Scalable ML Model Training with Databricks Clusters

Question

Pulumi · Accepted Answer

When you want to perform scalable machine learning (ML) model training using Databricks on the cloud, you leverage Databricks clusters. These clusters provide a managed environment for analytics and ML tasks. Here's how you can create a Databricks cluster using Pulumi:

1. **Import the Databricks Provider**: The first step is to import the Databricks provider into your Pulumi program. This provider allows you to interact with the resources that Databricks offers.

2. **Create a Databricks Cluster**: You define a Databricks cluster resource, configure its properties, and set the cluster to autoscale within specified limits. Autoscaling allows the cluster to automatically adjust the number of worker nodes to handle the workload efficiently.

3. **Attach Libraries**: ML training often requires various libraries. You can configure your cluster to include specific libraries for Python, Scala, R, or other programs, like machine learning frameworks.

4. **Set up Cluster Policies (Optional)**: Policies help enforce certain configurations like the type of instances, restrictions on maximum or minimum number of nodes, etc., for the clusters.

5. **Model Training**: You would normally use a Databricks notebook or submit a job for ML training on the cluster, but this isn't specified within the infrastructure code.

6. **Clean up and Resource Management**: Pulumi allows you to not only create and manage resources but also to clean them up when they're no longer needed, ensuring you only pay for what you use.

Now, let's see how this translates into a Pulumi Python program:

```python
import pulumi
import pulumi_databricks as databricks

# 1. Import the Databricks Provider
# Ensure you have the Databricks provider configured with appropriate access credentials.
# This can be set up using the Pulumi configuration system or environment variables depending on your setup.

# 2. Define the autoscaling limits for your Databricks cluster.
autoscale = databricks.ClusterAutoscaleArgs(
    min_workers=2,
    max_workers=50,
)

# 3. Choose the type of node to use for the driver & workers in your cluster.
node_type_id = "Standard_D3_v2" # This is an example node type, ensure to choose the one that suits your training needs.
driver_node_type_id = "Standard_D3_v2" # This is an example node type.

# 4. Define the cluster specifics, like the Spark version and environments.
cluster = databricks.Cluster("ml-training-cluster",
    autoscale=autoscale,
    node_type_id=node_type_id,
    driver_node_type_id=driver_node_type_id,
    spark_version="7.3.x-scala2.12",
    spark_conf={
        "spark.speculation": True,
    },
    # Specify the cluster mode: high concurrency, standard, or single node
    cluster_mode="HighConcurrency",
    # Optionally define an instance pool for more fine-grained control over instances
    instance_pool_id="your-instance-pool-id",
)

# 5. Attach libraries as needed - you can specify Maven, PyPI, CRAN, etc.
library = databricks.Library("ml-library",
    # Example: Attach a Python library using PyPI
    pypi=databricks.LibraryPypiArgs(
        package="tensorflow",
    ),
    cluster_id=cluster.id,
)

# 6. Export the Cluster ID so you can reference it to interact with the cluster later.
pulumi.export("cluster_id", cluster.id)
```

In the above program:

- We set up the cluster to autoscale between 2 and 50 worker nodes.
- We have chosen a node type for our worker and driver nodes. You will need to choose the node type that best fits your budget and compute requirements.
- By setting `spark_version` and `spark_conf`, you tailor the environment of the cluster to suit your ML workloads.
- If necessary, dependencies that your ML models use can be included as libraries.
- We export the cluster ID for future use, perhaps in other automation scripts or CI/CD pipelines.

After deploying this program using Pulumi, you'll have a running Databricks cluster that can handle ML training workloads scaled according to your specified parameters. Keep in mind to replace placeholders like the instance pool ID with actual values from your Databricks environment.