remove kubeflow training (#1902)
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## Overview
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The `kubeflow-training-classification.py` pipeline creates a TensorFlow model on structured data and image URLs (Google Cloud Storage). It works for both classification and regression.
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Everything runs inside the pipeline cluster (Kubeflow). The only possible dependency is Google Cloud DataFlow if you enable the "*cloud*" mode for
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the preprocessing or prediction step.
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## The requirements
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By default, the preprocessing and prediction steps use the "*local*" mode and run inside the cluster. If you specify the value of "*preprocess_mode*" as "*cloud*", you must enable the
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[DataFlow API](https://cloud.google.com/endpoints/docs/openapi/enable-api) for the given GCP project so that the preprocessing step
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can use Cloud DataFlow.
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Note: The trainer depends on Kubeflow API version v1alpha2.
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## Compiling the pipeline template
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Follow the guide to [building a pipeline](https://www.kubeflow.org/docs/guides/pipelines/build-pipeline/) to install the Kubeflow Pipelines SDK, then run the following command to compile the sample Python into a workflow specification. The specification takes the form of a YAML file compressed into a `.tar.gz` file.
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```bash
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dsl-compile --py kubeflow-training-classification.py --output kubeflow-training-classification.tar.gz
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```
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## Deploying the pipeline
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Open the Kubeflow pipelines UI. Create a new pipeline, and then upload the compiled specification (`.tar.gz` file) as a new pipeline template.
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The pipeline requires one argument:
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1. An output directory in a Google Cloud Storage bucket, of the form `gs://<BUCKET>/<PATH>`.
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## Components source
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Preprocessing:
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[source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft/src),
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[container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/tft)
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Training:
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[source code](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher/src),
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[container](https://github.com/kubeflow/pipelines/tree/master/components/kubeflow/launcher)
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Prediction:
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[source code](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/predict/src),
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[container](https://github.com/kubeflow/pipelines/tree/master/components/dataflow/predict)
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Confusion Matrix:
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[source code](https://github.com/kubeflow/pipelines/tree/master/components/local/confusion_matrix/src),
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[container](https://github.com/kubeflow/pipelines/tree/master/components/local/confusion_matrix)
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@ -1,91 +0,0 @@
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#!/usr/bin/env python3
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# Copyright 2019 Google LLC
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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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import kfp
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from kfp import components
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from kfp import dsl
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from kfp import gcp
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dataflow_tf_transform_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/dataflow/tft/component.yaml')
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kubeflow_tf_training_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/kubeflow/dnntrainer/component.yaml')
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dataflow_tf_predict_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/dataflow/predict/component.yaml')
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confusion_matrix_op = components.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/48dd338c8ab328084633c51704cda77db79ac8c2/components/local/confusion_matrix/component.yaml')
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@dsl.pipeline(
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name='TF training and prediction pipeline',
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description=''
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)
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def kubeflow_training(output, project,
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evaluation='gs://ml-pipeline-playground/flower/eval100.csv',
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train='gs://ml-pipeline-playground/flower/train200.csv',
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schema='gs://ml-pipeline-playground/flower/schema.json',
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learning_rate=0.1,
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hidden_layer_size='100,50',
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steps=2000,
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target='label',
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workers=0,
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pss=0,
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preprocess_mode='local',
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predict_mode='local',
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):
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output_template = str(output) + '/{{workflow.uid}}/{{pod.name}}/data'
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# set the flag to use GPU trainer
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use_gpu = False
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preprocess = dataflow_tf_transform_op(
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training_data_file_pattern=train,
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evaluation_data_file_pattern=evaluation,
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schema=schema,
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gcp_project=project,
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run_mode=preprocess_mode,
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preprocessing_module='',
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transformed_data_dir=output_template
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).apply(gcp.use_gcp_secret('user-gcp-sa'))
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training = kubeflow_tf_training_op(
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transformed_data_dir=preprocess.output,
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schema=schema,
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learning_rate=learning_rate,
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hidden_layer_size=hidden_layer_size,
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steps=steps,
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target=target,
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preprocessing_module='',
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training_output_dir=output_template
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).apply(gcp.use_gcp_secret('user-gcp-sa'))
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if use_gpu:
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training.image = 'gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer-gpu:0517114dc2b365a4a6d95424af6157ead774eff3',
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training.set_gpu_limit(1)
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prediction = dataflow_tf_predict_op(
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data_file_pattern=evaluation,
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schema=schema,
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target_column=target,
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model=training.output,
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run_mode=predict_mode,
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gcp_project=project,
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predictions_dir=output_template
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).apply(gcp.use_gcp_secret('user-gcp-sa'))
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confusion_matrix = confusion_matrix_op(
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predictions=prediction.output,
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output_dir=output_template
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).apply(gcp.use_gcp_secret('user-gcp-sa'))
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if __name__ == '__main__':
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kfp.compiler.Compiler().compile(kubeflow_training, __file__ + '.zip')
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@ -96,15 +96,6 @@ def main():
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'steps':
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'5'
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}
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elif args.testname == 'kubeflow_training_classification':
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params = {
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'output': args.output,
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'project': 'ml-pipeline-test',
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'evaluation': 'gs://ml-pipeline-dataset/sample-test/flower/eval15.csv',
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'train': 'gs://ml-pipeline-dataset/sample-test/flower/train30.csv',
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'hidden-layer-size': '10,5',
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'steps': '5'
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}
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elif args.testname == 'xgboost_training_cm':
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params = {
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'output': args.output,
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@ -150,22 +141,7 @@ def main():
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###### Validate the results for specific test cases ######
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#TODO: Add result check for tfx-cab-classification after launch.
