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training_jobs.py
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training_jobs.py
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# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed 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.
#
import datetime
import time
from typing import Dict, List, Optional, Sequence, Tuple, Union
import abc
from google.auth import credentials as auth_credentials
from google.cloud.aiplatform import base
from google.cloud.aiplatform import constants
from google.cloud.aiplatform import datasets
from google.cloud.aiplatform import initializer
from google.cloud.aiplatform import models
from google.cloud.aiplatform import schema
from google.cloud.aiplatform import utils
from google.cloud.aiplatform.utils import console_utils
from google.cloud.aiplatform.compat.types import (
env_var as gca_env_var,
io as gca_io,
model as gca_model,
pipeline_state as gca_pipeline_state,
training_pipeline as gca_training_pipeline,
)
from google.cloud.aiplatform.utils import _timestamped_gcs_dir
from google.cloud.aiplatform.utils import source_utils
from google.cloud.aiplatform.utils import worker_spec_utils
from google.cloud.aiplatform.v1.schema.trainingjob import (
definition_v1 as training_job_inputs,
)
from google.rpc import code_pb2
from google.rpc import status_pb2
import proto
_LOGGER = base.Logger(__name__)
_PIPELINE_COMPLETE_STATES = set(
[
gca_pipeline_state.PipelineState.PIPELINE_STATE_SUCCEEDED,
gca_pipeline_state.PipelineState.PIPELINE_STATE_FAILED,
gca_pipeline_state.PipelineState.PIPELINE_STATE_CANCELLED,
gca_pipeline_state.PipelineState.PIPELINE_STATE_PAUSED,
]
)
class _TrainingJob(base.VertexAiResourceNounWithFutureManager):
client_class = utils.PipelineClientWithOverride
_is_client_prediction_client = False
_resource_noun = "trainingPipelines"
_getter_method = "get_training_pipeline"
_list_method = "list_training_pipelines"
_delete_method = "delete_training_pipeline"
def __init__(
self,
display_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
):
"""Constructs a Training Job.
Args:
display_name (str):
Required. The user-defined name of this TrainingPipeline.
project (str):
Optional project to retrieve model from. If not set, project set in
aiplatform.init will be used.
location (str):
Optional location to retrieve model from. If not set, location set in
aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Optional credentials to use to retrieve the model.
training_encryption_spec_key_name (Optional[str]):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this TrainingPipeline will be secured by this key.
Note: Model trained by this TrainingPipeline is also secured
by this key if ``model_to_upload`` is not set separately.
Overrides encryption_spec_key_name set in aiplatform.init.
model_encryption_spec_key_name (Optional[str]):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the model. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, the trained Model will be secured by this key.
Overrides encryption_spec_key_name set in aiplatform.init.
"""
utils.validate_display_name(display_name)
super().__init__(project=project, location=location, credentials=credentials)
self._display_name = display_name
self._training_encryption_spec = initializer.global_config.get_encryption_spec(
encryption_spec_key_name=training_encryption_spec_key_name
)
self._model_encryption_spec = initializer.global_config.get_encryption_spec(
encryption_spec_key_name=model_encryption_spec_key_name
)
self._gca_resource = None
@property
@classmethod
@abc.abstractmethod
def _supported_training_schemas(cls) -> Tuple[str]:
"""List of supported schemas for this training job."""
pass
@property
def start_time(self) -> Optional[datetime.datetime]:
"""Time when the TrainingJob entered the `PIPELINE_STATE_RUNNING` for
the first time."""
self._sync_gca_resource()
return getattr(self._gca_resource, "start_time")
@property
def end_time(self) -> Optional[datetime.datetime]:
"""Time when the TrainingJob resource entered the `PIPELINE_STATE_SUCCEEDED`,
`PIPELINE_STATE_FAILED`, `PIPELINE_STATE_CANCELLED` state."""
self._sync_gca_resource()
return getattr(self._gca_resource, "end_time")
@property
def error(self) -> Optional[status_pb2.Status]:
"""Detailed error info for this TrainingJob resource. Only populated when
the TrainingJob's state is `PIPELINE_STATE_FAILED` or
`PIPELINE_STATE_CANCELLED`."""
self._sync_gca_resource()
return getattr(self._gca_resource, "error")
@classmethod
def get(
cls,
resource_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
) -> "_TrainingJob":
"""Get Training Job for the given resource_name.
