Organizar los recursos con etiquetas

En este documento, se explica cómo usar etiquetas para organizar tus recursos de Batch.

Las etiquetas son pares clave-valor que se aplican a los recursos para agruparlos y describirlos. El procesamiento por lotes tiene etiquetas predefinidas, que se aplican automáticamente a los recursos, y etiquetas personalizadas, que puedes definir y aplicar cuando creas un trabajo.

Las etiquetas te permiten filtrar los resultados de las listas de recursos y los informes de Facturación de Cloud. Por ejemplo, puedes usar etiquetas para lo siguiente:

  • Explica y organiza la lista de tareas de tu proyecto.

  • Distingue los ejecutables de un trabajo con etiquetas para describir el tipo de contenedor o secuencia de comandos que especifican.

  • Para analizar los costos, filtra los informes de Facturación de Cloud en busca de los recursos que creó Batch o trabajos específicos.

Para obtener más información sobre las etiquetas, consulta la documentación de Compute Engine sobre las etiquetas.

Antes de comenzar

  1. Si nunca usaste Batch, consulta Cómo comenzar a usar Batch y habilita Batch completando los requisitos previos para proyectos y usuarios.

  2. Para obtener los permisos que necesitas para crear un trabajo, pídele a tu administrador que te otorgue los siguientes roles de IAM:

    Para obtener más información sobre cómo otorgar roles, consulta Administra el acceso a proyectos, carpetas y organizaciones.

    También puedes obtener los permisos necesarios mediante roles personalizados o cualquier otro rol predefinido.

Restricciones

Además de los requisitos para las etiquetas que se especifican en la documentación de Compute Engine, aplicar etiquetas a un trabajo por lotes y sus recursos tiene las siguientes restricciones:

  • Por lotes solo admite etiquetas para recursos que se crean con por lotes y de los siguientes tipos:

  • Después de tener en cuenta las etiquetas predefinidas que Batch aplica automáticamente a un trabajo, puedes definir las siguientes cantidades de etiquetas personalizadas:

    • Puedes definir un máximo de 63 etiquetas personalizadas para aplicarlas a la tarea y a sus ejecutables.

    • Puedes definir un máximo de 61 etiquetas personalizadas para aplicarlas a cada GPU, disco persistente y VM creada para la tarea.

  • El procesamiento por lotes solo admite la definición de etiquetas personalizadas con nombres únicos. Esto tiene las siguientes consecuencias:

    • Si intentas anular una etiqueta predefinida, se generarán errores.

    • Si defines una etiqueta personalizada duplicada, se anulará la etiqueta personalizada existente.

  • Batch solo admite la definición de etiquetas cuando se crea un trabajo.

    • No se pueden agregar, actualizar ni quitar etiquetas de trabajos y ejecutables.

    • Aunque es posible usar Compute Engine para agregar, actualizar o remover etiquetas de los discos persistentes y las VMs creadas para trabajos, no se recomienda. No se puede estimar de forma confiable el período en el que existen los recursos de un trabajo, y es posible que los cambios no funcionen correctamente con Batch.

  • Para usar etiquetas y filtrar tu lista de trabajos, debes ver tu lista de trabajos con la CLI de gcloud o la API de Batch.

Etiquetas predefinidas

Cada etiqueta predefinida tiene una clave que comienza con el prefijo batch-. De forma predeterminada, Batch aplica automáticamente las siguientes etiquetas predefinidas:

  • Para cada trabajo que crees, sigue estos pasos:

    • batch-job-id: El valor de esta etiqueta se establece en el nombre del trabajo.

  • A cada GPU, disco persistente y VM creada para un trabajo, haz lo siguiente:

    • batch-job-id: El valor de esta etiqueta se establece en el nombre del trabajo.

    • batch-job-uid: El valor de esta etiqueta se establece en el identificador único (UID) del trabajo.

    • batch-node: El valor de esta etiqueta es nulo, solo agrupa todas las GPUs, los discos persistentes y las VMs que se crean para las tareas. Por ejemplo, usa esta etiqueta cuando visualices un informe de Facturación de Cloud para identificar los costos de todas las GPUs, los discos persistentes y las VMs que creó Batch.

Define etiquetas personalizadas

De manera opcional, puedes definir una o más etiquetas personalizadas cuando creas un trabajo. Puedes definir etiquetas personalizadas con claves nuevas o claves que ya usa tu proyecto. Para definir etiquetas personalizadas, selecciona uno o más de los siguientes métodos en este documento según el propósito de la etiqueta:

  • Define etiquetas personalizadas para la tarea y sus recursos.

    En esta sección, se explica cómo aplicar una o más etiquetas personalizadas a la tarea y a cada GPU, disco persistente y VM creada para la tarea. Después de crear el trabajo, puedes usar estas etiquetas para filtrar los informes de Facturación de Cloud y las listas de trabajos, discos persistentes y VMs de tu proyecto.

  • Define etiquetas personalizadas para el trabajo.

    En esta sección, se explica cómo aplicar una o más etiquetas personalizadas al trabajo. Después de crear la tarea, puedes usar estas etiquetas para filtrar las listas de tareas de tu proyecto.

  • Define etiquetas personalizadas para elementos ejecutables.

    En esta sección, se explica cómo aplicar una o más etiquetas personalizadas a uno o más elementos ejecutables para el trabajo. Después de crear el trabajo, puedes usar estas etiquetas para filtrar las listas de trabajos de tu proyecto.

Define etiquetas personalizadas para la tarea y sus recursos

Las etiquetas definidas en el campo labels para la política de asignación de un trabajo se aplican al trabajo, así como a cada GPU (si la hay), al disco persistente (todos los discos de inicio y los volúmenes de almacenamiento nuevos) y a la VM creada para el trabajo.

