Class CustomTrainingJob (0.9.0)

Version 0.9.0

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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,
    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 AI Platform using a script.

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

Inheritance

builtins.object > google.cloud.aiplatform.base.AiPlatformResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.AiPlatformResourceNounWithFutureManager > 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,
    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)

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')

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 AI Platform, 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 AI Platform 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 AI Platform 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 AI Platform.
model_serving_container_health_route	str If the training produces a managed AI Platform 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 AI Platform 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.
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,
    base_output_dir: Optional[str] = None,
    service_account: Optional[str] = None,
    network: Optional[str] = None,
    bigquery_destination: Optional[str] = None,
    args: Optional[List[Union[str, float, int]]] = None,
    environment_variables: Optional[Dict[str, str]] = None,
    replica_count: int = 0,
    machine_type: str = "n1-standard-4",
    accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED",
    accelerator_count: int = 0,
    training_fraction_split: float = 0.8,
    validation_fraction_split: float = 0.1,
    test_fraction_split: float = 0.1,
    predefined_split_column_name: 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

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 AI Platform.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.

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 AI Platform 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.
base_output_dir	str GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. AI Platform 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.
training_fraction_split	float 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 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 The fraction of the input data that is to be used to evaluate the Model. 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.
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 AI Platform Model resource or None if training did not produce an AI Platform Model.