Args:
resource_name (str):
Required. A fully-qualified resource name or ID.
project (str):
Optional project to retrieve training job from. If not set, project
set in aiplatform.init will be used.
location (str):
Optional location to retrieve training job from. If not set, location
set in aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
Raises:
ValueError: If the retrieved training job's training task definition
doesn't match the custom training task definition.
Returns:
A Vertex AI Training Job
"""
# Create job with dummy parameters
# These parameters won't be used as user can not run the job again.
# If they try, an exception will be raised.
self = cls._empty_constructor(
project=project,
location=location,
credentials=credentials,
resource_name=resource_name,
)
self._gca_resource = self._get_gca_resource(resource_name=resource_name)
if (
self._gca_resource.training_task_definition
not in cls._supported_training_schemas
):
raise ValueError(
f"The retrieved job's training task definition "
f"is {self._gca_resource.training_task_definition}, "
f"which is not compatible with {cls.__name__}."
)
return self
@classmethod
def _get_and_return_subclass(
cls,
resource_name: str,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
) -> "_TrainingJob":
"""Retrieve Training Job subclass for the given resource_name without
knowing the training_task_definition.
Example usage:
```
aiplatform.training_jobs._TrainingJob._get_and_return_subclass(
'projects/.../locations/.../trainingPipelines/12345'
)
# Returns: <google.cloud.aiplatform.training_jobs.AutoMLImageTrainingJob>
```
Args:
resource_name (str):
Required. A fully-qualified resource name or ID.
project (str):
Optional project to retrieve dataset from. If not set, project
set in aiplatform.init will be used.
location (str):
Optional location to retrieve dataset from. If not set, location
set in aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to upload this model. Overrides
credentials set in aiplatform.init.
Returns:
A Vertex AI Training Job
"""
# Retrieve training pipeline resource before class construction
client = cls._instantiate_client(location=location, credentials=credentials)
gca_training_pipeline = getattr(client, cls._getter_method)(name=resource_name)
schema_uri = gca_training_pipeline.training_task_definition
# Collect all AutoML training job classes and CustomTrainingJob
class_list = [
c for c in cls.__subclasses__() if c.__name__.startswith("AutoML")
] + [CustomTrainingJob]
# Identify correct training job subclass, construct and return object
for c in class_list:
if schema_uri in c._supported_training_schemas:
return c._empty_constructor(
project=project,
location=location,
credentials=credentials,
resource_name=resource_name,
)
@property
@abc.abstractmethod
def _model_upload_fail_string(self) -> str:
"""Helper property for model upload failure."""
pass
@abc.abstractmethod
def run(self) -> Optional[models.Model]:
"""Runs the training job.
Should call _run_job internally
"""
pass
@staticmethod
def _create_input_data_config(
dataset: Optional[datasets._Dataset] = None,
annotation_schema_uri: Optional[str] = None,
training_fraction_split: float = 0.8,
validation_fraction_split: float = 0.1,
test_fraction_split: float = 0.1,
predefined_split_column_name: Optional[str] = None,
gcs_destination_uri_prefix: Optional[str] = None,
bigquery_destination: Optional[str] = None,
) -> Optional[gca_training_pipeline.InputDataConfig]:
"""Constructs a input data config to pass to the training pipeline.
Args:
dataset (datasets._Dataset):
The dataset within the same Project from which data will be used to train the Model. The
Dataset must use schema compatible with Model being trained,
and what is compatible should be described in the used
TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
For tabular Datasets, all their data is exported to
training, to pick and choose from.
