New-Item -ItemType directory -Path $gradleInit
echo "Copy .ci/init.gradle to $gradleInit"
Copy-Item .ci/init.gradle -Destination $gradleInit

[Environment]::SetEnvironmentVariable("JAVA_HOME", $null, "Machine")
$env:PATH="C:\Users\jenkins\.java\$env:ES_BUILD_JAVA\bin\;$env:PATH"
$env:JAVA_HOME=$null
$env:SYSTEM_JAVA_HOME="C:\Users\jenkins\.java\$env:ES_RUNTIME_JAVA"
Remove-Item -Recurse -Force \tmp -ErrorAction Ignore

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

- Kube-apiserver now reports metrics for authorization decisions in the `apiserver_authorization_decisions_total` metric, labeled by authorizer type, name, and decision. ([#123333](https://github.com/kubernetes/kubernetes/pull/123333), [@liggitt](https://github.com/liggitt)) [SIG API Machinery, Auth and Testing]

- Kube-apiserver: fixes a 1.27+ regression watching a single namespace via the deprecated /api/v1/watch/namespaces/$name endpoint where watch events were not delivered after the watch was established ([#126150](https://github.com/kubernetes/kubernetes/pull/126150), [@xyz-li](https://github.com/xyz-li)) [SIG API Machinery and Testing]

New-Item -ItemType directory -Path $gradleInit
echo "Copy .ci/init.gradle to $gradleInit"
Copy-Item .ci/init.gradle -Destination $gradleInit

[Environment]::SetEnvironmentVariable("JAVA_HOME", $null, "Machine")
$env:PATH="C:\Users\jenkins\.java\$env:ES_BUILD_JAVA\bin\;$env:PATH"
$env:JAVA_HOME=$null
$env:SYSTEM_JAVA_HOME="C:\Users\jenkins\.java\$env:ES_RUNTIME_JAVA"
Remove-Item -Recurse -Force \tmp -ErrorAction Ignore

## `fastapi run` { #fastapi-run }

Exécuter `fastapi run` démarre FastAPI en mode production par défaut.

### Concurrency + Parallelism: Web + Machine Learning { #concurrency-parallelism-web-machine-learning }

With **FastAPI** you can take advantage of concurrency that is very common for web development (the same main attraction of NodeJS).

But you can also exploit the benefits of parallelism and multiprocessing (having multiple processes running in parallel) for **CPU bound** workloads like those in Machine Learning systems.

- Fix counting error in service/nodeport/loadbalancer quota check ([#97826](https://github.com/kubernetes/kubernetes/pull/97826), [@pacoxu](https://github.com/pacoxu)) [SIG API Machinery and Network]
- Fix missing cadvisor machine metrics. ([#97006](https://github.com/kubernetes/kubernetes/pull/97006), [@lingsamuel](https://github.com/lingsamuel)) [SIG Node]

- Fixes a spurious "namespace not found" error possible in default configurations in 1.30+ when using ValidatingAdmissionPolicy or MutatingAdmissionPolicy to intercept namespaced objects in newly-created namespaces ([#135443](https://github.com/kubernetes/kubernetes/pull/135443), [@lalitc375](https://github.com/lalitc375)) [SIG API Machinery]

- Kube-apiserver: fixes a potential crash serving CustomResourceDefinitions that combine an invalid schema and CEL validation rules. ([#126167](https://github.com/kubernetes/kubernetes/pull/126167), [@cici37](https://github.com/cici37)) [SIG API Machinery and Testing]