Google Cloud Native v0.32.0, Nov 29 23

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

google-native.ml/v1.getJob

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Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

Using getJob

Two invocation forms are available. The direct form accepts plain arguments and either blocks until the result value is available, or returns a Promise-wrapped result. The output form accepts Input-wrapped arguments and returns an Output-wrapped result.

TypeScript
Python
Go
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Java
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function getJob(args: GetJobArgs, opts?: InvokeOptions): Promise<GetJobResult>
function getJobOutput(args: GetJobOutputArgs, opts?: InvokeOptions): Output<GetJobResult>

def get_job(job_id: Optional[str] = None,
            project: Optional[str] = None,
            opts: Optional[InvokeOptions] = None) -> GetJobResult
def get_job_output(job_id: Optional[pulumi.Input[str]] = None,
            project: Optional[pulumi.Input[str]] = None,
            opts: Optional[InvokeOptions] = None) -> Output[GetJobResult]

func LookupJob(ctx *Context, args *LookupJobArgs, opts ...InvokeOption) (*LookupJobResult, error)
func LookupJobOutput(ctx *Context, args *LookupJobOutputArgs, opts ...InvokeOption) LookupJobResultOutput

> Note: This function is named LookupJob in the Go SDK.

public static class GetJob 
{
    public static Task<GetJobResult> InvokeAsync(GetJobArgs args, InvokeOptions? opts = null)
    public static Output<GetJobResult> Invoke(GetJobInvokeArgs args, InvokeOptions? opts = null)
}

public static CompletableFuture<GetJobResult> getJob(GetJobArgs args, InvokeOptions options)
// Output-based functions aren't available in Java yet

fn::invoke:
  function: google-native:ml/v1:getJob
  arguments:
    # arguments dictionary

The following arguments are supported:

JobId string
Project string

JobId string
Project string

jobId String
project String

jobId string
project string

job_id str
project str

jobId String
project String

getJob Result

The following output properties are available:

CreateTime string: When the job was created.
EndTime string: When the job processing was completed.
ErrorMessage string: The details of a failure or a cancellation.
Etag string: 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.
JobId string: The user-specified id of the job.
JobPosition string: 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 Dictionary<string, string>: 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.
PredictionInput Pulumi.GoogleNative.Ml.V1.Outputs.GoogleCloudMlV1__PredictionInputResponse: Input parameters to create a prediction job.
PredictionOutput Pulumi.GoogleNative.Ml.V1.Outputs.GoogleCloudMlV1__PredictionOutputResponse: The current prediction job result.
StartTime string: When the job processing was started.
State string: The detailed state of a job.
TrainingInput Pulumi.GoogleNative.Ml.V1.Outputs.GoogleCloudMlV1__TrainingInputResponse: Input parameters to create a training job.
TrainingOutput Pulumi.GoogleNative.Ml.V1.Outputs.GoogleCloudMlV1__TrainingOutputResponse: The current training job result.

CreateTime string: When the job was created.
EndTime string: When the job processing was completed.
ErrorMessage string: The details of a failure or a cancellation.
Etag string: 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.
JobId string: The user-specified id of the job.
JobPosition string: 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 map[string]string: 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.
PredictionInput GoogleCloudMlV1__PredictionInputResponse: Input parameters to create a prediction job.
PredictionOutput GoogleCloudMlV1__PredictionOutputResponse: The current prediction job result.
StartTime string: When the job processing was started.
State string: The detailed state of a job.
TrainingInput GoogleCloudMlV1__TrainingInputResponse: Input parameters to create a training job.
TrainingOutput GoogleCloudMlV1__TrainingOutputResponse: The current training job result.

createTime String: When the job was created.
endTime String: When the job processing was completed.
errorMessage String: The details of a failure or a cancellation.
etag String: 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.
jobId String: The user-specified id of the job.
jobPosition String: 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 Map<String,String>: 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.
predictionInput GoogleCloudMlV1__PredictionInputResponse: Input parameters to create a prediction job.
predictionOutput GoogleCloudMlV1__PredictionOutputResponse: The current prediction job result.
startTime String: When the job processing was started.
state String: The detailed state of a job.
trainingInput GoogleCloudMlV1__TrainingInputResponse: Input parameters to create a training job.
trainingOutput GoogleCloudMlV1__TrainingOutputResponse: The current training job result.

createTime string: When the job was created.
endTime string: When the job processing was completed.
errorMessage string: The details of a failure or a cancellation.
etag string: 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.
jobId string: The user-specified id of the job.
jobPosition string: 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: string]: string}: 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.
predictionInput GoogleCloudMlV1__PredictionInputResponse: Input parameters to create a prediction job.
predictionOutput GoogleCloudMlV1__PredictionOutputResponse: The current prediction job result.
startTime string: When the job processing was started.
state string: The detailed state of a job.
trainingInput GoogleCloudMlV1__TrainingInputResponse: Input parameters to create a training job.
trainingOutput GoogleCloudMlV1__TrainingOutputResponse: The current training job result.

