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)
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
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).
"""
template_fields = [
'region',
'project_id',
'impersonation_chain',
]
operator_extra_links = (VertexAITrainingLink(), )
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: Union[List[Dict], List[gapic.WorkerPoolSpec]],
base_output_dir: Optional[str] = None,
custom_job_labels: Optional[Dict[str, str]] = None,
custom_job_encryption_spec_key_name: Optional[str] = None,
staging_bucket: Optional[str] = None,
# END: CustomJob param
max_failed_trial_count: int = 0,
search_algorithm: Optional[str] = None,
measurement_selection: Optional[str] = "best",
hyperparameter_tuning_job_labels: Optional[Dict[str, str]] = None,
hyperparameter_tuning_job_encryption_spec_key_name: Optional[
str] = None,
# START: run param
service_account: Optional[str] = None,
network: Optional[str] = None,
timeout: Optional[int] = None, # seconds
restart_job_on_worker_restart: bool = False,
enable_web_access: bool = False,
tensorboard: Optional[str] = None,
sync: bool = True,
# END: run param
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = 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 = None # type: Optional[HyperparameterTuningJobHook]
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
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()
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).
"""
template_fields = ("region", "hyperparameter_tuning_job_id", "project_id",
"impersonation_chain")
operator_extra_links = (VertexAITrainingLink(), )
def __init__(
self,
*,
region: str,
project_id: str,
hyperparameter_tuning_job_id: str,
retry: Union[Retry, _MethodDefault] = DEFAULT,
timeout: Optional[float] = None,
metadata: Sequence[Tuple[str, str]] = (),
gcp_conn_id: str = "google_cloud_default",
delegate_to: Optional[str] = None,
impersonation_chain: Optional[Union[str, Sequence[str]]] = 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
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)