google-native.aiplatform/v1beta1.getCustomJob

func LookupCustomJob(ctx *Context, args *LookupCustomJobArgs, opts ...InvokeOption) (*LookupCustomJobResult, error)
func LookupCustomJobOutput(ctx *Context, args *LookupCustomJobOutputArgs, opts ...InvokeOption) LookupCustomJobResultOutput

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

CustomJobId string

Location string

Project string

CustomJobId string

Location string

Project string

customJobId String

location String

project String

customJobId string

location string

project string

custom_job_id str

location str

project str

customJobId String

location String

project String

CreateTime string

Time when the CustomJob was created.

DisplayName string

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

EncryptionSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1EncryptionSpecResponse

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

EndTime string

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Error Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleRpcStatusResponse

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

JobSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Outputs.GoogleCloudAiplatformV1beta1CustomJobSpecResponse

Job spec.

Labels Dictionary<string, string>

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

Name string

Resource name of a CustomJob.

StartTime string

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

State string

The detailed state of the job.

UpdateTime string

Time when the CustomJob was most recently updated.

WebAccessUris Dictionary<string, string>

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

CreateTime string

Time when the CustomJob was created.

DisplayName string

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

EncryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

EndTime string

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

Error GoogleRpcStatusResponse

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

JobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse

Job spec.

Labels map[string]string

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

Name string

Resource name of a CustomJob.

StartTime string

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

State string

The detailed state of the job.

UpdateTime string

Time when the CustomJob was most recently updated.

WebAccessUris map[string]string

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime String

Time when the CustomJob was created.

displayName String

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

endTime String

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

error GoogleRpcStatusResponse

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

jobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse

Job spec.

labels Map<String,String>

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

name String

Resource name of a CustomJob.

startTime String

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

state String

The detailed state of the job.

updateTime String

Time when the CustomJob was most recently updated.

webAccessUris Map<String,String>

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime string

Time when the CustomJob was created.

displayName string

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

encryptionSpec GoogleCloudAiplatformV1beta1EncryptionSpecResponse

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

endTime string

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

error GoogleRpcStatusResponse

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

jobSpec GoogleCloudAiplatformV1beta1CustomJobSpecResponse

Job spec.

labels {[key: string]: string}

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

name string

Resource name of a CustomJob.

startTime string

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

state string

The detailed state of the job.

updateTime string

Time when the CustomJob was most recently updated.

webAccessUris {[key: string]: string}

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

create_time str

Time when the CustomJob was created.

display_name str

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

encryption_spec GoogleCloudAiplatformV1beta1EncryptionSpecResponse

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

end_time str

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

error GoogleRpcStatusResponse

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

job_spec GoogleCloudAiplatformV1beta1CustomJobSpecResponse

Job spec.

labels Mapping[str, str]

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

name str

Resource name of a CustomJob.

start_time str

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

state str

The detailed state of the job.

update_time str

Time when the CustomJob was most recently updated.

web_access_uris Mapping[str, str]

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

createTime String

Time when the CustomJob was created.

displayName String

The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.

encryptionSpec Property Map

Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.

endTime String

Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.

error Property Map

Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.

jobSpec Property Map

Job spec.

labels Map<String>

The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

name String

Resource name of a CustomJob.

startTime String

Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.

state String

The detailed state of the job.

updateTime String

Time when the CustomJob was most recently updated.

webAccessUris Map<String>

URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.

Args List<string>

The arguments to be passed when starting the container.

Command List<string>

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>

Environment variables to be passed to the container. Maximum limit is 100.

ImageUri string

The URI of a container image in the Container Registry that is to be run on each worker replica.

Args []string

The arguments to be passed when starting the container.

Command []string

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

Env []GoogleCloudAiplatformV1beta1EnvVarResponse

Environment variables to be passed to the container. Maximum limit is 100.