Puedes definir etiquetas para un trabajo y sus recursos cuando lo creas con la CLI de gcloud o la API de Batch.

gcloud

Por ejemplo, para crear un trabajo de contenedor básico en us-central1 que defina dos etiquetas personalizadas que se apliquen al trabajo y a los recursos creados para él, sigue estos pasos:

  1. Crea un archivo JSON que especifique los detalles de configuración del trabajo y el campo allocationPolicy.labels.

    {
  "allocationPolicy": {
    "instances": [
      {
        "policy": {
          "machineType": "e2-standard-4"
        }
      }
    ],
    "labels": {
      "VM_LABEL_NAME1": "VM_LABEL_VALUE1",
      "VM_LABEL_NAME2": "VM_LABEL_VALUE2"
    }
  },
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello world!"
              ]
            }
          }
        ]
      }
    }
  ]
}

    Reemplaza lo siguiente:

    • VM_LABEL_NAME1: Es el nombre de la primera etiqueta que se aplicará a las VMs creadas para la tarea.

    • VM_LABEL_VALUE1: Es el valor de la primera etiqueta que se aplicará a las VMs creadas para la tarea.

    • VM_LABEL_NAME2: Es el nombre de la segunda etiqueta que se aplicará a las VMs creadas para la tarea.

    • VM_LABEL_VALUE2: Es el valor de la segunda etiqueta que se aplicará a las VMs creadas para la tarea.

  2. Crea el trabajo en us-central1 con el comando gcloud batch jobs submit.

    gcloud batch jobs submit example-job \
    --config=JSON_CONFIGURATION_FILE \
    --location=us-central1

    Reemplaza JSON_CONFIGURATION_FILE por la ruta de acceso al archivo JSON con los detalles de configuración del trabajo que creaste en el paso anterior.

API

Por ejemplo, para crear un trabajo de contenedor básico en us-central1 que defina dos etiquetas personalizadas que se apliquen al trabajo y a los recursos creados para él, realiza una solicitud POST al método jobs.create y especifica el campo allocationPolicy.labels.

POST https://batch.googleapis.com/v1/projects/example-project/locations/us-central1/jobs?job_id=example-job

{
  "allocationPolicy": {
    "instances": [
      {
        "policy": {
          "machineType": "e2-standard-4"
        }
      }
    ],
    "labels": {
      "VM_LABEL_NAME1": "VM_LABEL_VALUE1",
      "VM_LABEL_NAME2": "VM_LABEL_VALUE2"
    }
  },
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello world!"
              ]
            }
          }
        ]
      }
    }
  ]
}

Reemplaza lo siguiente:

  • VM_LABEL_NAME1: Es el nombre de la primera etiqueta que se aplicará a las VMs creadas para la tarea.

  • VM_LABEL_VALUE1: Es el valor de la primera etiqueta que se aplicará a las VMs creadas para la tarea.

  • VM_LABEL_NAME2: Es el nombre de la segunda etiqueta que se aplicará a las VMs creadas para la tarea.

  • VM_LABEL_VALUE2: Es el valor de la segunda etiqueta que se aplicará a las VMs creadas para la tarea.

Java 


import com.google.cloud.batch.v1.AllocationPolicy;
import com.google.cloud.batch.v1.BatchServiceClient;
import com.google.cloud.batch.v1.ComputeResource;
import com.google.cloud.batch.v1.CreateJobRequest;
import com.google.cloud.batch.v1.Job;
import com.google.cloud.batch.v1.LogsPolicy;
import com.google.cloud.batch.v1.Runnable;
import com.google.cloud.batch.v1.TaskGroup;
import com.google.cloud.batch.v1.TaskSpec;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class CreateBatchAllocationPolicyLabel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    // Project ID or project number of the Google Cloud project you want to use.
    String projectId = "YOUR_PROJECT_ID";
    // Name of the region you want to use to run the job. Regions that are
    // available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
    String region = "us-central1";
    // The name of the job that will be created.
    // It needs to be unique for each project and region pair.
    String jobName = "example-job";
    // Name of the label1 to be applied for your Job.
    String labelName1 = "VM_LABEL_NAME1";
    // Value for the label1 to be applied for your Job.
    String labelValue1 = "VM_LABEL_VALUE1";
    // Name of the label2 to be applied for your Job.
    String labelName2 = "VM_LABEL_NAME2";
    // Value for the label2 to be applied for your Job.
    String labelValue2 = "VM_LABEL_VALUE2";

    createBatchAllocationPolicyLabel(projectId, region, jobName, labelName1,
        labelValue1, labelName2, labelValue2);
  }

  // This method shows how to create a job with labels defined 
  // in the labels field of a job's allocation policy. These are 
  // applied to the job, as well as to each GPU (if any), persistent disk 
  // (all boot disks and any new storage volumes), and VM created for the job.
  public static Job createBatchAllocationPolicyLabel(String projectId, String region,
                               String jobName, String labelName1,
                               String labelValue1, String labelName2, String labelValue2)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (BatchServiceClient batchServiceClient = BatchServiceClient.create()) {

      // Define what will be done as part of the job.
      Runnable runnable =
          Runnable.newBuilder()
              .setContainer(
                  Runnable.Container.newBuilder()
                      .setImageUri("gcr.io/google-containers/busybox")
                      .setEntrypoint("/bin/sh")
                      .addCommands("-c")
                      .addCommands(
                          "echo Hello world! This is task ${BATCH_TASK_INDEX}. "
                              + "This job has a total of ${BATCH_TASK_COUNT} tasks.")
                      .build())
              .build();

      // We can specify what resources are requested by each task.
      ComputeResource computeResource =
          ComputeResource.newBuilder()
              // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
              .setCpuMilli(2000)
              // In MiB.
              .setMemoryMib(2000)
              .build();

