Updates a specific job resource. Currently the only supported fields to update are labels.

Scopes

You will need authorization for the https://www.googleapis.com/auth/cloud-platform scope to make a valid call.

If unset, the scope for this method defaults to https://www.googleapis.com/auth/cloud-platform. You can set the scope for this method like this: ml1 --scope <scope> projects jobs-patch ...

Required Scalar Argument

  • <name> (string)
    • Required. The job name.

Required Request Value

The request value is a data-structure with various fields. Each field may be a simple scalar or another data-structure. In the latter case it is advised to set the field-cursor to the data-structure's field to specify values more concisely.

For example, a structure like this:

GoogleCloudMlV1__Job:
  create-time: string
  end-time: string
  error-message: string
  etag: string
  job-id: string
  job-position: string
  labels: { string: string }
  prediction-input:
    batch-size: string
    data-format: string
    input-paths: [string]
    max-worker-count: int64
    model-name: string
    output-data-format: string
    output-path: string
    region: string
    runtime-version: string
    signature-name: string
    uri: string
    version-name: string
  prediction-output:
    error-count: int64
    node-hours: number
    output-path: string
    prediction-count: int64
  start-time: string
  state: string
  training-input:
    args: [string]
    enable-web-access: boolean
    encryption-config:
      kms-key-name: string
    evaluator-config:
      accelerator-config:
        count: string
        type: string
      container-args: [string]
      container-command: [string]
      disk-config:
        boot-disk-size-gb: integer
        boot-disk-type: string
      image-uri: string
      tpu-tf-version: string
    evaluator-count: int64
    evaluator-type: string
    hyperparameters:
      algorithm: string
      enable-trial-early-stopping: boolean
      goal: string
      hyperparameter-metric-tag: string
      max-failed-trials: integer
      max-parallel-trials: integer
      max-trials: integer
      resume-previous-job-id: string
    job-dir: string
    master-config:
      accelerator-config:
        count: string
        type: string
      container-args: [string]
      container-command: [string]
      disk-config:
        boot-disk-size-gb: integer
        boot-disk-type: string
      image-uri: string
      tpu-tf-version: string
    master-type: string
    network: string
    package-uris: [string]
    parameter-server-config:
      accelerator-config:
        count: string
        type: string
      container-args: [string]
      container-command: [string]
      disk-config:
        boot-disk-size-gb: integer
        boot-disk-type: string
      image-uri: string
      tpu-tf-version: string
    parameter-server-count: int64
    parameter-server-type: string
    python-module: string
    python-version: string
    region: string
    runtime-version: string
    scale-tier: string
    scheduling:
      max-running-time: string
      max-wait-time: string
      priority: integer
    service-account: string
    use-chief-in-tf-config: boolean
    worker-config:
      accelerator-config:
        count: string
        type: string
      container-args: [string]
      container-command: [string]
      disk-config:
        boot-disk-size-gb: integer
        boot-disk-type: string
      image-uri: string
      tpu-tf-version: string
    worker-count: int64
    worker-type: string
  training-output:
    built-in-algorithm-output:
      framework: string
      model-path: string
      python-version: string
      runtime-version: string
    completed-trial-count: int64
    consumed-ml-units: number
    hyperparameter-metric-tag: string
    is-built-in-algorithm-job: boolean
    is-hyperparameter-tuning-job: boolean
    web-access-uris: { string: string }

can be set completely with the following arguments which are assumed to be executed in the given order. Note how the cursor position is adjusted to the respective structures, allowing simple field names to be used most of the time.

