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Update to KFP pipelines codelab code (GH summarization) (kubeflow#638)
* checkpointing * checkpointing * refactored pipeline that uses pre-emptible VMs * checkpointing. istio routing for the webapp. * checkpointing * - temp testing components - initial v of metadata logging 'component' - new dirs; file rename * public md log image; add md server connect retry * update pipeline to include md logging steps * - file rename, notebook updates - update compiled pipeline; fix component name typo - change DAG to allow md logging concurrently; update pre-emptible VMS PL * pylint cleanup, readme/tutorial update/deprecation, minor tweaks * file cleanup * update the tfjob api version for an (unrelated) test to address presubmit issues * try annotating test_train in github_issue_summarization/testing/tfjob_test.py with @unittest.expectedFailure * try commenting out a (likely) problematic unittest unrelated to the code changes in this PR * try adding @test_util.expectedFailure annotation instead of commenting out test * update the codelab shortlink; revert to commenting out a problematic unit test
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‎github_issue_summarization/ks_app/components/tfjob.jsonnet

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@@ -7,7 +7,7 @@ local name = params.name;
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local namespace = env.namespace;
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local tfjob = {
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apiVersion: "kubeflow.org/v1beta1",
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apiVersion: "kubeflow.org/v1",
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kind: "TFJob",
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metadata: {
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name: name,

‎github_issue_summarization/pipelines/README.md

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This Kubeflow Pipelines example shows how to build a web app that summarizes GitHub issues using Kubeflow Pipelines to train and serve a model.
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The pipeline trains a [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor/) model on GitHub issue data, learning to predict issue titles from issue bodies. It then exports the trained model and deploys the exported model using [Tensorflow Serving](https://github.com/tensorflow/serving). The final step in the pipeline launches a web app, which interacts with the TF-Serving instance in order to get model predictions.
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You can follow this example as a codelab: [g.co/codelabs/kubecon18](https://g.co/codelabs/kubecon18).
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Or, you can run it as a [Cloud shell Tutorial](https://console.cloud.google.com/?cloudshell=true&cloudshell_git_repo=https://github.com/kubeflow/examples&working_dir=github_issue_summarization/pipelines&cloudshell_tutorial=tutorial.md). The source for the Cloud Shell tutorial is [here](tutorial.md).
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You can follow this example as a codelab: [g.co/codelabs/kfp-gis](https://g.co/codelabs/kfp-gis).
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<!-- Or, you can run it as a [Cloud shell Tutorial](https://console.cloud.google.com/?cloudshell=true&cloudshell_git_repo=https://github.com/kubeflow/examples&working_dir=github_issue_summarization/pipelines&cloudshell_tutorial=tutorial.md). The source for the Cloud Shell tutorial is [here](tutorial.md). -->
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‎github_issue_summarization/pipelines/components/t2t/containers/metadata-logger/Dockerfile

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# Copyright 2018 Google Inc. All Rights Reserved.
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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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FROM ubuntu:18.04
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RUN apt-get update \
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&& apt-get install -y python3-pip python3-dev \
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&& cd /usr/local/bin \
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&& ln -s /usr/bin/python3 python \
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&& pip3 install --upgrade pip
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RUN apt-get install -y wget unzip git
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# RUN pip install pyyaml==3.12 six==1.11.0 requests==2.18.4
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# RUN pip install tensorflow==1.12.0
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RUN pip install --upgrade pip
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RUN pip install kfmd urllib3 certifi retrying
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# RUN wget -nv https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.zip && \
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# unzip -qq google-cloud-sdk.zip -d tools && \
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# rm google-cloud-sdk.zip && \
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# tools/google-cloud-sdk/install.sh --usage-reporting=false \
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# --path-update=false --bash-completion=false \
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# --disable-installation-options && \
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# tools/google-cloud-sdk/bin/gcloud -q components update \
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# gcloud core gsutil && \
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# tools/google-cloud-sdk/bin/gcloud -q components install kubectl && \
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# tools/google-cloud-sdk/bin/gcloud config set component_manager/disable_update_check true && \
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# touch /tools/google-cloud-sdk/lib/third_party/google.py
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ADD build /ml
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ENTRYPOINT ["python", "/ml/log-metadata.py"]
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‎github_issue_summarization/pipelines/components/t2t/containers/metadata-logger/build.sh

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#!/bin/bash -e
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# Copyright 2018 Google Inc. All Rights Reserved.
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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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if [ -z "$1" ]
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then
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PROJECT_ID=$(gcloud config config-helper --format "value(configuration.properties.core.project)")
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else
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PROJECT_ID=$1
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fi
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mkdir -p ./build
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rsync -arvp "../../metadata-logger"/ ./build/
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docker build -t ml-pipeline-metadata-logger .
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rm -rf ./build
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docker tag ml-pipeline-metadata-logger gcr.io/${PROJECT_ID}/ml-pipeline-metadata-logger
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docker push gcr.io/${PROJECT_ID}/ml-pipeline-metadata-logger

‎github_issue_summarization/pipelines/components/t2t/datacopy_component.yaml

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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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name: Copy training checkpoint data
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description: |
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A Kubeflow Pipeline component to copy training checkpoint data from one bucket
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to another
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metadata:
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labels:
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add-pod-env: 'true'
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inputs:
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- name: working_dir
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description: '...'
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type: GCSPath
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- name: data_dir
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description: '...'
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type: GCSPath
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- name: checkpoint_dir
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description: '...'
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type: GCSPath
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- name: model_dir
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description: '...'
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type: GCSPath
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- name: action
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description: '...'
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type: String
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implementation:
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container:
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image: gcr.io/google-samples/ml-pipeline-t2ttrain:v2ap
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args: [
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--data-dir, {inputValue: data_dir},
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--checkpoint-dir, {inputValue: checkpoint_dir},
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--action, {inputValue: action},
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--working-dir, {inputValue: working_dir},
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--model-dir, {inputValue: model_dir}
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]
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env:
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KFP_POD_NAME: "{{pod.name}}"