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if args.testname == 'kubeflow_training_classification':
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cm_tar_path = './confusion_matrix.tar.gz'
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utils.get_artifact_in_minio(workflow_json, 'confusion-matrix', cm_tar_path,
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'mlpipeline-ui-metadata')
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with tarfile.open(cm_tar_path) as tar_handle:
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file_handles = tar_handle.getmembers()
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assert len(file_handles) == 1
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with tar_handle.extractfile(file_handles[0]) as f:
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cm_data = json.load(io.TextIOWrapper(f))
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utils.add_junit_test(
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test_cases, 'confusion matrix format',
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(len(cm_data['outputs'][0]['schema']) == 3),
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'the column number of the confusion matrix output is not equal to three'
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)
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elif args.testname == 'xgboost_training_cm':
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if args.testname == 'xgboost_training_cm':
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cm_tar_path = './confusion_matrix.tar.gz'
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utils.get_artifact_in_minio(workflow_json, 'confusion-matrix', cm_tar_path,
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'mlpipeline-ui-metadata')
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@ -203,17 +203,6 @@ xgboost_training_cm_injection() {
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sed -i "s|gcr.io/ml-pipeline/ml-pipeline-local-roc:\([a-zA-Z0-9_.-]\)\+|${LOCAL_ROC_IMAGE}|g" ${TEST_NAME}.yaml
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}
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################################################################################
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# Utility function to inject correct images to python files for
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# kubeflow_training_classification test.
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################################################################################
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kubeflow_training_classification_injection() {
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sed -i "s|gcr.io/ml-pipeline/ml-pipeline-dataflow-tft:\([a-zA-Z0-9_.-]\)\+|${DATAFLOW_TFT_IMAGE}|g" ${TEST_NAME}.py
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sed -i "s|gcr.io/ml-pipeline/ml-pipeline-kubeflow-tf-trainer:\([a-zA-Z0-9_.-]\)\+|${KUBEFLOW_DNNTRAINER_IMAGE}|g" ${TEST_NAME}.py
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sed -i "s|gcr.io/ml-pipeline/ml-pipeline-dataflow-tf-predict:\([a-zA-Z0-9_.-]\)\+|${DATAFLOW_PREDICT_IMAGE}|g" ${TEST_NAME}.py
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sed -i "s|gcr.io/ml-pipeline/ml-pipeline-local-confusion-matrix:\([a-zA-Z0-9_.-]\)\+|${LOCAL_CONFUSIONMATRIX_IMAGE}|g" ${TEST_NAME}.py
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}
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if [[ -z "$RESULTS_GCS_DIR" ]]; then
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usage
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exit 1
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@ -231,17 +220,7 @@ echo "Run the sample tests..."
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# Run the tests
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preparation ${TEST_NAME}
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if [[ "${TEST_NAME}" == "kubeflow_training_classification" ]]; then
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#TODO(numerology): convert the sed commands to sed -e
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# 's|gcr.io/ml-pipeline/|gcr.io/ml-pipeline-test/' and tag replacement. Also
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# let the postsubmit tests refer to yaml files.
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if [ -n "${DATAFLOW_TFT_IMAGE}" ];then
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kubeflow_training_classification_injection
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fi
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dsl-compile --py "${TEST_NAME}.py" --output "${TEST_NAME}.yaml"
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check_result ${TEST_NAME}
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elif [[ "${TEST_NAME}" == "tfx_cab_classification" ]]; then
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if [[ "${TEST_NAME}" == "tfx_cab_classification" ]]; then
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dsl-compile --py "${TEST_NAME}.py" --output "${TEST_NAME}.yaml"
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if [[ -n "${DATAFLOW_TFT_IMAGE}" ]]; then
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tfx_cab_classification_injection
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@ -67,7 +67,6 @@ spec:
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- name: test-name
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value: "{{item}}"
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withItems:
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- kubeflow_training_classification
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- tfx_cab_classification
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- xgboost_training_cm
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- kubeflow_pipeline_using_TFX_OSS_components
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