def run_submission_with_function_preprocessor(capsys,
deployer="job",
builder="append",
namespace="default",
dockerfile_path=None,
cleanup=False):
py_version = ".".join([str(x) for x in sys.version_info[0:3]])
base_image = 'registry.hub.docker.com/library/python:{}'.format(py_version)
if builder == 'cluster':
if dockerfile_path is None:
fairing.config.set_builder(
builder,
base_image=base_image,
registry=DOCKER_REGISTRY,
pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
context_source=gcs_context.GCSContextSource(
namespace=namespace),
namespace=namespace)
else:
fairing.config.set_builder(
builder,
registry=DOCKER_REGISTRY,
dockerfile_path=dockerfile_path,
pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
context_source=gcs_context.GCSContextSource(
namespace=namespace),
namespace=namespace)
else:
fairing.config.set_builder(builder,
base_image=base_image,
registry=DOCKER_REGISTRY)
expected_result = str(uuid.uuid4())
fairing.config.set_deployer(deployer,
namespace=namespace,
cleanup=cleanup,
labels={'pytest-id': expected_result})
remote_train = fairing.config.fn(lambda: train_fn(expected_result))
remote_train()
captured = capsys.readouterr()
assert expected_result in captured.out
if deployer == "tfjob":
if cleanup:
assert expected_result not in str(
get_tfjobs_with_labels('pytest-id=' + expected_result))
else:
assert expected_result in str(
get_tfjobs_with_labels('pytest-id=' + expected_result))
def get_builder(
self,
preprocessor,
base_image,
registry,
needs_deps_installation=True, # pylint:disable=arguments-differ
pod_spec_mutators=None):
"""Creates a builder instance with right config for GCP
:param preprocessor: Preprocessor to use to modify inputs
:param base_image: Base image to use for this job
:param registry: Registry to push image to. Example: gcr.io/kubeflow-images
:param needs_deps_installation: need depends on installation(Default value = True)
:param pod_spec_mutators: list of functions that is used to mutate the podsspec.
e.g. fairing.cloud.gcp.add_gcp_credentials_if_exists
This can used to set things like volumes and security context.
(Default value =None)
"""
pod_spec_mutators = pod_spec_mutators or []
pod_spec_mutators.append(gcp.add_gcp_credentials_if_exists)
pod_spec_mutators.append(docker.add_docker_credentials_if_exists)
# TODO (karthikv2k): Add cloud build as the deafult
# once https://github.com/kubeflow/fairing/issues/145 is fixed
if not needs_deps_installation:
return AppendBuilder(preprocessor=preprocessor,
base_image=base_image,
registry=registry)
elif utils.is_running_in_k8s():
return ClusterBuilder(
preprocessor=preprocessor,
base_image=base_image,
registry=registry,
pod_spec_mutators=pod_spec_mutators,
context_source=gcs_context.GCSContextSource(
namespace=utils.get_default_target_namespace()))
elif ml_tasks_utils.is_docker_daemon_exists():
return DockerBuilder(preprocessor=preprocessor,
base_image=base_image,
registry=registry)
else:
# TODO (karthikv2k): Add more info on how to reolve this issue
raise RuntimeError(
"Not able to guess the right builder for this job!")
def __init__(self, namespace=None, build_context_source=None):
super(GKEBackend, self).__init__(namespace, build_context_source)
self._build_context_source = gcs_context.GCSContextSource(
namespace=self._namespace)