Source code for airflow.providers.google.cloud.operators.vertex_ai.hyperparameter_tuning_job

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""This module contains Google Vertex AI operators.

.. spelling::

    irreproducible
    codepoints
    Tensorboard
    aiplatform
    myVPC
"""
from __future__ import annotations

from typing import TYPE_CHECKING, Sequence

from google.api_core.exceptions import NotFound
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.aiplatform import gapic, hyperparameter_tuning
from google.cloud.aiplatform_v1.types import HyperparameterTuningJob

from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.vertex_ai.hyperparameter_tuning_job import (
    HyperparameterTuningJobHook,
)
from airflow.providers.google.cloud.links.vertex_ai import (
    VertexAIHyperparameterTuningJobListLink,
    VertexAITrainingLink,
)

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class CreateHyperparameterTuningJobOperator(BaseOperator): """ Create Hyperparameter Tuning job :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param display_name: Required. The user-defined name of the HyperparameterTuningJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. :param metric_spec: Required. Dictionary representing metrics to optimize. The dictionary key is the metric_id, which is reported by your training job, and the dictionary value is the optimization goal of the metric('minimize' or 'maximize'). example: metric_spec = {'loss': 'minimize', 'accuracy': 'maximize'} :param parameter_spec: Required. Dictionary representing parameters to optimize. The dictionary key is the metric_id, which is passed into your training job as a command line key word argument, and the dictionary value is the parameter specification of the metric. :param max_trial_count: Required. The desired total number of Trials. :param parallel_trial_count: Required. The desired number of Trials to run in parallel. :param worker_pool_specs: Required. The spec of the worker pools including machine type and Docker image. Can provided as a list of dictionaries or list of WorkerPoolSpec proto messages. :param base_output_dir: Optional. GCS output directory of job. If not provided a timestamped directory in the staging directory will be used. :param custom_job_labels: Optional. The labels with user-defined metadata to organize CustomJobs. 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. :param custom_job_encryption_spec_key_name: Optional.Customer-managed encryption key name for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key. :param staging_bucket: Optional. Bucket for produced custom job artifacts. Overrides staging_bucket set in aiplatform.init. :param max_failed_trial_count: Optional. The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Vertex AI decides how many Trials must fail before the whole job fails. :param search_algorithm: The search algorithm specified for the Study. Accepts one of the following: `None` - If you do not specify an algorithm, your job uses the default Vertex AI algorithm. The default algorithm applies Bayesian optimization to arrive at the optimal solution with a more effective search over the parameter space. 'grid' - A simple grid search within the feasible space. This option is particularly useful if you want to specify a quantity of trials that is greater than the number of points in the feasible space. In such cases, if you do not specify a grid search, the Vertex AI default algorithm may generate duplicate suggestions. To use grid search, all parameter specs must be of type `IntegerParameterSpec`, `CategoricalParameterSpace`, or `DiscreteParameterSpec`. 'random' - A simple random search within the feasible space. :param measurement_selection: This indicates which measurement to use if/when the service automatically selects the final measurement from previously reported intermediate measurements. Accepts: 'best', 'last' Choose this based on two considerations: A) Do you expect your measurements to monotonically improve? If so, choose 'last'. On the other hand, if you're in a situation where your system can "over-train" and you expect the performance to get better for a while but then start declining, choose 'best'. B) Are your measurements significantly noisy and/or irreproducible? If so, 'best' will tend to be over-optimistic, and it may be better to choose 'last'. If both or neither of (A) and (B) apply, it doesn't matter which selection type is chosen. :param hyperparameter_tuning_job_labels: Optional. The labels with user-defined metadata to organize HyperparameterTuningJobs. 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. :param hyperparameter_tuning_job_encryption_spec_key_name: Optional. Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key. :param service_account: Optional. Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. :param network: Optional. 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. :param timeout: The maximum job running time in seconds. The default is 7 days. :param restart_job_on_worker_restart: 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. :param enable_web_access: 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 :param tensorboard: Optional. The name of a Vertex AI [Tensorboard][google.cloud.aiplatform.v1beta1.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 :param sync: Whether to execute this method synchronously. If False, this method will unblock and it will be executed in a concurrent Future. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = [ "region", "project_id", "impersonation_chain",
