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CustomContainerTrainingJob(
display_name: str,
container_uri: str,
command: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_image_uri: typing.Optional[str] = None,
model_serving_container_predict_route: typing.Optional[str] = None,
model_serving_container_health_route: typing.Optional[str] = None,
model_serving_container_command: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_args: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_environment_variables: typing.Optional[
typing.Dict[str, str]
] = None,
model_serving_container_ports: typing.Optional[typing.Sequence[int]] = None,
model_description: typing.Optional[str] = None,
model_instance_schema_uri: typing.Optional[str] = None,
model_parameters_schema_uri: typing.Optional[str] = None,
model_prediction_schema_uri: typing.Optional[str] = None,
explanation_metadata: typing.Optional[
google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
] = None,
explanation_parameters: typing.Optional[
google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
training_encryption_spec_key_name: typing.Optional[str] = None,
model_encryption_spec_key_name: typing.Optional[str] = None,
staging_bucket: typing.Optional[str] = None,
)
Class to launch a Custom Training Job in Vertex AI using a Container.
Properties
create_time
Time this resource was created.
display_name
Display name of this resource.
encryption_spec
Customer-managed encryption key options for this Vertex AI resource.
If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.
end_time
Optional. The time when the training job entered the
PIPELINE_STATE_SUCCEEDED
, PIPELINE_STATE_FAILED
, or
PIPELINE_STATE_CANCELLED
state.
error
Optional. Detailed error information for this training job resource.
Error information is created only when the state of the training job is
PIPELINE_STATE_FAILED
or PIPELINE_STATE_CANCELLED
.
gca_resource
The underlying resource proto representation.
has_failed
Returns true
if the training job failed, otherwise false
.
labels
User-defined labels containing metadata about this resource.
Read more about labels at https://goo.gl/xmQnxf
name
Name of this resource.
network
The full name of the Google Compute Engine
network to which this
CustomTrainingJob
should be peered.
Specify the name of the network using the format
projects/{project}/global/networks/{network}
. Replace {project} with
the project number, such as 12345
, and {network} with a network name.
Before specifying a network, private services access must be configured for the network. If private services access isn't configured, then the custom training job can't be peered with a network.
resource_name
Full qualified resource name.
start_time
Optional. The time when the training job first entered the
PIPELINE_STATE_RUNNING
state.
state
Current training state.
update_time
Time this resource was last updated.
web_access_uris
Returns the URIs used to access the custom training job.
Methods
CustomContainerTrainingJob
CustomContainerTrainingJob(
display_name: str,
container_uri: str,
command: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_image_uri: typing.Optional[str] = None,
model_serving_container_predict_route: typing.Optional[str] = None,
model_serving_container_health_route: typing.Optional[str] = None,
model_serving_container_command: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_args: typing.Optional[typing.Sequence[str]] = None,
model_serving_container_environment_variables: typing.Optional[
typing.Dict[str, str]
] = None,
model_serving_container_ports: typing.Optional[typing.Sequence[int]] = None,
model_description: typing.Optional[str] = None,
model_instance_schema_uri: typing.Optional[str] = None,
model_parameters_schema_uri: typing.Optional[str] = None,
model_prediction_schema_uri: typing.Optional[str] = None,
explanation_metadata: typing.Optional[
google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata
] = None,
explanation_parameters: typing.Optional[
google.cloud.aiplatform_v1.types.explanation.ExplanationParameters
] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
labels: typing.Optional[typing.Dict[str, str]] = None,
training_encryption_spec_key_name: typing.Optional[str] = None,
model_encryption_spec_key_name: typing.Optional[str] = None,
staging_bucket: typing.Optional[str] = None,
)
Constructs a Custom Container Training Job.
job = aiplatform.CustomContainerTrainingJob( display_name='test-train', container_uri='gcr.io/my_project_id/my_image_name:tag', command=['python3', 'run_script.py'] 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)
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. |
container_uri |
str
Required: Uri of the training container image in the GCR. |
command |
Sequence[str]
The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided |
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 |
model_parameters_schema_uri |
str
Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via |
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 |
explanation_metadata |
explain.ExplanationMetadata
Optional. Metadata describing the Model's input and output for explanation. |
explanation_parameters |
explain.ExplanationParameters
Optional. Parameters to configure explaining for Model's predictions. For more details, see |
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: |
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: |
staging_bucket |
str
Bucket used to stage source and training artifacts. Overrides staging_bucket set in aiplatform.init. |
cancel
cancel() -> None
Asynchronously attempts to cancel a training job.
The server makes a best effort to cancel the job, but the training job
can't always be cancelled. If the training job is canceled, its state
transitions to CANCELLED
and it's not deleted.
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If this training job isn't running, then a runtime error is raised. |
delete
delete(sync: bool = True) -> None
Deletes this Vertex AI resource. WARNING: This deletion is permanent.
done
done() -> bool
Method indicating whether a job has completed.
get
get(
resource_name: str,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> google.cloud.aiplatform.training_jobs._TrainingJob
Gets a training job using the resource_name
that's passed in.
