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* feat(frontend): Runtime DAG in RunDetailsV2 * remove debug log
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,103 @@ | ||
| // Copyright 2021 The Kubeflow Authors | ||
| // | ||
| // Licensed under the Apache License, Version 2.0 (the "License"); | ||
| // you may not use this file except in compliance with the License. | ||
| // You may obtain a copy of the License at | ||
| // | ||
| // http://www.apache.org/licenses/LICENSE-2.0 | ||
| // | ||
| // Unless required by applicable law or agreed to in writing, software | ||
| // distributed under the License is distributed on an "AS IS" BASIS, | ||
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| // See the License for the specific language governing permissions and | ||
| // limitations under the License. | ||
|
|
||
| import * as TWO_STEP_PIPELINE from 'src/data/test/mock_lightweight_python_functions_v2_pipeline.json'; | ||
| import { PipelineSpec } from 'src/generated/pipeline_spec'; | ||
| import { ml_pipelines } from 'src/generated/pipeline_spec/pbjs_ml_pipelines'; | ||
| import { Artifact, Event, Execution, Value } from 'src/third_party/mlmd'; | ||
| import { TASK_NAME_KEY, updateFlowElementsState } from './DynamicFlow'; | ||
| import { convertFlowElements } from './StaticFlow'; | ||
|
|
||
| describe('DynamicFlow', () => { | ||
| it('update node status based on MLMD', () => { | ||
| // Prepare MLMD objects. | ||
| const EXECUTION_PREPROCESS = new Execution() | ||
| .setId(3) | ||
| .setLastKnownState(Execution.State.COMPLETE); | ||
| EXECUTION_PREPROCESS.getCustomPropertiesMap().set( | ||
| TASK_NAME_KEY, | ||
| new Value().setStringValue('preprocess'), | ||
| ); | ||
| const EXECUTION_TRAIN = new Execution().setId(4).setLastKnownState(Execution.State.FAILED); | ||
| EXECUTION_TRAIN.getCustomPropertiesMap().set( | ||
| TASK_NAME_KEY, | ||
| new Value().setStringValue('train'), | ||
| ); | ||
|
|
||
| const ARTIFACT_OUTPUT_DATA_ONE = new Artifact().setId(1).setState(Artifact.State.LIVE); | ||
| const ARTIFACT_OUTPUT_DATA_TWO = new Artifact().setId(2).setState(Artifact.State.PENDING); | ||
| const ARTIFACT_MODEL = new Artifact().setId(3).setState(Artifact.State.DELETED); | ||
|
|
||
| const EVENT_PREPROCESS_OUTPUT_DATA_ONE = new Event() | ||
| .setExecutionId(3) | ||
| .setArtifactId(1) | ||
| .setType(Event.Type.OUTPUT) | ||
| .setPath(new Event.Path().setStepsList([new Event.Path.Step().setKey('output_dataset_one')])); | ||
| const EVENT_PREPROCESS_OUTPUT_DATA_TWO = new Event() | ||
| .setExecutionId(3) | ||
| .setArtifactId(2) | ||
| .setType(Event.Type.OUTPUT) | ||
| .setPath( | ||
| new Event.Path().setStepsList([new Event.Path.Step().setKey('output_dataset_two_path')]), | ||
| ); | ||
| const EVENT_OUTPUT_DATA_ONE_TRAIN = new Event().setExecutionId(4).setArtifactId(1); | ||
| const EVENT_OUTPUT_DATA_TWO_TRAIN = new Event().setExecutionId(4).setArtifactId(2); | ||
| const EVENT_TRAIN_MODEL = new Event() | ||
| .setExecutionId(4) | ||
| .setArtifactId(3) | ||
| .setType(Event.Type.OUTPUT) | ||
| .setPath(new Event.Path().setStepsList([new Event.Path.Step().setKey('model')])); | ||
|
|
||
| // Converts to static graph first, its type is Elements<any>. | ||
| const jsonObject = TWO_STEP_PIPELINE; | ||
| const message = ml_pipelines.PipelineSpec.fromObject(jsonObject['pipelineSpec']); | ||
| const buffer = ml_pipelines.PipelineSpec.encode(message).finish(); | ||
| const pipelineSpec = PipelineSpec.deserializeBinary(buffer); | ||
| const graph = convertFlowElements(pipelineSpec); | ||
|
|
||
| // MLMD objects to provide node states. | ||
| const executions: Execution[] = [EXECUTION_PREPROCESS, EXECUTION_TRAIN]; | ||
| const events: Event[] = [ | ||
| EVENT_PREPROCESS_OUTPUT_DATA_ONE, | ||
| EVENT_PREPROCESS_OUTPUT_DATA_TWO, | ||
| EVENT_OUTPUT_DATA_ONE_TRAIN, | ||
| EVENT_OUTPUT_DATA_TWO_TRAIN, | ||
| EVENT_TRAIN_MODEL, | ||
| ]; | ||
| const artifacts: Artifact[] = [ | ||
| ARTIFACT_OUTPUT_DATA_ONE, | ||
| ARTIFACT_OUTPUT_DATA_TWO, | ||
| ARTIFACT_MODEL, | ||
| ]; | ||
|
|
||
| updateFlowElementsState(graph, executions, events, artifacts); | ||
| for (let element of graph) { | ||
| graph | ||
| .filter(e => e.id === element.id) | ||
| .forEach(e => { | ||
| if (e.id === 'task.preprocess') { | ||
| expect(e.data.state).toEqual(EXECUTION_PREPROCESS.getLastKnownState()); | ||
| } else if (e.id === 'task.train') { | ||
| expect(e.data.state).toEqual(EXECUTION_TRAIN.getLastKnownState()); | ||
| } else if (e.id === 'artifact.preprocess.output_dataset_one') { | ||
| expect(e.data.state).toEqual(ARTIFACT_OUTPUT_DATA_ONE.getState()); | ||
| } else if (e.id === 'artifact.preprocess.output_dataset_two_path') { | ||
| expect(e.data.state).toEqual(ARTIFACT_OUTPUT_DATA_TWO.getState()); | ||
| } else if (e.id === 'artifact.train.model') { | ||
| expect(e.data.state).toEqual(ARTIFACT_MODEL.getState()); | ||
| } | ||
| }); | ||
| } | ||
| }); | ||
| }); |
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