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Don't let resource exhaustion leave your users hanging: A guide to handling 429 errors
- Large language models (LLMs) demand significant computational resources, which means it's essential to anticipate and handle potential resource exhaustion.
- Exponential backoff and retry logic in the code can help in handling resource exhaustion or API unavailability also apply to LLMs.
- In Python, tenacity is a useful general-purpose retrying library written in Python to simplify the task of adding retry behavior to your code.
- Fallbacks can be implemented in code along with backoff and retry methods for greater resilience of your LLM applications.
- Circuit breaking with Apigee can be used to manage traffic distribution and graceful failure handling.
- Dynamic shared quota is one way that Google Cloud manages resource allocation for certain LLMs, which aims to provide a more flexible and efficient user experience.
- Provisioned Throughput from Google Cloud is a service that allows you to reserve dedicated capacity for generative AI models on the Vertex AI platform.
- Backoff and retry mechanisms should be combined with dynamic shared quota, especially as request volume and token size increase.
- Provisioned Throughput offers predictable performance, reserved capacity, cost-effectiveness, scalable services & can help in computationally-intensive AI tasks.
- Implementing the above 3 practical strategies can help in achieving reliability and improved performance.
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