create_time str: When the job was created.
end_time str: When the job processing was completed.
error_message str: The details of a failure or a cancellation.
etag str: 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 str: The user-specified id of the job.
job_position str: 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 Mapping[str, str]: 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.
prediction_input GoogleCloudMlV1PredictionInputResponse: Input parameters to create a prediction job.
prediction_output GoogleCloudMlV1PredictionOutputResponse: The current prediction job result.
start_time str: When the job processing was started.
state str: The detailed state of a job.
training_input GoogleCloudMlV1TrainingInputResponse: Input parameters to create a training job.
training_output GoogleCloudMlV1TrainingOutputResponse: The current training job result.

createTime String: When the job was created.
endTime String: When the job processing was completed.
errorMessage String: The details of a failure or a cancellation.
etag String: 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.
jobId String: The user-specified id of the job.
jobPosition String: 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 Map<String>: 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.
predictionInput Property Map: Input parameters to create a prediction job.
predictionOutput Property Map: The current prediction job result.
startTime String: When the job processing was started.
state String: The detailed state of a job.
trainingInput Property Map: Input parameters to create a training job.
trainingOutput Property Map: The current training job result.

Supporting Types

GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse

ObjectiveValue double: The objective value at this training step.
TrainingStep string: The global training step for this metric.

ObjectiveValue float64: The objective value at this training step.
TrainingStep string: The global training step for this metric.

objectiveValue Double: The objective value at this training step.
trainingStep String: The global training step for this metric.

objectiveValue number: The objective value at this training step.
trainingStep string: The global training step for this metric.

objective_value float: The objective value at this training step.
training_step str: The global training step for this metric.

objectiveValue Number: The objective value at this training step.
trainingStep String: The global training step for this metric.

GoogleCloudMlV1__AcceleratorConfigResponse

Count string: The number of accelerators to attach to each machine running the job.
Type string: The type of accelerator to use.

Count string: The number of accelerators to attach to each machine running the job.
Type string: The type of accelerator to use.

count String: The number of accelerators to attach to each machine running the job.
type String: The type of accelerator to use.

count string: The number of accelerators to attach to each machine running the job.
type string: The type of accelerator to use.

count str: The number of accelerators to attach to each machine running the job.
type str: The type of accelerator to use.

count String: The number of accelerators to attach to each machine running the job.
type String: The type of accelerator to use.

GoogleCloudMlV1__BuiltInAlgorithmOutputResponse

Framework string: Framework on which the built-in algorithm was trained.
ModelPath string: 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.
PythonVersion string: Python version on which the built-in algorithm was trained.
RuntimeVersion string: AI Platform runtime version on which the built-in algorithm was trained.

Framework string: Framework on which the built-in algorithm was trained.
ModelPath string: 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.
PythonVersion string: Python version on which the built-in algorithm was trained.
RuntimeVersion string: AI Platform runtime version on which the built-in algorithm was trained.

framework String: Framework on which the built-in algorithm was trained.
modelPath String: 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.
pythonVersion String: Python version on which the built-in algorithm was trained.
runtimeVersion String: AI Platform runtime version on which the built-in algorithm was trained.

framework string: Framework on which the built-in algorithm was trained.
modelPath string: 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.
pythonVersion string: Python version on which the built-in algorithm was trained.
runtimeVersion string: AI Platform runtime version on which the built-in algorithm was trained.

framework str: Framework on which the built-in algorithm was trained.
model_path str: 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 str: Python version on which the built-in algorithm was trained.
runtime_version str: AI Platform runtime version on which the built-in algorithm was trained.

framework String: Framework on which the built-in algorithm was trained.
modelPath String: 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.
pythonVersion String: Python version on which the built-in algorithm was trained.
runtimeVersion String: AI Platform runtime version on which the built-in algorithm was trained.

GoogleCloudMlV1__DiskConfigResponse

BootDiskSizeGb int: Size in GB of the boot disk (default is 100GB).
BootDiskType string: 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).

BootDiskSizeGb int: Size in GB of the boot disk (default is 100GB).
BootDiskType string: 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).

bootDiskSizeGb Integer: Size in GB of the boot disk (default is 100GB).
bootDiskType String: 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).

bootDiskSizeGb number: Size in GB of the boot disk (default is 100GB).
bootDiskType string: 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).

boot_disk_size_gb int: Size in GB of the boot disk (default is 100GB).
boot_disk_type str: 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).

bootDiskSizeGb Number: Size in GB of the boot disk (default is 100GB).
bootDiskType String: 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).

