Remove-Item $zulu_zip; \
  $env:PATH = [Environment]::GetEnvironmentVariable(\"PATH\", \"Machine\") + \";${zulu_root}\\bin\"; \
  [Environment]::SetEnvironmentVariable(\"PATH\", $env:PATH, \"Machine\"); \
  $env:JAVA_HOME = $zulu_root; \
  [Environment]::SetEnvironmentVariable(\"JAVA_HOME\", $env:JAVA_HOME, \"Machine\")

# Point to the LLVM installation.
# The Bazel Windows guide claims it can find LLVM automatically,

Lorsqu'on se réfère à la machine distante, il est courant de l'appeler **serveur**, mais aussi **machine**, **VM** (machine virtuelle), **nœud**. Tout cela fait référence à un type de machine distante, exécutant  Linux, en règle générale, sur laquelle vous exécutez des programmes.


## Installer le programme serveur

Vous pouvez installer un serveur compatible ASGI avec :

//// tab | Uvicorn

When referring to the remote machine, it's common to call it **server**, but also **machine**, **VM** (virtual machine), **node**. Those all refer to some type of remote machine, normally running Linux, where you run programs.

## Install the Server Program

When you install FastAPI, it comes with a production server, Uvicorn, and you can start it with the `fastapi run` command.

But you can also install an ASGI server manually.

## Use Case

Let's start with an example **use case** and then see how to solve it with this.

Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖

		}
	}

	return host, port, nil
}

// isLocalHost - checks if the given parameter
// correspond to one of the local IP of the
// current machine
func isLocalHost(host string, port string, localPort string) (bool, error) {
	hostIPs, err := getHostIP(host)
	if err != nil {
		return false, err
	}

	nonInterIPV4s := mustGetLocalIP4().Intersection(hostIPs)

[ ] Extensions and Telemetry
[ ] Security
[ ] Test and Release
[ ] User Experience
[ ] Developer Infrastructure

**Affected features (please put an X in all that apply)**

[ ] Multi Cluster
[ ] Virtual Machine
[ ] Multi Control Plane

* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.

## 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

- [ ] Performance and Scalability
- [ ] Extensions and Telemetry
- [ ] Security
- [ ] Test and Release
- [ ] User Experience
- [ ] Developer Infrastructure
- [ ] Upgrade
- [ ] Multi Cluster
- [ ] Virtual Machine
- [ ] Control Plane Revisions

**Please check any characteristics that apply to this pull request.**

message NodeSystemInfo {
  // MachineID reported by the node. For unique machine identification
  // in the cluster this field is preferred. Learn more from man(5)
  // machine-id: http://man7.org/linux/man-pages/man5/machine-id.5.html
  optional string machineID = 1;

  // SystemUUID reported by the node. For unique machine identification
  // MachineID is preferred. This field is specific to Red Hat hosts