      TaskSpec task =
          TaskSpec.newBuilder()
              // Jobs can be divided into tasks. In this case, we have only one task.
              .addRunnables(runnable)
              .setComputeResource(computeResource)
              .setMaxRetryCount(2)
              .setMaxRunDuration(Duration.newBuilder().setSeconds(3600).build())
              .build();

      // Tasks are grouped inside a job using TaskGroups.
      // Currently, it's possible to have only one task group.
      TaskGroup taskGroup = TaskGroup.newBuilder().setTaskCount(1).setTaskSpec(task).build();

      // Policies are used to define on what kind of virtual machines the tasks will run on.
      // In this case, we tell the system to use "e2-standard-4" machine type.
      // Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
      AllocationPolicy.InstancePolicy instancePolicy =
          AllocationPolicy.InstancePolicy.newBuilder().setMachineType("e2-standard-4").build();

      AllocationPolicy allocationPolicy =
          AllocationPolicy.newBuilder()
              .addInstances(AllocationPolicy.InstancePolicyOrTemplate.newBuilder()
                  .setPolicy(instancePolicy)
                  .build())
              // Labels and their value to be applied to the job and its resources
              .putLabels(labelName1, labelValue1)
              .putLabels(labelName2, labelValue2)
              .build();

      Job job =
          Job.newBuilder()
              .addTaskGroups(taskGroup)
              .setAllocationPolicy(allocationPolicy)
              // We use Cloud Logging as it's an out of the box available option.
              .setLogsPolicy(LogsPolicy.newBuilder()
                      .setDestination(LogsPolicy.Destination.CLOUD_LOGGING).build())
              .build();

      CreateJobRequest createJobRequest =
          CreateJobRequest.newBuilder()
              // The job's parent is the region in which the job will run.
              .setParent(String.format("projects/%s/locations/%s", projectId, region))
              .setJob(job)
              .setJobId(jobName)
              .build();

      Job result =
          batchServiceClient
              .createJobCallable()
              .futureCall(createJobRequest)
              .get(5, TimeUnit.MINUTES);

      System.out.printf("Successfully created the job: %s", result.getName());

      return result;
    }
  }

}

Node.js 

// Imports the Batch library
const batchLib = require('@google-cloud/batch');
const batch = batchLib.protos.google.cloud.batch.v1;

// Instantiates a client
const batchClient = new batchLib.v1.BatchServiceClient();

/**
 * TODO(developer): Update these variables before running the sample.
 */
// Project ID or project number of the Google Cloud project you want to use.
const projectId = await batchClient.getProjectId();
// Name of the region you want to use to run the job. Regions that are
// available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
const region = 'europe-central2';
// The name of the job that will be created.
// It needs to be unique for each project and region pair.
const jobName = 'batch-labels-allocation-job';
// Name of the label1 to be applied for your Job.
const labelName1 = 'vm_label_name_1';
// Value for the label1 to be applied for your Job.
const labelValue1 = 'vmLabelValue1';
// Name of the label2 to be applied for your Job.
const labelName2 = 'vm_label_name_2';
// Value for the label2 to be applied for your Job.
const labelValue2 = 'vmLabelValue2';

// Define what will be done as part of the job.
const runnable = new batch.Runnable({
  script: new batch.Runnable.Script({
    commands: ['-c', 'echo Hello world! This is task ${BATCH_TASK_INDEX}.'],
  }),
});

// Specify what resources are requested by each task.
const computeResource = new batch.ComputeResource({
  // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
  cpuMilli: 500,
  // In MiB.
  memoryMib: 16,
});

const task = new batch.TaskSpec({
  runnables: [runnable],
  computeResource,
  maxRetryCount: 2,
  maxRunDuration: {seconds: 3600},
});

// Tasks are grouped inside a job using TaskGroups.
const group = new batch.TaskGroup({
  taskCount: 3,
  taskSpec: task,
});

// Policies are used to define on what kind of virtual machines the tasks will run on.
// In this case, we tell the system to use "e2-standard-4" machine type.
// Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
const instancePolicy = new batch.AllocationPolicy.InstancePolicy({
  machineType: 'e2-standard-4',
});

const allocationPolicy = new batch.AllocationPolicy({
  instances: [{policy: instancePolicy}],
});
// Labels and their value to be applied to the job and its resources.
allocationPolicy.labels[labelName1] = labelValue1;
allocationPolicy.labels[labelName2] = labelValue2;

const job = new batch.Job({
  name: jobName,
  taskGroups: [group],
  labels: {env: 'testing', type: 'script'},
  allocationPolicy,
  // We use Cloud Logging as it's an option available out of the box
  logsPolicy: new batch.LogsPolicy({
    destination: batch.LogsPolicy.Destination.CLOUD_LOGGING,
  }),
});

// The job's parent is the project and region in which the job will run
const parent = `projects/${projectId}/locations/${region}`;

async function callCreateBatchLabelsAllocation() {
  // Construct request
  const request = {
    parent,
    jobId: jobName,
    job,
  };

  // Run request
  const [response] = await batchClient.createJob(request);
  console.log(JSON.stringify(response));
}

await callCreateBatchLabelsAllocation();

Python 

from google.cloud import batch_v1


def create_job_with_custom_allocation_policy_labels(
    project_id: str, region: str, job_name: str, labels: dict
) -> batch_v1.Job:
    """
    This method shows the creation of a Batch job with custom labels which describe the allocation policy.
    Args:
        project_id (str): project ID or project number of the Cloud project you want to use.
        region (str): name of the region you want to use to run the job. Regions that are
            available for Batch are listed on: https://cloud.google.com/batch/docs/locations
        job_name (str): the name of the job that will be created.
        labels (dict): a dictionary of key-value pairs that will be used as labels
            E.g., {"label_key1": "label_value2", "label_key2": "label_value2"}
    Returns:
        batch_v1.Job: The created Batch job object containing configuration details.
    """
    client = batch_v1.BatchServiceClient()

    runnable = batch_v1.Runnable()
    runnable.container = batch_v1.Runnable.Container()
    runnable.container.image_uri = "gcr.io/google-containers/busybox"
    runnable.container.entrypoint = "/bin/sh"
    runnable.container.commands = [
        "-c",
        "echo Hello world!",
    ]