GoogleCloudMlV1__EncryptionConfigResponse

KmsKeyName string: 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}

KmsKeyName string: 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}

kmsKeyName String: 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}

kmsKeyName string: 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}

kms_key_name str: 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}

kmsKeyName String: 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}

GoogleCloudMlV1__HyperparameterOutputResponse

AllMetrics List<Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse>: All recorded object metrics for this trial. This field is not currently populated.
BuiltInAlgorithmOutput Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
EndTime string: End time for the trial.
FinalMetric Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse: The final objective metric seen for this trial.
Hyperparameters Dictionary<string, string>: The hyperparameters given to this trial.
IsTrialStoppedEarly bool: True if the trial is stopped early.
StartTime string: Start time for the trial.
State string: The detailed state of the trial.
TrialId string: The trial id for these results.
WebAccessUris Dictionary<string, string>: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

AllMetrics []GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse: All recorded object metrics for this trial. This field is not currently populated.
BuiltInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
EndTime string: End time for the trial.
FinalMetric GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse: The final objective metric seen for this trial.
Hyperparameters map[string]string: The hyperparameters given to this trial.
IsTrialStoppedEarly bool: True if the trial is stopped early.
StartTime string: Start time for the trial.
State string: The detailed state of the trial.
TrialId string: The trial id for these results.
WebAccessUris map[string]string: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

allMetrics List<GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse>: All recorded object metrics for this trial. This field is not currently populated.
builtInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
endTime String: End time for the trial.
finalMetric GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse: The final objective metric seen for this trial.
hyperparameters Map<String,String>: The hyperparameters given to this trial.
isTrialStoppedEarly Boolean: True if the trial is stopped early.
startTime String: Start time for the trial.
state String: The detailed state of the trial.
trialId String: The trial id for these results.
webAccessUris Map<String,String>: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

allMetrics GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse[]: All recorded object metrics for this trial. This field is not currently populated.
builtInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
endTime string: End time for the trial.
finalMetric GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse: The final objective metric seen for this trial.
hyperparameters {[key: string]: string}: The hyperparameters given to this trial.
isTrialStoppedEarly boolean: True if the trial is stopped early.
startTime string: Start time for the trial.
state string: The detailed state of the trial.
trialId string: The trial id for these results.
webAccessUris {[key: string]: string}: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

all_metrics Sequence[GoogleCloudMlV1HyperparameterOutput_HyperparameterMetricResponse]: All recorded object metrics for this trial. This field is not currently populated.
built_in_algorithm_output GoogleCloudMlV1BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
end_time str: End time for the trial.
final_metric GoogleCloudMlV1HyperparameterOutput_HyperparameterMetricResponse: The final objective metric seen for this trial.
hyperparameters Mapping[str, str]: The hyperparameters given to this trial.
is_trial_stopped_early bool: True if the trial is stopped early.
start_time str: Start time for the trial.
state str: The detailed state of the trial.
trial_id str: The trial id for these results.
web_access_uris Mapping[str, str]: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

allMetrics List<Property Map>: All recorded object metrics for this trial. This field is not currently populated.
builtInAlgorithmOutput Property Map: Details related to built-in algorithms jobs. Only set for trials of built-in algorithms jobs that have succeeded.
endTime String: End time for the trial.
finalMetric Property Map: The final objective metric seen for this trial.
hyperparameters Map<String>: The hyperparameters given to this trial.
isTrialStoppedEarly Boolean: True if the trial is stopped early.
startTime String: Start time for the trial.
state String: The detailed state of the trial.
trialId String: The trial id for these results.
webAccessUris Map<String>: URIs for accessing interactive shells (one URI for each training node). Only available if this trial is part of a hyperparameter tuning job and the job's 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.

GoogleCloudMlV1__HyperparameterSpecResponse

Algorithm string: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
EnableTrialEarlyStopping bool: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
Goal string: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
HyperparameterMetricTag string: 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.
MaxFailedTrials int: 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.
MaxParallelTrials int: 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.
MaxTrials int: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
Params List<Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__ParameterSpecResponse>: The set of parameters to tune.
ResumePreviousJobId string: 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.

