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Kubeflow Pipelines Frontend Contributing Guide

Node setup

Install the node version specified in the .nvmrc file. You can use nvm or fnm to manage your node installations; fnm is basically a faster version of nvm implemented in Rust. Installation instructions are available at their corresponding GitHub repositories.

fnm

fnm install 22.14.0
fnm use 22.14.0

nvm

nvm install 22.14.0
nvm use 22.14.0

Manage dev environment with npm

Pre-requisites

Clone the Kubeflow Pipelines repo:

# Set this to your working directory
WORKING_DIRECTORY= ...
git clone https://github.com/kubeflow/pipelines.git ${WORKING_DIRECTORY}

Navigate to frontend folder:

cd ${WORKING_DIRECTORY}/frontend

Install the NPM dependencies:

npm ci.

npm ci ensures exact dependency versions are installed according to package-lock.json. You should run it:

When first setting up the project
After package.json or package-lock.json changes
In CI/CD pipelines

[!Note] This command deletes the existing node_modules folder and requires a package-lock.json file.

Daily workflow

You will see a lot of npm run xxx commands in the instructions below, the actual script being run is defined in the "scripts" field of package.json. Common development scripts are maintained in package.json, and we use npm to call them conveniently.

npm next step

You can learn more about npm in https://docs.npmjs.com/about-npm/.

Start frontend development server

You can then do npm start to run a webpack dev server at port 3000 that watches the source files. It also redirects api requests to localhost:3001. For example, requesting the pipelines page sends a fetch request to http://localhost:3000/apis/v1beta1/pipelines, which is proxied by the webserver to http://localhost:3001/apis/v1beta1/pipelines, which should return the list of pipelines. To override the port used by webpack, you can set the PORT environment variable.

Follow the next section to start an API server (mock or proxy) to let localhost:3001 respond to API requests.

Start api mock/proxy server

Api mock server

This is the easiest way to start developing, but it does not support all apis during development.

Run npm run mock:api to start a mock backend api server handler so it can serve basic api calls with mock data.

If you want to port real MLMD store to be used for mock backend scenario, you can run the following command. Note that a mock MLMD store doesn't exist yet.

kubectl port-forward svc/metadata-envoy-service 9090:9090

Proxy to a real cluster

You can proxy requests from the UI running on your host machine to an actual KFP deployment running on a remote or local Kubernetes cluster. This dramatically improves iteration time, especially since the docker build can take 20+ minutes.

KFP can be deployed in single-user or multi-user mode. Since there's a delta in logic between between the two modes, automated tests and manual validation against a single-user cluster can still fail when deployed to a multi-user cluster.

Single-user

Deploy a standalone KFP instance by running the following:

git clone https://github.com/kubeflow/pipelines.git ${WORKING_DIRECTORY}
cd ${WORKING_DIRECTORY}/backend
make kind-cluster-agnostic

Use the following table to determine which script to run.

What to develop?	Script to run	Extra notes
Client UI	`npm run start:proxy`
Client UI + Node server	`npm run start:proxy-and-server`	You need to rerun the script every time you edit node server code.
Client UI + Node server (debug mode)	`npm run start:proxy-and-server-inspect`	Same as above, and you can use chrome to debug the server.

Multi-user

Install Kubernetes and deploy KFP to it on your your local machine by following the multi-user Kubeflow installation instructions.
Run cd frontend.
Run the following code block.
```
export REACT_APP_NAMESPACE=kubeflow-user-example-com
npm run build
```
If you're targeting the cluster installed in step 1, the target namespace defaults to kubeflow-user-example-com. If you're targeting a different cluster / namespace, make sure to update the REACT_APP_NAMESPACE environment variable.
Install mod-header for Chrome.
Open the mod-header modal.
If you're targeting the cluster installed in step 1, add a header with a key of kubeflow-userid and a value of user@example.com. If you're targeting a different cluster / namespace, add the corresponding userid / namespace.
Add localhost:3001 to the filter field in the modal.
Run the commands from the Single-user section above, e.g. npm run start:proxy-and-server.
Navigate to http://localhost:3001. The UI running on your host machine should now be able to communicate to a KFP backend deployed in multi-user mode.
If you want the local UI server to target a single-user cluster again, you'll need to run the following first:
```
unset REACT_APP_NAMESPACE
npm run build
```

Unit testing FAQ

There are a few types of tests during pre-submit:

formatting, refer to Code Style Section
linting, you can also run locally with npm run lint
client UI unit tests, you can run locally with npm test
UI node server unit tests, you can run locally with cd server && npm test

There is a special type of unit test called snapshot tests. When snapshot tests are failing, you can update them automatically with npm test -u and run all tests. Then commit the snapshot changes.

Production Build

You can do npm run build to build the frontend code for production, which creates a ./build directory with the minified bundle. You can test this bundle using server/server.js. Note you need to have an API server running, which you can then feed its address (host + port) as environment variables into server.js. See the usage instructions in that file for more.

Container Build

You can also do npm run docker if you have docker installed to build an image containing the production bundle and the server pieces. In order to run this image, you'll need to port forward 3000, and pass the environment variables ML_PIPELINE_SERVICE_HOST and ML_PIPELINE_SERVICE_PORT with the details of the API server.

Package management

Run npm install --save <package> (or npm i -S <package> for short) to install runtime dependencies and save them to package.json. Run npm install --save-dev <package> (or npm i -D <package> for short) to install dev dependencies and save them to package.json.

