def deploy(self,
mdl_serve_class,
mdl_serve,
mdl_file_list=['model.dat'],
reqs_file='requirements.txt'):
my_file_list = []
my_file_list.extend(mdl_file_list)
my_file_list.append(reqs_file)
my_output_map = {
key: value
for (key, value) in self.MY_DEPLOYMENT_FILE_DICT.items()
}
#Add vessel ML core files to deployment image (MY_DEPLOYMENT_FILE_DICT dictionary has source and desired dest paths)
self.preprocessor = FunctionPreProcessor(function_obj=mdl_serve,
output_map=my_output_map,
input_files=my_file_list)
self.builder = ClusterBuilder(
registry=self.DOCKER_REGISTRY,
base_image=self.BASE_IMAGE,
preprocessor=self.preprocessor,
pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
context_source=gcs_context.GCSContextSource())
self.builder.build()
self.pod_spec = self.builder.generate_pod_spec()
# TODO: Add support for deploying into custom user-provided namespaces (use namespace param of Serving class)
self.infer_deployer = Serving(serving_class=mdl_serve_class)
self.endpoint = self.infer_deployer.deploy(self.pod_spec)
return self.endpoint
def get_builder(
self,
preprocessor,
base_image,
registry,
needs_deps_installation=True, # pylint:disable=arguments-differ
pod_spec_mutators=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 fairing.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=fairing.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,
registry=None,
context_source=gcs_context.GCSContextSource(),
preprocessor=None,
base_image=constants.DEFAULT_BASE_IMAGE,
dockerfile_path=None):
super().__init__(
registry=registry,
preprocessor=preprocessor,
base_image=base_image,
)
self.manager = KubeManager()
self.context_source = context_source
def train_v1(self,
mdl_trainer,
train_data_file_list=['data/train.csv'],
reqs_file='requirements.txt'):
#TODO: optimize if sequence doesn't matter
#logging.info("Creating docker context: %s", output_file)
my_file_list = []
my_file_list.append(mdl_trainer)
my_file_list.extend(train_data_file_list)
my_file_list.append(reqs_file)
my_output_map = {
key: value
for (key, value) in self.MY_DEPLOYMENT_FILE_DICT.items()
}
#for f in train_data_file_list:
#fname = f.split("/")[-1]
#my_output_map[os.path.normpath(f)] = "/app/data/{0}".format(fname)
self.preprocessor = BasePreProcessor(input_files=my_file_list,
output_map=my_output_map)
self.builder = ClusterBuilder(
registry=self.DOCKER_REGISTRY,
base_image=self.BASE_IMAGE,
preprocessor=self.preprocessor,
pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
context_source=gcs_context.GCSContextSource())
self.builder.build()
self.pod_spec = self.builder.generate_pod_spec()
self.train_deployer = TfJob(pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
worker_count=1,
chief_count=0)
j_name = self.train_deployer.deploy(self.pod_spec)
return (self.train_deployer.job_id, j_name)
def run_submission_with_function_preprocessor(capsys,
deployer="job",
builder="append",
namespace="default",
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':
fairing.config.set_builder(
builder,
base_image=base_image,
registry=DOCKER_REGISTRY,
pod_spec_mutators=[fairing.cloud.gcp.add_gcp_credentials],
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 __init__(self,
registry=None,
image_name=constants.DEFAULT_IMAGE_NAME,
context_source=None,
preprocessor=None,
push=True,
base_image=constants.DEFAULT_BASE_IMAGE,
pod_spec_mutators=None,
namespace="kubeflow",
dockerfile_path=None):
super().__init__(
registry=registry,
image_name=image_name,
push=push,
preprocessor=preprocessor,
base_image=base_image,
)
self.manager = KubeManager()
if context_source is None:
context_source = gcs_context.GCSContextSource(namespace=namespace)
self.context_source = context_source
self.pod_spec_mutators = pod_spec_mutators or []
self.namespace = namespace
def train(self,
mdl_trainer,
train_data_file_list=['train.csv'],
reqs_file='requirements.txt'):
my_file_list = []
my_file_list.append(reqs_file)
my_output_map = {
key: value
for (key, value) in self.MY_DEPLOYMENT_FILE_DICT.items()
}
for f in train_data_file_list:
fname = f.split("/")[-1]
my_output_map[os.path.normpath(f)] = "/app/data/{0}".format(fname)
self.preprocessor = FunctionPreProcessor(function_obj=mdl_trainer,
output_map=my_output_map,
input_files=my_file_list)
self.builder = ClusterBuilder(
registry=self.DOCKER_REGISTRY,
base_image=self.BASE_IMAGE,
preprocessor=self.preprocessor,
pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
context_source=gcs_context.GCSContextSource())
self.builder.build()
self.pod_spec = self.builder.generate_pod_spec()
self.train_deployer = TfJob(pod_spec_mutators=[
fairing.cloud.gcp.add_gcp_credentials_if_exists
],
worker_count=1,
chief_count=0)
j_name = self.train_deployer.deploy(self.pod_spec)
return (self.train_deployer.job_id, j_name)
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)
def __init__(self, namespace=None, build_context_source=None):
build_context_source = build_context_source or gcs_context.GCSContextSource(
namespace=namespace)
super(GKEBackend, self).__init__(namespace, build_context_source)
