216 lines
7.7 KiB
Python
216 lines
7.7 KiB
Python
# Copyright 2021 The Kubeflow Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Base class for MLMD artifact in KFP SDK."""
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from typing import Any, Optional
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from absl import logging
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import importlib
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import yaml
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from google.protobuf import json_format
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from google.protobuf import struct_pb2
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from kfp.pipeline_spec import pipeline_spec_pb2
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from kfp.deprecated.dsl import serialization_utils
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from kfp.deprecated.dsl import artifact_utils
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KFP_ARTIFACT_ONTOLOGY_MODULE = 'kfp.dsl.ontology_artifacts'
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DEFAULT_ARTIFACT_SCHEMA = 'title: kfp.Artifact\ntype: object\nproperties:\n'
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class Artifact(object):
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"""KFP Artifact Python class.
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Artifact Python class/object mainly serves following purposes in different
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period of its lifecycle.
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1. During compile time, users can use Artifact class to annotate I/O types of
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their components.
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2. At runtime, Artifact objects provide helper function/utilities to access
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the underlying RuntimeArtifact pb message, and provide additional layers
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of validation to ensure type compatibility for fields specified in the
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instance schema.
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"""
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TYPE_NAME = "kfp.Artifact"
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# Initialization flag to support setattr / getattr behavior.
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_initialized = False
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def __init__(self, instance_schema: Optional[str] = None):
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"""Constructs an instance of Artifact.
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Setups up self._metadata_fields to perform type checking and
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initialize RuntimeArtifact.
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"""
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if self.__class__ == Artifact:
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if not instance_schema:
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raise ValueError(
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'The "instance_schema" argument must be set for Artifact.')
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self._instance_schema = instance_schema
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else:
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if instance_schema:
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raise ValueError(
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'The "instance_schema" argument must not be passed for Artifact \
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subclass: {}'.format(self.__class__))
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# setup self._metadata_fields
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self.TYPE_NAME, self._metadata_fields = artifact_utils.parse_schema(
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self._instance_schema)
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# Instantiate a RuntimeArtifact pb message as the POD data structure.
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self._artifact = pipeline_spec_pb2.RuntimeArtifact()
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# Stores the metadata for the Artifact.
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self.metadata = {}
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self._artifact.type.CopyFrom(
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pipeline_spec_pb2.ArtifactTypeSchema(
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instance_schema=self._instance_schema))
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self._initialized = True
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@property
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def type_schema(self) -> str:
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"""Gets the instance_schema for this Artifact object."""
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return self._instance_schema
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def __getattr__(self, name: str) -> Any:
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"""Custom __getattr__ to allow access to artifact metadata."""
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if name not in self._metadata_fields:
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raise AttributeError(
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'No metadata field: {} in artifact.'.format(name))
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return self.metadata[name]
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def __setattr__(self, name: str, value: Any):
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"""Custom __setattr__ to allow access to artifact metadata."""
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if not self._initialized:
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object.__setattr__(self, name, value)
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return
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metadata_fields = {}
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if self._metadata_fields:
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metadata_fields = self._metadata_fields
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if name not in self._metadata_fields:
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if (name in self.__dict__ or
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any(name in c.__dict__ for c in self.__class__.mro())):
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# Use any provided getter / setter if available.
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object.__setattr__(self, name, value)
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return
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# In the case where we do not handle this via an explicit getter /
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# setter, we assume that the user implied an artifact attribute store,
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# and we raise an exception since such an attribute was not explicitly
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# defined in the Artifact PROPERTIES dictionary.
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raise AttributeError('Cannot set an unspecified metadata field:{} \
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on artifact. Only fields specified in instance schema can be \
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set.'.format(name))
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# Type checking to be performed during serialization.
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self.metadata[name] = value
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def _update_runtime_artifact(self):
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"""Verifies metadata is well-formed and updates artifact instance."""
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artifact_utils.verify_schema_instance(self._instance_schema,
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self.metadata)
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if len(self.metadata) != 0:
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metadata_protobuf_struct = struct_pb2.Struct()
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metadata_protobuf_struct.update(self.metadata)
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self._artifact.metadata.CopyFrom(metadata_protobuf_struct)
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@property
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def type(self):
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return self.__class__
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@property
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def type_name(self):
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return self.TYPE_NAME
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@property
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def uri(self) -> str:
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return self._artifact.uri
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@uri.setter
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def uri(self, uri: str) -> None:
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self._artifact.uri = uri
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@property
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def name(self) -> str:
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return self._artifact.name
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@name.setter
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def name(self, name: str) -> None:
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self._artifact.name = name
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@property
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def runtime_artifact(self) -> pipeline_spec_pb2.RuntimeArtifact:
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self._update_runtime_artifact()
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return self._artifact
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@runtime_artifact.setter
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def runtime_artifact(self, artifact: pipeline_spec_pb2.RuntimeArtifact):
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self._artifact = artifact
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def serialize(self) -> str:
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"""Serializes an Artifact to JSON dict format."""
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self._update_runtime_artifact()
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return json_format.MessageToJson(self._artifact, sort_keys=True)
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@classmethod
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def get_artifact_type(cls) -> str:
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"""Gets the instance_schema according to the Python schema spec."""
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result_map = {'title': cls.TYPE_NAME, 'type': 'object'}
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return serialization_utils.yaml_dump(result_map)
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@classmethod
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def get_ir_type(cls) -> pipeline_spec_pb2.ArtifactTypeSchema:
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return pipeline_spec_pb2.ArtifactTypeSchema(
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instance_schema=cls.get_artifact_type())
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@classmethod
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def get_from_runtime_artifact(
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cls, artifact: pipeline_spec_pb2.RuntimeArtifact) -> Any:
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"""Deserializes an Artifact object from RuntimeArtifact message."""
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instance_schema = yaml.safe_load(artifact.type.instance_schema)
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type_name = instance_schema['title'][len('kfp.'):]
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result = None
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try:
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artifact_cls = getattr(
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importlib.import_module(KFP_ARTIFACT_ONTOLOGY_MODULE),
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type_name)
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result = artifact_cls()
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except (AttributeError, ImportError, ValueError) as err:
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logging.warning('Failed to instantiate Ontology Artifact:{} \
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instance'.format(type_name))
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if not result:
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# Otherwise generate a generic Artifact object.
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result = Artifact(instance_schema=artifact.type.instance_schema)
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result.runtime_artifact = artifact
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result.metadata = json_format.MessageToDict(artifact.metadata)
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return result
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@classmethod
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def deserialize(cls, data: str) -> Any:
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"""Deserializes an Artifact object from JSON dict."""
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artifact = pipeline_spec_pb2.RuntimeArtifact()
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json_format.Parse(data, artifact, ignore_unknown_fields=True)
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return cls.get_from_runtime_artifact(artifact)
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