## Development Workflow This section describes the steps necessary to build Elyra in a development environment. #### Requirements * [Python 3 Miniconda](https://docs.conda.io/en/latest/miniconda.html) * [Node.js 22](https://nodejs.org/en/) * [Yarn](https://yarnpkg.com/lang/en/docs/install) * [GNU Make](https://www.gnu.org/software/make/) ### Setting up your development environment * Install Miniconda Download and install a [Python 3 version of Miniconda](https://docs.conda.io/en/latest/miniconda.html) according to your Operating System * Create a new Python environment using a version that is [supported by Elyra](../getting_started/installation.html#prerequisites). ``` conda create -n python ``` The Python version of your environment will match the miniconda version you installed. You can override the default by explicitly setting `python=3.10`, for example. * Activate the new environment ``` conda activate ``` * Verify your miniconda environment ``` python --version # should yield a version that is supported by Elyra which python # displays current `python` path pip3 --version # should be a recent version to avoid build issues which pip3 # displays current `pip` path ``` Python path must be under miniconda envs folder. Confirm pip3 location matches where miniconda is installed. * Install a version of Node.js that is [supported by Elyra](../getting_started/installation.html#prerequisites). ``` conda install -y -c conda-forge/label/main nodejs ``` * Verify node is installed correctly ``` node --version ``` * Install Yarn ``` conda install -y -c conda-forge/label/main yarn ``` * Verify yarn is installed correctly ``` yarn --version ``` * Install GNU Make Refer to the following link for installation instructions: [GNU Make](https://www.gnu.org/software/make/) To verify the installation, run `make`. If you have yet to [set up the repository](#setting-up-your-development-environment), you should see a message like the following: ``` make: *** No targets specified and no makefile found. Stop. ``` Once the repository is set up, running `make` from that location should display the available tasks that are listed in the [Build & Installation](#build-installation) section below. ### Setting up your Elyra Github repository * Fork the [Elyra Github repository](https://github.com/elyra-ai/elyra) (if you haven't already) * Make a local copy of Elyra fork ``` git clone https://github.com//elyra.git cd elyra ``` * Set `upstream` as described in the [GitHub documentation](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/configuring-a-remote-for-a-fork) ### Building Elyra is divided in two parts, a collection of Jupyter Notebook backend extensions, and their respective JupyterLab UI extensions. Our JupyterLab extensions are located in our `packages` directory. #### Build & Installation Elyra uses `make` to automate some of the development workflow tasks. Issuing a `make` command with no task specified will provide a list of the currently supported tasks. ```bash $ make build-ui-dev Build UI packages for development build-ui-prod Build UI packages for production clean Make a clean source tree and uninstall extensions container-images Build all container images docs Build docs elyra-image-env Creates a conda env consisting of the dependencies used in images install-all-dev Build and install for development, including examples install-all-prod Build and install for production, including examples install-dev Build and install for development install-examples Install example pipeline components install-prod Build and install for production install-server Build and install backend lint Run linters package-ui-dev Package UI for development package-ui-prod Package UI for production publish-container-images Publish all container images release Build wheel file for release test Run all tests (backend, frontend and cypress integration tests) watch Watch packages. For use alongside jupyter lab --watch ``` You can build and install all Elyra packages with: ```bash make clean install-prod ``` You can check that the notebook server extension was successfully installed with: ```bash jupyter serverextension list ``` You can check that the JupyterLab extension was successfully installed with: ```bash jupyter labextension list ``` > NOTE: When switching between Elyra major versions, it is recommended to clean your JupyterLab environment before a build. The `clean-jupyterlab` removes your JupyterLab packages and completely deletes your Jupyter workspace. Make sure to backup any important data in your environment before running the script. To clean your environment and install the latest JupyterLab: `etc/scripts/clean-jupyterlab.sh` To specify a JupyterLab version `x.y.z` to be installed: `etc/scripts/clean-jupyterlab.sh --version x.y.z` #### Parallel Development with @elyra/pipeline-editor You can install Elyra using a