ImageUri string

The URI of a container image in the Container Registry that is to be run on each worker replica.

args List<String>

The arguments to be passed when starting the container.

command List<String>

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

env List<GoogleCloudAiplatformV1beta1EnvVarResponse>

Environment variables to be passed to the container. Maximum limit is 100.

imageUri String

The URI of a container image in the Container Registry that is to be run on each worker replica.

args string[]

The arguments to be passed when starting the container.

command string[]

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

env GoogleCloudAiplatformV1beta1EnvVarResponse[]

Environment variables to be passed to the container. Maximum limit is 100.

imageUri string

The URI of a container image in the Container Registry that is to be run on each worker replica.

args Sequence[str]

The arguments to be passed when starting the container.

command Sequence[str]

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]

Environment variables to be passed to the container. Maximum limit is 100.

image_uri str

The URI of a container image in the Container Registry that is to be run on each worker replica.

args List<String>

The arguments to be passed when starting the container.

command List<String>

The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.

env List<Property Map>

Environment variables to be passed to the container. Maximum limit is 100.

imageUri String

The URI of a container image in the Container Registry that is to be run on each worker replica.

BaseOutputDirectory Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1GcsDestinationResponse

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

EnableDashboardAccess bool

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

EnableWebAccess bool

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

Experiment string

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

ExperimentRun string

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

Network string

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

PersistentResourceId string

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

ProtectedArtifactLocationId string

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

ReservedIpRanges List<string>

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

Scheduling Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1SchedulingResponse

Scheduling options for a CustomJob.

ServiceAccount string

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

Tensorboard string

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

WorkerPoolSpecs List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse>

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

BaseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

EnableDashboardAccess bool

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

EnableWebAccess bool

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

Experiment string

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

ExperimentRun string

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

Network string

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

PersistentResourceId string

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

ProtectedArtifactLocationId string

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

ReservedIpRanges []string

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

Scheduling GoogleCloudAiplatformV1beta1SchedulingResponse

Scheduling options for a CustomJob.

ServiceAccount string

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

Tensorboard string

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

WorkerPoolSpecs []GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

enableDashboardAccess Boolean

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

enableWebAccess Boolean

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

experiment String

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

experimentRun String

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

network String

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

persistentResourceId String

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

protectedArtifactLocationId String

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

reservedIpRanges List<String>

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

scheduling GoogleCloudAiplatformV1beta1SchedulingResponse

Scheduling options for a CustomJob.

serviceAccount String

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

tensorboard String

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs List<GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse>

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory GoogleCloudAiplatformV1beta1GcsDestinationResponse

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

enableDashboardAccess boolean

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

enableWebAccess boolean

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

experiment string

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

experimentRun string

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

network string

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

persistentResourceId string

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

protectedArtifactLocationId string

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

reservedIpRanges string[]

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

scheduling GoogleCloudAiplatformV1beta1SchedulingResponse

Scheduling options for a CustomJob.

serviceAccount string

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

tensorboard string

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse[]

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

base_output_directory GoogleCloudAiplatformV1beta1GcsDestinationResponse

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

enable_dashboard_access bool

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

enable_web_access bool

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

experiment str

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

experiment_run str

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

network str

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

persistent_resource_id str

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

protected_artifact_location_id str

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

reserved_ip_ranges Sequence[str]

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

scheduling GoogleCloudAiplatformV1beta1SchedulingResponse

Scheduling options for a CustomJob.

service_account str

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

tensorboard str

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

worker_pool_specs Sequence[GoogleCloudAiplatformV1beta1WorkerPoolSpecResponse]

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

baseOutputDirectory Property Map

The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Vertex AI environment variables will be passed to containers or python modules when this field is set: For CustomJob: * AIP_MODEL_DIR = /model/ * AIP_CHECKPOINT_DIR = /checkpoints/ * AIP_TENSORBOARD_LOG_DIR = /logs/ For CustomJob backing a Trial of HyperparameterTuningJob: * AIP_MODEL_DIR = //model/ * AIP_CHECKPOINT_DIR = //checkpoints/ * AIP_TENSORBOARD_LOG_DIR = //logs/

enableDashboardAccess Boolean

Optional. Whether you want Vertex AI to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

enableWebAccess Boolean

Optional. Whether you want Vertex AI to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).

experiment String

Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}

experimentRun String

Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}

network String

Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Vertex AI. If this field is left unspecified, the job is not peered with any network.

persistentResourceId String

Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

protectedArtifactLocationId String

The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations

reservedIpRanges List<String>

Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].

scheduling Property Map

Scheduling options for a CustomJob.

serviceAccount String

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Vertex AI Custom Code Service Agent for the CustomJob's project is used.

tensorboard String

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

workerPoolSpecs List<Property Map>

The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.

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).

KmsKeyName string

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

KmsKeyName string

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

kmsKeyName String

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

kmsKeyName string

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

kms_key_name str

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

kmsKeyName String

The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key. The key needs to be in the same region as where the compute resource is created.

