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UPSTREAM: <carry>: Add the upstream code rebase document
Signed-off-by: Ricardo M. Oliveira <[email protected]>
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| # KFP -> DSP Rebase process | ||
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| This document describes the process to upgrade Data Science Pipelines (DSP) code from a specific Kubeflow Pipelines (KFP) version tag. The following repositories must be updated, in the following order: | ||
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| - https://github.com/opendatahub-io/data-science-pipelines | ||
| - https://github.com/opendatahub-io/argo-workflows | ||
| - https://github.com/opendatahub-io/data-science-pipelines-operator | ||
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| ## Checklist | ||
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| - The new rebase branch has been created from the upstream tag | ||
| - The new rebase branch includes relevant carries from target branch | ||
| - The upstream tag is pushed to kubeflow/pipelines to ensure that build artifacts are versioned correctly | ||
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| ## Getting Started | ||
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| ### Data Science Pipelines repository | ||
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| Preparing the local repo clone | ||
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| Clone from a personal fork, and add the remote for upstream and opendatahub, fetching its branches: | ||
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| ``` | ||
| git remote add --fetch kubeflow https://github.com/kubeflow/pipelines | ||
| git remote add --fetch opendatahub https://github.com/opendatahub-io/data-science-pipelines | ||
| ``` | ||
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| ### Creating a new local branch for the new rebase | ||
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| Branch the target release to a new branch: | ||
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| ``` | ||
| TAG=2.2.0 | ||
| git checkout -b rebase-$TAG $TAG | ||
| ``` | ||
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| Merge opendatahub(master) branch into the `rebase-\$TAG` branch with merge strategy ours. It discards all changes from the other branch (opendatahub/master) and create a merge commit. This leaves the content of your branch unchanged, and when you next merge with the other branch, Git will only consider changes made from this point forward. (Do not confuse this with ours conflict resolution strategy for recursive merge strategy, -X option.) | ||
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| ``` | ||
| git merge opendatahub/master | ||
| ``` | ||
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| This action will need to resolve some conflicts manually. Some recommentations when working in this task are: | ||
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| * Dockerfiles are not expected to have any merge conflicts. We should have our dsp images stored in a separate path from the kfp ones. | ||
| * Any changes in generated files (go.mod, go.sum, package.json, package-lock.json) should always prioritize upstream changes. | ||
| * In case of changes in backend code that diverges completelly between kfp and dsp, you should use `git blame` to find the author(s) of the changes and work together to fix the conflicts. Do not try to fix by yourself, you are not alone in this. | ||
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| After resolving all conflicts, remember to run a pre-flight check before going to the next task. | ||
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| ``` | ||
| pre-commit | ||
| make unittest | ||
| make functest | ||
| ``` | ||
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| ### Create the Pull-Request in opendatahub-io/data-science-pipelines repository | ||
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| Create a PR with the result of the previous tasks with the following description: `UPSTREAM <carry>: Rebase code to kfp x.y.z` | ||
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| ## Argo Workflows Repo | ||
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| If the kfp code you are rebasing uses a newer Argo workflows version, you must update opendatahub-io/argo-workflows repository. | ||
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| ### Preparing the local repo clone | ||
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| Clone from a personal fork, and add the remote for upstream and opendatahub, fetching its branches: | ||
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| ``` | ||
| git clone [email protected]:<user id>/data-science-pipelines | ||
| git remote add --fetch argo https://github.com/argoproj/argo-workflows | ||
| git remote add --fetch opendatahub https://github.com/opendatahub-io/argo-workflows | ||
| ``` | ||
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| ### Creating a backup branch form the dsp repo | ||