Code Style

We use prettier for code formatting, our prettier config is here.

To understand more what prettier is: What is Prettier.

IDE Integration

For vscode, install the plugin "Prettier - Code formatter" and it will pick this project's config automatically. Recommend setting the following in settings.json for vscode to autoformat on save.
```
"[typescript]": {
  "editor.formatOnSave": true,
  "files.trimTrailingWhitespace": false,
},
"[typescriptreact]": {
  "editor.formatOnSave": true,
  "files.trimTrailingWhitespace": false,
},
```
Also, vscode builtin trailing whitespace conflicts with jest inline snapshot, so recommend disabling it.
For others, refer to https://prettier.io/docs/en/editors.html.

Format Code Manually

Run npm run format.

Escape hatch

If there's some code that you don't want being formatted by prettier, follow guide here. (Most likely you don't need this.)

Api client code generation

If you made any changes to protos (see backend/README), you'll need to regenerate the Typescript client library from swagger. We use swagger-codegen-cli@2.4.7, which you can get here. Make sure to add the jar file to $PATH with the name swagger-codegen-cli.jar, then run npm run apis for v1 api or npm run apis:v2beta1 for v2 api.

// add jar file to $PATH
JAR_PATH=<folder-path-to-jar-file>
export PATH="$JAR_PATH:$PATH"

After code generation, you should run npm run format to format the output and avoid creating a large PR.

MLMD components

src/mlmd - components for visualizing data from an ml-metadata store. For more information see the google/ml-metadata repository.

This module previously lived in kubeflow/frontend repository. It contains tsx files for visualizing MLMD components.

MLMD protos lives in pipelines/third_party/ml-metadata/ml_metadata/, and the generated JS files live in pipelines/frontend/src/third_party/mlmd.

Building generated metadata Protocol Buffers

build:protos - for compiling Protocol Buffer definitions

This project contains a mix of natively defined classes and classes generated by the Protocol Buffer Compiler from definitions in the pipelines/third_party/ml-metadata/ml_metadata/ directory. Copies of the generated classes are included in the pipelines/frontend/src/third_party/mlmd directory to allow the build process to succeed without a dependency on the Protocol Buffer compiler, protoc, being in the system PATH.

If a file in pipelines/third_party/ml-metadata/ml_metadata/proto is modified or you need to manually re-generate the protos, you'll need to:

Add protoc (download) to your system PATH

# Example:
apt install -y protobuf-compiler=3.15.8

Add protoc-gen-grpc-web (download) to your system PATH

# Example:
curl -LO https://github.com/grpc/grpc-web/releases/download/1.4.2/protoc-gen-grpc-web-1.4.2-linux-x86_64
mv protoc-gen-grpc-web-1.4.2-linux-x86_64 /usr/local/bin/protoc-gen-grpc-web
chmod +x /usr/local/bin/protoc-gen-grpc-web

Replace metadata_store.proto and metadata_store_service.proto proto files with target mlmd version by running
```
npm run build:replace -- {mlmd_versions}
// example:
// npm run build:replace -- 1.0.0
```
Generate new protos by running
```
npm run build:protos
```

The script run by npm run build:replace can be found at scripts/replace_protos.js. The script run by npm run build:protos can be found at scripts/gen_grpc_web_protos.js.

The current TypeScript proto library was generated with protoc-gen-grpc-web version 1.2.1 with protoc version 3.17.3.

The Protocol Buffers in pipelines/third_party/ml-metadata/ml_metadata/proto are taken from the target version(v1.0.0 by default) of the ml_metadata proto package from google/ml-metadata.

Pipeline Spec (IR) API

For KFP v2, we use pipeline spec or IR(Intermediate Representation) to represent a Pipeline definition. It is saved as json payload when transmitted. You can find the API in api/v2alpha1/pipeline_spec.proto. To take the latest of this file and compile it to Typescript classes, follow the below step:

npm run build:pipeline-spec

See the explanation on what it does below:

Convert buffer to a runtime object using protoc

Prerequisite: Add protoc (download) to your system PATH

Compile pipeline_spec.proto to Typed classes in TypeScript, so it can convert a payload stream to a PipelineSpec object during runtime.

You can check out the result like pipeline_spec_pb.js, pipeline_spec_pb.d.ts in frontend/src/generated/pipeline_spec.

The plugin tool for conversion we currently use is ts-proto. We previously use protobuf.js but it doesn't natively support Protobuf.Value processing.

You can checkout the generated TypeScript interfaces in frontend/src/generated/pipeline_spec/pipeline_spec.ts

Platform Spec API

For KFP v2, we use platform spec to represent a platform definition.

Kubernetes

The details of Kubernetes platform is in kubernetes_platform/proto/kubernetes_executor_config.proto. To take the latest of this file and compile it to Typescript classes, follow the below step:

npm run build:platform-spec:kubernetes-platform

Large features development

To accommodate KFP v2 development, we create a frontend feature flag capability which hides features under development behind a flag. Only when developer explicitly enables these flags, they can see those features. To control the visibility of these features, check out a webpage similar to pattern http://localhost:3000/#/frontend_features.

To manage feature flags default values, visit frontend/src/feature.ts for const features. To apply the default feature flags locally in your browser, run localStorage.setItem('flags', "") in browser console.

Storybook

For component driven UI development, KFP UI integrates with Storybook to develop v2 features. To run Storybook locally, run npm run storybook and visit localhost:6006 in browser.

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