Algorithm string: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
EnableTrialEarlyStopping bool: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
Goal string: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
HyperparameterMetricTag string: 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.
MaxFailedTrials int: 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.
MaxParallelTrials int: 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.
MaxTrials int: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
Params []GoogleCloudMlV1__ParameterSpecResponse: The set of parameters to tune.
ResumePreviousJobId string: 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.

algorithm String: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
enableTrialEarlyStopping Boolean: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
goal String: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
hyperparameterMetricTag String: 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.
maxFailedTrials Integer: 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.
maxParallelTrials Integer: 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.
maxTrials Integer: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
params List<GoogleCloudMlV1__ParameterSpecResponse>: The set of parameters to tune.
resumePreviousJobId String: 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.

algorithm string: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
enableTrialEarlyStopping boolean: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
goal string: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
hyperparameterMetricTag string: 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.
maxFailedTrials number: 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.
maxParallelTrials number: 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.
maxTrials number: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
params GoogleCloudMlV1__ParameterSpecResponse[]: The set of parameters to tune.
resumePreviousJobId string: 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.

algorithm str: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
enable_trial_early_stopping bool: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
goal str: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
hyperparameter_metric_tag str: 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 int: 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 int: 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 int: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
params Sequence[GoogleCloudMlV1ParameterSpecResponse]: The set of parameters to tune.
resume_previous_job_id str: 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.

algorithm String: Optional. The search algorithm specified for the hyperparameter tuning job. Uses the default AI Platform hyperparameter tuning algorithm if unspecified.
enableTrialEarlyStopping Boolean: Optional. Indicates if the hyperparameter tuning job enables auto trial early stopping.
goal String: The type of goal to use for tuning. Available types are MAXIMIZE and MINIMIZE. Defaults to MAXIMIZE.
hyperparameterMetricTag String: 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.
maxFailedTrials Number: 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.
maxParallelTrials Number: 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.
maxTrials Number: Optional. How many training trials should be attempted to optimize the specified hyperparameters. Defaults to one.
params List<Property Map>: The set of parameters to tune.
resumePreviousJobId String: 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.

GoogleCloudMlV1__ParameterSpecResponse

CategoricalValues List<string>: Required if type is CATEGORICAL. The list of possible categories.
DiscreteValues List<double>: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
MaxValue double: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
MinValue double: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
ParameterName string: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
ScaleType string: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
Type string: The type of the parameter.

CategoricalValues []string: Required if type is CATEGORICAL. The list of possible categories.
DiscreteValues []float64: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
MaxValue float64: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
MinValue float64: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
ParameterName string: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
ScaleType string: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
Type string: The type of the parameter.

categoricalValues List<String>: Required if type is CATEGORICAL. The list of possible categories.
discreteValues List<Double>: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
maxValue Double: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
minValue Double: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
parameterName String: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
scaleType String: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
type String: The type of the parameter.

categoricalValues string[]: Required if type is CATEGORICAL. The list of possible categories.
discreteValues number[]: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
maxValue number: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
minValue number: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
parameterName string: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
scaleType string: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
type string: The type of the parameter.

categorical_values Sequence[str]: Required if type is CATEGORICAL. The list of possible categories.
discrete_values Sequence[float]: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
max_value float: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
min_value float: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
parameter_name str: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
scale_type str: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
type str: The type of the parameter.

categoricalValues List<String>: Required if type is CATEGORICAL. The list of possible categories.
discreteValues List<Number>: Required if type is DISCRETE. A list of feasible points. The list should be in strictly increasing order. For instance, this parameter might have possible settings of 1.5, 2.5, and 4.0. This list should not contain more than 1,000 values.
maxValue Number: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
minValue Number: Required if type is DOUBLE or INTEGER. This field should be unset if type is CATEGORICAL. This value should be integers if type is INTEGER.
parameterName String: The parameter name must be unique amongst all ParameterConfigs in a HyperparameterSpec message. E.g., "learning_rate".
scaleType String: Optional. How the parameter should be scaled to the hypercube. Leave unset for categorical parameters. Some kind of scaling is strongly recommended for real or integral parameters (e.g., UNIT_LINEAR_SCALE).
type String: The type of the parameter.

GoogleCloudMlV1__PredictionInputResponse

BatchSize string: 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.
DataFormat string: The format of the input data files.
InputPaths List<string>: The Cloud Storage location of the input data files. May contain wildcards.
MaxWorkerCount string: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
ModelName string: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
OutputDataFormat string: Optional. Format of the output data files, defaults to JSON.
OutputPath string: The output Google Cloud Storage location.
Region string: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
RuntimeVersion string: 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.
SignatureName string: 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 string: Use this field if you want to specify a Google Cloud Storage path for the model to use.
VersionName string: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