Parameters | |
---|---|
Name | Description |
resource_name |
str
Required. A fully-qualified resource name or ID. |
project |
str
Optional. The name of the Google Cloud project to retrieve the training job from. This overrides the project that was set by |
location |
str
Optional. The Google Cloud region from where the training job is retrieved. This region overrides the region that was set by |
credentials |
auth_credentials.Credentials
Optional. The credentials that are used to upload this model. These credentials override the credentials set by |
Exceptions | |
---|---|
Type | Description |
ValueError |
A ValueError is raised if the task definition of the retrieved training job doesn't match the custom training task definition. |
get_model
get_model(sync=True) -> google.cloud.aiplatform.models.Model
Returns the Vertex AI model produced by this training job.
Parameter | |
---|---|
Name | Description |
sync |
bool
If set to |
Exceptions | |
---|---|
Type | Description |
RuntimeError |
A runtime error is raised if the training job failed or if a model wasn't produced by the training job. |
list
list(
filter: typing.Optional[str] = None,
order_by: typing.Optional[str] = None,
project: typing.Optional[str] = None,
location: typing.Optional[str] = None,
credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.base.VertexAiResourceNoun]
Lists all instances of this training job resource.
The following shows an example of how to call CustomTrainingJob.list
:
aiplatform.CustomTrainingJob.list(
filter='display_name="experiment_a27"',
order_by='create_time desc'
)
Parameters | |
---|---|
Name | Description |
filter |
str
Optional. An expression for filtering the results of the request. For field names, snake_case and camelCase are supported. |
order_by |
str
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 |
project |
str
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 |
location |
str
Optional. The Google Cloud region from where the training job resources are retrieved. This region overrides the region that was set by |
credentials |
auth_credentials.Credentials
Optional. The credentials that are used to retrieve list. These credentials override the credentials set by |
Returns | |
---|---|
Type | Description |
List[VertexAiResourceNoun] |
A list of training job resources. |
run
run(
dataset: typing.Optional[
typing.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: typing.Optional[str] = None,
model_display_name: typing.Optional[str] = None,
model_labels: typing.Optional[typing.Dict[str, str]] = None,
model_id: typing.Optional[str] = None,
parent_model: typing.Optional[str] = None,
is_default_version: typing.Optional[bool] = True,
model_version_aliases: typing.Optional[typing.Sequence[str]] = None,
model_version_description: typing.Optional[str] = None,
base_output_dir: typing.Optional[str] = None,
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
bigquery_destination: typing.Optional[str] = None,
args: typing.Optional[typing.List[typing.Union[str, float, int]]] = None,
environment_variables: typing.Optional[typing.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: typing.Optional[str] = None,
reduction_server_container_uri: typing.Optional[str] = None,
training_fraction_split: typing.Optional[float] = None,
validation_fraction_split: typing.Optional[float] = None,
test_fraction_split: typing.Optional[float] = None,
training_filter_split: typing.Optional[str] = None,
validation_filter_split: typing.Optional[str] = None,
test_filter_split: typing.Optional[str] = None,
predefined_split_column_name: typing.Optional[str] = None,
timestamp_split_column_name: typing.Optional[str] = None,
timeout: typing.Optional[int] = None,
restart_job_on_worker_restart: bool = False,
enable_web_access: bool = False,
enable_dashboard_access: bool = False,
tensorboard: typing.Optional[str] = None,
sync=True,
create_request_timeout: typing.Optional[float] = None,
disable_retries: bool = False,
persistent_resource_id: typing.Optional[str] = None,
tpu_topology: typing.Optional[str] = None,
scheduling_strategy: typing.Optional[
google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy
] = None,
reservation_affinity_type: typing.Optional[
typing.Literal["NO_RESERVATION", "ANY_RESERVATION", "SPECIFIC_RESERVATION"]
] = None,
reservation_affinity_key: typing.Optional[str] = None,
reservation_affinity_values: typing.Optional[typing.List[str]] = None,
max_wait_duration: typing.Optional[int] = None,
) -> typing.Optional[google.cloud.aiplatform.models.Model]
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 |
dataset |
Union[datasets.ImageDataset,datasets.TabularDataset,datasets.TextDataset,datasets.VideoDataset]
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"] |
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 |
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. |
model_id |
str
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 |
parent_model |
str
Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of |
is_default_version |
bool
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. |
model_version_aliases |
Sequence[str]
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 |
model_version_description |
str
Optional. The description of the model version being uploaded by this job. |
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 network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
bigquery_destination |
str
Provide this field if |
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 |
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 { |
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 |
timeout |
int
The maximum job running time in seconds. The default is 7 days. |
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. |
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 |
enable_dashboard_access |
bool
Whether you want Vertex AI to enable access to the customized dashboard to training containers. |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
disable_retries |
bool
Indicates if the job should retry for internal errors after the job starts running. If True, overrides |
persistent_resource_id |
str
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. |
tpu_topology |
str
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. |
scheduling_strategy |
