def testLogFiltersWithTask(self): filters = log_utils.LogFilters('job1', task_name='task1') self.assertIn( '(resource.type="ml_job" OR resource.type="cloudml_job")', filters) self.assertIn('resource.labels.job_id="job1"', filters) self.assertIn( '(resource.labels.task_name="task1" OR labels.task_name="task1")', filters)
def StreamLogs(job, task_name, polling_interval, allow_multiline_logs): log_fetcher = stream.LogFetcher( filters=log_utils.LogFilters(job, task_name), polling_interval=polling_interval, continue_interval=_CONTINUE_INTERVAL, continue_func=log_utils.MakeContinueFunction(job)) return log_utils.SplitMultiline(log_fetcher.YieldLogs(), allow_multiline=allow_multiline_logs)
def SubmitTraining(jobs_client, job, job_dir=None, staging_bucket=None, packages=None, package_path=None, scale_tier=None, config=None, module_name=None, runtime_version=None, python_version=None, stream_logs=None, user_args=None, labels=None, custom_train_server_config=None): """Submit a training job.""" region = properties.VALUES.compute.region.Get(required=True) staging_location = jobs_prep.GetStagingLocation( staging_bucket=staging_bucket, job_id=job, job_dir=job_dir) try: uris = jobs_prep.UploadPythonPackages( packages=packages, package_path=package_path, staging_location=staging_location) except jobs_prep.NoStagingLocationError: raise flags.ArgumentError( 'If local packages are provided, the `--staging-bucket` or ' '`--job-dir` flag must be given.') log.debug('Using {0} as trainer uris'.format(uris)) scale_tier_enum = jobs_client.training_input_class.ScaleTierValueValuesEnum scale_tier = scale_tier_enum(scale_tier) if scale_tier else None try: job = jobs_client.BuildTrainingJob( path=config, module_name=module_name, job_name=job, trainer_uri=uris, region=region, job_dir=job_dir.ToUrl() if job_dir else None, scale_tier=scale_tier, user_args=user_args, runtime_version=runtime_version, python_version=python_version, labels=labels, custom_train_server_config=custom_train_server_config) except jobs_prep.NoStagingLocationError: raise flags.ArgumentError( 'If `--package-path` is not specified, at least one Python package ' 'must be specified via `--packages`.') project_ref = resources.REGISTRY.Parse( properties.VALUES.core.project.Get(required=True), collection='ml.projects') job = jobs_client.Create(project_ref, job) if not stream_logs: PrintSubmitFollowUp(job.jobId, print_follow_up_message=True) return job else: PrintSubmitFollowUp(job.jobId, print_follow_up_message=False) log_fetcher = stream.LogFetcher( filters=log_utils.LogFilters(job.jobId), polling_interval=properties.VALUES.ml_engine.polling_interval.GetInt(), continue_interval=_CONTINUE_INTERVAL, continue_func=log_utils.MakeContinueFunction(job.jobId)) printer = resource_printer.Printer(log_utils.LOG_FORMAT, out=log.err) with execution_utils.RaisesKeyboardInterrupt(): try: printer.Print(log_utils.SplitMultiline(log_fetcher.YieldLogs())) except KeyboardInterrupt: log.status.Print('Received keyboard interrupt.\n') log.status.Print( _FOLLOW_UP_MESSAGE.format(job_id=job.jobId, project=project_ref.Name())) except exceptions.HttpError as err: log.status.Print('Polling logs failed:\n{}\n'.format( six.text_type(err))) log.info('Failure details:', exc_info=True) log.status.Print( _FOLLOW_UP_MESSAGE.format(job_id=job.jobId, project=project_ref.Name())) job_ref = resources.REGISTRY.Parse( job.jobId, params={'projectsId': properties.VALUES.core.project.GetOrFail}, collection='ml.projects.jobs') job = jobs_client.Get(job_ref) return job
def testLogFilters(self): filters = log_utils.LogFilters('job1') self.assertIn( '(resource.type="ml_job" OR resource.type="cloudml_job")', filters) self.assertIn('resource.labels.job_id="job1"', filters)