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 testMakeContinueFunction(self):
response1 = self.short_msgs.Job(endTime=None)
response2 = self.short_msgs.Job(endTime='2017-01-20T17:28:19.002754Z')
# Job is completed after the second check.
self.client.projects_jobs.Get.Expect(
request=self.msgs.MlProjectsJobsGetRequest(
name='projects/proj/jobs/job1'),
response=response1)
self.client.projects_jobs.Get.Expect(
request=self.msgs.MlProjectsJobsGetRequest(
name='projects/proj/jobs/job1'),
response=response2)
should_continue = log_utils.MakeContinueFunction('job1')
# Tests that we should always continue when we've had < 1 empty poll.
self.assertTrue(should_continue(0))
self.assertTrue(should_continue(1))
# Tests that we should continue if job is still running
self.assertTrue(should_continue(2))
# Tests that we should stop after 2 empty polls and a finished job.
self.assertFalse(should_continue(2))
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
