Class CustomTrainingJob (1.6.2)

CustomTrainingJob(
    display_name: str,
    script_path: str,
    container_uri: str,
    requirements: Optional[Sequence[str]] = None,
    model_serving_container_image_uri: Optional[str] = None,
    model_serving_container_predict_route: Optional[str] = None,
    model_serving_container_health_route: Optional[str] = None,
    model_serving_container_command: Optional[Sequence[str]] = None,
    model_serving_container_args: Optional[Sequence[str]] = None,
    model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
    model_serving_container_ports: Optional[Sequence[int]] = None,
    model_description: Optional[str] = None,
    model_instance_schema_uri: Optional[str] = None,
    model_parameters_schema_uri: Optional[str] = None,
    model_prediction_schema_uri: Optional[str] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    labels: Optional[Dict[str, str]] = None,
    training_encryption_spec_key_name: Optional[str] = None,
    model_encryption_spec_key_name: Optional[str] = None,
    staging_bucket: Optional[str] = None,
)

Class to launch a Custom Training Job in Vertex AI using a script.

Takes a training implementation as a python script and executes that script in Cloud Vertex AI Training.

Inheritance

builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > google.cloud.aiplatform.training_jobs._TrainingJob > google.cloud.aiplatform.training_jobs._CustomTrainingJob > CustomTrainingJob

Methods

CustomTrainingJob

CustomTrainingJob(
    display_name: str,
    script_path: str,
    container_uri: str,
    requirements: Optional[Sequence[str]] = None,
    model_serving_container_image_uri: Optional[str] = None,
    model_serving_container_predict_route: Optional[str] = None,
    model_serving_container_health_route: Optional[str] = None,
    model_serving_container_command: Optional[Sequence[str]] = None,
    model_serving_container_args: Optional[Sequence[str]] = None,
    model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
    model_serving_container_ports: Optional[Sequence[int]] = None,
    model_description: Optional[str] = None,
    model_instance_schema_uri: Optional[str] = None,
    model_parameters_schema_uri: Optional[str] = None,
    model_prediction_schema_uri: Optional[str] = None,
    project: Optional[str] = None,
    location: Optional[str] = None,
    credentials: Optional[google.auth.credentials.Credentials] = None,
    labels: Optional[Dict[str, str]] = None,
    training_encryption_spec_key_name: Optional[str] = None,
    model_encryption_spec_key_name: Optional[str] = None,
    staging_bucket: Optional[str] = None,
)

Constructs a Custom Training Job from a Python script.

job = aiplatform.CustomTrainingJob( display_name='test-train', script_path='test_script.py', requirements=['pandas', 'numpy'], container_uri='gcr.io/cloud-aiplatform/training/tf-cpu.2-2:latest', model_serving_container_image_uri='gcr.io/my-trainer/serving:1', model_serving_container_predict_route='predict', model_serving_container_health_route='metadata, labels={'key': 'value'}, )

Usage with Dataset:

ds = aiplatform.TabularDataset( 'projects/my-project/locations/us-central1/datasets/12345')

job.run( ds, replica_count=1, model_display_name='my-trained-model', model_labels={'key': 'value'}, )

Usage without Dataset:

job.run(replica_count=1, model_display_name='my-trained-model)

TODO(b/169782082) add documentation about traning utilities To ensure your model gets saved in Vertex AI, write your saved model to os.environ["AIP_MODEL_DIR"] in your provided training script.

Parameters

Name	Description
display_name	`str` Required. The user-defined name of this TrainingPipeline.
script_path	`str` Required. Local path to training script.
container_uri	`str` Required: Uri of the training container image in the GCR.
requirements	`Sequence[str]` List of python packages dependencies of script.
model_serving_container_image_uri	`str` 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.
model_serving_container_predict_route	`str` 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.
model_serving_container_health_route	`str` 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.
model_serving_container_command	`Sequence[str]` 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.
model_serving_container_args	`Sequence[str]` 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.
model_serving_container_environment_variables	`Dict[str, str]` 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.
model_serving_container_ports	`Sequence[int]` 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.
model_description	`str` The description of the Model.
model_instance_schema_uri	`str` 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.
model_parameters_schema_uri	`str` 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.
model_prediction_schema_uri	`str` 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.
project	`str` Project to run training in. Overrides project set in aiplatform.init.
location	`str` Location to run training in. Overrides location set in aiplatform.init.
credentials	`auth_credentials.Credentials` Custom credentials to use to run call training service. Overrides credentials set in aiplatform.init.
labels	`Dict[str, str]` 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.
training_encryption_spec_key_name	`Optional[str]` 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.
model_encryption_spec_key_name	`Optional[str]` 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.
staging_bucket	`str` Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init.