  • -r . create-time=et
    • Output only. When the job was created.
  • end-time=tempor
    • Output only. When the job processing was completed.
  • error-message=aliquyam
    • Output only. The details of a failure or a cancellation.
  • etag=ipsum
    • etag is used for optimistic concurrency control as a way to help prevent simultaneous updates of a job from overwriting each other. It is strongly suggested that systems make use of the etag in the read-modify-write cycle to perform job updates in order to avoid race conditions: An etag is returned in the response to GetJob, and systems are expected to put that etag in the request to UpdateJob to ensure that their change will be applied to the same version of the job.
  • job-id=et
    • Required. The user-specified id of the job.
  • job-position=sanctus
    • Output only. It's only effect when the job is in QUEUED state. If it's positive, it indicates the job's position in the job scheduler. It's 0 when the job is already scheduled.
  • labels=key=lorem
    • Optional. One or more labels that you can add, to organize your jobs. Each label is a key-value pair, where both the key and the value are arbitrary strings that you supply. For more information, see the documentation on using labels.
    • the value will be associated with the given key
  • prediction-input batch-size=est
    • Optional. Number of records per batch, defaults to 64. The service will buffer batch_size number of records in memory before invoking one Tensorflow prediction call internally. So take the record size and memory available into consideration when setting this parameter.
  • data-format=sed
    • Required. The format of the input data files.
  • input-paths=diam
    • Required. The Cloud Storage location of the input data files. May contain wildcards.
    • Each invocation of this argument appends the given value to the array.
  • max-worker-count=-19
    • Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
  • model-name=dolores
    • Use this field if you want to use the default version for the specified model. The string must use the following format: &#34;projects/YOUR_PROJECT/models/YOUR_MODEL&#34;
  • output-data-format=et
    • Optional. Format of the output data files, defaults to JSON.
  • output-path=sed
    • Required. The output Google Cloud Storage location.
  • region=no
    • Required. The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
  • runtime-version=et
    • Optional. The AI Platform runtime version to use for this batch prediction. If not set, AI Platform will pick the runtime version used during the CreateVersion request for this model version, or choose the latest stable version when model version information is not available such as when the model is specified by uri.
  • signature-name=elitr
    • Optional. The name of the signature defined in the SavedModel to use for this job. Please refer to SavedModel for information about how to use signatures. Defaults to DEFAULT_SERVING_SIGNATURE_DEF_KEY , which is "serving_default".
  • uri=sed
    • Use this field if you want to specify a Google Cloud Storage path for the model to use.

  • version-name=no

    • Use this field if you want to specify a version of the model to use. The string is formatted the same way as model_version, with the addition of the version information: &#34;projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION&#34;

  • ..prediction-output error-count=-91

    • The number of data instances which resulted in errors.
  • node-hours=0.1918654921610582
    • Node hours used by the batch prediction job.
  • output-path=aliquyam
    • The output Google Cloud Storage location provided at the job creation time.

  • prediction-count=-69

    • The number of generated predictions.

  • .. start-time=sadipscing

    • Output only. When the job processing was started.
  • state=erat
    • Output only. The detailed state of a job.
  • training-input args=aliquyam
    • Optional. Command-line arguments passed to the training application when it starts. If your job uses a custom container, then the arguments are passed to the container's ENTRYPOINT command.
    • Each invocation of this argument appends the given value to the array.
  • enable-web-access=true
    • Optional. Whether you want AI Platform Training to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by TrainingOutput.web_access_uris or HyperparameterOutput.web_access_uris (within TrainingOutput.trials).

  • encryption-config kms-key-name=est

    • The Cloud KMS resource identifier of the customer-managed encryption key used to protect a resource, such as a training job. It has the following format: projects/{PROJECT_ID}/locations/{REGION}/keyRings/{KEY_RING_NAME}/cryptoKeys/{KEY_NAME}

  • ..evaluator-config.accelerator-config count=et

    • The number of accelerators to attach to each machine running the job.

  • type=sea

    • The type of accelerator to use.

  • .. container-args=consetetur

    • Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • container-command=consetetur
    • The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • disk-config boot-disk-size-gb=36
    • Size in GB of the boot disk (default is 100GB).

  • boot-disk-type=est

    • Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

  • .. image-uri=aliquyam

  • tpu-tf-version=elitr

    • The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a runtime version that currently supports training with TPUs. Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different patch version. In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow 1.x.y, specify 1.x.

