Atlas Project Setup for LLM Training Data Management

Question

Pulumi · Accepted Answer

To set up an Atlas project for managing training data for a Language Model (LLM), you will want to organize and manage datasets, experiments, models, and computational resources. In the context of Pulumi, Atlas is not directly referenced; however, cloud provider services such as Azure Machine Learning can be configured using Pulumi to achieve these goals.

For this purpose in Azure, we can use resources like Machine Learning Workspaces, Datastores, Datasets, and Compute Clusters to build a robust environment for our LLM projects.

Here is a high-level outline of the steps we'll take to set up an Azure Machine Learning Workspace for LLM training data management using Pulumi:

1. **Azure Resource Group**: This is a container that holds related resources for an Azure solution. In this case, it will contain our Machine Learning resources.

2. **Azure Machine Learning Workspace**: This resource provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace holds your experiments, compute targets, models, datastores, etc.

3. **Azure Machine Learning Datastore**: Represents storage, over which we can manage and access our data, we'll use it to store our LLM training datasets.

4. **Azure Machine Learning Dataset**: Datasets are resource containers that make it easier to manage your data, and work with it during model training and inference.

Please note, as a novice, you will need an Azure Subscription, and you'd have to have set up the Pulumi CLI with the appropriate configurations to communicate with Azure, such as setting up credentials and selecting the right subscription.

Below is a Python program using Pulumi to set up the environment for managing your LLM training data:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Provision an Azure Machine Learning Workspace
aml_workspace = azure_native.machinelearningservices.Workspace("aml_workspace",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=azure_native.machinelearningservices.SkuArgs(name="Basic"),  # Choose the SKU that best suits your needs
    identity=azure_native.machinelearningservices.IdentityArgs(type="SystemAssigned"),
    opts=pulumi.ResourceOptions(depends_on=[resource_group])
)

# Create a Datastore within the ML Workspace to store the LLM training datasets
datastore = azure_native.machinelearningservices.Datastore("datastore",
    resource_group_name=resource_group.name,
    workspace_name=aml_workspace.name,
    datastore_properties=azure_native.machinelearningservices.DatastorePropertiesResourceArgs(
        datastore_type="Blob",
        storage_account_parameters=azure_native.machinelearningservices.StorageAccountParametersArgs(
            account_name="",  # Provide your Azure Blob Storage account name
            container_name="",  # Provide the container name where the data will be stored
        )
    ),
    opts=pulumi.ResourceOptions(depends_on=[aml_workspace])
)

# Output the details of the Machine Learning Workspace
pulumi.export("workspace_name", aml_workspace.name)
pulumi.export("workspace_url", aml_workspace.workspace_url)
pulumi.export("datastore_name", datastore.name)
```

Explanation of the Pulumi program:

- We start by importing the required Pulumi packages.
- Then, we create a new Resource Group which is needed to group all our resources.
- Following that, we provision a new Azure ML Workspace, which requires details like the name of the Resource Group and the location. We also specify the type of identity for the workspace and the SKU ("Basic" is used here as an example).
- After that, we set up a Datastore within the Machine Learning Workspace. This involves specifying the type of datastore (Azure Blob storage in this case), and providing the Blob Storage account name and container name that you have set up to store your LLM datasets.
- Lastly, we export the name and URL of the ML Workspace along with the name of the Datastore, which allows you to easily retrieve these details after deployment.

To apply this pulumi program, run `pulumi up` in the command line, from the directory where this file is located. The command line will report the progress and any errors that may occur. After successful execution, the outputs "workspace_name", "workspace_url", and "datastore_name" will be visible in the console, providing you with the necessary information about your resources.