st ``` The MinIO deployment starts using default root credentials `minioadmin:minioadmin`. You can test the deployment using the MinIO Console, an embedded web-based object browser built into MinIO Server. Point a web browser running on the host machine to <http://127.0.0.1:9000> and log in with the root credentials. You can use the Browser to create buckets, upload objects, and browse the contents of the MinIO server. You can also connect using any S3-compatible tool, such as the MinIO Client `mc`...

### Serverspeicher

Wenn Ihr Code beispielsweise ein Machine-Learning-Modell mit **1 GB Größe** lädt und Sie einen Prozess mit Ihrer API ausführen, verbraucht dieser mindestens 1 GB RAM. Und wenn Sie **4 Prozesse** (4 Worker) starten, verbraucht jeder 1 GB RAM. Insgesamt verbraucht Ihre API also **4 GB RAM**.

#include "tensorflow/c/experimental/grappler/grappler_internal.h"
#include "tensorflow/c/tf_buffer_internal.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/grappler/clusters/single_machine.h"
#include "tensorflow/core/grappler/costs/graph_properties.h"
#include "tensorflow/core/grappler/grappler_item.h"
#include "tensorflow/core/grappler/inputs/trivial_test_graph_input_yielder.h"

!!! tip
    If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

### Declare a *path parameter*

Then create a *path parameter* with a type annotation using the enum class you created (`ModelName`):

See our web documentation on [Deploying MinIO in Standalone Mode](Deploy Standalone MinIO in a Container) for a more structured tutorial on deploying MinIO in a container.

## Prerequisites

Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).

## Run Standalone MinIO on Docker

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

| IMPORTANT |
| -------------------------- |

			// spurious blank caused by Split on "\n"
			output = output[1:]
			continue
		}
		t.Errorf("unexpected output: %q", output[0])
		output = output[1:]
	}

	// Checked printing.
	// Now check machine code layout.

	top := pList.Firstpc
	var text *obj.LSym
	ok = true
	ctxt.DiagFunc = func(format string, args ...interface{}) {
		t.Errorf(format, args...)
		ok = false
	}

!!! tip "Astuce"
    Pour ceux qui se demandent, "AlexNet", "ResNet", et "LeNet" sont juste des noms de <abbr title="Techniquement, des architectures de modèles">modèles</abbr> de Machine Learning.

### Déclarer un paramètre de chemin

Créez ensuite un *paramètre de chemin* avec une annotation de type désignant l'énumération créée précédemment (`ModelName`) :

```Python hl_lines="16"

build:rbe_cross_compile_base --remote_instance_name=projects/tensorflow-testing/instances/default_instance

# XLA depends on some local Python headers that are configured as Genrule. They
# are present on the local host machine but not on the remote execution machine,
# leading to build failures. To resolve the issue, the following line is added
# to make sure all Genrule targets are excuted locally.
build:rbe_cross_compile_base_xla --config=rbe_cross_compile_base

!!! tip "Consejo"
    Si lo estás dudando, "AlexNet", "ResNet", y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

### Declara un *parámetro de path*

Luego, crea un *parámetro de path* con anotaciones de tipos usando la clase enum que creaste (`ModelName`):

```Python hl_lines="16"