Cache Model Predictions for Fast API Response

Question

Pulumi · Accepted Answer

Caching model predictions is critical for ensuring that your application can respond quickly to user requests, particularly when the underlying machine learning inference can be time-consuming. With Pulumi, you can automate the provisioning of infrastructure that effectively caches model predictions.

For this use case, we are assuming we want to deploy a machine learning model to a cloud service, make predictions, and cache these predictions. I will use AWS as our cloud provider for this example, but the concepts can be applied to other cloud providers as well.

In AWS, you would typically deploy a model to an endpoint using SageMaker, which is AWS's managed service for building, training, and deploying machine learning models. To cache the predictions made by this endpoint, we could potentially use a service like ElastiCache, which provides a high-performance, in-memory cache that could store these predictions.

Here's a simplified Pulumi program that:
1. Creates a SageMaker model.
2. Deploys a SageMaker endpoint configuration and endpoint.
3. Creates an ElastiCache Redis cluster to cache our predictions.

To make predictions and interact with the cache, you'd need to write application logic, potentially as a Lambda function, that calls the SageMaker endpoint for predictions, then caches the result in Redis.

Please note that Pulumi programs do not include application code and thus will not directly handle API calls for making predictions or caching them; instead, they set up the infrastructure where such code would run.

Below is the Pulumi program for setting up such infrastructure:

```python
import pulumi
import pulumi_aws as aws

# Create a role for SageMaker to access AWS resources
sagemaker_role = aws.iam.Role("sagemaker-role",
    assume_role_policy="""{
      "Version": "2012-10-17",
      "Statement": [
        {
          "Effect": "Allow",
          "Principal": {"Service": "sagemaker.amazonaws.com"},
          "Action": "sts:AssumeRole"
        }
      ]
    }"""
)

# Attach policies to the role so SageMaker can access necessary AWS services
aws.iam.RolePolicyAttachment("sagemaker-access",
                             policy_arn="arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess",
                             role=sagemaker_role.name)

aws.iam.RolePolicyAttachment("sagemaker-full",
                             policy_arn="arn:aws:iam::aws:policy/AmazonSageMakerFullAccess",
                             role=sagemaker_role.name)

# Define the model, assuming you already have a trained model in S3
model = aws.sagemaker.Model("model",
    execution_role_arn=sagemaker_role.arn,
    primary_container={
        "image": "174872318107.dkr.ecr.us-west-2.amazonaws.com/kmeans:1",
        "model_data_url": "s3://my-bucket/my-model/model.tar.gz"
    })

# Set up a SageMaker endpoint configuration
endpoint_config = aws.sagemaker.EndpointConfiguration("endpoint-config",
    production_variants=[{
        "variant_name": "default",
        "model_name": model.name,
        "initial_instance_count": 1,
        "instance_type": "ml.m4.xlarge",
    }]
)

# Deploy a SageMaker endpoint
endpoint = aws.sagemaker.Endpoint("endpoint",
    endpoint_config_name=endpoint_config.name)

# Set up an ElastiCache Redis cluster to cache predictions
cache_subnet_group = aws.elasticache.SubnetGroup("cache-subnet-group",
    subnet_ids=["subnet-xxxxxxxxxxxxxxxxx", "subnet-xxxxxxxxxxxxxxxxx"])

redis_cluster = aws.elasticache.Cluster("redis-cluster",
    cluster_id="model-predictions-cache",
    engine="redis",
    node_type="cache.t2.micro",
    num_cache_nodes=1,
    parameter_group_name="default.redis3.2",
    port=6379,
    subnet_group_name=cache_subnet_group.name)

# Export the SageMaker endpoint name and the ElastiCache Redis endpoint
pulumi.export("sagemaker_endpoint_name", endpoint.endpoint_name)
pulumi.export("redis_cluster_endpoint", redis_cluster.cache_nodes.apply(lambda nodes: nodes[0]["address"]))
```

In this code, we start by defining an IAM role for SageMaker, including permissions to access necessary AWS services like S3. Then we describe the SageMaker model, pulling from an S3 bucket where the trained model is already saved.

Following that, we configure a SageMaker endpoint for serving predictions from this model. We then allocate an AWS ElastiCache Redis cluster for caching. The cache is set up with a subnet group and includes the necessary details for configuring the Redis instance.

Once this infrastructure is provisioned by Pulumi, you would write your application logic to communicate with both the SageMaker endpoint and the Redis cache. Specifically, your application should query the Redis cache first to see if a prediction result is already stored. If it's not available in the cache, then it should call the SageMaker endpoint for a prediction and store the result in the cache for future requests.

This program demonstrates infrastructure setup, but the implementation of prediction requests and caching logic would typically be part of your application code.