BatchSize string: 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.
DataFormat string: The format of the input data files.
InputPaths []string: The Cloud Storage location of the input data files. May contain wildcards.
MaxWorkerCount string: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
ModelName string: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
OutputDataFormat string: Optional. Format of the output data files, defaults to JSON.
OutputPath string: The output Google Cloud Storage location.
Region string: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
RuntimeVersion string: 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.
SignatureName string: 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 string: Use this field if you want to specify a Google Cloud Storage path for the model to use.
VersionName string: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

batchSize String: 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.
dataFormat String: The format of the input data files.
inputPaths List<String>: The Cloud Storage location of the input data files. May contain wildcards.
maxWorkerCount String: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
modelName String: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
outputDataFormat String: Optional. Format of the output data files, defaults to JSON.
outputPath String: The output Google Cloud Storage location.
region String: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
runtimeVersion String: 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.
signatureName String: 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 String: Use this field if you want to specify a Google Cloud Storage path for the model to use.
versionName String: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

batchSize string: 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.
dataFormat string: The format of the input data files.
inputPaths string[]: The Cloud Storage location of the input data files. May contain wildcards.
maxWorkerCount string: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
modelName string: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
outputDataFormat string: Optional. Format of the output data files, defaults to JSON.
outputPath string: The output Google Cloud Storage location.
region string: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
runtimeVersion string: 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.
signatureName string: 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 string: Use this field if you want to specify a Google Cloud Storage path for the model to use.
versionName string: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

batch_size str: 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 str: The format of the input data files.
input_paths Sequence[str]: The Cloud Storage location of the input data files. May contain wildcards.
max_worker_count str: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
model_name str: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
output_data_format str: Optional. Format of the output data files, defaults to JSON.
output_path str: The output Google Cloud Storage location.
region str: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
runtime_version str: 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 str: 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 str: Use this field if you want to specify a Google Cloud Storage path for the model to use.
version_name str: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

batchSize String: 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.
dataFormat String: The format of the input data files.
inputPaths List<String>: The Cloud Storage location of the input data files. May contain wildcards.
maxWorkerCount String: Optional. The maximum number of workers to be used for parallel processing. Defaults to 10 if not specified.
modelName String: Use this field if you want to use the default version for the specified model. The string must use the following format: "projects/YOUR_PROJECT/models/YOUR_MODEL"
outputDataFormat String: Optional. Format of the output data files, defaults to JSON.
outputPath String: The output Google Cloud Storage location.
region String: The Google Compute Engine region to run the prediction job in. See the available regions for AI Platform services.
runtimeVersion String: 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.
signatureName String: 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 String: Use this field if you want to specify a Google Cloud Storage path for the model to use.
versionName String: 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: "projects/YOUR_PROJECT/models/YOUR_MODEL/versions/YOUR_VERSION"

GoogleCloudMlV1__PredictionOutputResponse

ErrorCount string: The number of data instances which resulted in errors.
NodeHours double: Node hours used by the batch prediction job.
OutputPath string: The output Google Cloud Storage location provided at the job creation time.
PredictionCount string: The number of generated predictions.

ErrorCount string: The number of data instances which resulted in errors.
NodeHours float64: Node hours used by the batch prediction job.
OutputPath string: The output Google Cloud Storage location provided at the job creation time.
PredictionCount string: The number of generated predictions.

errorCount String: The number of data instances which resulted in errors.
nodeHours Double: Node hours used by the batch prediction job.
outputPath String: The output Google Cloud Storage location provided at the job creation time.
predictionCount String: The number of generated predictions.

errorCount string: The number of data instances which resulted in errors.
nodeHours number: Node hours used by the batch prediction job.
outputPath string: The output Google Cloud Storage location provided at the job creation time.
predictionCount string: The number of generated predictions.

error_count str: The number of data instances which resulted in errors.
node_hours float: Node hours used by the batch prediction job.
output_path str: The output Google Cloud Storage location provided at the job creation time.
prediction_count str: The number of generated predictions.

errorCount String: The number of data instances which resulted in errors.
nodeHours Number: Node hours used by the batch prediction job.
outputPath String: The output Google Cloud Storage location provided at the job creation time.
predictionCount String: The number of generated predictions.

GoogleCloudMlV1__ReplicaConfigResponse

AcceleratorConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__AcceleratorConfigResponse: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
ContainerArgs List<string>: 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.
ContainerCommand List<string>: 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.
DiskConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__DiskConfigResponse: Represents the configuration of disk options.
ImageUri string: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
TpuTfVersion string: 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.

AcceleratorConfig GoogleCloudMlV1__AcceleratorConfigResponse: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
ContainerArgs []string: 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.
ContainerCommand []string: 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.
DiskConfig GoogleCloudMlV1__DiskConfigResponse: Represents the configuration of disk options.
ImageUri string: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
TpuTfVersion string: 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.

acceleratorConfig GoogleCloudMlV1__AcceleratorConfigResponse: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
containerArgs List<String>: 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.
containerCommand List<String>: 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.
diskConfig GoogleCloudMlV1__DiskConfigResponse: Represents the configuration of disk options.
imageUri String: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
tpuTfVersion String: 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.

