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import re
from googleapiclient.errors import HttpError
from airflow.contrib.hooks.gcp_mlengine_hook import MLEngineHook
from airflow.exceptions import AirflowException
from airflow.operators import BaseOperator
from airflow.utils.decorators import apply_defaults
from airflow.utils.log.logging_mixin import LoggingMixin
[docs]log = LoggingMixin().log
[docs]def _normalize_mlengine_job_id(job_id):
"""
Replaces invalid MLEngine job_id characters with '_'.
This also adds a leading 'z' in case job_id starts with an invalid
character.
Args:
job_id: A job_id str that may have invalid characters.
Returns:
A valid job_id representation.
"""
# Add a prefix when a job_id starts with a digit or a template
match = re.search(r'\d|\{{2}', job_id)
if match and match.start() == 0:
job = 'z_{}'.format(job_id)
else:
job = job_id
# Clean up 'bad' characters except templates
tracker = 0
cleansed_job_id = ''
for m in re.finditer(r'\{{2}.+?\}{2}', job):
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_',
job[tracker:m.start()])
cleansed_job_id += job[m.start():m.end()]
tracker = m.end()
# Clean up last substring or the full string if no templates
cleansed_job_id += re.sub(r'[^0-9a-zA-Z]+', '_', job[tracker:])
return cleansed_job_id
[docs]class MLEngineBatchPredictionOperator(BaseOperator):
"""
Start a Google Cloud ML Engine prediction job.
NOTE: For model origin, users should consider exactly one from the
three options below:
1. Populate ``uri`` field only, which should be a GCS location that
points to a tensorflow savedModel directory.
2. Populate ``model_name`` field only, which refers to an existing
model, and the default version of the model will be used.
3. Populate both ``model_name`` and ``version_name`` fields, which
refers to a specific version of a specific model.
In options 2 and 3, both model and version name should contain the
minimal identifier. For instance, call::
MLEngineBatchPredictionOperator(
...,
model_name='my_model',
version_name='my_version',
...)
if the desired model version is
``projects/my_project/models/my_model/versions/my_version``.
See https://cloud.google.com/ml-engine/reference/rest/v1/projects.jobs
for further documentation on the parameters.
:param project_id: The Google Cloud project name where the
prediction job is submitted. (templated)
:type project_id: str
:param job_id: A unique id for the prediction job on Google Cloud
ML Engine. (templated)
:type job_id: str
:param data_format: The format of the input data.
It will default to 'DATA_FORMAT_UNSPECIFIED' if is not provided
or is not one of ["TEXT", "TF_RECORD", "TF_RECORD_GZIP"].
:type data_format: str
:param input_paths: A list of GCS paths of input data for batch
prediction. Accepting wildcard operator ``*``, but only at the end. (templated)
:type input_paths: list[str]
:param output_path: The GCS path where the prediction results are
written to. (templated)
:type output_path: str
:param region: The Google Compute Engine region to run the
prediction job in. (templated)
:type region: str
:param model_name: The Google Cloud ML Engine model to use for prediction.
If version_name is not provided, the default version of this
model will be used.
Should not be None if version_name is provided.
Should be None if uri is provided. (templated)
:type model_name: str
:param version_name: The Google Cloud ML Engine model version to use for
prediction.
Should be None if uri is provided. (templated)
:type version_name: str
:param uri: The GCS path of the saved model to use for prediction.
Should be None if model_name is provided.
It should be a GCS path pointing to a tensorflow SavedModel. (templated)
:type uri: str
:param max_worker_count: The maximum number of workers to be used
for parallel processing. Defaults to 10 if not specified.
:type max_worker_count: int
:param runtime_version: The Google Cloud ML Engine runtime version to use
for batch prediction.
:type runtime_version: str
:param signature_name: The name of the signature defined in the SavedModel
to use for this job.
:type signature_name: str
:param gcp_conn_id: The connection ID used for connection to Google
Cloud Platform.
:type gcp_conn_id: str
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must
have domain-wide delegation enabled.
:type delegate_to: str
:raises: ``ValueError``: if a unique model/version origin cannot be
determined.
"""
[docs] template_fields = [
'_project_id',
'_job_id',
'_region',
'_input_paths',
'_output_path',
'_model_name',
'_version_name',
'_uri',
]
@apply_defaults
def __init__(self,
project_id,
job_id,
region,
data_format,
input_paths,
output_path,
model_name=None,
version_name=None,
uri=None,
max_worker_count=None,
runtime_version=None,
signature_name=None,
gcp_conn_id='google_cloud_default',
delegate_to=None,
*args,
**kwargs):
super(MLEngineBatchPredictionOperator, self).__init__(*args, **kwargs)
self._project_id = project_id
self._job_id = job_id
self._region = region
self._data_format = data_format
self._input_paths = input_paths
self._output_path = output_path
self._model_name = model_name
self._version_name = version_name
self._uri = uri
self._max_worker_count = max_worker_count
self._runtime_version = runtime_version
self._signature_name = signature_name
self._gcp_conn_id = gcp_conn_id
self._delegate_to = delegate_to
if not self._project_id:
raise AirflowException('Google Cloud project id is required.')
if not self._job_id:
raise AirflowException(
'An unique job id is required for Google MLEngine prediction '
'job.')
if self._uri:
if self._model_name or self._version_name:
raise AirflowException('Ambiguous model origin: Both uri and '
'model/version name are provided.')
if self._version_name and not self._model_name:
raise AirflowException(
'Missing model: Batch prediction expects '
'a model name when a version name is provided.')
if not (self._uri or self._model_name):
raise AirflowException(
'Missing model origin: Batch prediction expects a model, '
'a model & version combination, or a URI to a savedModel.')
[docs] def execute(self, context):
job_id = _normalize_mlengine_job_id(self._job_id)
prediction_request = {
'jobId': job_id,
'predictionInput': {
'dataFormat': self._data_format,
'inputPaths': self._input_paths,
'outputPath': self._output_path,
'region': self._region
}
}
if self._uri:
prediction_request['predictionInput']['uri'] = self._uri
elif self._model_name:
origin_name = 'projects/{}/models/{}'.format(
self._project_id, self._model_name)
if not self._version_name:
prediction_request['predictionInput'][
'modelName'] = origin_name
else:
prediction_request['predictionInput']['versionName'] = \
origin_name + '/versions/{}'.format(self._version_name)
if self._max_worker_count:
prediction_request['predictionInput'][
'maxWorkerCount'] = self._max_worker_count
if self._runtime_version:
prediction_request['predictionInput'][
'runtimeVersion'] = self._runtime_version
if self._signature_name:
prediction_request['predictionInput'][
'signatureName'] = self._signature_name
hook = MLEngineHook(self._gcp_conn_id, self._delegate_to)
# Helper method to check if the existing job's prediction input is the
# same as the request we get here.
def check_existing_job(existing_job):
return existing_job.get('predictionInput', None) == \
prediction_request['predictionInput']
try:
finished_prediction_job = hook.create_job(
self._project_id, prediction_request, check_existing_job)
except HttpError:
raise
if finished_prediction_job['state'] != 'SUCCEEDED':
self.log.error(
'MLEngine batch prediction job failed: %s', str(finished_prediction_job)
)
raise RuntimeError(finished_prediction_job['errorMessage'])
return finished_prediction_job['predictionOutput']
[docs]class MLEngineModelOperator(BaseOperator):
"""
Operator for managing a Google Cloud ML Engine model.
:param project_id: The Google Cloud project name to which MLEngine
model belongs. (templated)
:type project_id: str
:param model: A dictionary containing the information about the model.
If the `operation` is `create`, then the `model` parameter should
contain all the information about this model such as `name`.
If the `operation` is `get`, the `model` parameter
should contain the `name` of the model.
:type model: dict
:param operation: The operation to perform. Available operations are:
* ``create``: Creates a new model as provided by the `model` parameter.
* ``get``: Gets a particular model where the name is specified in `model`.
:type operation: str
:param gcp_conn_id: The connection ID to use when fetching connection info.
:type gcp_conn_id: str
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must have
domain-wide delegation enabled.
:type delegate_to: str
"""
[docs] template_fields = [
'_model',
]
@apply_defaults
def __init__(self,
project_id,
model,
operation='create',
gcp_conn_id='google_cloud_default',
delegate_to=None,
*args,
**kwargs):
super(MLEngineModelOperator, self).__init__(*args, **kwargs)
self._project_id = project_id
self._model = model
self._operation = operation
self._gcp_conn_id = gcp_conn_id
self._delegate_to = delegate_to
[docs] def execute(self, context):
hook = MLEngineHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
if self._operation == 'create':
return hook.create_model(self._project_id, self._model)
elif self._operation == 'get':
return hook.get_model(self._project_id, self._model['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))
[docs]class MLEngineVersionOperator(BaseOperator):
"""
Operator for managing a Google Cloud ML Engine version.
:param project_id: The Google Cloud project name to which MLEngine
model belongs.
:type project_id: str
:param model_name: The name of the Google Cloud ML Engine model that the version
belongs to. (templated)
:type model_name: str
:param version_name: A name to use for the version being operated upon.
If not None and the `version` argument is None or does not have a value for
the `name` key, then this will be populated in the payload for the
`name` key. (templated)
:type version_name: str
:param version: A dictionary containing the information about the version.
If the `operation` is `create`, `version` should contain all the
information about this version such as name, and deploymentUrl.
If the `operation` is `get` or `delete`, the `version` parameter
should contain the `name` of the version.
If it is None, the only `operation` possible would be `list`. (templated)
:type version: dict
:param operation: The operation to perform. Available operations are:
* ``create``: Creates a new version in the model specified by `model_name`,
in which case the `version` parameter should contain all the
information to create that version
(e.g. `name`, `deploymentUrl`).
* ``get``: Gets full information of a particular version in the model
specified by `model_name`.
The name of the version should be specified in the `version`
parameter.
* ``list``: Lists all available versions of the model specified
by `model_name`.
* ``delete``: Deletes the version specified in `version` parameter from the
model specified by `model_name`).
The name of the version should be specified in the `version`
parameter.
:type operation: str
:param gcp_conn_id: The connection ID to use when fetching connection info.
:type gcp_conn_id: str
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must have
domain-wide delegation enabled.
:type delegate_to: str
"""
[docs] template_fields = [
'_model_name',
'_version_name',
'_version',
]
@apply_defaults
def __init__(self,
project_id,
model_name,
version_name=None,
version=None,
operation='create',
gcp_conn_id='google_cloud_default',
delegate_to=None,
*args,
**kwargs):
super(MLEngineVersionOperator, self).__init__(*args, **kwargs)
self._project_id = project_id
self._model_name = model_name
self._version_name = version_name
self._version = version or {}
self._operation = operation
self._gcp_conn_id = gcp_conn_id
self._delegate_to = delegate_to
[docs] def execute(self, context):
if 'name' not in self._version:
self._version['name'] = self._version_name
hook = MLEngineHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
if self._operation == 'create':
if not self._version:
raise ValueError("version attribute of {} could not "
"be empty".format(self.__class__.__name__))
return hook.create_version(self._project_id, self._model_name,
self._version)
elif self._operation == 'set_default':
return hook.set_default_version(self._project_id, self._model_name,
self._version['name'])
elif self._operation == 'list':
return hook.list_versions(self._project_id, self._model_name)
elif self._operation == 'delete':
return hook.delete_version(self._project_id, self._model_name,
self._version['name'])
else:
raise ValueError('Unknown operation: {}'.format(self._operation))
[docs]class MLEngineTrainingOperator(BaseOperator):
"""
Operator for launching a MLEngine training job.
:param project_id: The Google Cloud project name within which MLEngine
training job should run (templated).
:type project_id: str
:param job_id: A unique templated id for the submitted Google MLEngine
training job. (templated)
:type job_id: str
:param package_uris: A list of package locations for MLEngine training job,
which should include the main training program + any additional
dependencies. (templated)
:type package_uris: str
:param training_python_module: The Python module name to run within MLEngine
training job after installing 'package_uris' packages. (templated)
:type training_python_module: str
:param training_args: A list of templated command line arguments to pass to
the MLEngine training program. (templated)
:type training_args: str
:param region: The Google Compute Engine region to run the MLEngine training
job in (templated).
:type region: str
:param scale_tier: Resource tier for MLEngine training job. (templated)
:type scale_tier: str
:param master_type: Cloud ML Engine machine name.
Must be set when scale_tier is CUSTOM. (templated)
:type master_type: str
:param runtime_version: The Google Cloud ML runtime version to use for
training. (templated)
:type runtime_version: str
:param python_version: The version of Python used in training. (templated)
:type python_version: str
:param job_dir: A Google Cloud Storage path in which to store training
outputs and other data needed for training. (templated)
:type job_dir: str
:param gcp_conn_id: The connection ID to use when fetching connection info.
:type gcp_conn_id: str
:param delegate_to: The account to impersonate, if any.
For this to work, the service account making the request must have
domain-wide delegation enabled.
:type delegate_to: str
:param mode: Can be one of 'DRY_RUN'/'CLOUD'. In 'DRY_RUN' mode, no real
training job will be launched, but the MLEngine training job request
will be printed out. In 'CLOUD' mode, a real MLEngine training job
creation request will be issued.
:type mode: str
"""
[docs] template_fields = [
'_project_id',
'_job_id',
'_package_uris',
'_training_python_module',
'_training_args',
'_region',
'_scale_tier',
'_master_type',
'_runtime_version',
'_python_version',
'_job_dir'
]
@apply_defaults
def __init__(self,
project_id,
job_id,
package_uris,
training_python_module,
training_args,
region,
scale_tier=None,
master_type=None,
runtime_version=None,
python_version=None,
job_dir=None,
gcp_conn_id='google_cloud_default',
delegate_to=None,
mode='PRODUCTION',
*args,
**kwargs):
super(MLEngineTrainingOperator, self).__init__(*args, **kwargs)
self._project_id = project_id
self._job_id = job_id
self._package_uris = package_uris
self._training_python_module = training_python_module
self._training_args = training_args
self._region = region
self._scale_tier = scale_tier
self._master_type = master_type
self._runtime_version = runtime_version
self._python_version = python_version
self._job_dir = job_dir
self._gcp_conn_id = gcp_conn_id
self._delegate_to = delegate_to
self._mode = mode
if not self._project_id:
raise AirflowException('Google Cloud project id is required.')
if not self._job_id:
raise AirflowException(
'An unique job id is required for Google MLEngine training '
'job.')
if not package_uris:
raise AirflowException(
'At least one python package is required for MLEngine '
'Training job.')
if not training_python_module:
raise AirflowException(
'Python module name to run after installing required '
'packages is required.')
if not self._region:
raise AirflowException('Google Compute Engine region is required.')
if self._scale_tier is not None and self._scale_tier.upper() == "CUSTOM" and not self._master_type:
raise AirflowException(
'master_type must be set when scale_tier is CUSTOM')
[docs] def execute(self, context):
job_id = _normalize_mlengine_job_id(self._job_id)
training_request = {
'jobId': job_id,
'trainingInput': {
'scaleTier': self._scale_tier,
'packageUris': self._package_uris,
'pythonModule': self._training_python_module,
'region': self._region,
'args': self._training_args,
}
}
if self._runtime_version:
training_request['trainingInput']['runtimeVersion'] = self._runtime_version
if self._python_version:
training_request['trainingInput']['pythonVersion'] = self._python_version
if self._job_dir:
training_request['trainingInput']['jobDir'] = self._job_dir
if self._scale_tier is not None and self._scale_tier.upper() == "CUSTOM":
training_request['trainingInput']['masterType'] = self._master_type
if self._mode == 'DRY_RUN':
self.log.info('In dry_run mode.')
self.log.info('MLEngine Training job request is: %s', training_request)
return
hook = MLEngineHook(
gcp_conn_id=self._gcp_conn_id, delegate_to=self._delegate_to)
# Helper method to check if the existing job's training input is the
# same as the request we get here.
def check_existing_job(existing_job):
return existing_job.get('trainingInput', None) == \
training_request['trainingInput']
try:
finished_training_job = hook.create_job(
self._project_id, training_request, check_existing_job)
except HttpError:
raise
if finished_training_job['state'] != 'SUCCEEDED':
self.log.error('MLEngine training job failed: %s', str(finished_training_job))
raise RuntimeError(finished_training_job['errorMessage'])