    # Create a task specification and assign the runnable and volume to it
    task = batch_v1.TaskSpec()
    task.runnables = [runnable]

    # Specify what resources are requested by each task.
    resources = batch_v1.ComputeResource()
    resources.cpu_milli = 2000  # in milliseconds per cpu-second. This means the task requires 2 whole CPUs.
    resources.memory_mib = 16  # in MiB
    task.compute_resource = resources

    task.max_retry_count = 2
    task.max_run_duration = "3600s"

    # Create a task group and assign the task specification to it
    group = batch_v1.TaskGroup()
    group.task_count = 3
    group.task_spec = task

    # Policies are used to define on what kind of virtual machines the tasks will run on.
    # In this case, we tell the system to use "e2-standard-4" machine type.
    # Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
    policy = batch_v1.AllocationPolicy.InstancePolicy()
    policy.machine_type = "e2-standard-4"
    instances = batch_v1.AllocationPolicy.InstancePolicyOrTemplate()
    instances.policy = policy
    allocation_policy = batch_v1.AllocationPolicy()
    allocation_policy.instances = [instances]

    # Assign the provided labels to the allocation policy
    allocation_policy.labels = labels

    # Create the job and assign the task group and allocation policy to it
    job = batch_v1.Job()
    job.task_groups = [group]
    job.allocation_policy = allocation_policy

    # We use Cloud Logging as it's an out of the box available option
    job.logs_policy = batch_v1.LogsPolicy()
    job.logs_policy.destination = batch_v1.LogsPolicy.Destination.CLOUD_LOGGING

    # Create the job request and set the job and job ID
    create_request = batch_v1.CreateJobRequest()
    create_request.job = job
    create_request.job_id = job_name
    # The job's parent is the region in which the job will run
    create_request.parent = f"projects/{project_id}/locations/{region}"

    return client.create_job(create_request)

Define etiquetas personalizadas para el trabajo

Las etiquetas definidas en el campo labels para el trabajo solo se aplican a este.

Puedes definir etiquetas para un trabajo cuando lo creas con la CLI de gcloud o la API de Batch.

gcloud

Por ejemplo, para crear un trabajo de contenedor básico en us-central1 que defina dos etiquetas personalizadas que se apliquen al trabajo en sí, sigue estos pasos:

  1. Crea un archivo JSON que especifique los detalles de configuración del trabajo y el campo labels.

    {
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello World!"
              ]
            }
          }
        ]
      }
    }
  ],
  "labels": {
    "JOB_LABEL_NAME1": "JOB_LABEL_VALUE1",
    "JOB_LABEL_NAME2": "JOB_LABEL_VALUE2"
  }
}

    Reemplaza lo siguiente:

    • JOB_LABEL_NAME1: Es el nombre de la primera etiqueta que se aplicará a tu trabajo.

    • JOB_LABEL_VALUE1: Es el valor de la primera etiqueta que se aplicará a tu trabajo.

    • JOB_LABEL_NAME2: Es el nombre de la segunda etiqueta que se aplicará a tu trabajo.

    • JOB_LABEL_VALUE2: Es el valor de la segunda etiqueta que se aplicará a tu trabajo.

  2. Crea el trabajo en us-central1 con el comando gcloud batch jobs submit y las siguientes marcas:

    gcloud batch jobs submit example-job \
    --config=JSON_CONFIGURATION_FILE \
    --location=us-central1

    Reemplaza JSON_CONFIGURATION_FILE por la ruta de acceso al archivo JSON con los detalles de configuración del trabajo que creaste en el paso anterior.

API

Por ejemplo, para crear un trabajo de contenedor en us-central1 que defina dos etiquetas personalizadas para aplicarlas a los trabajos, realiza una solicitud POST al método jobs.create y especifica el campo labels.

POST https://batch.googleapis.com/v1/projects/example-project/locations/us-central1/jobs?job_id=example-job

{
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello World!"
              ]
            }
          }
        ]
      }
    }
  ],
  "labels": {
    "JOB_LABEL_NAME1": "JOB_LABEL_VALUE1",
    "JOB_LABEL_NAME2": "JOB_LABEL_VALUE2"
  }
}

Reemplaza lo siguiente:

  • JOB_LABEL_NAME1: Es el nombre de la primera etiqueta que se aplicará a tu trabajo.

  • JOB_LABEL_VALUE1: Es el valor de la primera etiqueta que se aplicará a tu trabajo.

  • JOB_LABEL_NAME2: Es el nombre de la segunda etiqueta que se aplicará a tu trabajo.

  • JOB_LABEL_VALUE2: Es el valor de la segunda etiqueta que se aplicará a tu trabajo.