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](https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#schema-object) The schema files
that can be used here are found in
gs://google-cloud-aiplatform/schema/dataset/annotation/,
note that the chosen schema must be consistent with
``metadata``
of the Dataset specified by
``dataset_id``.
Only Annotations that both match this schema and belong to
DataItems not ignored by the split method are used in
respectively training, validation or test role, depending on
the role of the DataItem they are on.
When used in conjunction with
``annotations_filter``,
the Annotations used for training are filtered by both
``annotations_filter``
and
``annotation_schema_uri``.
training_fraction_split (float):
The fraction of the input data that is to be
used to train the Model. This is ignored if Dataset is not provided.
training_fraction_split (float):
The fraction of the input data that is to be
used to train the Model. This is ignored if Dataset is not provided.
validation_fraction_split (float):
The fraction of the input data that is to be
used to validate the Model. This is ignored if Dataset is not provided.
test_fraction_split (float):
The fraction of the input data that is to be
used to evaluate the Model. This is ignored if Dataset is not provided.
predefined_split_column_name (str):
Optional. The key is a name of one of the Dataset's data
columns. The value of the key (either the label's value or
value in the column) must be one of {``training``,
``validation``, ``test``}, and it defines to which set the
given piece of data is assigned. If for a piece of data the
key is not present or has an invalid value, that piece is
ignored by the pipeline.
Supported only for tabular and time series Datasets.
gcs_destination_uri_prefix (str):
Optional. The Google Cloud Storage location.
The Vertex AI environment variables representing Google
Cloud Storage data URIs will always be represented in the
Google Cloud Storage wildcard format to support sharded
data.
- AIP_DATA_FORMAT = "jsonl".
- AIP_TRAINING_DATA_URI = "gcs_destination/training-*"
- AIP_VALIDATION_DATA_URI = "gcs_destination/validation-*"
- AIP_TEST_DATA_URI = "gcs_destination/test-*".
bigquery_destination (str):
The BigQuery project location where the training data is to
be written to. In the given project a new dataset is created
with name
``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``
where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All
training input data will be written into that dataset. In
the dataset three tables will be created, ``training``,
``validation`` and ``test``.
- AIP_DATA_FORMAT = "bigquery".
- AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"
- AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
"""
input_data_config = None
if dataset:
# Create fraction split spec
fraction_split = gca_training_pipeline.FractionSplit(
training_fraction=training_fraction_split,
validation_fraction=validation_fraction_split,
test_fraction=test_fraction_split,
)
# Create predefined split spec
predefined_split = None
if predefined_split_column_name:
if dataset._gca_resource.metadata_schema_uri not in (
schema.dataset.metadata.tabular,
schema.dataset.metadata.time_series,
):
raise ValueError(
"A pre-defined split may only be used with a tabular or time series Dataset"
)
predefined_split = gca_training_pipeline.PredefinedSplit(
key=predefined_split_column_name
)
# Create GCS destination
gcs_destination = None
if gcs_destination_uri_prefix:
gcs_destination = gca_io.GcsDestination(
output_uri_prefix=gcs_destination_uri_prefix
)
# TODO(b/177416223) validate managed BQ dataset is passed in
bigquery_destination_proto = None
if bigquery_destination:
bigquery_destination_proto = gca_io.BigQueryDestination(
output_uri=bigquery_destination
)
# create input data config
input_data_config = gca_training_pipeline.InputDataConfig(
fraction_split=fraction_split,
predefined_split=predefined_split,
dataset_id=dataset.name,
annotation_schema_uri=annotation_schema_uri,
gcs_destination=gcs_destination,
bigquery_destination=bigquery_destination_proto,
)
return input_data_config
def _run_job(
self,
training_task_definition: str,
training_task_inputs: Union[dict, proto.Message],
dataset: Optional[datasets._Dataset],
training_fraction_split: float,
validation_fraction_split: float,
test_fraction_split: float,
annotation_schema_uri: Optional[str] = None,
predefined_split_column_name: Optional[str] = None,
model: Optional[gca_model.Model] = None,
gcs_destination_uri_prefix: Optional[str] = None,
bigquery_destination: Optional[str] = None,
) -> Optional[models.Model]:
"""Runs the training job.