] def __init__( self, *, project_id: str, region: str, display_name: str, metric_spec: dict[str, str], parameter_spec: dict[str, hyperparameter_tuning._ParameterSpec], max_trial_count: int, parallel_trial_count: int, # START: CustomJob param worker_pool_specs: list[dict] | list[gapic.WorkerPoolSpec], base_output_dir: str | None = None, custom_job_labels: dict[str, str] | None = None, custom_job_encryption_spec_key_name: str | None = None, staging_bucket: str | None = None, # END: CustomJob param max_failed_trial_count: int = 0, search_algorithm: str | None = None, measurement_selection: str | None = "best", hyperparameter_tuning_job_labels: dict[str, str] | None = None, hyperparameter_tuning_job_encryption_spec_key_name: str | None = None, # START: run param service_account: str | None = None, network: str | None = None, timeout: int | None = None, # seconds restart_job_on_worker_restart: bool = False, enable_web_access: bool = False, tensorboard: str | None = None, sync: bool = True, # END: run param gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.display_name = display_name self.metric_spec = metric_spec self.parameter_spec = parameter_spec self.max_trial_count = max_trial_count self.parallel_trial_count = parallel_trial_count self.worker_pool_specs = worker_pool_specs self.base_output_dir = base_output_dir self.custom_job_labels = custom_job_labels self.custom_job_encryption_spec_key_name = custom_job_encryption_spec_key_name self.staging_bucket = staging_bucket self.max_failed_trial_count = max_failed_trial_count self.search_algorithm = search_algorithm self.measurement_selection = measurement_selection self.hyperparameter_tuning_job_labels = hyperparameter_tuning_job_labels self.hyperparameter_tuning_job_encryption_spec_key_name = ( hyperparameter_tuning_job_encryption_spec_key_name ) self.service_account = service_account self.network = network self.timeout = timeout self.restart_job_on_worker_restart = restart_job_on_worker_restart self.enable_web_access = enable_web_access self.tensorboard = tensorboard self.sync = sync self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain self.hook: HyperparameterTuningJobHook | None = None
[docs] def execute(self, context: Context): self.log.info("Creating Hyperparameter Tuning job") self.hook = HyperparameterTuningJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) result = self.hook.create_hyperparameter_tuning_job( project_id=self.project_id, region=self.region, display_name=self.display_name, metric_spec=self.metric_spec, parameter_spec=self.parameter_spec, max_trial_count=self.max_trial_count, parallel_trial_count=self.parallel_trial_count, worker_pool_specs=self.worker_pool_specs, base_output_dir=self.base_output_dir, custom_job_labels=self.custom_job_labels, custom_job_encryption_spec_key_name=self.custom_job_encryption_spec_key_name, staging_bucket=self.staging_bucket, max_failed_trial_count=self.max_failed_trial_count, search_algorithm=self.search_algorithm, measurement_selection=self.measurement_selection, hyperparameter_tuning_job_labels=self.hyperparameter_tuning_job_labels, hyperparameter_tuning_job_encryption_spec_key_name=( self.hyperparameter_tuning_job_encryption_spec_key_name ), service_account=self.service_account, network=self.network, timeout=self.timeout, restart_job_on_worker_restart=self.restart_job_on_worker_restart, enable_web_access=self.enable_web_access, tensorboard=self.tensorboard, sync=self.sync, ) hyperparameter_tuning_job = result.to_dict() hyperparameter_tuning_job_id = self.hook.extract_hyperparameter_tuning_job_id( hyperparameter_tuning_job ) self.log.info("Hyperparameter Tuning job was created. Job id: %s", hyperparameter_tuning_job_id) self.xcom_push(context, key="hyperparameter_tuning_job_id", value=hyperparameter_tuning_job_id) VertexAITrainingLink.persist( context=context, task_instance=self, training_id=hyperparameter_tuning_job_id ) return hyperparameter_tuning_job
[docs] def on_kill(self) -> None: """ Callback called when the operator is killed. Cancel any running job. """ if self.hook: self.hook.cancel_hyperparameter_tuning_job()