acceleratorConfig GoogleCloudMlV1__AcceleratorConfigResponse: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
containerArgs string[]: 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.
containerCommand string[]: 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.
diskConfig GoogleCloudMlV1__DiskConfigResponse: Represents the configuration of disk options.
imageUri string: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
tpuTfVersion string: 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.

accelerator_config GoogleCloudMlV1AcceleratorConfigResponse: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
container_args Sequence[str]: 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.
container_command Sequence[str]: 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.
disk_config GoogleCloudMlV1DiskConfigResponse: Represents the configuration of disk options.
image_uri str: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
tpu_tf_version str: 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.

acceleratorConfig Property Map: Represents the type and number of accelerators used by the replica. Learn about restrictions on accelerator configurations for training.
containerArgs List<String>: 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.
containerCommand List<String>: 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.
diskConfig Property Map: Represents the configuration of disk options.
imageUri String: The Docker image to run on the replica. This image must be in Container Registry. Learn more about configuring custom containers.
tpuTfVersion String: 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.

GoogleCloudMlV1__SchedulingResponse

MaxRunningTime string: 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
MaxWaitTime string: 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 int: 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.

MaxRunningTime string: 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
MaxWaitTime string: 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 int: 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.

maxRunningTime String: 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
maxWaitTime String: 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 Integer: 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.

maxRunningTime string: 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
maxWaitTime string: 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 number: 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.

max_running_time str: 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 str: 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 int: 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.

maxRunningTime String: 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
maxWaitTime String: 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 Number: 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.

GoogleCloudMlV1__TrainingInputResponse

Args List<string>: 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.
EnableWebAccess bool: 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).
EncryptionConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__EncryptionConfigResponse: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
EvaluatorConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
EvaluatorCount string: 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.
EvaluatorType string: 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 Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__HyperparameterSpecResponse: Optional. The set of Hyperparameters to tune.
JobDir string: 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.
MasterConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
MasterType string: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
Network string: 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..
PackageUris List<string>: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
ParameterServerConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
ParameterServerCount string: 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.
ParameterServerType string: 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.
PythonModule string: The Python module name to run after installing the packages.
PythonVersion string: 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 string: The region to run the training job in. See the available regions for AI Platform Training.
RuntimeVersion string: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
ScaleTier string: Specifies the machine types, the number of replicas for workers and parameter servers.
Scheduling Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__SchedulingResponse: Optional. Scheduling options for a training job.
ServiceAccount string: 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.
UseChiefInTfConfig bool: 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.
WorkerConfig Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
WorkerCount string: 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.
WorkerType string: 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.

Args []string: 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.
EnableWebAccess bool: 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).
EncryptionConfig GoogleCloudMlV1__EncryptionConfigResponse: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
EvaluatorConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
EvaluatorCount string: 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.
EvaluatorType string: 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 GoogleCloudMlV1__HyperparameterSpecResponse: Optional. The set of Hyperparameters to tune.
JobDir string: 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.
MasterConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
MasterType string: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
Network string: 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..
PackageUris []string: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
ParameterServerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
ParameterServerCount string: 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.
ParameterServerType string: 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.
PythonModule string: The Python module name to run after installing the packages.
PythonVersion string: 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 string: The region to run the training job in. See the available regions for AI Platform Training.
RuntimeVersion string: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
ScaleTier string: Specifies the machine types, the number of replicas for workers and parameter servers.
Scheduling GoogleCloudMlV1__SchedulingResponse: Optional. Scheduling options for a training job.
ServiceAccount string: 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.
UseChiefInTfConfig bool: 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.
WorkerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
WorkerCount string: 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.
WorkerType string: 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.

args List<String>: 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.
enableWebAccess Boolean: 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).
encryptionConfig GoogleCloudMlV1__EncryptionConfigResponse: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
evaluatorConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
evaluatorCount String: 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.
evaluatorType String: 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 GoogleCloudMlV1__HyperparameterSpecResponse: Optional. The set of Hyperparameters to tune.
jobDir String: 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.
masterConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
masterType String: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
network String: 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..
packageUris List<String>: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
parameterServerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
parameterServerCount String: 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.
parameterServerType String: 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.
pythonModule String: The Python module name to run after installing the packages.
pythonVersion String: 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 String: The region to run the training job in. See the available regions for AI Platform Training.
runtimeVersion String: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
scaleTier String: Specifies the machine types, the number of replicas for workers and parameter servers.
scheduling GoogleCloudMlV1__SchedulingResponse: Optional. Scheduling options for a training job.
serviceAccount String: 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.
useChiefInTfConfig Boolean: 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.
workerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
workerCount String: 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.
workerType String: 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.

