def setUp(self):
super(PlaceholderUtilsTest, self).setUp()
examples = [standard_artifacts.Examples()]
examples[0].uri = "/tmp"
examples[0].split_names = artifact_utils.encode_split_names(
["train", "eval"])
self._serving_spec = infra_validator_pb2.ServingSpec()
self._serving_spec.tensorflow_serving.tags.extend(
["latest", "1.15.0-gpu"])
self._resolution_context = placeholder_utils.ResolutionContext(
exec_info=data_types.ExecutionInfo(
input_dict={
"model": [standard_artifacts.Model()],
"examples": examples,
},
output_dict={"blessing": [standard_artifacts.ModelBlessing()]},
exec_properties={
"proto_property":
json_format.MessageToJson(message=self._serving_spec,
sort_keys=True,
preserving_proto_field_name=True,
indent=0)
},
execution_output_uri="test_executor_output_uri",
stateful_working_dir="test_stateful_working_dir",
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
type=metadata_store_pb2.ExecutionType(
name="infra_validator"))),
pipeline_info=pipeline_pb2.PipelineInfo(
id="test_pipeline_id")),
executor_spec=executable_spec_pb2.PythonClassExecutableSpec(
class_path="test_class_path"),
)
# Resolution context to simulate missing optional values.
self._none_resolution_context = placeholder_utils.ResolutionContext(
exec_info=data_types.ExecutionInfo(
input_dict={
"model": [],
"examples": [],
},
output_dict={"blessing": []},
exec_properties={},
pipeline_node=pipeline_pb2.PipelineNode(
node_info=pipeline_pb2.NodeInfo(
type=metadata_store_pb2.ExecutionType(
name="infra_validator"))),
pipeline_info=pipeline_pb2.PipelineInfo(
id="test_pipeline_id")),
executor_spec=None,
platform_config=None)
def run_executor(
self, execution_info: data_types.ExecutionInfo
) -> execution_result_pb2.ExecutorOutput:
"""Execute underlying component implementation."""
context = placeholder_utils.ResolutionContext(
exec_info=execution_info,
executor_spec=self._executor_spec,
platform_config=self._platform_config)
component_executor_spec = (
executor_specs.TemplatedExecutorContainerSpec(
image=self._container_executor_spec.image,
command=[
placeholder_utils.resolve_placeholder_expression(
cmd, context)
for cmd in self._container_executor_spec.commands
]))
docker_config = docker_component_config.DockerComponentConfig()
logging.info('Container spec: %s', vars(component_executor_spec))
logging.info('Docker config: %s', vars(docker_config))
# Call client.containers.run and wait for completion.
# ExecutorContainerSpec follows k8s container spec which has different
# names to Docker's container spec. It's intended to set command to docker's
# entrypoint and args to docker's command.
if docker_config.docker_server_url:
client = docker.DockerClient(
base_url=docker_config.docker_server_url)
else:
client = docker.from_env()
run_args = docker_config.to_run_args()
container = client.containers.run(
image=component_executor_spec.image,
command=component_executor_spec.command,
detach=True,
**run_args)
# Streaming logs
for log in container.logs(stream=True):
logging.info('Docker: %s', log.decode('utf-8'))
exit_code = container.wait()['StatusCode']
if exit_code != 0:
raise RuntimeError(
'Container exited with error code "{}"'.format(exit_code))
# TODO(b/141192583): Report data to publisher
# - report container digest
# - report replaced command line entrypoints
# - report docker run args
return execution_result_pb2.ExecutorOutput()
def resolve_artifacts(
self, metadata_handler: metadata.Metadata,
input_dict: Dict[str, List[types.Artifact]]
) -> Optional[Dict[str, List[types.Artifact]]]:
for placeholder_pb in self._predicates:
context = placeholder_utils.ResolutionContext(
exec_info=portable_data_types.ExecutionInfo(
input_dict=input_dict))
predicate_result = placeholder_utils.resolve_placeholder_expression(
placeholder_pb, context)
if not isinstance(predicate_result, bool):
raise ValueError(
"Predicate evaluates to a non-boolean result.")
if not predicate_result:
raise exceptions.SkipSignal("Predicate evaluates to False.")