  • .. evaluator-count=-20

    • Optional. The number of evaluator replicas to use for the training job. Each replica in the cluster will be of the type specified in evaluator_type. This value can only be used when scale_tier is set to CUSTOM. If you set this value, you must also set evaluator_type. The default value is zero.
  • evaluator-type=diam
    • Optional. Specifies the type of virtual machine to use for your training job's evaluator nodes. The supported values are the same as those described in the entry for masterType. This value must be consistent with the category of machine type that masterType uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when scaleTier is set to CUSTOM and evaluatorCount is greater than zero.
  • hyperparameters algorithm=est
    • Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
  • enable-trial-early-stopping=true
    • Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
  • goal=sed
    • Required. The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
  • hyperparameter-metric-tag=eos
    • Optional. The TensorFlow summary tag name to use for optimizing trials. For current versions of TensorFlow, this tag name should exactly match what is shown in TensorBoard, including all scopes. For versions of TensorFlow prior to 0.12, this should be only the tag passed to tf.Summary. By default, "training/hptuning/metric" will be used.
  • max-failed-trials=45
    • Optional. The number of failed trials that need to be seen before failing the hyperparameter tuning job. You can specify this field to override the default failing criteria for AI Platform hyperparameter tuning jobs. Defaults to zero, which means the service decides when a hyperparameter job should fail.
  • max-parallel-trials=84
    • Optional. The number of training trials to run concurrently. You can reduce the time it takes to perform hyperparameter tuning by adding trials in parallel. However, each trail only benefits from the information gained in completed trials. That means that a trial does not get access to the results of trials running at the same time, which could reduce the quality of the overall optimization. Each trial will use the same scale tier and machine types. Defaults to one.
  • max-trials=86
    • Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.

  • resume-previous-job-id=dolores

    • Optional. The prior hyperparameter tuning job id that users hope to continue with. The job id will be used to find the corresponding vizier study guid and resume the study.

  • .. job-dir=eos

    • Optional. A Google Cloud Storage path in which to store training outputs and other data needed for training. This path is passed to your TensorFlow program as the '--job-dir' command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training.
  • master-config.accelerator-config count=et
    • The number of accelerators to attach to each machine running the job.

  • type=sea

    • The type of accelerator to use.

  • .. container-args=et

    • Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • container-command=at
    • The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • disk-config boot-disk-size-gb=17
    • Size in GB of the boot disk (default is 100GB).

  • boot-disk-type=eirmod

    • Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

  • .. image-uri=lorem

  • tpu-tf-version=accusam

    • The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a runtime version that currently supports training with TPUs. Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different patch version. In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow 1.x.y, specify 1.x.

  • .. master-type=amet

  • network=erat
    • Optional. The full name of the Compute Engine network to which the Job is peered. For example, projects/12345/global/networks/myVPC. The format of this field is projects/{project}/global/networks/{network}, where {project} is a project number (like 12345) and {network} is network name. Private services access must already be configured for the network. If left unspecified, the Job is not peered with any network. Learn about using VPC Network Peering..
  • package-uris=dolores
    • Required. The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
    • Each invocation of this argument appends the given value to the array.
  • parameter-server-config.accelerator-config count=erat
    • The number of accelerators to attach to each machine running the job.

  • type=accusam

    • The type of accelerator to use.

  • .. container-args=sea

    • Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • container-command=takimata
    • The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • disk-config boot-disk-size-gb=50
    • Size in GB of the boot disk (default is 100GB).

  • boot-disk-type=et

    • Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

  • .. image-uri=at

  • tpu-tf-version=dolor

    • The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a runtime version that currently supports training with TPUs. Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different patch version. In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow 1.x.y, specify 1.x.