args string[]: 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.
enableWebAccess boolean: 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).
encryptionConfig GoogleCloudMlV1__EncryptionConfigResponse: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
evaluatorConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
evaluatorCount string: 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.
evaluatorType string: 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 GoogleCloudMlV1__HyperparameterSpecResponse: Optional. The set of Hyperparameters to tune.
jobDir string: 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.
masterConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
masterType string: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
network string: 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..
packageUris string[]: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
parameterServerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
parameterServerCount string: 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.
parameterServerType string: 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.
pythonModule string: The Python module name to run after installing the packages.
pythonVersion string: 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 string: The region to run the training job in. See the available regions for AI Platform Training.
runtimeVersion string: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
scaleTier string: Specifies the machine types, the number of replicas for workers and parameter servers.
scheduling GoogleCloudMlV1__SchedulingResponse: Optional. Scheduling options for a training job.
serviceAccount string: 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.
useChiefInTfConfig boolean: 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.
workerConfig GoogleCloudMlV1__ReplicaConfigResponse: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
workerCount string: 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.
workerType string: 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.

args Sequence[str]: 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.
enable_web_access bool: 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 GoogleCloudMlV1EncryptionConfigResponse: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
evaluator_config GoogleCloudMlV1ReplicaConfigResponse: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
evaluator_count str: 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 str: 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 GoogleCloudMlV1HyperparameterSpecResponse: Optional. The set of Hyperparameters to tune.
job_dir str: 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 GoogleCloudMlV1ReplicaConfigResponse: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
master_type str: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
network str: 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 Sequence[str]: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
parameter_server_config GoogleCloudMlV1ReplicaConfigResponse: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
parameter_server_count str: 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 str: 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 str: The Python module name to run after installing the packages.
python_version str: 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 str: The region to run the training job in. See the available regions for AI Platform Training.
runtime_version str: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
scale_tier str: Specifies the machine types, the number of replicas for workers and parameter servers.
scheduling GoogleCloudMlV1SchedulingResponse: Optional. Scheduling options for a training job.
service_account str: 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 bool: 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 GoogleCloudMlV1ReplicaConfigResponse: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
worker_count str: 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 str: 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.

args List<String>: 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.
enableWebAccess Boolean: 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).
encryptionConfig Property Map: Optional. Options for using customer-managed encryption keys (CMEK) to protect resources created by a training job, instead of using Google's default encryption. If this is set, then all resources created by the training job will be encrypted with the customer-managed encryption key that you specify. Learn how and when to use CMEK with AI Platform Training.
evaluatorConfig Property Map: Optional. The configuration for evaluators. You should only set evaluatorConfig.acceleratorConfig if evaluatorType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set evaluatorConfig.imageUri only if you build a custom image for your evaluator. If evaluatorConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
evaluatorCount String: 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.
evaluatorType String: 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 Property Map: Optional. The set of Hyperparameters to tune.
jobDir String: 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.
masterConfig Property Map: Optional. The configuration for your master worker. You should only set masterConfig.acceleratorConfig if masterType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set masterConfig.imageUri only if you build a custom image. Only one of masterConfig.imageUri and runtimeVersion should be set. Learn more about configuring custom containers.
masterType String: Optional. Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when scaleTier is set to CUSTOM. You can use certain Compute Engine machine types directly in this field. See the list of compatible Compute Engine machine types. Alternatively, you can use the certain legacy machine types in this field. See the list of legacy machine types. Finally, if you want to use a TPU for training, specify cloud_tpu in this field. Learn more about the special configuration options for training with TPUs.
network String: 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..
packageUris List<String>: The Google Cloud Storage location of the packages with the training program and any additional dependencies. The maximum number of package URIs is 100.
parameterServerConfig Property Map: Optional. The configuration for parameter servers. You should only set parameterServerConfig.acceleratorConfig if parameterServerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set parameterServerConfig.imageUri only if you build a custom image for your parameter server. If parameterServerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
parameterServerCount String: 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.
parameterServerType String: 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.
pythonModule String: The Python module name to run after installing the packages.
pythonVersion String: 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 String: The region to run the training job in. See the available regions for AI Platform Training.
runtimeVersion String: Optional. The AI Platform runtime version to use for training. You must either specify this field or specify masterConfig.imageUri. For more information, see the runtime version list and learn how to manage runtime versions.
scaleTier String: Specifies the machine types, the number of replicas for workers and parameter servers.
scheduling Property Map: Optional. Scheduling options for a training job.
serviceAccount String: 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.
useChiefInTfConfig Boolean: 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.
workerConfig Property Map: Optional. The configuration for workers. You should only set workerConfig.acceleratorConfig if workerType is set to a Compute Engine machine type. Learn about restrictions on accelerator configurations for training. Set workerConfig.imageUri only if you build a custom image for your worker. If workerConfig.imageUri has not been set, AI Platform uses the value of masterConfig.imageUri. Learn more about configuring custom containers.
workerCount String: 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.
workerType String: 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.