Args:
training_task_definition (str):
Required. A Google Cloud Storage path to the
YAML file that defines the training task which
is responsible for producing the model artifact,
and may also include additional auxiliary work.
The definition files that can be used here are
found in gs://google-cloud-
aiplatform/schema/trainingjob/definition/. Note:
The URI given on output will be immutable and
probably different, including the URI scheme,
than the one given on input. The output URI will
point to a location where the user only has a
read access.
training_task_inputs (Union[dict, proto.Message]):
Required. The training task's input that corresponds to the training_task_definition parameter.
dataset (datasets._Dataset):
The dataset within the same Project from which data will be used to train the Model. The
Dataset must use schema compatible with Model being trained,
and what is compatible should be described in the used
TrainingPipeline's [training_task_definition]
[google.cloud.aiplatform.v1beta1.TrainingPipeline.training_task_definition].
For tabular Datasets, all their data is exported to
training, to pick and choose from.
training_fraction_split (float):
The fraction of the input data that is to be
used to train the Model. This is ignored if Dataset is not provided.
validation_fraction_split (float):
The fraction of the input data that is to be
used to validate the Model. This is ignored if Dataset is not provided.
test_fraction_split (float):
The fraction of the input data that is to be
used to evaluate the Model. This is ignored if Dataset is not provided.
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](https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#schema-object) The schema files
that can be used here are found in
gs://google-cloud-aiplatform/schema/dataset/annotation/,
note that the chosen schema must be consistent with
``metadata``
of the Dataset specified by
``dataset_id``.
Only Annotations that both match this schema and belong to
DataItems not ignored by the split method are used in
respectively training, validation or test role, depending on
the role of the DataItem they are on.
When used in conjunction with
``annotations_filter``,
the Annotations used for training are filtered by both
``annotations_filter``
and
``annotation_schema_uri``.
predefined_split_column_name (str):
Optional. The key is a name of one of the Dataset's data
columns. The value of the key (either the label's value or
value in the column) must be one of {``training``,
``validation``, ``test``}, and it defines to which set the
given piece of data is assigned. If for a piece of data the
key is not present or has an invalid value, that piece is
ignored by the pipeline.
Supported only for tabular and time series Datasets.
model (~.model.Model):
Optional. Describes the Model that may be uploaded (via
[ModelService.UploadMode][]) by this TrainingPipeline. The
TrainingPipeline's
``training_task_definition``
should make clear whether this Model description should be
populated, and if there are any special requirements
regarding how it should be filled. If nothing is mentioned
in the
``training_task_definition``,
then it should be assumed that this field should not be
filled and the training task either uploads the Model
without a need of this information, or that training task
does not support uploading a Model as part of the pipeline.
When the Pipeline's state becomes
``PIPELINE_STATE_SUCCEEDED`` and the trained Model had been
uploaded into Vertex AI, then the model_to_upload's
resource ``name``
is populated. The Model is always uploaded into the Project
and Location in which this pipeline is.
gcs_destination_uri_prefix (str):
Optional. The Google Cloud Storage location.
The Vertex AI environment variables representing Google
Cloud Storage data URIs will always be represented in the
Google Cloud Storage wildcard format to support sharded
data.
- AIP_DATA_FORMAT = "jsonl".
- AIP_TRAINING_DATA_URI = "gcs_destination/training-*"
- AIP_VALIDATION_DATA_URI = "gcs_destination/validation-*"
- AIP_TEST_DATA_URI = "gcs_destination/test-*".
bigquery_destination (str):
The BigQuery project location where the training data is to
be written to. In the given project a new dataset is created
with name
``dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call>``
where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All
training input data will be written into that dataset. In
the dataset three tables will be created, ``training``,
``validation`` and ``test``.