return input_dict
def setUp(self):
super(PlaceholderUtilsTest, self).setUp()
examples = [standard_artifacts.Examples()]
examples[0].uri = "/tmp"
examples[0].split_names = artifact_utils.encode_split_names(
["train", "eval"])
serving_spec = infra_validator_pb2.ServingSpec()
serving_spec.tensorflow_serving.tags.extend(["latest", "1.15.0-gpu"])
self._resolution_context = placeholder_utils.ResolutionContext(
input_dict={
"model": [standard_artifacts.Model()],
"examples": examples,
},
output_dict={"blessing": [standard_artifacts.ModelBlessing()]},
exec_properties={
"proto_property": serving_spec.SerializeToString(),
"double_list_property": [0.7, 0.8, 0.9],
})
def setUp(self):
super(PlaceholderUtilsTest, self).setUp()
examples = [standard_artifacts.Examples()]
examples[0].uri = "/tmp"
examples[0].split_names = artifact_utils.encode_split_names(
["train", "eval"])
serving_spec = infra_validator_pb2.ServingSpec()
serving_spec.tensorflow_serving.tags.extend(["latest", "1.15.0-gpu"])
self._resolution_context = placeholder_utils.ResolutionContext(
input_dict={
"model": [standard_artifacts.Model()],
"examples": examples,
},
output_dict={"blessing": [standard_artifacts.ModelBlessing()]},
exec_properties={
"proto_property":
json_format.MessageToJson(message=serving_spec,
sort_keys=True,
preserving_proto_field_name=True)
})
def run_executor(
self, execution_info: data_types.ExecutionInfo
) -> execution_result_pb2.ExecutorOutput:
"""Execute underlying component implementation.
Runs executor container in a Kubernetes Pod and wait until it goes into
`Succeeded` or `Failed` state.
Args:
execution_info: All the information that the launcher provides.
Raises:
RuntimeError: when the pod is in `Failed` state or unexpected failure from
Kubernetes API.
Returns:
An ExecutorOutput instance
"""
context = placeholder_utils.ResolutionContext(
exec_info=execution_info,
executor_spec=self._executor_spec,
platform_config=self._platform_config)
container_spec = executor_specs.TemplatedExecutorContainerSpec(
image=self._container_executor_spec.image,
command=[
placeholder_utils.resolve_placeholder_expression(cmd, context)
for cmd in self._container_executor_spec.commands
] or None,
args=[
placeholder_utils.resolve_placeholder_expression(arg, context)
for arg in self._container_executor_spec.args
] or None,
)
pod_name = self._build_pod_name(execution_info)
# TODO(hongyes): replace the default value from component config.
try:
namespace = kube_utils.get_kfp_namespace()
except RuntimeError:
namespace = 'kubeflow'
pod_manifest = self._build_pod_manifest(pod_name, container_spec)
core_api = kube_utils.make_core_v1_api()
if kube_utils.is_inside_kfp():
launcher_pod = kube_utils.get_current_kfp_pod(core_api)
pod_manifest['spec'][
'serviceAccount'] = launcher_pod.spec.service_account
pod_manifest['spec'][
'serviceAccountName'] = launcher_pod.spec.service_account_name
pod_manifest['metadata'][
'ownerReferences'] = container_common.to_swagger_dict(
launcher_pod.metadata.owner_references)
else:
pod_manifest['spec'][
'serviceAccount'] = kube_utils.TFX_SERVICE_ACCOUNT
pod_manifest['spec'][
'serviceAccountName'] = kube_utils.TFX_SERVICE_ACCOUNT
logging.info('Looking for pod "%s:%s".', namespace, pod_name)
resp = kube_utils.get_pod(core_api, pod_name, namespace)
if not resp:
logging.info('Pod "%s:%s" does not exist. Creating it...',
namespace, pod_name)
logging.info('Pod manifest: %s', pod_manifest)
try:
resp = core_api.create_namespaced_pod(namespace=namespace,
body=pod_manifest)
except client.rest.ApiException as e:
raise RuntimeError(
'Failed to created container executor pod!\nReason: %s\nBody: %s'
% (e.reason, e.body))
# Wait up to 300 seconds for the pod to move from pending to another status.
logging.info('Waiting for pod "%s:%s" to start.', namespace, pod_name)
kube_utils.wait_pod(
core_api,
pod_name,
namespace,
exit_condition_lambda=kube_utils.pod_is_not_pending,
condition_description='non-pending status',
timeout_sec=300)
logging.info('Start log streaming for pod "%s:%s".', namespace,
pod_name)
try:
logs = core_api.read_namespaced_pod_log(
name=pod_name,
namespace=namespace,
container=kube_utils.ARGO_MAIN_CONTAINER_NAME,
follow=True,
_preload_content=False).stream()
except client.rest.ApiException as e:
raise RuntimeError(
'Failed to stream the logs from the pod!\nReason: %s\nBody: %s'
% (e.reason, e.body))
for log in logs:
logging.info(log.decode().rstrip('\n'))
# Wait indefinitely for the pod to complete.
resp = kube_utils.wait_pod(
core_api,
pod_name,
namespace,
exit_condition_lambda=kube_utils.pod_is_done,
condition_description='done state')
if resp.status.phase == kube_utils.PodPhase.FAILED.value:
raise RuntimeError('Pod "%s:%s" failed with status "%s".' %
(namespace, pod_name, resp.status))
logging.info('Pod "%s:%s" is done.', namespace, pod_name)
return execution_result_pb2.ExecutorOutput()