Name string

Name of the environment variable. Must be a valid C identifier.

Value string

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

Name string

Name of the environment variable. Must be a valid C identifier.

Value string

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

name String

Name of the environment variable. Must be a valid C identifier.

value String

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

name string

Name of the environment variable. Must be a valid C identifier.

value string

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

name str

Name of the environment variable. Must be a valid C identifier.

value str

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

name String

Name of the environment variable. Must be a valid C identifier.

value String

Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

OutputUriPrefix string

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

OutputUriPrefix string

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

outputUriPrefix String

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

outputUriPrefix string

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

output_uri_prefix str

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

outputUriPrefix String

Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

AcceleratorCount int

The number of accelerators to attach to the machine.

AcceleratorType string

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

MachineType string

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

TpuTopology string

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

AcceleratorCount int

The number of accelerators to attach to the machine.

AcceleratorType string

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

MachineType string

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

TpuTopology string

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount Integer

The number of accelerators to attach to the machine.

acceleratorType String

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

machineType String

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

tpuTopology String

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount number

The number of accelerators to attach to the machine.

acceleratorType string

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

machineType string

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

tpuTopology string

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

accelerator_count int

The number of accelerators to attach to the machine.

accelerator_type str

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

machine_type str

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

tpu_topology str

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

acceleratorCount Number

The number of accelerators to attach to the machine.

acceleratorType String

Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.

machineType String

Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.

tpuTopology String

Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").

MountPoint string

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

Path string

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

Server string

IP address of the NFS server.

MountPoint string

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

Path string

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

Server string

IP address of the NFS server.

mountPoint String

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

path String

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

server String

IP address of the NFS server.

mountPoint string

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

path string

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

server string

IP address of the NFS server.

mount_point str

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

path str

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

server str

IP address of the NFS server.

mountPoint String

Destination mount path. The NFS will be mounted for the user under /mnt/nfs/

path String

Source path exported from NFS server. Has to start with '/', and combined with the ip address, it indicates the source mount path in the form of server:path

server String

IP address of the NFS server.

Args List<string>

Command line arguments to be passed to the Python task.

Env List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1EnvVarResponse>

Environment variables to be passed to the python module. Maximum limit is 100.

ExecutorImageUri string

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

PackageUris List<string>

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

PythonModule string

The Python module name to run after installing the packages.

Args []string

Command line arguments to be passed to the Python task.

Env []GoogleCloudAiplatformV1beta1EnvVarResponse

Environment variables to be passed to the python module. Maximum limit is 100.

ExecutorImageUri string

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

PackageUris []string

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

PythonModule string

The Python module name to run after installing the packages.

args List<String>

Command line arguments to be passed to the Python task.

env List<GoogleCloudAiplatformV1beta1EnvVarResponse>

Environment variables to be passed to the python module. Maximum limit is 100.

executorImageUri String

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

packageUris List<String>

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

pythonModule String

The Python module name to run after installing the packages.

args string[]

Command line arguments to be passed to the Python task.

env GoogleCloudAiplatformV1beta1EnvVarResponse[]

Environment variables to be passed to the python module. Maximum limit is 100.

executorImageUri string

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

packageUris string[]

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

pythonModule string

The Python module name to run after installing the packages.

args Sequence[str]

Command line arguments to be passed to the Python task.

env Sequence[GoogleCloudAiplatformV1beta1EnvVarResponse]

Environment variables to be passed to the python module. Maximum limit is 100.

executor_image_uri str

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

package_uris Sequence[str]

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

python_module str

The Python module name to run after installing the packages.

args List<String>

Command line arguments to be passed to the Python task.

env List<Property Map>

Environment variables to be passed to the python module. Maximum limit is 100.

executorImageUri String

The URI of a container image in Artifact Registry that will run the provided Python package. Vertex AI provides a wide range of executor images with pre-installed packages to meet users' various use cases. See the list of pre-built containers for training. You must use an image from this list.

packageUris List<String>

The Google Cloud Storage location of the Python package files which are the training program and its dependent packages. The maximum number of package URIs is 100.

pythonModule String

The Python module name to run after installing the packages.

DisableRetries bool

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

RestartJobOnWorkerRestart bool

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

Timeout string

The maximum job running time. The default is 7 days.