‎github_issue_summarization/pipelines/components/t2t/metadata-logger/log-metadata.py

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# Copyright 2019 Google Inc. All Rights Reserved.
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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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# https://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 argparse
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from datetime import datetime
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import logging
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import retrying
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from kfmd import metadata
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DATASET = 'dataset'
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MODEL = 'model'
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METADATA_SERVICE = "metadata-service.kubeflow:8080"
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def get_or_create_workspace(ws_name):
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return metadata.Workspace(
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# Connect to metadata-service in namesapce kubeflow in the k8s cluster.
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backend_url_prefix=METADATA_SERVICE,
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name=ws_name,
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description="a workspace for the GitHub summarization task",
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labels={"n1": "v1"})
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def get_or_create_workspace_run(md_workspace, run_name):
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return metadata.Run(
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workspace=md_workspace,
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name=run_name,
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description="Metadata run for workflow %s" % run_name,
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)
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@retrying.retry(stop_max_delay=180000)
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def log_model_info(ws, ws_run, model_uri):
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exec2 = metadata.Execution(
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name="execution" + datetime.utcnow().isoformat("T"),
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workspace=ws,
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run=ws_run,
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description="train action",
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)
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_ = exec2.log_input(
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metadata.Model(
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description="t2t model",
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name="t2t-model",
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owner="[email protected]",
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uri=model_uri,
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version="v1.0.0"
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))
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@retrying.retry(stop_max_delay=180000)
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def log_dataset_info(ws, ws_run, data_uri):
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exec1 = metadata.Execution(
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name="execution" + datetime.utcnow().isoformat("T"),
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workspace=ws,
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run=ws_run,
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description="copy action",
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)
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_ = exec1.log_input(
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metadata.DataSet(
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description="gh summarization data",
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name="gh-summ-data",
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owner="[email protected]",
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uri=data_uri,
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version="v1.0.0"
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))
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def main():
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parser = argparse.ArgumentParser(description='Serving webapp')
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parser.add_argument(
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'--log-type',
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help='...',
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required=True)
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parser.add_argument(
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'--workspace-name',
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help='...',
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required=True)
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parser.add_argument(
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'--run-name',
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help='...',
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required=True)
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parser.add_argument(
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'--data-uri',
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help='...',
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)
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parser.add_argument(
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'--model-uri',
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help='...',
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)
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parser.add_argument('--cluster', type=str,
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help='GKE cluster set up for kubeflow. If set, zone must be provided. ' +
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'If not set, assuming this runs in a GKE container and current ' +
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'cluster is used.')
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parser.add_argument('--zone', type=str, help='zone of the kubeflow cluster.')
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args = parser.parse_args()
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ws = get_or_create_workspace(args.workspace_name)
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ws_run = get_or_create_workspace_run(ws, args.run_name)
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if args.log_type.lower() == DATASET:
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log_dataset_info(ws, ws_run, args.data_uri)
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elif args.log_type.lower() == MODEL:
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log_model_info(ws, ws_run, args.model_uri)
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else:
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logging.warning("Error: unknown metadata logging type %s", args.log_type)
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if __name__ == "__main__":
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main()

‎github_issue_summarization/pipelines/components/t2t/metadata_log_component.yaml

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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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name: log_metadata
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description: |
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A Kubeflow Pipeline component to log dataset or model metadata
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metadata:
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labels:
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add-pod-env: 'true'
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inputs:
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- name: log_type
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description: '...'
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type: String
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- name: workspace_name
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description: '...'
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type: String
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- name: run_name
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description: '...'
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type: String
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- name: data_uri
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description: '...'
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type: GCSPath
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default: ''
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- name: model_uri
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description: '...'
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type: GCSPath
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default: ''
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implementation:
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container:
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image: gcr.io/google-samples/ml-pipeline-metadata-logger:v1
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args: [
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--log-type, {inputValue: log_type},
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--workspace-name, {inputValue: workspace_name},
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--run-name, {inputValue: run_name},
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--data-uri, {inputValue: data_uri},
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--model-uri, {inputValue: model_uri}
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]
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env:
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KFP_POD_NAME: "{{pod.name}}"

‎github_issue_summarization/pipelines/components/t2t/t2t-app/app/ghsumm/trainer/problem.py

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from tensor2tensor.data_generators import text_problems
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@registry.register_problem
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@registry.register_problem # pylint: disable=abstract-method
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class GhProblem(text_problems.Text2TextProblem):
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"""... predict GH issue title from body..."""
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‎github_issue_summarization/pipelines/components/t2t/t2t-proc/ghsumm/trainer/problem.py

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from tensor2tensor.data_generators import text_problems
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@registry.register_problem
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@registry.register_problem # pylint: disable=abstract-method
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class GhProblem(text_problems.Text2TextProblem):
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"""... predict GH issue title from body..."""
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‎github_issue_summarization/pipelines/components/t2t/t2t-train/ghsumm/trainer/problem.py

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from tensor2tensor.data_generators import text_problems
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@registry.register_problem
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@registry.register_problem # pylint: disable=abstract-method
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class GhProblem(text_problems.Text2TextProblem):
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"""... predict GH issue title from body..."""
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