run

run(
    dataset: Optional[
        Union[
            google.cloud.aiplatform.datasets.image_dataset.ImageDataset,
            google.cloud.aiplatform.datasets.tabular_dataset.TabularDataset,
            google.cloud.aiplatform.datasets.text_dataset.TextDataset,
            google.cloud.aiplatform.datasets.video_dataset.VideoDataset,
        ]
    ] = None,
    annotation_schema_uri: Optional[str] = None,
    model_display_name: Optional[str] = None,
    model_labels: Optional[Dict[str, str]] = None,
    base_output_dir: Optional[str] = None,
    service_account: Optional[str] = None,
    network: Optional[str] = None,
    bigquery_destination: Optional[str] = None,
    args: Optional[List[Union[float, int, str]]] = None,
    environment_variables: Optional[Dict[str, str]] = None,
    replica_count: int = 1,
    machine_type: str = "n1-standard-4",
    accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
    accelerator_count: int = 0,
    boot_disk_type: str = "pd-ssd",
    boot_disk_size_gb: int = 100,
    reduction_server_replica_count: int = 0,
    reduction_server_machine_type: Optional[str] = None,
    reduction_server_container_uri: Optional[str] = None,
    training_fraction_split: Optional[float] = None,
    validation_fraction_split: Optional[float] = None,
    test_fraction_split: Optional[float] = None,
    training_filter_split: Optional[str] = None,
    validation_filter_split: Optional[str] = None,
    test_filter_split: Optional[str] = None,
    predefined_split_column_name: Optional[str] = None,
    timestamp_split_column_name: Optional[str] = None,
    enable_web_access: bool = False,
    tensorboard: Optional[str] = None,
    sync=True,
)

Runs the custom training job.

Distributed Training Support: 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. ie: replica_count = 10 will result in 1 chief and 9 workers All replicas have same machine_type, accelerator_type, and accelerator_count

If training on a Vertex AI dataset, you can use one of the following split configurations: Data fraction splits: Any of training_fraction_split, validation_fraction_split and test_fraction_split may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data will be used for training, 10% for validation, and 10% for test.

Data filter splits:
Assigns input data to training, validation, and test sets
based on the given filters, data pieces not matched by any
filter are ignored. Currently only supported for Datasets
containing DataItems.
If any of the filters in this message are to match nothing, then
they can be set as '-' (the minus sign).
If using filter splits, all of ``training_filter_split``, ``validation_filter_split`` and
``test_filter_split`` must be provided.
Supported only for unstructured Datasets.

Predefined splits:
Assigns input data to training, validation, and test sets based on the value of a provided key.
If using predefined splits, ``predefined_split_column_name`` must be provided.
Supported only for tabular Datasets.

Timestamp splits:
Assigns input data to training, validation, and test sets
based on a provided timestamps. The youngest data pieces are
assigned to training set, next to validation set, and the oldest
to the test set.
Supported only for tabular Datasets.

Parameters

Name	Description
annotation_schema_uri	`str` 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`.
model_display_name	`str` 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.
model_labels	`Dict[str, str]` 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.
base_output_dir	`str` 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/
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.
network	`str` 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 job is not peered with any network.
bigquery_destination	`str` Provide this field if `dataset` is a BiqQuery 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_<dataset-id>_<annotation-type>_<timestamp-of-training-call>` 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"
args	`List[Unions[str, int, float]]` Command line arguments to be passed to the Python script.
environment_variables	`Dict[str, str]` 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' }
replica_count	`int` 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.
machine_type	`str` The type of machine to use for training.
accelerator_type	`str` Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4
accelerator_count	`int` The number of accelerators to attach to a worker replica.
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).
boot_disk_size_gb	`int` Size in GB of the boot disk, default is 100GB. boot disk size must be within the range of [100, 64000].
reduction_server_replica_count	`int` The number of reduction server replicas, default is 0.
reduction_server_machine_type	`str` Optional. The type of machine to use for reduction server.
reduction_server_container_uri	`str` 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
training_fraction_split	`float` Optional. The fraction of the input data that is to be used to train the Model. This is ignored if Dataset is not provided.
validation_fraction_split	`float` Optional. The fraction of the input data that is to be used to validate the Model. This is ignored if Dataset is not provided.
test_fraction_split	`float` Optional. The fraction of the input data that is to be used to evaluate the Model. This is ignored if Dataset is not provided.
training_filter_split	`str` 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.
validation_filter_split	`str` 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.
test_filter_split	`str` 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.
predefined_split_column_name	`str` 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.
timestamp_split_column_name	`str` 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.
enable_web_access	`bool` 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
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}` 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
sync	`bool` 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.

Returns

Type	Description
model	The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model.