## Server Machine and Server Program { #server-machine-and-server-program }

There's a small detail about names to keep in mind. 💡

The word "**server**" is commonly used to refer to both the remote/cloud computer (the physical or virtual machine) and also the program that is running on that machine (e.g. Uvicorn).

  JAVA_HOME("java.home"),

  /** Java Virtual Machine specification version. */
  JAVA_VM_SPECIFICATION_VERSION("java.vm.specification.version"),

  /** Java Virtual Machine specification vendor. */
  JAVA_VM_SPECIFICATION_VENDOR("java.vm.specification.vendor"),

  /** Java Virtual Machine specification name. */
  JAVA_VM_SPECIFICATION_NAME("java.vm.specification.name"),

  /** Java Virtual Machine implementation version. */

  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,

# The RBE machine itself has older kernel mode driver, and it requires
# nvidia driver to be installed.
nvidia-driver-580-open
# TODO(b/445248346): The Docker image shouldn't have cuda-compat installed.
# However, hermetic CUDA forward-compatibility mode is still missing some
# libraries.

## Use Case { #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. 🤖

## Caso de uso { #use-case }

Vamos começar com um exemplo de **caso de uso** e então ver como resolvê-lo com isso.

Vamos imaginar que você tem alguns **modelos de machine learning** que deseja usar para lidar com as requisições. 🤖

## Caso de Uso { #use-case }

Empecemos con un ejemplo de **caso de uso** y luego veamos cómo resolverlo con esto.

Imaginemos que tienes algunos **modelos de machine learning** que quieres usar para manejar 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)

### 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.

## 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