Frontend

ML Pipeline Management Frontend

Develop: You need npm, install dependencies using npm install.

You can then do npm start to run a static file server at port 3000 that watches the source files. A separate mock backend process also needs to be started to handle all backend API requests. This can be started in a separate shell by running npm run mock:api. The mock backend includes some sample data to showcase many of the UI interactions.

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 (first build the server by running npm run build inside the server directory. 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, which you can run using npm run mock:api separately.