Java 


import com.google.cloud.batch.v1.BatchServiceClient;
import com.google.cloud.batch.v1.ComputeResource;
import com.google.cloud.batch.v1.CreateJobRequest;
import com.google.cloud.batch.v1.Job;
import com.google.cloud.batch.v1.LogsPolicy;
import com.google.cloud.batch.v1.Runnable;
import com.google.cloud.batch.v1.TaskGroup;
import com.google.cloud.batch.v1.TaskSpec;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;


public class CreateBatchLabelJob {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    // Project ID or project number of the Google Cloud project you want to use.
    String projectId = "YOUR_PROJECT_ID";
    // Name of the region you want to use to run the job. Regions that are
    // available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
    String region = "us-central1";
    // The name of the job that will be created.
    // It needs to be unique for each project and region pair.
    String jobName = "example-job";
    // Name of the label1 to be applied for your Job.
    String labelName1 = "JOB_LABEL_NAME1";
    // Value for the label1 to be applied for your Job.
    String labelValue1 = "JOB_LABEL_VALUE1";
    // Name of the label2 to be applied for your Job.
    String labelName2 = "JOB_LABEL_NAME2";
    // Value for the label2 to be applied for your Job.
    String labelValue2 = "JOB_LABEL_VALUE2";

    createBatchLabelJob(projectId, region, jobName, labelName1,
        labelValue1, labelName2, labelValue2);
  }

  // Creates a job with labels defined in the labels field.
  public static Job createBatchLabelJob(String projectId, String region, String jobName,
                    String labelName1, String labelValue1, String labelName2, String labelValue2)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (BatchServiceClient batchServiceClient = BatchServiceClient.create()) {

      // Define what will be done as part of the job.
      Runnable runnable =
          Runnable.newBuilder()
              .setContainer(
                  Runnable.Container.newBuilder()
                      .setImageUri("gcr.io/google-containers/busybox")
                      .setEntrypoint("/bin/sh")
                      .addCommands("-c")
                      .addCommands(
                          "echo Hello world! This is task ${BATCH_TASK_INDEX}. "
                              + "This job has a total of ${BATCH_TASK_COUNT} tasks.")
                      .build())
              .build();

      // We can specify what resources are requested by each task.
      ComputeResource computeResource =
          ComputeResource.newBuilder()
              // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
              .setCpuMilli(2000)
              // In MiB.
              .setMemoryMib(2000)
              .build();

      TaskSpec task =
          TaskSpec.newBuilder()
              // Jobs can be divided into tasks. In this case, we have only one task.
              .addRunnables(runnable)
              .setComputeResource(computeResource)
              .setMaxRetryCount(2)
              .setMaxRunDuration(Duration.newBuilder().setSeconds(3600).build())
              .build();

      // Tasks are grouped inside a job using TaskGroups.
      // Currently, it's possible to have only one task group.
      TaskGroup taskGroup = TaskGroup.newBuilder().setTaskCount(1).setTaskSpec(task).build();

      Job job =
          Job.newBuilder()
              .addTaskGroups(taskGroup)
              // We use Cloud Logging as it's an out of the box available option.
              .setLogsPolicy(LogsPolicy.newBuilder()
              .setDestination(LogsPolicy.Destination.CLOUD_LOGGING).build())
              // Labels and their value to be applied to the job.
              .putLabels(labelName1, labelValue1)
              .putLabels(labelName2, labelValue2)
              .build();

      CreateJobRequest createJobRequest =
          CreateJobRequest.newBuilder()
              // The job's parent is the region in which the job will run.
              .setParent(String.format("projects/%s/locations/%s", projectId, region))
              .setJob(job)
              .setJobId(jobName)
              .build();

      Job result =
          batchServiceClient
              .createJobCallable()
              .futureCall(createJobRequest)
              .get(5, TimeUnit.MINUTES);

      System.out.printf("Successfully created the job: %s", result.getName());

      return result;
    }
  }

}

Node.js 

// Imports the Batch library
const batchLib = require('@google-cloud/batch');
const batch = batchLib.protos.google.cloud.batch.v1;

// Instantiates a client
const batchClient = new batchLib.v1.BatchServiceClient();

/**
 * TODO(developer): Update these variables before running the sample.
 */
// Project ID or project number of the Google Cloud project you want to use.
const projectId = await batchClient.getProjectId();
// Name of the region you want to use to run the job. Regions that are
// available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
const region = 'europe-central2';
// The name of the job that will be created.
// It needs to be unique for each project and region pair.
const jobName = 'batch-labels-job';
// Name of the label1 to be applied for your Job.
const labelName1 = 'job_label_name_1';
// Value for the label1 to be applied for your Job.
const labelValue1 = 'job_label_value1';
// Name of the label2 to be applied for your Job.
const labelName2 = 'job_label_name_2';
// Value for the label2 to be applied for your Job.
const labelValue2 = 'job_label_value2';

// Define what will be done as part of the job.
const runnable = new batch.Runnable({
  container: new batch.Runnable.Container({
    imageUri: 'gcr.io/google-containers/busybox',
    entrypoint: '/bin/sh',
    commands: ['-c', 'echo Hello world! This is task ${BATCH_TASK_INDEX}.'],
  }),
});

// Specify what resources are requested by each task.
const computeResource = new batch.ComputeResource({
  // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
  cpuMilli: 500,
  // In MiB.
  memoryMib: 16,
});

const task = new batch.TaskSpec({
  runnables: [runnable],
  computeResource,
  maxRetryCount: 2,
  maxRunDuration: {seconds: 3600},
});

// Tasks are grouped inside a job using TaskGroups.
const group = new batch.TaskGroup({
  taskCount: 3,
  taskSpec: task,
});

const job = new batch.Job({
  name: jobName,
  taskGroups: [group],
  // We use Cloud Logging as it's an option available out of the box
  logsPolicy: new batch.LogsPolicy({
    destination: batch.LogsPolicy.Destination.CLOUD_LOGGING,
  }),
});

// Labels and their value to be applied to the job and its resources.
job.labels[labelName1] = labelValue1;
job.labels[labelName2] = labelValue2;

// The job's parent is the project and region in which the job will run
const parent = `projects/${projectId}/locations/${region}`;

async function callCreateBatchLabelsJob() {
  // Construct request
  const request = {
    parent,
    jobId: jobName,
    job,
  };

  // Run request
  const [response] = await batchClient.createJob(request);
  console.log(JSON.stringify(response));
}

await callCreateBatchLabelsJob();