- AIP_DATA_FORMAT = "bigquery".
- AIP_TRAINING_DATA_URI ="bigquery_destination.dataset_*.training"
- AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset_*.validation"
- AIP_TEST_DATA_URI = "bigquery_destination.dataset_*.test"
"""
input_data_config = self._create_input_data_config(
dataset=dataset,
annotation_schema_uri=annotation_schema_uri,
training_fraction_split=training_fraction_split,
validation_fraction_split=validation_fraction_split,
test_fraction_split=test_fraction_split,
predefined_split_column_name=predefined_split_column_name,
gcs_destination_uri_prefix=gcs_destination_uri_prefix,
bigquery_destination=bigquery_destination,
)
# create training pipeline
training_pipeline = gca_training_pipeline.TrainingPipeline(
display_name=self._display_name,
training_task_definition=training_task_definition,
training_task_inputs=training_task_inputs,
model_to_upload=model,
input_data_config=input_data_config,
encryption_spec=self._training_encryption_spec,
)
training_pipeline = self.api_client.create_training_pipeline(
parent=initializer.global_config.common_location_path(
self.project, self.location
),
training_pipeline=training_pipeline,
)
self._gca_resource = training_pipeline
_LOGGER.info("View Training:\n%s" % self._dashboard_uri())
model = self._get_model()
if model is None:
_LOGGER.warning(
"Training did not produce a Managed Model returning None. "
+ self._model_upload_fail_string
)
return model
def _is_waiting_to_run(self) -> bool:
"""Returns True if the Job is pending on upstream tasks False
otherwise."""
self._raise_future_exception()
if self._latest_future:
_LOGGER.info(
"Training Job is waiting for upstream SDK tasks to complete before"
" launching."
)
return True
return False
@property
def state(self) -> Optional[gca_pipeline_state.PipelineState]:
"""Current training state."""
if self._assert_has_run():
return
self._sync_gca_resource()
return self._gca_resource.state
def get_model(self, sync=True) -> models.Model:
"""Vertex AI Model produced by this training, if one was produced.
Args:
sync (bool):
Whether to execute this method synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
Returns:
model: Vertex AI Model produced by this training
Raises:
RuntimeError: If training failed or if a model was not produced by this training.
"""
self._assert_has_run()
if not self._gca_resource.model_to_upload:
raise RuntimeError(self._model_upload_fail_string)
return self._force_get_model(sync=sync)
@base.optional_sync()
def _force_get_model(self, sync: bool = True) -> models.Model:
"""Vertex AI Model produced by this training, if one was produced.
Args:
sync (bool):
Whether to execute this method synchronously. If False, this method
will be executed in concurrent Future and any downstream object will
be immediately returned and synced when the Future has completed.
Returns:
model: Vertex AI Model produced by this training
Raises:
RuntimeError: If training failed or if a model was not produced by this training.
"""
model = self._get_model()
if model is None:
raise RuntimeError(self._model_upload_fail_string)
return model
def _get_model(self) -> Optional[models.Model]:
"""Helper method to get and instantiate the Model to Upload.
Returns:
model: Vertex AI Model if training succeeded and produced a Vertex AI
Model. None otherwise.
Raises:
RuntimeError: If Training failed.
"""
self._block_until_complete()
if self.has_failed:
raise RuntimeError(
f"Training Pipeline {self.resource_name} failed. No model available."
)
if not self._gca_resource.model_to_upload:
return None
if self._gca_resource.model_to_upload.name:
fields = utils.extract_fields_from_resource_name(
self._gca_resource.model_to_upload.name
)
return models.Model(
fields.id,
project=fields.project,
location=fields.location,
)
def _wait_callback(self):
"""Callback performs custom logging during _block_until_complete. Override in subclass."""
pass
def _block_until_complete(self):
"""Helper method to block and check on job until complete."""