  • .. parameter-server-count=-22

    • Optional. The number of parameter server replicas to use for the training job. Each replica in the cluster will be of the type specified in parameter_server_type. This value can only be used when scale_tier is set to CUSTOM. If you set this value, you must also set parameter_server_type. The default value is zero.
  • parameter-server-type=sit
    • Optional. Specifies the type of virtual machine to use for your training job's parameter server. The supported values are the same as those described in the entry for master_type. This value must be consistent with the category of machine type that masterType uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. This value must be present when scaleTier is set to CUSTOM and parameter_server_count is greater than zero.
  • python-module=erat
    • Required. The Python module name to run after installing the packages.
  • python-version=sea
    • Optional. The version of Python used in training. You must either specify this field or specify masterConfig.imageUri. The following Python versions are available: * Python '3.7' is available when runtime_version is set to '1.15' or later. * Python '3.5' is available when runtime_version is set to a version from '1.4' to '1.14'. * Python '2.7' is available when runtime_version is set to '1.15' or earlier. Read more about the Python versions available for each runtime version.
  • region=nonumy
    • Required. The region to run the training job in. See the available regions for AI Platform Training.
  • runtime-version=et
  • scale-tier=gubergren
    • Required. Specifies the machine types, the number of replicas for workers and parameter servers.
  • scheduling max-running-time=justo
    • Optional. The maximum job running time, expressed in seconds. The field can contain up to nine fractional digits, terminated by s. If not specified, this field defaults to 604800s (seven days). If the training job is still running after this duration, AI Platform Training cancels it. The duration is measured from when the job enters the RUNNING state; therefore it does not overlap with the duration limited by Scheduling.max_wait_time. For example, if you want to ensure your job runs for no more than 2 hours, set this field to 7200s (2 hours * 60 minutes / hour * 60 seconds / minute). If you submit your training job using the gcloud tool, you can specify this field in a config.yaml file. For example: yaml trainingInput: scheduling: maxRunningTime: 7200s
  • max-wait-time=sea
    • Optional. The maximum job wait time, expressed in seconds. The field can contain up to nine fractional digits, terminated by s. If not specified, there is no limit to the wait time. The minimum for this field is 1800s (30 minutes). If the training job has not entered the RUNNING state after this duration, AI Platform Training cancels it. After the job begins running, it can no longer be cancelled due to the maximum wait time. Therefore the duration limited by this field does not overlap with the duration limited by Scheduling.max_running_time. For example, if the job temporarily stops running and retries due to a VM restart, this cannot lead to a maximum wait time cancellation. However, independently of this constraint, AI Platform Training might stop a job if there are too many retries due to exhausted resources in a region. The following example describes how you might use this field: To cancel your job if it doesn't start running within 1 hour, set this field to 3600s (1 hour * 60 minutes / hour * 60 seconds / minute). If the job is still in the QUEUED or PREPARING state after an hour of waiting, AI Platform Training cancels the job. If you submit your training job using the gcloud tool, you can specify this field in a config.yaml file. For example: yaml trainingInput: scheduling: maxWaitTime: 3600s

  • priority=5

    • Optional. Job scheduling will be based on this priority, which in the range [0, 1000]. The bigger the number, the higher the priority. Default to 0 if not set. If there are multiple jobs requesting same type of accelerators, the high priority job will be scheduled prior to ones with low priority.

  • .. service-account=sit

    • Optional. The email address of a service account to use when running the training appplication. You must have the iam.serviceAccounts.actAs permission for the specified service account. In addition, the AI Platform Training Google-managed service account must have the roles/iam.serviceAccountAdmin role for the specified service account. Learn more about configuring a service account. If not specified, the AI Platform Training Google-managed service account is used by default.
  • use-chief-in-tf-config=false
    • Optional. Use chief instead of master in the TF_CONFIG environment variable when training with a custom container. Defaults to false. Learn more about this field. This field has no effect for training jobs that don't use a custom container.
  • worker-config.accelerator-config count=dolores
    • The number of accelerators to attach to each machine running the job.

  • type=consetetur

    • The type of accelerator to use.

  • .. container-args=gubergren

    • Arguments to the entrypoint command. The following rules apply for container_command and container_args: - If you do not supply command or args: The defaults defined in the Docker image are used. - If you supply a command but no args: The default EntryPoint and the default Cmd defined in the Docker image are ignored. Your command is run without any arguments. - If you supply only args: The default Entrypoint defined in the Docker image is run with the args that you supplied. - If you supply a command and args: The default Entrypoint and the default Cmd defined in the Docker image are ignored. Your command is run with your args. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • container-command=dolor
    • The command with which the replica's custom container is run. If provided, it will override default ENTRYPOINT of the docker image. If not provided, the docker image's ENTRYPOINT is used. It cannot be set if custom container image is not provided. Note that this field and [TrainingInput.args] are mutually exclusive, i.e., both cannot be set at the same time.
    • Each invocation of this argument appends the given value to the array.
  • disk-config boot-disk-size-gb=69
    • Size in GB of the boot disk (default is 100GB).

  • boot-disk-type=no

    • Type of the boot disk (default is "pd-ssd"). Valid values: "pd-ssd" (Persistent Disk Solid State Drive) or "pd-standard" (Persistent Disk Hard Disk Drive).

  • .. image-uri=amet.

  • tpu-tf-version=ipsum

    • The AI Platform runtime version that includes a TensorFlow version matching the one used in the custom container. This field is required if the replica is a TPU worker that uses a custom container. Otherwise, do not specify this field. This must be a runtime version that currently supports training with TPUs. Note that the version of TensorFlow included in a runtime version may differ from the numbering of the runtime version itself, because it may have a different patch version. In this field, you must specify the runtime version (TensorFlow minor version). For example, if your custom container runs TensorFlow 1.x.y, specify 1.x.

