def StreamLogs(api_version, 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, api_version))
return log_utils.SplitMultiline(
log_fetcher.YieldLogs(), allow_multiline=allow_multiline_logs)
def Run(self, args):
"""Run the stream-logs command."""
log_fetcher = stream.LogFetcher(
filters=log_utils.LogFilters(args.job, args.task_name),
polling_interval=args.polling_interval,
continue_func=log_utils.MakeContinueFunction(args.job))
return log_utils.SplitMultiline(
log_fetcher.YieldLogs(), allow_multiline=args.allow_multiline_logs)
def Run(self, args):
"""This is what gets called when the user runs this command.
Args:
args: an argparse namespace. All the arguments that were provided to this
command invocation.
Returns:
Some value that we want to have printed later.
"""
region = properties.VALUES.compute.region.Get(required=True)
staging_location = jobs_prep.GetStagingLocation(
staging_bucket=args.staging_bucket,
job_id=args.job,
job_dir=args.job_dir)
try:
uris = jobs_prep.UploadPythonPackages(
packages=args.packages,
package_path=args.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.GetMessagesModule(
).GoogleCloudMlV1beta1TrainingInput.ScaleTierValueValuesEnum)
scale_tier = scale_tier_enum(
args.scale_tier) if args.scale_tier else None
job = jobs.BuildTrainingJob(
path=args.config,
module_name=args.module_name,
job_name=args.job,
trainer_uri=uris,
region=region,
job_dir=args.job_dir.ToUrl() if args.job_dir else None,
scale_tier=scale_tier,
user_args=args.user_args,
runtime_version=args.runtime_version)
jobs_client = jobs.JobsClient()
project_ref = resources.REGISTRY.Parse(
properties.VALUES.core.project.Get(required=True),
collection='ml.projects')
job = jobs_client.Create(project_ref, job)
log.status.Print('Job [{}] submitted successfully.'.format(job.jobId))
if args. async:
log.status.Print(_FOLLOW_UP_MESSAGE.format(job_id=job.jobId))
return job
log_fetcher = stream.LogFetcher(
filters=log_utils.LogFilters(job.jobId),
polling_interval=_POLLING_INTERVAL,
continue_func=log_utils.MakeContinueFunction(job.jobId))
printer = resource_printer.Printer(log_utils.LOG_FORMAT, out=log.err)
def _CtrlCHandler(signal, frame):
del signal, frame # Unused
raise KeyboardInterrupt
with execution_utils.CtrlCSection(_CtrlCHandler):
try:
printer.Print(log_utils.SplitMultiline(
log_fetcher.YieldLogs()))
except KeyboardInterrupt:
log.status.Print('Received keyboard interrupt.')
log.status.Print(_FOLLOW_UP_MESSAGE.format(job_id=job.jobId))
job_ref = resources.REGISTRY.Parse(job.jobId,
collection='ml.projects.jobs')
job = jobs_client.Get(job_ref)
# If the job itself failed, we will return a failure status.
if job.state is not job.StateValueValuesEnum.SUCCEEDED:
self.exit_code = 1
return job
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,
stream_logs=None, user_args=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
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)
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(str(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