DisableRetries bool

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

RestartJobOnWorkerRestart bool

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

Timeout string

The maximum job running time. The default is 7 days.

disableRetries Boolean

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

restartJobOnWorkerRestart Boolean

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

timeout String

The maximum job running time. The default is 7 days.

disableRetries boolean

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

restartJobOnWorkerRestart boolean

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

timeout string

The maximum job running time. The default is 7 days.

disable_retries bool

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

restart_job_on_worker_restart bool

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

timeout str

The maximum job running time. The default is 7 days.

disableRetries Boolean

Optional. Indicates if the job should retry for internal errors after the job starts running. If true, overrides Scheduling.restart_job_on_worker_restart to false.

restartJobOnWorkerRestart Boolean

Restarts the entire CustomJob if a worker gets restarted. This feature can be used by distributed training jobs that are not resilient to workers leaving and joining a job.

timeout String

The maximum job running time. The default is 7 days.

ContainerSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1ContainerSpecResponse

The custom container task.

DiskSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1DiskSpecResponse

Disk spec.

MachineSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1MachineSpecResponse

Optional. Immutable. The specification of a single machine.

NfsMounts List<Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1NfsMountResponse>

Optional. List of NFS mount spec.

PythonPackageSpec Pulumi.GoogleNative.Aiplatform.V1Beta1.Inputs.GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

The Python packaged task.

ReplicaCount string

Optional. The number of worker replicas to use for this worker pool.

ContainerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse

The custom container task.

DiskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse

Disk spec.

MachineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse

Optional. Immutable. The specification of a single machine.

NfsMounts []GoogleCloudAiplatformV1beta1NfsMountResponse

Optional. List of NFS mount spec.

PythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

The Python packaged task.

ReplicaCount string

Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse

The custom container task.

diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse

Disk spec.

machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse

Optional. Immutable. The specification of a single machine.

nfsMounts List<GoogleCloudAiplatformV1beta1NfsMountResponse>

Optional. List of NFS mount spec.

pythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

The Python packaged task.

replicaCount String

Optional. The number of worker replicas to use for this worker pool.

containerSpec GoogleCloudAiplatformV1beta1ContainerSpecResponse

The custom container task.

diskSpec GoogleCloudAiplatformV1beta1DiskSpecResponse

Disk spec.

machineSpec GoogleCloudAiplatformV1beta1MachineSpecResponse

Optional. Immutable. The specification of a single machine.

nfsMounts GoogleCloudAiplatformV1beta1NfsMountResponse[]

Optional. List of NFS mount spec.

pythonPackageSpec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

The Python packaged task.

replicaCount string

Optional. The number of worker replicas to use for this worker pool.

container_spec GoogleCloudAiplatformV1beta1ContainerSpecResponse

The custom container task.

disk_spec GoogleCloudAiplatformV1beta1DiskSpecResponse

Disk spec.

machine_spec GoogleCloudAiplatformV1beta1MachineSpecResponse

Optional. Immutable. The specification of a single machine.

nfs_mounts Sequence[GoogleCloudAiplatformV1beta1NfsMountResponse]

Optional. List of NFS mount spec.

python_package_spec GoogleCloudAiplatformV1beta1PythonPackageSpecResponse

The Python packaged task.

replica_count str

Optional. The number of worker replicas to use for this worker pool.

containerSpec Property Map

The custom container task.

diskSpec Property Map

Disk spec.

machineSpec Property Map

Optional. Immutable. The specification of a single machine.

nfsMounts List<Property Map>

Optional. List of NFS mount spec.

pythonPackageSpec Property Map

The Python packaged task.

replicaCount String

Optional. The number of worker replicas to use for this worker pool.

Code int

The status code, which should be an enum value of google.rpc.Code.

Details List<ImmutableDictionary<string, string>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

Message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

Code int

The status code, which should be an enum value of google.rpc.Code.

Details []map[string]string

A list of messages that carry the error details. There is a common set of message types for APIs to use.

Message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Integer

The status code, which should be an enum value of google.rpc.Code.

details List<Map<String,String>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message String

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code number

The status code, which should be an enum value of google.rpc.Code.

details {[key: string]: string}[]

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message string

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code int

The status code, which should be an enum value of google.rpc.Code.

details Sequence[Mapping[str, str]]

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message str

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.

code Number

The status code, which should be an enum value of google.rpc.Code.

details List<Map<String>>

A list of messages that carry the error details. There is a common set of message types for APIs to use.

message String

A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.