gca_custom_job_compat.Scheduling.Strategy
Optional. Indicates the job scheduling strategy. |
reservation_affinity_type |
str
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. |
reservation_affinity_key |
str
Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use |
reservation_affinity_values |
List[str]
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}' |
max_wait_duration |
int
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. |
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. |
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If Training job has already been run, staging_bucket has not been set, or model_display_name was provided but required arguments were not provided in constructor. |
Returns | |
---|---|
Type | Description |
model |
The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model. |
submit
submit(
dataset: typing.Optional[
typing.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: typing.Optional[str] = None,
model_display_name: typing.Optional[str] = None,
model_labels: typing.Optional[typing.Dict[str, str]] = None,
model_id: typing.Optional[str] = None,
parent_model: typing.Optional[str] = None,
is_default_version: typing.Optional[bool] = True,
model_version_aliases: typing.Optional[typing.Sequence[str]] = None,
model_version_description: typing.Optional[str] = None,
base_output_dir: typing.Optional[str] = None,
service_account: typing.Optional[str] = None,
network: typing.Optional[str] = None,
bigquery_destination: typing.Optional[str] = None,
args: typing.Optional[typing.List[typing.Union[str, float, int]]] = None,
environment_variables: typing.Optional[typing.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: typing.Optional[str] = None,
reduction_server_container_uri: typing.Optional[str] = None,
training_fraction_split: typing.Optional[float] = None,
validation_fraction_split: typing.Optional[float] = None,
test_fraction_split: typing.Optional[float] = None,
training_filter_split: typing.Optional[str] = None,
validation_filter_split: typing.Optional[str] = None,
test_filter_split: typing.Optional[str] = None,
predefined_split_column_name: typing.Optional[str] = None,
timestamp_split_column_name: typing.Optional[str] = None,
timeout: typing.Optional[int] = None,
restart_job_on_worker_restart: bool = False,
enable_web_access: bool = False,
enable_dashboard_access: bool = False,
tensorboard: typing.Optional[str] = None,
sync=True,
create_request_timeout: typing.Optional[float] = None,
disable_retries: bool = False,
persistent_resource_id: typing.Optional[str] = None,
tpu_topology: typing.Optional[str] = None,
scheduling_strategy: typing.Optional[
google.cloud.aiplatform_v1.types.custom_job.Scheduling.Strategy
] = None,
reservation_affinity_type: typing.Optional[
typing.Literal["NO_RESERVATION", "ANY_RESERVATION", "SPECIFIC_RESERVATION"]
] = None,
reservation_affinity_key: typing.Optional[str] = None,
reservation_affinity_values: typing.Optional[typing.List[str]] = None,
max_wait_duration: typing.Optional[int] = None,
) -> typing.Optional[google.cloud.aiplatform.models.Model]
Submits the custom training job without blocking until completion.
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 |
dataset |
Union[datasets.ImageDataset,datasets.TabularDataset,datasets.TextDataset,datasets.VideoDataset]
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"] |
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 |
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. |
model_id |
str
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 |
parent_model |
str
Optional. The resource name or model ID of an existing model. The new model uploaded by this job will be a version of |
is_default_version |
bool
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. |
model_version_aliases |
Sequence[str]
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 |
model_version_description |
str
Optional. The description of the model version being uploaded by this job. |
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 network set in aiplatform.init will be used. Otherwise, the job is not peered with any network. |
bigquery_destination |
str
Provide this field if |
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 |
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 { |
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 |
timeout |
int
The maximum job running time in seconds. The default is 7 days. |
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. |
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 |
enable_dashboard_access |
bool
Whether you want Vertex AI to enable access to the customized dashboard to training containers. |
tensorboard |
str
Optional. The name of a Vertex AI Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
disable_retries |
bool
Indicates if the job should retry for internal errors after the job starts running. If True, overrides |
persistent_resource_id |
str
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. |
tpu_topology |
str
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. |
scheduling_strategy |
gca_custom_job_compat.Scheduling.Strategy
Optional. Indicates the job scheduling strategy. |
reservation_affinity_type |
str
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. |
reservation_affinity_key |
str
Optional. Corresponds to the label key of a reservation resource. To target a SPECIFIC_RESERVATION by name, use |
reservation_affinity_values |
List[str]
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}' |
max_wait_duration |
int
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. |
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. |
Exceptions | |
---|---|
Type | Description |
RuntimeError |
If Training job has already been run, staging_bucket has not been set, or model_display_name was provided but required arguments were not provided in constructor. |
Returns | |
---|---|
Type | Description |
model |
The trained Vertex AI Model resource or None if training did not produce a Vertex AI Model. |
to_dict
to_dict() -> typing.Dict[str, typing.Any]
Returns the resource proto as a dictionary.
wait
wait()
Helper method that blocks until all futures are complete.
wait_for_resource_creation
wait_for_resource_creation() -> None
Waits until the resource has been created.