution!