Class CustomContainerTrainingJob (1.90.0)

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Required. The user-defined name of this TrainingPipeline.

Required: Uri of the training container image in the GCR.

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

If the training produces a managed Vertex AI Model, the URI of the Model serving container suitable for serving the model produced by the training script.

If the training produces a managed Vertex AI Model, An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI.

If the training produces a managed Vertex AI Model, an HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by AI Platform.

The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. 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.

The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. 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.

The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names.

Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network.

The description of the Model.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in PredictRequest.instances, ExplainRequest.instances and BatchPredictionJob.input_config. The schema is defined as an OpenAPI 3.0.2 Schema Object https://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via PredictRequest.parameters, ExplainRequest.parameters and BatchPredictionJob.model_parameters. The schema is defined as an OpenAPI 3.0.2 Schema Object https://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via PredictResponse.predictions, ExplainResponse.explanations, and BatchPredictionJob.output_config. The schema is defined as an OpenAPI 3.0.2 Schema Object https://tinyurl.com/y538mdwt#schema-object__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.

Optional. Metadata describing the Model's input and output for explanation. explanation_metadata is optional while explanation_parameters must be specified when used. For more details, see Ref docs http://tinyurl.com/1igh60kt

Optional. Parameters to configure explaining for Model's predictions. For more details, see Ref docs http://tinyurl.com/1an4zake

Project to run training in. Overrides project set in aiplatform.init.

Location to run training in. Overrides location set in aiplatform.init.

Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.

Optional. The labels with user-defined metadata to organize TrainingPipelines. 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.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the training pipeline. 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. If set, this TrainingPipeline will be secured by this key. Note: Model trained by this TrainingPipeline is also secured by this key if model_to_upload is not set separately. Overrides encryption_spec_key_name set in aiplatform.init.

Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. 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. If set, the trained Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init.

Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init.

Required. A fully-qualified resource name or ID.

Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by aiplatform.init.

Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by aiplatform.init.

Optional. The credentials that are used to upload this model. These credentials override the credentials set by aiplatform.init.

If set to true, this method runs synchronously. If false, this method runs asynchronously.

Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported.

Optional. A comma-separated list of fields used to sort the returned traing job resources. The defauilt sorting order is ascending. To sort by a field name in descending order, use desc after the field name. The following fields are supported: display_name, create_time, update_time.

Optional. The name of the Google Cloud project to which to retrieve the list of training job resources. This overrides the project that was set by aiplatform.init.

Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by aiplatform.init.

Optional. The credentials that are used to retrieve list. These credentials override the credentials set by aiplatform.init.

Vertex AI to fit this training against. Custom training script should retrieve datasets through passed in environment variables uris: os.environ["AIP_TRAINING_DATA_URI"] os.environ["AIP_VALIDATION_DATA_URI"] os.environ["AIP_TEST_DATA_URI"] Additionally the dataset format is passed in as: os.environ["AIP_DATA_FORMAT"]

Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used.

Optional. The labels with user-defined metadata to organize your Models. 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.

Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of the model produced by this job without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded by this job.

GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI sets the following environment variables when it runs your training code: - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/ - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/ - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

Provide this field if dataset is a BigQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, training, validation and test. - AIP_DATA_FORMAT = "bigquery". - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_.training" - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_.validation" - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"

Command line arguments to be passed to the Python script.

Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. environment_variables = { 'MY_KEY': 'MY_VALUE' }

The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

The type of machine to use for training.

Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

The number of accelerators to attach to a worker replica.

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).

Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

The number of reduction server replicas, default is 0.

Optional. The type of machine to use for reduction server.

Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets.

Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets.

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

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.

Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

Whether you want Vertex AI to enable access to the customized dashboard to training containers.

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} The training script should write Tensorboard to following Vertex AI environment variable: AIP_TENSORBOARD_LOG_DIR service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Optional. The timeout for the create request in seconds.

Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

Optional. Indicates the job scheduling strategy.

Optional. The type of reservation affinity. One of: * "NO_RESERVATION" : No reservation is used. * "ANY_RESERVATION" : Any reservation that matches machine spec can be used. * "SPECIFIC_RESERVATION" : A specific reservation must be use used. See reservation_affinity_key and reservation_affinity_values for how to specify the reservation.

Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: 'projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}'

This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 30 minutes.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

Vertex AI to fit this training against. Custom training script should retrieve datasets through passed in environment variables uris: os.environ["AIP_TRAINING_DATA_URI"] os.environ["AIP_VALIDATION_DATA_URI"] os.environ["AIP_TEST_DATA_URI"] Additionally the dataset format is passed in as: os.environ["AIP_DATA_FORMAT"]

Google Cloud Storage URI points to a YAML file describing annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.

