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']
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: {}'.format( 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 errors.HttpError: raise if finished_training_job['state'] != 'SUCCEEDED': self.log.error('MLEngine training job failed: {}'.format( str(finished_training_job))) raise RuntimeError(finished_training_job['errorMessage'])
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 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']
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: {}'.format( 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: {}'.format( str(finished_training_job))) raise RuntimeError(finished_training_job['errorMessage'])
def execute(self, context): conf = context['dag_run'].conf job_id = conf['jobName'] print('jobID: {}'.format(job_id)) training_request = { 'jobId': job_id, 'trainingInput': { 'scaleTier': 'BASIC_GPU', 'packageUris': [ 'gs://elvos/cloud-ml/blueno-0.1.0.tar.gz', 'gs://elvos/cloud-ml/c3d/cloudml-c3d-0.0.2.tar.gz' ], 'pythonModule': 'trainer.task', 'region': 'us-east1', 'args': '', 'runtimeVersion': '1.4', 'pythonVersion': '3.5', } } 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 errors.HttpError: raise if finished_training_job['state'] != 'SUCCEEDED': self.log.error('MLEngine training job failed: {}'.format( str(finished_training_job))) raise RuntimeError(finished_training_job['errorMessage'])
def execute(self, context): hook = MLEngineHook(self.gcp_conn_id, self.delegate_to) def check_existing_job(existing_job): return existing_job.get('predictionInput', None) == \ self.prediction_job_request['predictionInput'] try: finished_prediction_job = hook.create_job( self.project_id, self.prediction_job_request, check_existing_job) except errors.HttpError: raise if finished_prediction_job['state'] != 'SUCCEEDED': self.log.error('Batch prediction job failed: %s', str(finished_prediction_job)) raise RuntimeError(finished_prediction_job['errorMessage']) return finished_prediction_job['predictionOutput']
def execute(self, context): hook = MLEngineHook(self.gcp_conn_id, self.delegate_to) def check_existing_job(existing_job): return existing_job.get('predictionInput', None) == \ self.prediction_job_request['predictionInput'] try: finished_prediction_job = hook.create_job( self.project_id, self.prediction_job_request, check_existing_job) except errors.HttpError: raise if finished_prediction_job['state'] != 'SUCCEEDED': self.log.error( 'Batch prediction job failed: %s', str(finished_prediction_job)) raise RuntimeError(finished_prediction_job['errorMessage']) return finished_prediction_job['predictionOutput']