Python 

from google.cloud import batch_v1


def create_job_with_custom_job_labels(
    project_id: str,
    region: str,
    job_name: str,
    labels: dict,
) -> batch_v1.Job:
    """
    This method creates a Batch job with custom labels.
    Args:
        project_id (str): project ID or project number of the Cloud project you want to use.
        region (str): name of the region you want to use to run the job. Regions that are
            available for Batch are listed on: https://cloud.google.com/batch/docs/locations
        job_name (str): the name of the job that will be created.
        labels (dict): A dictionary of custom labels to be added to the job.
            E.g., {"label_key1": "label_value2", "label_key2": "label_value2"}
    Returns:
        batch_v1.Job: The created Batch job object containing configuration details.
    """
    client = batch_v1.BatchServiceClient()

    runnable = batch_v1.Runnable()
    runnable.container = batch_v1.Runnable.Container()
    runnable.container.image_uri = "gcr.io/google-containers/busybox"
    runnable.container.entrypoint = "/bin/sh"
    runnable.container.commands = [
        "-c",
        "echo Hello world!",
    ]

    # Create a task specification and assign the runnable and volume to it
    task = batch_v1.TaskSpec()
    task.runnables = [runnable]

    # Specify what resources are requested by each task.
    resources = batch_v1.ComputeResource()
    resources.cpu_milli = 2000  # in milliseconds per cpu-second. This means the task requires 2 whole CPUs.
    resources.memory_mib = 16  # in MiB
    task.compute_resource = resources

    task.max_retry_count = 2
    task.max_run_duration = "3600s"

    # Create a task group and assign the task specification to it
    group = batch_v1.TaskGroup()
    group.task_count = 3
    group.task_spec = task

    # Policies are used to define on what kind of virtual machines the tasks will run on.
    # In this case, we tell the system to use "e2-standard-4" machine type.
    # Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
    policy = batch_v1.AllocationPolicy.InstancePolicy()
    policy.machine_type = "e2-standard-4"
    instances = batch_v1.AllocationPolicy.InstancePolicyOrTemplate()
    instances.policy = policy
    allocation_policy = batch_v1.AllocationPolicy()
    allocation_policy.instances = [instances]

    # Create the job and assign the task group and allocation policy to it
    job = batch_v1.Job()
    job.task_groups = [group]
    job.allocation_policy = allocation_policy

    # Set the labels for the job
    job.labels = labels

    # We use Cloud Logging as it's an out of the box available option
    job.logs_policy = batch_v1.LogsPolicy()
    job.logs_policy.destination = batch_v1.LogsPolicy.Destination.CLOUD_LOGGING

    # Create the job request and set the job and job ID
    create_request = batch_v1.CreateJobRequest()
    create_request.job = job
    create_request.job_id = job_name
    # The job's parent is the region in which the job will run
    create_request.parent = f"projects/{project_id}/locations/{region}"

    return client.create_job(create_request)

Define etiquetas personalizadas para elementos ejecutables

Las etiquetas definidas en el campo labels para un elemento ejecutable solo se aplican a ese elemento.

Puedes definir etiquetas para uno o más elementos ejecutables cuando creas un trabajo con la CLI de gcloud o la API de Batch.

gcloud

Por ejemplo, para crear un trabajo en us-central1 que defina dos etiquetas personalizadas, una etiqueta personalizada para cada uno de los ejecutables del trabajo, sigue estos pasos:

  1. Crea un archivo JSON que especifique los detalles de configuración de la tarea y los campos runnables.labels.

    {
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello from task ${BATCH_TASK_INDEX}!"
              ]
            },
            "labels": {
              "RUNNABLE1_LABEL_NAME1": "RUNNABLE1_LABEL_VALUE1"
            }
          },
          {
            "script": {
              "text": "echo Hello from task ${BATCH_TASK_INDEX}!"
            },
            "labels": {
              "RUNNABLE2_LABEL_NAME1": "RUNNABLE2_LABEL_VALUE1"
            }
          }
        ]
      }
    }
  ]
}

    Reemplaza lo siguiente:

    • RUNNABLE1_LABEL_NAME1: Es el nombre de la etiqueta que se aplicará al primer elemento ejecutable de la tarea.

    • RUNNABLE1_LABEL_VALUE1: Es el valor de la etiqueta que se aplicará al primer elemento ejecutable de la tarea.

    • RUNNABLE2_LABEL_NAME1: Es el nombre de la etiqueta que se aplicará al segundo elemento ejecutable de la tarea.

    • RUNNABLE2_LABEL_VALUE1: Es el valor de la etiqueta que se aplicará al segundo elemento ejecutable del trabajo.

  2. Crea el trabajo en us-central1 con el comando gcloud batch jobs submit.

    gcloud batch jobs submit example-job \
    --config=JSON_CONFIGURATION_FILE \
    --location=us-central1

    Reemplaza JSON_CONFIGURATION_FILE por la ruta de acceso al archivo JSON con los detalles de configuración del trabajo que creaste en el paso anterior.

API

Por ejemplo, para crear un trabajo en us-central1 que defina dos etiquetas personalizadas, una para cada uno de los ejecutables del trabajo, realiza una solicitud POST al método jobs.create y especifica los campos runnables.labels.

POST https://batch.googleapis.com/v1/projects/example-project/locations/us-central1/jobs?job_id=example-job

{
  "taskGroups": [
    {
      "taskSpec": {
        "runnables": [
          {
            "container": {
              "imageUri": "gcr.io/google-containers/busybox",
              "entrypoint": "/bin/sh",
              "commands": [
                "-c",
                "echo Hello from ${BATCH_TASK_INDEX}!"
              ]
            },
            "labels": {
              "RUNNABLE1_LABEL_NAME1": "RUNNABLE1_LABEL_VALUE1"
            }
          },
          {
            "script": {
              "text": "echo Hello from ${BATCH_TASK_INDEX}!"
            },
            "labels": {
              "RUNNABLE2_LABEL_NAME1": "RUNNABLE2_LABEL_VALUE1"
            }
          }
        ]
      }
    }
  ]
}

Reemplaza lo siguiente:

  • RUNNABLE1_LABEL_NAME1: Es el nombre de la etiqueta que se aplicará al ejecutable del primer trabajo.

