Caching Features for Azure-based LLMs

Question

Pulumi · Accepted Answer

Caching is an essential feature for improving the performance of language learning models (LLMs) by reducing latency and offloading some of the workload from the computation layer to the cache. Azure provides several caching features that can be leveraged for this purpose.

For instance, in Azure API Management (APIM), you can apply caching policies to APIs to cache responses and improve performance. Azure also offers Redis Cache, which can be used to store commonly accessed data in memory to speed up access times. Azure CDN (Content Delivery Network) can also be used to cache static assets closer to the user, reducing load times.

I'll provide you with a Pulumi program that sets up an Azure Redis Cache, which you can use for caching within your LLM application. This program will deploy an Azure Redis Cache instance.

Here is a breakdown of the resources that will be used in the program:

- `azure_native.cache.Redis`: Represents an Azure Redis Cache instance. This managed service provides a secure and highly available cache that you can use for your LLM application. Redis is a popular choice for caching because of its speed and rich feature set.

The below Pulumi program in Python creates an Azure Redis Cache:

```python
import pulumi
from pulumi_azure_native import resources, cache

# Create an Azure Resource Group
resource_group = resources.ResourceGroup('resource_group')

# Create an Azure Cache for Redis instance
redis_cache = cache.Redis(
    'redisCache',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    sku=cache.SkuArgs(
        name=cache.SkuName.BASIC,
        family=cache.SkuFamily.C,
        capacity=0  # The size of the Redis cache to deploy, for example 0 (250MB), 1 (1GB), 2 (2.5GB), etc.
    ),
    enable_non_ssl_port=True,
    minimum_tls_version='1.2',
    redis_configuration={},
)

# Export the primary key of the Redis cache
primary_key = pulumi.Output.secret(redis_cache.redis_access_keys.apply(lambda keys: keys.primary_key))
pulumi.export('redis_primary_key', primary_key)

# Export the connection string of the Redis cache (useful for configuring your application)
connection_string = pulumi.Output.all(resource_group.name, redis_cache.name).apply(
    lambda args: f"{args[1]}.redis.cache.windows.net:6380,password={primary_key.result},ssl=True,abortConnect=False"
)
pulumi.export('redis_connection_string', connection_string)
```

In the program:

- We begin by creating an Azure Resource Group using the `ResourceGroup` resource from the `pulumi_azure_native.resources` module. Resource groups are containers that hold related resources for an Azure solution.

- Then, we create an Azure Redis Cache instance with the `Redis` resource from the `pulumi_azure_native.cache` module. In the `sku` property, we define the pricing tier (Basic, Standard, Premium), family (C for basic/standard, P for premium), and capacity (0 for the smallest size).

- We set the `enable_non_ssl_port` to `True` and the `minimum_tls_version` to '1.2' for security reasons. The redis_configuration is kept empty, but you can specify additional configurations as needed.

- Lastly, we export the primary key for the Redis Cache, which you'll need to access the cache from your application, and we construct and export the connection string to use when connecting to the Redis instance.

To deploy this program, you would need to have the Pulumi CLI installed and configured to use Azure. With the Pulumi CLI installed and the program written, you could deploy it by running `pulumi up` in the same directory as the program file. Upon successful deployment, Pulumi will output the primary key and connection string, which you should securely store for use in your LLM application. Remember not to expose these secrets in your source code or version control.

This setup will provide you a robust caching layer for your LLMs on Azure, which should greatly enhance performance.