# Used these numbers so failures surface fast
wait = 5 # start at five seconds
log_wait = 5
max_wait = 60 * 5 # 5 minute wait
multiplier = 2 # scale wait by 2 every iteration
previous_time = time.time()
while self.state not in _PIPELINE_COMPLETE_STATES:
current_time = time.time()
if current_time - previous_time >= log_wait:
_LOGGER.info(
"%s %s current state:\n%s"
% (
self.__class__.__name__,
self._gca_resource.name,
self._gca_resource.state,
)
)
log_wait = min(log_wait * multiplier, max_wait)
previous_time = current_time
self._wait_callback()
time.sleep(wait)
self._raise_failure()
_LOGGER.log_action_completed_against_resource("run", "completed", self)
if self._gca_resource.model_to_upload and not self.has_failed:
_LOGGER.info(
"Model available at %s" % self._gca_resource.model_to_upload.name
)
def _raise_failure(self):
"""Helper method to raise failure if TrainingPipeline fails.
Raises:
RuntimeError: If training failed.
"""
if self._gca_resource.error.code != code_pb2.OK:
raise RuntimeError("Training failed with:\n%s" % self._gca_resource.error)
@property
def has_failed(self) -> bool:
"""Returns True if training has failed.
False otherwise.
"""
self._assert_has_run()
return self.state == gca_pipeline_state.PipelineState.PIPELINE_STATE_FAILED
def _dashboard_uri(self) -> str:
"""Helper method to compose the dashboard uri where training can be
viewed."""
fields = utils.extract_fields_from_resource_name(self.resource_name)
url = f"https://console.cloud.google.com/ai/platform/locations/{fields.location}/training/{fields.id}?project={fields.project}"
return url
def _sync_gca_resource(self):
"""Helper method to sync the local gca_source against the service."""
self._gca_resource = self.api_client.get_training_pipeline(
name=self.resource_name
)
@property
def _has_run(self) -> bool:
"""Helper property to check if this training job has been run."""
return self._gca_resource is not None
def _assert_has_run(self) -> bool:
"""Helper method to assert that this training has run."""
if not self._has_run:
if self._is_waiting_to_run():
return True
raise RuntimeError(
"TrainingPipeline has not been launched. You must run this"
" TrainingPipeline using TrainingPipeline.run. "
)
return False
@classmethod
def list(
cls,
filter: Optional[str] = None,
order_by: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
) -> List["base.VertexAiResourceNoun"]:
"""List all instances of this TrainingJob resource.
Example Usage:
aiplatform.CustomTrainingJob.list(
filter='display_name="experiment_a27"',
order_by='create_time desc'
)
Args:
filter (str):
Optional. An expression for filtering the results of the request.
For field names both snake_case and camelCase are supported.
order_by (str):
Optional. A comma-separated list of fields to order by, sorted in
ascending order. Use "desc" after a field name for descending.
Supported fields: `display_name`, `create_time`, `update_time`
project (str):
Optional. Project to retrieve list from. If not set, project
set in aiplatform.init will be used.
location (str):
Optional. Location to retrieve list from. If not set, location
set in aiplatform.init will be used.
credentials (auth_credentials.Credentials):
Optional. Custom credentials to use to retrieve list. Overrides
credentials set in aiplatform.init.
Returns:
List[VertexAiResourceNoun] - A list of TrainingJob resource objects
"""
training_job_subclass_filter = (
lambda gapic_obj: gapic_obj.training_task_definition
in cls._supported_training_schemas
)
return cls._list_with_local_order(
cls_filter=training_job_subclass_filter,
filter=filter,
order_by=order_by,
project=project,
location=location,
credentials=credentials,
)
def cancel(self) -> None:
"""Starts asynchronous cancellation on the TrainingJob. The server
makes a best effort to cancel the job, but success is not guaranteed.
