What techniques help build efficient caching mechanisms in code to speed up frequent model inference requests

Question

Can you name the techniques that help build efficient caching mechanisms in code to speed up frequent model inference requests?

Answer 1

To build efficient caching mechanisms for frequent model inference requests, You can refer to following :

In-Memory Caching (e.g., functools.lru_cache)

• Automatically caches results of function calls.

Redis Caching

• Leverages an external cache for scalability.

Batch Inference with Caching

• Groups request to reduce redundant computations.

Hashing Inputs

• Cache results using hashable representations of inputs to avoid recomputation.

In the code above, we are Using in-memory caching for local use, Redis for distributed setups, and batching to minimize repetitive calls.

Hence, using these techniques will help you build efficient caching mechanisms in code to speed up frequent model inference requests.