  • .. worker-count=-56

    • Optional. The number of worker replicas to use for the training job. Each replica in the cluster will be of the type specified in worker_type. This value can only be used when scale_tier is set to CUSTOM. If you set this value, you must also set worker_type. The default value is zero.

  • worker-type=accusam

    • Optional. Specifies the type of virtual machine to use for your training job's worker nodes. The supported values are the same as those described in the entry for masterType. This value must be consistent with the category of machine type that masterType uses. In other words, both must be Compute Engine machine types or both must be legacy machine types. If you use cloud_tpu for this value, see special instructions for configuring a custom TPU machine. This value must be present when scaleTier is set to CUSTOM and workerCount is greater than zero.

  • ..training-output.built-in-algorithm-output framework=gubergren

    • Framework on which the built-in algorithm was trained.
  • model-path=sadipscing
    • The Cloud Storage path to the model/ directory where the training job saves the trained model. Only set for successful jobs that don't use hyperparameter tuning.
  • python-version=at
    • Python version on which the built-in algorithm was trained.

  • runtime-version=sit

    • AI Platform runtime version on which the built-in algorithm was trained.

  • .. completed-trial-count=-20

    • The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
  • consumed-ml-units=0.38020685422472145
    • The amount of ML units consumed by the job.
  • hyperparameter-metric-tag=et
  • is-built-in-algorithm-job=true
    • Whether this job is a built-in Algorithm job.
  • is-hyperparameter-tuning-job=false
    • Whether this job is a hyperparameter tuning job.
  • web-access-uris=key=amet.
    • Output only. URIs for accessing interactive shells (one URI for each training node). Only available if training_input.enable_web_access is true. The keys are names of each node in the training job; for example, master-replica-0 for the master node, worker-replica-0 for the first worker, and ps-replica-0 for the first parameter server. The values are the URIs for each node's interactive shell.
    • the value will be associated with the given key

About Cursors

The cursor position is key to comfortably set complex nested structures. The following rules apply:

  • The cursor position is always set relative to the current one, unless the field name starts with the . character. Fields can be nested such as in -r f.s.o .
  • The cursor position is set relative to the top-level structure if it starts with ., e.g. -r .s.s
  • You can also set nested fields without setting the cursor explicitly. For example, to set a value relative to the current cursor position, you would specify -r struct.sub_struct=bar.
  • You can move the cursor one level up by using ... Each additional . moves it up one additional level. E.g. ... would go three levels up.

Optional Output Flags

The method's return value a JSON encoded structure, which will be written to standard output by default.

  • -o out
    • out specifies the destination to which to write the server's result to. It will be a JSON-encoded structure. The destination may be - to indicate standard output, or a filepath that is to contain the received bytes. If unset, it defaults to standard output.

Optional Method Properties

You may set the following properties to further configure the call. Please note that -p is followed by one or more key-value-pairs, and is called like this -p k1=v1 k2=v2 even though the listing below repeats the -p for completeness.

  • -p update-mask=string
    • Required. Specifies the path, relative to Job, of the field to update. To adopt etag mechanism, include etag field in the mask, and include the etag value in your job resource. For example, to change the labels of a job, the update_mask parameter would be specified as labels, etag, and the PATCH request body would specify the new value, as follows: { "labels": { "owner": "Google", "color": "Blue" } "etag": "33a64df551425fcc55e4d42a148795d9f25f89d4" } If etag matches the one on the server, the labels of the job will be replaced with the given ones, and the server end etag will be recalculated. Currently the only supported update masks are labels and etag.

Optional General Properties

The following properties can configure any call, and are not specific to this method.

  • -p $-xgafv=string

    • V1 error format.

  • -p access-token=string

    • OAuth access token.

  • -p alt=string

    • Data format for response.

  • -p callback=string

    • JSONP

  • -p fields=string

    • Selector specifying which fields to include in a partial response.

  • -p key=string

    • API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

  • -p oauth-token=string

    • OAuth 2.0 token for the current user.

  • -p pretty-print=boolean

    • Returns response with indentations and line breaks.

  • -p quota-user=string

    • Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.

  • -p upload-type=string

    • Legacy upload protocol for media (e.g. "media", "multipart").

  • -p upload-protocol=string

    • Upload protocol for media (e.g. "raw", "multipart").