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Copy file name to clipboardexpand all lines: docs/source/recipes/configure-airflow-as-a-runtime.md
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- A Kubernetes Cluster without Apache Airflow installed
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- Ensure Kubernetes is at least v1.18. Earlier versions might work but have not been tested.
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- Helm v3.0 or later
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- Use the [Helm chart](https://github.com/airflow-helm/charts/tree/main/charts/airflow) available in the Airflow source distribution with the [Elyra sample configuration](https://raw.githubusercontent.com/elyra-ai/elyra/main/etc/kubernetes/airflow/helm/values.yaml).
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- Use the [Helm chart](https://github.com/airflow-helm/charts/tree/v3.15.0/charts/airflow) available in the Airflow source distribution with the [Elyra sample configuration](https://raw.githubusercontent.com/elyra-ai/elyra/v3.15.0/etc/kubernetes/airflow/helm/values.yaml).
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OR
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In order to use Apache Airflow with Elyra, it must be configured to use a Git repository to store DAGs.
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- Create a private repository on github.com, GitHub Enterprise, gitlab.com, or GitLab Enterprise. (Elyra produces DAGs that contain credentials, which are not encrypted. Therefore you should not use a public repository.) Next, create a branch (e.g `main`) in your repository. This will be referenced later for storing the DAGs.
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- Create a private repository on github.com, GitHub Enterprise, gitlab.com, or GitLab Enterprise. (Elyra produces DAGs that contain credentials, which are not encrypted. Therefore you should not use a public repository.) Next, create a branch (e.g `v3.15.0`) in your repository. This will be referenced later for storing the DAGs.
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- Generate a personal access token with push access to the repository. This token is used by Elyra to upload DAGs.
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-[Instructions for GitHub](https://docs.github.com/en/github/authenticating-to-github/creating-a-personal-access-token)
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-[Instructions for GitLab](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html)
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Take note of the following information:
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- Git API endpoint (e.g. `https://api.github.com` for github.com or `https://gitlab.com` for gitlab.com)
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- Repository name (e.g. `your-git-org/your-dag-repo`)
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- Repository branch name (e.g. `main`)
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- Repository branch name (e.g. `v3.15.0`)
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- Personal access token (e.g. `4d79206e616d6520697320426f6e642e204a616d657320426f6e64`)
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You need to provide this information in addition to your cloud object storage credentials when you [create a runtime configuration](../user_guide/runtime-conf) in Elyra for the Apache Airflow deployment.
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2. Download, review, and customize the [sample `helm` configuration](https://raw.githubusercontent.com/elyra-ai/elyra/main/etc/kubernetes/airflow/helm/values.yaml) (or customize an existing configuration). This sample configuration will use the `KubernetesExecutor` by default.
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2. Download, review, and customize the [sample `helm` configuration](https://raw.githubusercontent.com/elyra-ai/elyra/v3.15.0/etc/kubernetes/airflow/helm/values.yaml) (or customize an existing configuration). This sample configuration will use the `KubernetesExecutor` by default.
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- Set `git.url` to the URL of the private repository you created earlier, e.g. `ssh://[email protected]/your-git-org/your-dag-repo`. **Note: Make sure your ssh URL contains only forward slashes.**
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- Set `git.ref` to the DAG branch, e.g. `main` you created earlier.
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- Set `git.ref` to the DAG branch, e.g. `v3.15.0` you created earlier.
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- Set `git.secret` to the name of the secret you created, e.g. `airflow-secret`.
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- Adjust the `git.gitSync.refreshTime` as desired.
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## the branch/tag/sha1 which we clone
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##
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ref: "main"
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ref: "v3.15.0"
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## the name of a pre-created secret containing files for ~/.ssh/
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##
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repository: elyra/airflow
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```
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The container image is created using [this `Dockerfile`](https://github.com/elyra-ai/elyra/tree/main/etc/docker/airflow) and published on [Docker Hub](https://hub.docker.com/r/elyra/airflow) and [quay.io](https://quay.io/repository/elyra/airflow).
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The container image is created using [this `Dockerfile`](https://github.com/elyra-ai/elyra/tree/v3.15.0/etc/docker/airflow) and published on [Docker Hub](https://hub.docker.com/r/elyra/airflow) and [quay.io](https://quay.io/repository/elyra/airflow).
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3. Install Apache Airflow using the customized configuration.
Copy file name to clipboardexpand all lines: docs/source/recipes/running-elyra-in-air-gapped-environment.md
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-**Container registry**: All pipeline nodes are executed in containers. The runtime environment must be configured to have read access to the registries (e.g. Docker Hub) where the container images are stored that the generic and custom components are referencing.
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-**Elyra runtime artifacts**: When processing pipeline nodes that are implemented using [generic components](../user_guide/pipeline-components.html#generic-components), Elyra downloads a few dependencies to the container. By default these dependencies are located in a _release-specific branch_ in the [Elyra GitHub repository](https://github.com/elyra-ai/elyra):
In air-gapped environments you must store a copy of these files in a location that is accessible via an anonymous HTTP `GET` request and configure the following environment variables _in the environment where JupyterLab is running_:
Copy file name to clipboardexpand all lines: docs/source/recipes/using-elyra-with-kubeflow-notebook-server.md
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OR
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Create a custom Elyra container image following the [instructions in this directory](https://github.com/elyra-ai/elyra/tree/main/etc/docker/kubeflow).
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Create a custom Elyra container image following the [instructions in this directory](https://github.com/elyra-ai/elyra/tree/3.15.0/etc/docker/kubeflow).
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## Launching Elyra in the Kubeflow Notebook Server
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1. In the default Kubeflow welcome page, in the left side menu, click on `Notebook Servers`
Copy file name to clipboardexpand all lines: docs/source/user_guide/jupyterlab-interface.md
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### Launcher
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Elyra adds a new category to the JupyterLab launcher, providing access to the [Visual Pipeline Editor](#visual-pipeline-editor), the [Python editor](enhanced-script-support.html#python-script-execution-support), the [R editor](enhanced-script-support.html#r-script-execution-support), and the [Elyra documentation](https://elyra.readthedocs.io/en/latest/).
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Elyra adds a new category to the JupyterLab launcher, providing access to the [Visual Pipeline Editor](#visual-pipeline-editor), the [Python editor](enhanced-script-support.html#python-script-execution-support), the [R editor](enhanced-script-support.html#r-script-execution-support), and the [Elyra documentation](https://elyra.readthedocs.io/en/v3.15.0/).
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