  • RUNNABLE1_LABEL_VALUE1: Es el valor de la etiqueta que se aplicará al ejecutable del primer trabajo.

  • RUNNABLE2_LABEL_NAME1: Es el nombre de la etiqueta que se aplicará al elemento ejecutable del segundo trabajo.

  • RUNNABLE2_LABEL_VALUE1: Es el valor de la etiqueta que se aplicará al ejecutable del segundo trabajo.

Java 


import com.google.cloud.batch.v1.BatchServiceClient;
import com.google.cloud.batch.v1.ComputeResource;
import com.google.cloud.batch.v1.CreateJobRequest;
import com.google.cloud.batch.v1.Job;
import com.google.cloud.batch.v1.LogsPolicy;
import com.google.cloud.batch.v1.Runnable;
import com.google.cloud.batch.v1.TaskGroup;
import com.google.cloud.batch.v1.TaskSpec;
import com.google.protobuf.Duration;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class CreateBatchRunnableLabel {
  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // TODO(developer): Replace these variables before running the sample.
    // Project ID or project number of the Google Cloud project you want to use.
    String projectId = "YOUR_PROJECT_ID";
    // Name of the region you want to use to run the job. Regions that are
    // available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
    String region = "us-central1";
    // The name of the job that will be created.
    // It needs to be unique for each project and region pair.
    String jobName = "example-job";
    // Name of the label1 to be applied for your Job.
    String labelName1 = "RUNNABLE_LABEL_NAME1";
    // Value for the label1 to be applied for your Job.
    String labelValue1 = "RUNNABLE_LABEL_VALUE1";
    // Name of the label2 to be applied for your Job.
    String labelName2 = "RUNNABLE_LABEL_NAME2";
    // Value for the label2 to be applied for your Job.
    String labelValue2 = "RUNNABLE_LABEL_VALUE2";

    createBatchRunnableLabel(projectId, region, jobName, labelName1,
        labelValue1, labelName2, labelValue2);
  }

  // Creates a job with labels defined in the labels field
  // for a runnable. The labels are only applied to that runnable.
  // In Batch, a runnable represents a single task or unit of work within a job.
  // It can be a container (like a Docker image) or a script.
  public static Job createBatchRunnableLabel(String projectId, String region, String jobName,
                   String labelName1, String labelValue1, String labelName2, String labelValue2)
      throws IOException, ExecutionException, InterruptedException, TimeoutException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests.
    try (BatchServiceClient batchServiceClient = BatchServiceClient.create()) {

      // Define what will be done as part of the job.
      Runnable runnable =
          Runnable.newBuilder()
              .setContainer(
                  Runnable.Container.newBuilder()
                      .setImageUri("gcr.io/google-containers/busybox")
                      .setEntrypoint("/bin/sh")
                      .addCommands("-c")
                      .addCommands(
                          "echo Hello world! This is task ${BATCH_TASK_INDEX}. "
                              + "This job has a total of ${BATCH_TASK_COUNT} tasks.")
                      .build())
              // Label and its value to be applied to the container
              // that processes data from a specific region.
              .putLabels(labelName1, labelValue1)
              .setScript(Runnable.Script.newBuilder()
              .setText("echo Hello world! This is task ${BATCH_TASK_INDEX}. ").build())
              // Label and its value to be applied to the script
              // that performs some analysis on the processed data.
              .putLabels(labelName2, labelValue2)
              .build();

      // We can specify what resources are requested by each task.
      ComputeResource computeResource =
          ComputeResource.newBuilder()
              // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
              .setCpuMilli(2000)
              // In MiB.
              .setMemoryMib(2000)
              .build();

      TaskSpec task =
          TaskSpec.newBuilder()
              // Jobs can be divided into tasks. In this case, we have only one task.
              .addRunnables(runnable)
              .setComputeResource(computeResource)
              .setMaxRetryCount(2)
              .setMaxRunDuration(Duration.newBuilder().setSeconds(3600).build())
              .build();

      // Tasks are grouped inside a job using TaskGroups.
      // Currently, it's possible to have only one task group.
      TaskGroup taskGroup = TaskGroup.newBuilder().setTaskCount(1).setTaskSpec(task).build();

      Job job =
          Job.newBuilder()
              .addTaskGroups(taskGroup)
              // We use Cloud Logging as it's an out of the box available option.
              .setLogsPolicy(LogsPolicy.newBuilder()
              .setDestination(LogsPolicy.Destination.CLOUD_LOGGING).build())
              .build();

      CreateJobRequest createJobRequest =
          CreateJobRequest.newBuilder()
              // The job's parent is the region in which the job will run for the specific project.
              .setParent(String.format("projects/%s/locations/%s", projectId, region))
              .setJob(job)
              .setJobId(jobName)
              .build();

      Job result =
          batchServiceClient
              .createJobCallable()
              .futureCall(createJobRequest)
              .get(5, TimeUnit.MINUTES);

      System.out.printf("Successfully created the job: %s", result.getName());

      return result;
    }
  }

}

Node.js 

// Imports the Batch library
const batchLib = require('@google-cloud/batch');
const batch = batchLib.protos.google.cloud.batch.v1;

// Instantiates a client
const batchClient = new batchLib.v1.BatchServiceClient();

