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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::CustomJobSpec.
Represents the spec of a CustomJob.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#base_output_directory
def base_output_directory() -> ::Google::Cloud::AIPlatform::V1::GcsDestination
-
(::Google::Cloud::AIPlatform::V1::GcsDestination) —
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 =
<base_output_directory>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/logs/
For CustomJob backing a Trial of HyperparameterTuningJob:
- AIP_MODEL_DIR =
<base_output_directory>/<trial_id>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/<trial_id>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/<trial_id>/logs/
- AIP_MODEL_DIR =
#base_output_directory=
def base_output_directory=(value) -> ::Google::Cloud::AIPlatform::V1::GcsDestination
-
value (::Google::Cloud::AIPlatform::V1::GcsDestination) —
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 =
<base_output_directory>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/logs/
For CustomJob backing a Trial of HyperparameterTuningJob:
- AIP_MODEL_DIR =
<base_output_directory>/<trial_id>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/<trial_id>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/<trial_id>/logs/
- AIP_MODEL_DIR =
-
(::Google::Cloud::AIPlatform::V1::GcsDestination) —
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 =
<base_output_directory>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/logs/
For CustomJob backing a Trial of HyperparameterTuningJob:
- AIP_MODEL_DIR =
<base_output_directory>/<trial_id>/model/
- AIP_CHECKPOINT_DIR =
<base_output_directory>/<trial_id>/checkpoints/
- AIP_TENSORBOARD_LOG_DIR =
<base_output_directory>/<trial_id>/logs/
- AIP_MODEL_DIR =
#enable_dashboard_access
def enable_dashboard_access() -> ::Boolean
-
(::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).
#enable_dashboard_access=
def enable_dashboard_access=(value) -> ::Boolean
-
value (::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).
-
(::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).
#enable_web_access
def enable_web_access() -> ::Boolean
-
(::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).
#enable_web_access=
def enable_web_access=(value) -> ::Boolean
-
value (::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).
-
(::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
def experiment() -> ::String
-
(::String) — Optional. The Experiment associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
#experiment=
def experiment=(value) -> ::String
-
value (::String) — Optional. The Experiment associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
-
(::String) — Optional. The Experiment associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
#experiment_run
def experiment_run() -> ::String
-
(::String) — Optional. The Experiment Run associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
#experiment_run=
def experiment_run=(value) -> ::String
-
value (::String) — Optional. The Experiment Run associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
-
(::String) — Optional. The Experiment Run associated with this job.
Format:
projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
#network
def network() -> ::String
-
(::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 formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, 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.
#network=
def network=(value) -> ::String
-
value (::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 formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, 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.
-
(::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 formprojects/{project}/global/networks/{network}
. Where {project} is a project number, as in12345
, 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.
#protected_artifact_location_id
def protected_artifact_location_id() -> ::String
- (::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
#protected_artifact_location_id=
def protected_artifact_location_id=(value) -> ::String
- value (::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
- (::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
#reserved_ip_ranges
def reserved_ip_ranges() -> ::Array<::String>
-
(::Array<::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'].
#reserved_ip_ranges=
def reserved_ip_ranges=(value) -> ::Array<::String>
-
value (::Array<::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'].
-
(::Array<::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
def scheduling() -> ::Google::Cloud::AIPlatform::V1::Scheduling
- (::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.
#scheduling=
def scheduling=(value) -> ::Google::Cloud::AIPlatform::V1::Scheduling
- value (::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.
- (::Google::Cloud::AIPlatform::V1::Scheduling) — Scheduling options for a CustomJob.
#service_account
def service_account() -> ::String
- (::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.
#service_account=
def service_account=(value) -> ::String
- value (::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.
- (::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
def tensorboard() -> ::String
-
(::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}
#tensorboard=
def tensorboard=(value) -> ::String
-
value (::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}
-
(::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}
#worker_pool_specs
def worker_pool_specs() -> ::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>
- (::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>) — Required. 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.
#worker_pool_specs=
def worker_pool_specs=(value) -> ::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>
- value (::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>) — Required. 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.
- (::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>) — Required. 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.