include with deployment or statefulset additionalAnnotation: [] ## Typically the deployment/statefulset includes checksums of secrets/config, ## So that when these change on a subsequent helm install, the deployment/statefulset ## is restarted. This can result in unnecessary restarts under GitOps tooling such as ## flux, so set to "true" to disable this behaviour. ignoreChartChecksums: false ## Additional arguments to pass to minio binary extraArgs: [] ## Internal port number for MinIO S3 API container...

    * AWS, Google Compute Engine, Google Container Engine, and OpenStack using
    * the default kube-up.sh scripts. This StorageClass is marked as default so that
    * a PersistentVolumeClaim without a StorageClass annotation now results in
    * automatic provisioning of storage (GCE PersistentDisk on Google Cloud, AWS
    * EBS on AWS, and Cinder on OpenStack). In previous versions of Kubernetes

include with deployment or statefulset additionalAnnotation: {} ## Typically the deployment/statefulset includes checksums of secrets/config, ## So that when these change on a subsequent helm install, the deployment/statefulset ## is restarted. This can result in unnecessary restarts under GitOps tooling such as ## flux, so set to "true" to disable this behaviour. ignoreChartChecksums: false ## Additional arguments to pass to minio binary extraArgs: [] # example for enabling FTP: # - --ftp=\"address=:8021\"...

  controller-manager, and scheduler server components, we no longer
  reference the individual architectures in the image names, specifically
  in the 'FROM' directive of the server image Dockerfiles.
  
  As a result, server images for non-amd64 images copy in the go-runner
  amd64 binary instead of the go-runner that matches that architecture.
  
  This commit explicitly sets the '--platform=linux/${arch}' to ensure

* fixes a regression in kubectl printing behavior when using go-template or jsonpath output that resulted in a "unable to match a printer" error message ([#65979](https://github.com/kubernetes/kubernetes/pull/65979), [@juanvallejo](https://github.com/juanvallejo))

    ml_models.clear()


app = FastAPI(lifespan=lifespan)


@app.get("/predict")
async def predict(x: float):
    result = ml_models["answer_to_everything"](x)
    return {"result": result}
```

**Note**: This is the recommended way going forward, instead of using `startup` and `shutdown` events.