airflow hyperparameter_tuning_job 源码
airflow hyperparameter_tuning_job 代码
文件路径：/airflow/providers/google/cloud/hooks/vertex_ai/hyperparameter_tuning_job.py
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"""This module contains a Google Cloud Vertex AI hook.
.. spelling::
irreproducible
codepoints
Tensorboard
aiplatform
myVPC
"""
from __future__ import annotations
from typing import Sequence
from google.api_core.client_options import ClientOptions
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.operation import Operation
from google.api_core.retry import Retry
from google.cloud.aiplatform import CustomJob, HyperparameterTuningJob, gapic, hyperparameter_tuning
from google.cloud.aiplatform_v1 import JobServiceClient, types
from google.cloud.aiplatform_v1.services.job_service.pagers import ListHyperparameterTuningJobsPager
from airflow import AirflowException
from airflow.providers.google.common.hooks.base_google import GoogleBaseHook
class HyperparameterTuningJobHook(GoogleBaseHook):
"""Hook for Google Cloud Vertex AI Hyperparameter Tuning Job APIs."""
def __init__(
self,
gcp_conn_id: str = "google_cloud_default",
delegate_to: str | None = None,
impersonation_chain: str | Sequence[str] | None = None,
) -> None:
super().__init__(
gcp_conn_id=gcp_conn_id,
delegate_to=delegate_to,
impersonation_chain=impersonation_chain,
)
self._hyperparameter_tuning_job: HyperparameterTuningJob | None = None
def get_job_service_client(self, region: str | None = None) -> JobServiceClient:
"""Returns JobServiceClient."""
if region and region != 'global':
client_options = ClientOptions(api_endpoint=f'{region}-aiplatform.googleapis.com:443')
else:
client_options = ClientOptions()
return JobServiceClient(
credentials=self.get_credentials(), client_info=self.client_info, client_options=client_options
)
def get_hyperparameter_tuning_job_object(
self,
display_name: str,
custom_job: CustomJob,
metric_spec: dict[str, str],
parameter_spec: dict[str, hyperparameter_tuning._ParameterSpec],
max_trial_count: int,
parallel_trial_count: int,
max_failed_trial_count: int = 0,
search_algorithm: str | None = None,
measurement_selection: str | None = "best",
project: str | None = None,
location: str | None = None,
labels: dict[str, str] | None = None,
encryption_spec_key_name: str | None = None,
) -> HyperparameterTuningJob:
"""Returns HyperparameterTuningJob object"""
return HyperparameterTuningJob(
display_name=display_name,
custom_job=custom_job,
metric_spec=metric_spec,
parameter_spec=parameter_spec,
max_trial_count=max_trial_count,
parallel_trial_count=parallel_trial_count,
max_failed_trial_count=max_failed_trial_count,
search_algorithm=search_algorithm,
measurement_selection=measurement_selection,
project=project,
location=location,
credentials=self.get_credentials(),
labels=labels,
encryption_spec_key_name=encryption_spec_key_name,
)
def get_custom_job_object(
self,
display_name: str,
worker_pool_specs: list[dict] | list[gapic.WorkerPoolSpec],
base_output_dir: str | None = None,
project: str | None = None,
location: str | None = None,
labels: dict[str, str] | None = None,
encryption_spec_key_name: str | None = None,
staging_bucket: str | None = None,
) -> CustomJob:
"""Returns CustomJob object"""
return CustomJob(
display_name=display_name,
worker_pool_specs=worker_pool_specs,
base_output_dir=base_output_dir,
project=project,
location=location,
credentials=self.get_credentials,
labels=labels,
encryption_spec_key_name=encryption_spec_key_name,
staging_bucket=staging_bucket,
)
@staticmethod
def extract_hyperparameter_tuning_job_id(obj: dict) -> str:
"""Returns unique id of the hyperparameter_tuning_job."""
return obj["name"].rpartition("/")[-1]
def wait_for_operation(self, operation: Operation, timeout: float | None = None):
"""Waits for long-lasting operation to complete."""
try:
return operation.result(timeout=timeout)
except Exception:
error = operation.exception(timeout=timeout)
raise AirflowException(error)
def cancel_hyperparameter_tuning_job(self) -> None:
"""Cancel HyperparameterTuningJob"""
if self._hyperparameter_tuning_job:
self._hyperparameter_tuning_job.cancel()
@GoogleBaseHook.fallback_to_default_project_id
def create_hyperparameter_tuning_job(
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
) -> HyperparameterTuningJob:
"""
Create 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 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.
"""
custom_job = self.get_custom_job_object(
project=project_id,
location=region,
display_name=display_name,
worker_pool_specs=worker_pool_specs,
base_output_dir=base_output_dir,
labels=custom_job_labels,
encryption_spec_key_name=custom_job_encryption_spec_key_name,
staging_bucket=staging_bucket,
)
self._hyperparameter_tuning_job = self.get_hyperparameter_tuning_job_object(
project=project_id,
location=region,
display_name=display_name,
custom_job=custom_job,
metric_spec=metric_spec,
parameter_spec=parameter_spec,
max_trial_count=max_trial_count,
parallel_trial_count=parallel_trial_count,
max_failed_trial_count=max_failed_trial_count,
search_algorithm=search_algorithm,
measurement_selection=measurement_selection,
labels=hyperparameter_tuning_job_labels,
encryption_spec_key_name=hyperparameter_tuning_job_encryption_spec_key_name,
)
self._hyperparameter_tuning_job.run(
service_account=service_account,
network=network,
timeout=timeout, # seconds
restart_job_on_worker_restart=restart_job_on_worker_restart,
enable_web_access=enable_web_access,
tensorboard=tensorboard,
sync=sync,
)
self._hyperparameter_tuning_job.wait()
return self._hyperparameter_tuning_job
@GoogleBaseHook.fallback_to_default_project_id
def get_hyperparameter_tuning_job(
self,
project_id: str,
region: str,
hyperparameter_tuning_job: str,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float | None = None,
metadata: Sequence[tuple[str, str]] = (),
) -> types.HyperparameterTuningJob:
"""
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: 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.
"""
client = self.get_job_service_client(region)
name = client.hyperparameter_tuning_job_path(project_id, region, hyperparameter_tuning_job)
result = client.get_hyperparameter_tuning_job(
request={
'name': name,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
@GoogleBaseHook.fallback_to_default_project_id
def list_hyperparameter_tuning_jobs(
self,
project_id: str,
region: str,
filter: str | None = None,
page_size: int | None = None,
page_token: str | None = None,
read_mask: str | None = None,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float | None = None,
metadata: Sequence[tuple[str, str]] = (),
) -> ListHyperparameterTuningJobsPager:
"""
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.
"""
client = self.get_job_service_client(region)
parent = client.common_location_path(project_id, region)
result = client.list_hyperparameter_tuning_jobs(
request={
'parent': parent,
'filter': filter,
'page_size': page_size,
'page_token': page_token,
'read_mask': read_mask,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
@GoogleBaseHook.fallback_to_default_project_id
def delete_hyperparameter_tuning_job(
self,
project_id: str,
region: str,
hyperparameter_tuning_job: str,
retry: Retry | _MethodDefault = DEFAULT,
timeout: float | None = None,
metadata: Sequence[tuple[str, str]] = (),
) -> Operation:
"""
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: 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.
"""
client = self.get_job_service_client(region)
name = client.hyperparameter_tuning_job_path(project_id, region, hyperparameter_tuning_job)
result = client.delete_hyperparameter_tuning_job(
request={
'name': name,
},
retry=retry,
timeout=timeout,
metadata=metadata,
)
return result
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