[docs]class GetHyperparameterTuningJobOperator(BaseOperator): """ Gets a HyperparameterTuningJob :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param hyperparameter_tuning_job_id: Required. The name of the HyperparameterTuningJob resource. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. :param gcp_conn_id: The connection ID to use connecting to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). """
[docs] template_fields = ("region", "hyperparameter_tuning_job_id", "project_id", "impersonation_chain")
def __init__( self, *, region: str, project_id: str, hyperparameter_tuning_job_id: str, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.hyperparameter_tuning_job_id = hyperparameter_tuning_job_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = HyperparameterTuningJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Get hyperparameter tuning job: %s", self.hyperparameter_tuning_job_id) result = hook.get_hyperparameter_tuning_job( project_id=self.project_id, region=self.region, hyperparameter_tuning_job=self.hyperparameter_tuning_job_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAITrainingLink.persist( context=context, task_instance=self, training_id=self.hyperparameter_tuning_job_id ) self.log.info("Hyperparameter tuning job was gotten.") return HyperparameterTuningJob.to_dict(result) except NotFound: self.log.info( "The Hyperparameter tuning job %s does not exist.", self.hyperparameter_tuning_job_id
)
[docs]class DeleteHyperparameterTuningJobOperator(BaseOperator): """ Deletes a HyperparameterTuningJob. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param hyperparameter_tuning_job_id: Required. The name of the HyperparameterTuningJob resource to be deleted. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. """
[docs] template_fields = ("region", "project_id", "hyperparameter_tuning_job_id", "impersonation_chain")
def __init__( self, *, hyperparameter_tuning_job_id: str, region: str, project_id: str, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.hyperparameter_tuning_job_id = hyperparameter_tuning_job_id self.region = region self.project_id = project_id self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = HyperparameterTuningJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) try: self.log.info("Deleting Hyperparameter Tuning job: %s", self.hyperparameter_tuning_job_id) operation = hook.delete_hyperparameter_tuning_job( region=self.region, project_id=self.project_id, hyperparameter_tuning_job=self.hyperparameter_tuning_job_id, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) hook.wait_for_operation(timeout=self.timeout, operation=operation) self.log.info("Hyperparameter Tuning job was deleted.") except NotFound: self.log.info( "The Hyperparameter Tuning Job ID %s does not exist.", self.hyperparameter_tuning_job_id
)
[docs]class ListHyperparameterTuningJobOperator(BaseOperator): """ Lists HyperparameterTuningJobs in a Location. :param project_id: Required. The ID of the Google Cloud project that the service belongs to. :param region: Required. The ID of the Google Cloud region that the service belongs to. :param filter: The standard list filter. Supported fields: - ``display_name`` supports = and !=. - ``state`` supports = and !=. - ``model_display_name`` supports = and != Some examples of using the filter are: - ``state="JOB_STATE_SUCCEEDED" AND display_name="my_job"`` - ``state="JOB_STATE_RUNNING" OR display_name="my_job"`` - ``NOT display_name="my_job"`` - ``state="JOB_STATE_FAILED"`` :param page_size: The standard list page size. :param page_token: The standard list page token. :param read_mask: Mask specifying which fields to read. :param retry: Designation of what errors, if any, should be retried. :param timeout: The timeout for this request. :param metadata: Strings which should be sent along with the request as metadata. """
[docs] template_fields = [ "region", "project_id", "impersonation_chain",
] def __init__( self, *, region: str, project_id: str, page_size: int | None = None, page_token: str | None = None, filter: str | None = None, read_mask: str | None = None, retry: Retry | _MethodDefault = DEFAULT, timeout: float | None = None, metadata: Sequence[tuple[str, str]] = (), gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, **kwargs, ) -> None: super().__init__(**kwargs) self.region = region self.project_id = project_id self.page_size = page_size self.page_token = page_token self.filter = filter self.read_mask = read_mask self.retry = retry self.timeout = timeout self.metadata = metadata self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: Context): hook = HyperparameterTuningJobHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) results = hook.list_hyperparameter_tuning_jobs( region=self.region, project_id=self.project_id, page_size=self.page_size, page_token=self.page_token, filter=self.filter, read_mask=self.read_mask, retry=self.retry, timeout=self.timeout, metadata=self.metadata, ) VertexAIHyperparameterTuningJobListLink.persist(context=context, task_instance=self) return [HyperparameterTuningJob.to_dict(result) for result in results]

Was this entry helpful?