Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine learning models. This guide walks you through deploying vLLM using native Kubernetes.
Deploying vLLM on Kubernetes is a scalable and efficient way to serve machine learning models. This guide walks you through deploying vLLM using native Kubernetes.
If the startup or readiness probe failureThreshold is too low for the time needed to startup the server, Kubernetes scheduler will kill the container. A couple of indications that this has happened:
2.`kubectl get events` shows message `Container $NAME failed startup probe, will be restarted`
To mitigate, increase the failureThreshold to allow more time for the model server to start serving. You can identify an ideal failureThreshold by removing the probes from the manifest and measuring how much time it takes for the model server to show it's ready to serve.
## Conclusion
## Conclusion
Deploying vLLM with Kubernetes allows for efficient scaling and management of ML models leveraging GPU resources. By following the steps outlined above, you should be able to set up and test a vLLM deployment within your Kubernetes cluster. If you encounter any issues or have suggestions, please feel free to contribute to the documentation.
Deploying vLLM with Kubernetes allows for efficient scaling and management of ML models leveraging GPU resources. By following the steps outlined above, you should be able to set up and test a vLLM deployment within your Kubernetes cluster. If you encounter any issues or have suggestions, please feel free to contribute to the documentation.
Anna Pendleton <pendleton@google.com>