GoogleCloudMlV1__TrainingOutputResponse

BuiltInAlgorithmOutput Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
CompletedTrialCount string: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
ConsumedMLUnits double: The amount of ML units consumed by the job.
HyperparameterMetricTag string: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
IsBuiltInAlgorithmJob bool: Whether this job is a built-in Algorithm job.
IsHyperparameterTuningJob bool: Whether this job is a hyperparameter tuning job.
Trials List<Pulumi.GoogleNative.Ml.V1.Inputs.GoogleCloudMlV1__HyperparameterOutputResponse>: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
WebAccessUris Dictionary<string, string>: 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.

BuiltInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
CompletedTrialCount string: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
ConsumedMLUnits float64: The amount of ML units consumed by the job.
HyperparameterMetricTag string: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
IsBuiltInAlgorithmJob bool: Whether this job is a built-in Algorithm job.
IsHyperparameterTuningJob bool: Whether this job is a hyperparameter tuning job.
Trials []GoogleCloudMlV1__HyperparameterOutputResponse: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
WebAccessUris map[string]string: 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.

builtInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
completedTrialCount String: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
consumedMLUnits Double: The amount of ML units consumed by the job.
hyperparameterMetricTag String: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
isBuiltInAlgorithmJob Boolean: Whether this job is a built-in Algorithm job.
isHyperparameterTuningJob Boolean: Whether this job is a hyperparameter tuning job.
trials List<GoogleCloudMlV1__HyperparameterOutputResponse>: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
webAccessUris Map<String,String>: 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.

builtInAlgorithmOutput GoogleCloudMlV1__BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
completedTrialCount string: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
consumedMLUnits number: The amount of ML units consumed by the job.
hyperparameterMetricTag string: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
isBuiltInAlgorithmJob boolean: Whether this job is a built-in Algorithm job.
isHyperparameterTuningJob boolean: Whether this job is a hyperparameter tuning job.
trials GoogleCloudMlV1__HyperparameterOutputResponse[]: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
webAccessUris {[key: string]: string}: 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.

built_in_algorithm_output GoogleCloudMlV1BuiltInAlgorithmOutputResponse: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
completed_trial_count str: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
consumed_ml_units float: The amount of ML units consumed by the job.
hyperparameter_metric_tag str: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
is_built_in_algorithm_job bool: Whether this job is a built-in Algorithm job.
is_hyperparameter_tuning_job bool: Whether this job is a hyperparameter tuning job.
trials Sequence[GoogleCloudMlV1HyperparameterOutputResponse]: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
web_access_uris Mapping[str, str]: 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.

builtInAlgorithmOutput Property Map: Details related to built-in algorithms jobs. Only set for built-in algorithms jobs.
completedTrialCount String: The number of hyperparameter tuning trials that completed successfully. Only set for hyperparameter tuning jobs.
consumedMLUnits Number: The amount of ML units consumed by the job.
hyperparameterMetricTag String: The TensorFlow summary tag name used for optimizing hyperparameter tuning trials. See HyperparameterSpec.hyperparameterMetricTag for more information. Only set for hyperparameter tuning jobs.
isBuiltInAlgorithmJob Boolean: Whether this job is a built-in Algorithm job.
isHyperparameterTuningJob Boolean: Whether this job is a hyperparameter tuning job.
trials List<Property Map>: Results for individual Hyperparameter trials. Only set for hyperparameter tuning jobs.
webAccessUris Map<String>: 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.

Package Details

Repository: Google Cloud Native pulumi/pulumi-google-native
License: Apache-2.0

Google Cloud Native is in preview. Google Cloud Classic is fully supported.

Google Cloud Native v0.32.0 published on Wednesday, Nov 29, 2023 by Pulumi

pulumi/pulumi-google-native

google-native.ml/v1.getJob

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Using getJob

getJob Result

Supporting Types

GoogleCloudMlV1_HyperparameterOutput_HyperparameterMetricResponse

GoogleCloudMlV1__AcceleratorConfigResponse

GoogleCloudMlV1__BuiltInAlgorithmOutputResponse

GoogleCloudMlV1__DiskConfigResponse

GoogleCloudMlV1__EncryptionConfigResponse

GoogleCloudMlV1__HyperparameterOutputResponse

GoogleCloudMlV1__HyperparameterSpecResponse

GoogleCloudMlV1__ParameterSpecResponse

GoogleCloudMlV1__PredictionInputResponse

GoogleCloudMlV1__PredictionOutputResponse

GoogleCloudMlV1__ReplicaConfigResponse

GoogleCloudMlV1__SchedulingResponse

GoogleCloudMlV1__TrainingInputResponse

GoogleCloudMlV1__TrainingOutputResponse

Package Details

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