local build of @elyra/pipeline-editor with: ```bash make clean install-dev ``` ### Back-end Development After making code changes to the back-end, you can re-build Elyra's Python package with: ```bash make install-server ``` This command builds and installs the updated Python package independently, skipping any UI component build. Restart JupyterLab to pick up the new code changes. ### Front-end Incremental Development Elyra supports incremental development using `--watch`. This allows you to make code changes to front-end packages and see them without running `make install` again. After installation run the following to watch for code changes and rebuild automatically: ```bash make watch ``` Then in a separate terminal, using the same Python environment, start JupyterLab in watch mode: ```bash jupyter lab --watch ``` When in watch mode JupyterLab will watch for changes in the build of each package and rebuild. To see your changes just refresh JupyterLab in your browser. > NOTE: JupyterLab watch mode will not pick up changes in package dependencies like `services`. So when making changes to services you will need to stop and restart `jupyter lab --watch` and not just refresh your browser. ### Building the Elyra Container Image Elyra's container image can be built in two ways (production and development): Development: ```bash make elyra-image ``` By default, the command above will build a container image (development) with the changes that exist in your local branch. Production: From main branch: ```bash make elyra-image TAG=3.7.0 ``` or after checking out a git tag e.g. `git checkout tags/v3.7.0` ```bash make elyra-image ``` In order to build from a particular release (production), you can pass a `TAG` parameter to the make command or you can checkout the respective tagged release and omit the `TAG` parameter. Official container images are published on [Docker Hub](https://hub.docker.com/r/elyra/elyra/tags) and [quay.io](https://quay.io/repository/elyra/elyra?tab=tags). ### Developing Elyra against the Jupyterlab source repo Sometimes it is useful to develop Elyra against a local build of Jupyterlab. To use a local build of Jupyterlab use the following steps in the same python environment. 1. Uninstall any pip installations of Jupyterlab. You can use `etc/scripts/clean-jupyterlab.sh --version dev` as mentioned above with `--version dev` to not reinstall Jupyterlab at the end of the script. 2. Build your local repo of Jupyterlab, step-by-step instructions can be found in the [Jupyterlab documentation](https://jupyterlab.readthedocs.io/en/latest/developer/contributing.html#installing-jupyterlab). Uninstalling in the previous step will also wipe any previous installations of a local build. 3. `cd` to the `builder/` directory in your Jupyterlab repo and run `yarn link`. The Elyra `Makefile` will use this yarn link in step 6. 4. In your Elyra repo, uncomment the following line in `tsconfig.base.json` to tell Typescript to use the local Jupyterlab packages when building: ```"paths": { "@jupyterlab/*": ["../jupyterlab/packages/*"] },``` 5. Comment out `jupyterlab` and `jupyterlab-lsp` in the `install_requires` section of `setup.py` in your Elyra repo. This will prevent Jupyterlab from being pip installed during the Elyra build. Note: `jupyterlab-lsp` also pip installs Jupyterlab when installed 6. Run `make install-dev` to install Elyra using the linked `@jupyterlab/builder` from step 3. 7. You can now start Jupyterlab by running `jupyter lab --dev-mode --extensions-in-dev-mode`, this will automatically watch for changes in the Jupyterlab repo. To also watch for changes in Elyra run `make watch` in a separate terminal in the same Python environment. When you want to switch back to developing Elyra against a Jupyterlab release, you just have to undo the comments in steps 4 and 5 and rebuild with `make clean install` ## Analyzing automated test failures The Elyra GitHub repository is configured to run automated tests whenever a pull request is opened. These tests include static [code quality analysis](https://github.com/elyra-ai/elyra/blob/main/.github/workflows/codeql-analysis.yml) and [UI, server, and integration tests](https://github.com/elyra-ai/elyra/blob/main/.github/workflows/build.yml). The test results can be accessed from the pull request or the _actions_ tab. If the test log does not include enough details to diagnose failures, download and review test artifacts that might have been generated. ### Accessing test artifacts 1. Open the [Elyra repository actions panel](https://github.com/elyra-ai/elyra/actions) (`https://github.com/elyra-ai/elyra/actions`). 1. Locate the failing workflow.  1. Open the workflow. 1. Click the 'home' (summary) button.  1. Locate the 'Artifacts' section. If present, it should contain a download link.  1. Download the archive, extract it, and review the artifacts.