/**
 * TODO(developer): Update these variables before running the sample.
 */
// Project ID or project number of the Google Cloud project you want to use.
const projectId = await batchClient.getProjectId();
// Name of the region you want to use to run the job. Regions that are
// available for Batch are listed on: https://cloud.google.com/batch/docs/get-started#locations
const region = 'us-central1';
// The name of the job that will be created.
// It needs to be unique for each project and region pair.
const jobName = 'example-job';
// Name of the label1 to be applied for your Job.
const labelName1 = 'RUNNABLE_LABEL_NAME1';
// Value for the label1 to be applied for your Job.
const labelValue1 = 'RUNNABLE_LABEL_VALUE1';
// Name of the label2 to be applied for your Job.
const labelName2 = 'RUNNABLE_LABEL_NAME2';
// Value for the label2 to be applied for your Job.
const labelValue2 = 'RUNNABLE_LABEL_VALUE2';

const container = new batch.Runnable.Container({
  imageUri: 'gcr.io/google-containers/busybox',
  entrypoint: '/bin/sh',
  commands: ['-c', 'echo Hello world! This is task ${BATCH_TASK_INDEX}.'],
});

const script = new batch.Runnable.Script({
  commands: ['-c', 'echo Hello world! This is task ${BATCH_TASK_INDEX}.'],
});

const runnable1 = new batch.Runnable({
  container,
  // Label and its value to be applied to the container
  // that processes data from a specific region.
  labels: {
    [labelName1]: labelValue1,
  },
});

const runnable2 = new batch.Runnable({
  script,
  // Label and its value to be applied to the script
  // that performs some analysis on the processed data.
  labels: {
    [labelName2]: labelValue2,
  },
});

// Specify what resources are requested by each task.
const computeResource = new batch.ComputeResource({
  // In milliseconds per cpu-second. This means the task requires 50% of a single CPUs.
  cpuMilli: 500,
  // In MiB.
  memoryMib: 16,
});

const task = new batch.TaskSpec({
  runnables: [runnable1, runnable2],
  computeResource,
  maxRetryCount: 2,
  maxRunDuration: {seconds: 3600},
});

// Tasks are grouped inside a job using TaskGroups.
const group = new batch.TaskGroup({
  taskCount: 3,
  taskSpec: task,
});

const job = new batch.Job({
  name: jobName,
  taskGroups: [group],
  // We use Cloud Logging as it's an option available out of the box
  logsPolicy: new batch.LogsPolicy({
    destination: batch.LogsPolicy.Destination.CLOUD_LOGGING,
  }),
});

// The job's parent is the project and region in which the job will run
const parent = `projects/${projectId}/locations/${region}`;

async function callCreateBatchLabelsRunnable() {
  // Construct request
  const request = {
    parent,
    jobId: jobName,
    job,
  };

  // Run request
  const [response] = await batchClient.createJob(request);
  console.log(JSON.stringify(response));
}

await callCreateBatchLabelsRunnable();

Python 

from google.cloud import batch_v1


def create_job_with_custom_runnables_labels(
    project_id: str,
    region: str,
    job_name: str,
    labels: dict,
) -> batch_v1.Job:
    """
    This method creates a Batch job with custom labels for runnable.
    Args:
        project_id (str): project ID or project number of the Cloud project you want to use.
        region (str): name of the region you want to use to run the job. Regions that are
            available for Batch are listed on: https://cloud.google.com/batch/docs/locations
        job_name (str): the name of the job that will be created.
        labels (dict): a dictionary of key-value pairs that will be used as labels
            E.g., {"label_key1": "label_value2"}
    Returns:
        batch_v1.Job: The created Batch job object containing configuration details.
    """
    client = batch_v1.BatchServiceClient()

    runnable = batch_v1.Runnable()
    runnable.display_name = "Script 1"
    runnable.script = batch_v1.Runnable.Script()
    runnable.script.text = "echo Hello world from Script 1 for task ${BATCH_TASK_INDEX}"
    # Add custom labels to the first runnable
    runnable.labels = labels

    # Create a task specification and assign the runnable and volume to it
    task = batch_v1.TaskSpec()
    task.runnables = [runnable]

    # Specify what resources are requested by each task.
    resources = batch_v1.ComputeResource()
    resources.cpu_milli = 2000  # in milliseconds per cpu-second. This means the task requires 2 whole CPUs.
    resources.memory_mib = 16  # in MiB
    task.compute_resource = resources

    task.max_retry_count = 2
    task.max_run_duration = "3600s"

    # Create a task group and assign the task specification to it
    group = batch_v1.TaskGroup()
    group.task_count = 3
    group.task_spec = task

    # Policies are used to define on what kind of virtual machines the tasks will run on.
    # In this case, we tell the system to use "e2-standard-4" machine type.
    # Read more about machine types here: https://cloud.google.com/compute/docs/machine-types
    policy = batch_v1.AllocationPolicy.InstancePolicy()
    policy.machine_type = "e2-standard-4"
    instances = batch_v1.AllocationPolicy.InstancePolicyOrTemplate()
    instances.policy = policy
    allocation_policy = batch_v1.AllocationPolicy()
    allocation_policy.instances = [instances]

    # Create the job and assign the task group and allocation policy to it
    job = batch_v1.Job()
    job.task_groups = [group]
    job.allocation_policy = allocation_policy

    # We use Cloud Logging as it's an out of the box available option
    job.logs_policy = batch_v1.LogsPolicy()
    job.logs_policy.destination = batch_v1.LogsPolicy.Destination.CLOUD_LOGGING

    # Create the job request and set the job and job ID
    create_request = batch_v1.CreateJobRequest()
    create_request.job = job
    create_request.job_id = job_name
    # The job's parent is the region in which the job will run
    create_request.parent = f"projects/{project_id}/locations/{region}"

    return client.create_job(create_request)

¿Qué sigue?