On successful cancellation, the TrainingJob is not deleted; instead it
becomes a job with state set to `CANCELLED`.
Raises:
RuntimeError: If this TrainingJob has not started running.
"""
if not self._has_run:
raise RuntimeError(
"This TrainingJob has not been launched, use the `run()` method "
"to start. `cancel()` can only be called on a job that is running."
)
self.api_client.cancel_training_pipeline(name=self.resource_name)
class _CustomTrainingJob(_TrainingJob):
"""ABC for Custom Training Pipelines.."""
_supported_training_schemas = (schema.training_job.definition.custom_task,)
def __init__(
self,
display_name: str,
container_uri: str,
model_serving_container_image_uri: Optional[str] = None,
model_serving_container_predict_route: Optional[str] = None,
model_serving_container_health_route: Optional[str] = None,
model_serving_container_command: Optional[Sequence[str]] = None,
model_serving_container_args: Optional[Sequence[str]] = None,
model_serving_container_environment_variables: Optional[Dict[str, str]] = None,
model_serving_container_ports: Optional[Sequence[int]] = None,
model_description: Optional[str] = None,
model_instance_schema_uri: Optional[str] = None,
model_parameters_schema_uri: Optional[str] = None,
model_prediction_schema_uri: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[auth_credentials.Credentials] = None,
training_encryption_spec_key_name: Optional[str] = None,
model_encryption_spec_key_name: Optional[str] = None,
staging_bucket: Optional[str] = None,
):
"""
Args:
display_name (str):
Required. The user-defined name of this TrainingPipeline.
container_uri (str):
Required: Uri of the training container image in the GCR.
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
``PredictRequest.instances``,
``ExplainRequest.instances``
and
``BatchPredictionJob.input_config``.
The schema is defined as an OpenAPI 3.0.2 `Schema
Object <https://tinyurl.com/y538mdwt#schema-object>`__.
AutoML Models always have this field populated by AI
Platform. Note: The URI given on output will be immutable
and probably different, including the URI scheme, than the
one given on input. The output URI will point to a location
where the user only has a read access.
model_parameters_schema_uri (str):
Optional. Points to a YAML file stored on Google Cloud
Storage describing the parameters of prediction and
explanation via
``PredictRequest.parameters``,
``ExplainRequest.parameters``
and
``BatchPredictionJob.model_parameters``.
The schema is defined as an OpenAPI 3.0.2 `Schema
Object <https://tinyurl.com/y538mdwt#schema-object>`__.
AutoML Models always have this field populated by AI
Platform, if no parameters are supported it is set to an
empty string. Note: The URI given on output will be
immutable and probably different, including the URI scheme,
than the one given on input. The output URI will point to a
location where the user only has a read access.
model_prediction_schema_uri (str):
Optional. Points to a YAML file stored on Google Cloud
Storage describing the format of a single prediction
produced by this Model, which are returned via
``PredictResponse.predictions``,
``ExplainResponse.explanations``,
and
``BatchPredictionJob.output_config``.
The schema is defined as an OpenAPI 3.0.2 `Schema
Object <https://tinyurl.com/y538mdwt#schema-object>`__.
AutoML Models always have this field populated by AI
Platform. Note: The URI given on output will be immutable
and probably different, including the URI scheme, than the
one given on input. The output URI will point to a location
where the user only has a read access.
project (str):
Project to run training in. Overrides project set in aiplatform.init.
location (str):
Location to run training in. Overrides location set in aiplatform.init.
credentials (auth_credentials.Credentials):
Custom credentials to use to run call training service. Overrides
credentials set in aiplatform.init.
training_encryption_spec_key_name (Optional[str]):
Optional. The Cloud KMS resource identifier of the customer
managed encryption key used to protect the training pipeline. Has the
form:
``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``.
The key needs to be in the same region as where the compute
resource is created.
If set, this TrainingPipeline will be secured by this key.