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| Argo Workflows git history diverges completely across versions, so it's important to create a backup branch from the current dsp repo in case we need to revert changes. | ||
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| ``` | ||
| git checkout -b dsp-backup opendatahub/main | ||
| ``` | ||
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| **NOTE:** Keep this branch for as long time as possible after Argo rebase, just in case we need to revert some changes. | ||
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| ### Creating a new local branch for the new rebase | ||
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| Branch the target release to a new branch: | ||
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| ``` | ||
| TAG=v3.4.17 | ||
| git checkout -b argo-upgrade $TAG | ||
| ``` | ||
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| ### Create the Pull-Request in opendatahub-io/argo-workflows repository | ||
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| Create a PR with the result of the previous tasks with the following description: `Upgrade argo-workflows code to x.y.z` | ||
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| ## Data Science Pipelines Operator repository | ||
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| ### Apply the DataSciencePipelinesApplication CustomResource from the opendatahub-io/data-science-pipelines Pull-Request | ||
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| With the Pull-Request opened in opendatahub-io/data-science-pipelines repository, you can get a DataSciencePipelinesApplication (DSPA) CustomResource with the resulting image builds from the bot comment like this. | ||
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| ``` | ||
| An OCP cluster where you are logged in as cluster admin is required. | ||
| The Data Science Pipelines team recommends testing this using the Data Science Pipelines Operator. Check here for more information on using the DSPO. | ||
| To use and deploy a DSP stack with these images (assuming the DSPO is deployed), first save the following YAML to a file named dspa.pr-76.yaml: | ||
| apiVersion: datasciencepipelinesapplications.opendatahub.io/v1alpha1 | ||
| kind: DataSciencePipelinesApplication | ||
| metadata: | ||
| name: pr-76 | ||
| spec: | ||
| dspVersion: v2 | ||
| apiServer: | ||
| image: "quay.io/opendatahub/ds-pipelines-api-server:pr-76" | ||
| argoDriverImage: "quay.io/opendatahub/ds-pipelines-driver:pr-76" | ||
| argoLauncherImage: "quay.io/opendatahub/ds-pipelines-launcher:pr-76" | ||
| persistenceAgent: | ||
| image: "quay.io/opendatahub/ds-pipelines-persistenceagent:pr-76" | ||
| scheduledWorkflow: | ||
| image: "quay.io/opendatahub/ds-pipelines-scheduledworkflow:pr-76" | ||
| mlmd: | ||
| deploy: true # Optional component | ||
| grpc: | ||
| image: "quay.io/opendatahub/mlmd-grpc-server:latest" | ||
| envoy: | ||
| image: "registry.redhat.io/openshift-service-mesh/proxyv2-rhel8:2.3.9-2" | ||
| mlpipelineUI: | ||
| deploy: true # Optional component | ||
| image: "quay.io/opendatahub/ds-pipelines-frontend:pr-76" | ||
| objectStorage: | ||
| minio: | ||
| deploy: true | ||
| image: 'quay.io/opendatahub/minio:RELEASE.2019-08-14T20-37-41Z-license-compliance' | ||
| Then run the following: | ||
| cd $(mktemp -d) | ||
| git clone [email protected]:opendatahub-io/data-science-pipelines.git | ||
| cd data-science-pipelines/ | ||
| git fetch origin pull/76/head | ||
| git checkout -b pullrequest f5a03d13022b1e1ba3ba09129e840633982522ac | ||
| oc apply -f dspa.pr-76.yaml | ||
| More instructions here on how to deploy and test a Data Science Pipelines Application. | ||
| ``` | ||
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| ### Fix the Data Science Pipelines Operator code | ||
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| Check if there are any breaking changes, and fix the code whenever is needed | ||
| One obvious change would be the tag references in params.env file | ||
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| ### Create the Pull-Request in opendatahub-io/data-science-pipelines-operator repository | ||
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| Create a PR with the changes in previous task with the following description: `Prepare to upgrade to the next DSP Release` | ||
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| ## Followup work | ||
| QE team has a Jenkins Job that can help test some basic features. Ask Diego Lovison to trigger the Jenkins job. You might need to create a separate branch DSPO from the code rebase to change `params.env` file with the values of the generated images from the previous PRs to run this Jenkins job. | ||
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| It is also good creating a follow-up task in JIRA to coordinate with QE to run some regression tests before merging the PRs. | ||
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| ## Updating with rebase.sh (experimental) | ||
| WIP | ||
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