Continuous Integration Workflows for AI Applications.

Question

Pulumi · Accepted Answer

Continuous Integration (CI) workflows for AI applications typically involve setting up a pipeline that orchestrates the process of building, training, testing, and deploying machine learning models. In the context of infrastructure as code (IaC) using Pulumi, we can set up resources that support CI for AI applications, such as computing resources for running training jobs, storage for datasets and models, and services to serve the models.

For the purpose of CI in AI applications, services like Azure Machine Learning, AWS Sagemaker, or Google AI Platform can be used. These services provide managed environments for developing, training, and deploying machine learning models. However, setting up a comprehensive CI workflow for AI may include additional components like code repositories, container services for packaging the application and model, and trigger mechanisms that initiate the CI process on code changes.

In this example, I will demonstrate how to provision a basic set of cloud resources that could form part of a CI workflow for AI applications on Azure, including:

- An Azure Machine Learning Workspace for managing the machine learning lifecycle.
- An Azure Container Registry (ACR) for storing Docker container images.
- An Azure Kubernetes Service (AKS) for deploying and serving models.

Before running the following Pulumi program, ensure you have:

- Installed the Pulumi CLI and set up the Azure provider.
- Logged in to Azure through the Pulumi CLI using `pulumi login`.
- Set your preferred Azure region and other config values either in the Pulumi stack config or by passing them as command-line arguments.

Here is the Pulumi program written in Python:

```python
import pulumi
import pulumi_azure_native as azure_native

# Config variables that may be set in the Pulumi stack config
config = pulumi.Config()
location = config.require("location")  # Azure region to deploy resources

# Create an Azure Resource Group
resource_group = azure_native.resources.ResourceGroup('ai-cicd-rg',
    location=location
)

# Create an Azure Machine Learning Workspace
aml_workspace = azure_native.machinelearningservices.Workspace('ai-cicd-aml-workspace',
    location=location,
    sku=azure_native.machinelearningservices.SkuArgs(name="Basic"),
    resource_group_name=resource_group.name,
    description="AML Workspace for CI/CD workflows of AI Applications"
)

# Create an Azure Container Registry
acr = azure_native.containerregistry.Registry('ai-cicd-acr',
    resource_group_name=resource_group.name,
    location=location,
    sku=azure_native.containerregistry.SkuArgs(name="Basic"),
    admin_user_enabled=True
)

# Create an Azure Kubernetes Service
aks = azure_native.containerservice.ManagedCluster('ai-cicd-aks',
    resource_group_name=resource_group.name,
    location=location,
    kubernetes_version='1.19.11',
    dns_prefix='ai-cicd-aks-dns',
    agent_pool_profiles=[{
        'count': 3,
        'max_pods': 110,
        'mode': 'System',
        'name': 'agentpool',
        'vm_size': 'Standard_DS2_v2',
        'os_type': 'Linux',
    }]
)

# Output the necessary configuration details to the CLI
pulumi.export('resource_group', resource_group.name)
pulumi.export('aml_workspace_name', aml_workspace.name)
pulumi.export('acr_login_server', acr.login_server)
pulumi.export('aks_cluster_name', aks.name)

# Note: You can integrate these resources with GitHub Actions, Azure Pipelines or other CI/CD tools to complete the workflow.
```

In this program, we are provisioning the foundational cloud infrastructure needed to support continuous integration and deployment of AI applications. We start by creating an Azure Resource Group that acts as a container for all our resources. Then, we create an Azure Machine Learning Workspace which is the central hub for managing the end-to-end machine learning lifecycle. This includes experiment tracking, model management, and operationalizing machine learning pipelines.

Next, we set up an Azure Container Registry (ACR) to store and manage Docker container images. We could potentially use these containers to package our AI models along with the inference code needed to serve predictions.

Lastly, we provision an Azure Kubernetes Service (AKS) cluster, which can be utilized to deploy and manage containers in a highly scalable fashion, making it suitable for serving machine learning models.

The output statements at the end of the program are crucial as they print out the names and other configuration details of the resources created. These outputs can be used to integrate these resources with additional tooling for continuous integration and continuous deployment.

This program aims to provide a starting point. Based on this infrastructure, you can further develop automation scripts for training machine learning models, packaging them into Docker images, pushing those images to ACR, and deploying them into AKS via CI/CD pipelines. The actual CI/CD workflow would likely involve additional Pulumi code or scripts that interface with CI/CD platforms like GitHub Actions or Azure Pipelines.