If the script produces a managed Vertex AI Model. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. If not provided upon creation, the job's display_name is used.

Optional. The labels with user-defined metadata to organize your Models. 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.

Optional. The ID to use for the Model produced by this job, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of parent_model. Only set this field when training a new version of an existing model.

Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of the model produced by this job without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the model version produced by this job will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased.

Optional. User provided version aliases so that the model version uploaded by this job can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is a-z][a-zA-Z0-9-]{0,126}[a-z0-9]

Optional. The description of the model version being uploaded by this job.

GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. Vertex AI sets the following environment variables when it runs your training code: - AIP_MODEL_DIR: a Cloud Storage URI of a directory intended for saving model artifacts, i.e. <base_output_dir>/model/ - AIP_CHECKPOINT_DIR: a Cloud Storage URI of a directory intended for saving checkpoints, i.e. <base_output_dir>/checkpoints/ - AIP_TENSORBOARD_LOG_DIR: a Cloud Storage URI of a directory intended for saving TensorBoard logs, i.e. <base_output_dir>/logs/

Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account.

The full name of the Compute Engine network to which the job should be peered. For example, projects/12345/global/networks/myVPC. Private services access must already be configured for the network. If left unspecified, the network set in aiplatform.init will be used. Otherwise, the job is not peered with any network.

Provide this field if dataset is a BigQuery dataset. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_ where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data will be written into that dataset. In the dataset three tables will be created, training, validation and test. - AIP_DATA_FORMAT = "bigquery". - AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_.training" - AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_.validation" - AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"

Command line arguments to be passed to the Python script.

Environment variables to be passed to the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. At most 10 environment variables can be specified. The Name of the environment variable must be unique. environment_variables = { 'MY_KEY': 'MY_VALUE' }

The number of worker replicas. If replica count = 1 then one chief replica will be provisioned. If replica_count > 1 the remainder will be provisioned as a worker replica pool.

The type of machine to use for training.

Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4

The number of accelerators to attach to a worker replica.

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).

Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].

The number of reduction server replicas, default is 0.

Optional. The type of machine to use for reduction server.

Optional. The Uri of the reduction server container image. See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server

Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.

Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order. This is ignored if Dataset is not provided.

Optional. The key is a name of one of the Dataset's data columns. The value of the key (either the label's value or value in the column) must be one of {training, validation, test}, and it defines to which set the given piece of data is assigned. If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets.

Optional. The key is a name of one of the Dataset's data columns. The value of the key values of the key (the values in the column) must be in RFC 3339 date-time format, where time-offset = "Z" (e.g. 1985-04-12T23:20:50.52Z). If for a piece of data the key is not present or has an invalid value, that piece is ignored by the pipeline. Supported only for tabular and time series Datasets.

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

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.

Whether you want Vertex AI to enable interactive shell access to training containers. https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell

Whether you want Vertex AI to enable access to the customized dashboard to training containers.

Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} The training script should write Tensorboard to following Vertex AI environment variable: AIP_TENSORBOARD_LOG_DIR service_account is required with provided tensorboard. For more information on configuring your service account please visit: https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-training

Optional. The timeout for the create request in seconds.

Indicates if the job should retry for internal errors after the job starts running. If True, overrides restart_job_on_worker_restart to False.

Optional. The ID of the PersistentResource in the same Project and Location. 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, CMEK, and node pool configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.

Optional. Specifies the tpu topology to be used for TPU training job. This field is required for TPU v5 versions. For details on the TPU topology, refer to https://cloud.google.com/tpu/docs/v5e#tpu-v5e-config. The topology must be a supported value for the TPU machine type.

Optional. Indicates the job scheduling strategy.

Optional. The type of reservation affinity. One of: * "NO_RESERVATION" : No reservation is used. * "ANY_RESERVATION" : Any reservation that matches machine spec can be used. * "SPECIFIC_RESERVATION" : A specific reservation must be use used. See reservation_affinity_key and reservation_affinity_values for how to specify the reservation.

Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use compute.googleapis.com/reservation-name as the key and specify the name of your reservation as its value.

Optional. Corresponds to the label values of a reservation resource. This must be the full resource name of the reservation. Format: 'projects/{project_id_or_number}/zones/{zone}/reservations/{reservation_name}'

This is the maximum duration that a job will wait for the requested resources to be provisioned in seconds. If set to 0, the job will wait indefinitely. The default is 30 minutes.

Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.