132 lines
9.7 KiB
Markdown
132 lines
9.7 KiB
Markdown
# Example Notebook Servers
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> These images are provided as __examples__, and are supported on a best-effort basis.
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> <br>
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> Contributions are greatly appreciated.
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## Images
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This chart shows how the images are related to each other (the nodes are clickable links to the Dockerfiles):
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```mermaid
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graph TD
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Base[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/base'>Base</a>] --> Jupyter[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter'>Jupyter</a>]
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Base --> Code-Server[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/codeserver'>code-server</a>]
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Base --> RStudio[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/rstudio'>RStudio</a>]
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Jupyter --> PyTorch[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-pytorch'>PyTorch</a>]
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Jupyter --> SciPy[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-scipy'>SciPy</a>]
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Jupyter --> TensorFlow[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-tensorflow'>TensorFlow</a>]
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Code-Server --> Code-Server-Conda-Python[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/codeserver-python'>Conda Python</a>]
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RStudio --> Tidyverse[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/rstudio-tidyverse'>Tidyverse</a>]
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PyTorch --> PyTorchFull[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-pytorch-full'>PyTorch Full</a>]
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TensorFlow --> TensorFlowFull[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-tensorflow-full'>TensorFlow Full</a>]
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Jupyter --> PyTorchCuda[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-pytorch-cuda'>PyTorch CUDA</a>]
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Jupyter --> TensorFlowCuda[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-tensorflow-cuda'>TensorFlow CUDA</a>]
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PyTorchCuda --> PyTorchCudaFull[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-pytorch-cuda-full'>PyTorch CUDA Full</a>]
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TensorFlowCuda --> TensorFlowCudaFull[<a href='https://github.com/kubeflow/kubeflow/tree/master/components/example-notebook-servers/jupyter-tensorflow-cuda-full'>TensorFlow CUDA Full</a>]
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```
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### Base Images
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These images provide a common starting point for Kubeflow Notebook containers.
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Dockerfile | Container Registry | Notes
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--- | --- | ---
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[`./base`](./base) | [`kubeflownotebookswg/base`](https://hub.docker.com/r/kubeflownotebookswg/base) | Common Base Image
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[`./codeserver`](./codeserver) | [`kubeflownotebookswg/codeserver`](https://hub.docker.com/r/kubeflownotebookswg/codeserver) | [code-server](https://github.com/coder/code-server) (Visual Studio Code)
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[`./jupyter`](./jupyter) | [`kubeflownotebookswg/jupyter`](https://hub.docker.com/r/kubeflownotebookswg/jupyter) | [JupyterLab](https://github.com/jupyterlab/jupyterlab)
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[`./rstudio`](./rstudio) | [`kubeflownotebookswg/rstudio`](https://hub.docker.com/r/kubeflownotebookswg/rstudio) | [RStudio](https://github.com/rstudio/rstudio)
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### Kubeflow Images
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These images extend the [base images](#images--base) with common packages used in the real world.
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Dockerfile | Container Registry | Notes
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--- | --- | ---
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[`./codeserver-python`](./codeserver-python) | [`kubeflownotebookswg/codeserver-python`](https://hub.docker.com/r/kubeflownotebookswg/codeserver-python) | code-server + Conda Python
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[`./rstudio-tidyverse`](./rstudio-tidyverse) | [`kubeflownotebookswg/rstudio-tidyverse`](https://hub.docker.com/r/kubeflownotebookswg/rstudio-tidyverse) | RStudio + [Tidyverse](https://www.tidyverse.org/)
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[`./jupyter-pytorch`](./jupyter-pytorch) | [`kubeflownotebookswg/jupyter-pytorch`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-pytorch) | JupyterLab + PyTorch
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[`./jupyter-pytorch-full`](./jupyter-pytorch-full) | [`kubeflownotebookswg/jupyter-pytorch-full`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-pytorch-full) | JupyterLab + PyTorch + Common Packages
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[`./jupyter-pytorch-cuda`](./jupyter-pytorch-cuda) | [`kubeflownotebookswg/jupyter-pytorch-cuda`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-pytorch-cuda) | JupyterLab + PyTorch + CUDA
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[`./jupyter-pytorch-cuda-full`](./jupyter-pytorch-cuda-full) | [`kubeflownotebookswg/jupyter-pytorch-cuda-full`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-pytorch-cuda-full) | JupyterLab + PyTorch + CUDA + Common Packages
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[`./jupyter-scipy`](./jupyter-scipy) | [`kubeflownotebookswg/jupyter-scipy`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-scipy) | JupyterLab + Common Packages
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[`./jupyter-tensorflow`](./jupyter-tensorflow) | [`kubeflownotebookswg/jupyter-tensorflow`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-tensorflow) | JupyterLab + TensorFlow
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[`./jupyter-tensorflow-full`](./jupyter-tensorflow-full) | [`kubeflownotebookswg/jupyter-tensorflow-full`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-tensorflow-full) | JupyterLab + TensorFlow + Common Packages
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[`./jupyter-tensorflow-cuda`](./jupyter-tensorflow-cuda) | [`kubeflownotebookswg/jupyter-tensorflow-cuda`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-tensorflow-cuda) | JupyterLab + TensorFlow + CUDA
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[`./jupyter-tensorflow-cuda-full`](./jupyter-tensorflow-cuda-full) | [`kubeflownotebookswg/jupyter-tensorflow-cuda-full`](https://hub.docker.com/r/kubeflownotebookswg/jupyter-tensorflow-cuda-full) | JupyterLab + TensorFlow + CUDA + Common Packages
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## Custom Images
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Packages installed by users __after spawning__ a Kubeflow Notebook will only last the lifetime of the pod (unless installed into a PVC-backed directory).
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To ensure packages are preserved throughout Pod restarts users will need to either:
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1. Build custom images that include them, or
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2. Ensure they are installed in a PVC-backed directory
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### Install Python Packages
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You may extend one of the images and install any `pip` or `conda` packages your Kubeflow Notebook users are likely to need.
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As a guide, look at [`./jupyter-pytorch-full/Dockerfile`](./jupyter-pytorch-full/Dockerfile) for a `pip install ...` example, and the [`./rstudio-tidyverse/Dockerfile`](./rstudio-tidyverse/Dockerfile) for `conda install ...`.
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> __NOTE:__
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>
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> A common cause of errors is users running `pip install --user ...`, causing the home-directory (which is backed by a PVC) to contain a different or incompatible version of a package contained in `/opt/conda/...`
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### Install Linux Packages
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You may extend one of the images and install any `apt-get` packages your Kubeflow Notebook users are likely to need.
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> __NOTE:__
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> Ensure you swap to `root` in the Dockerfile before running `apt-get`, and swap back to `$NB_USER` after.
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### Configure S6 Overlay
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Some use-cases might require custom scripts to run during the startup of the Notebook Server container, or advanced users might want to add additional services that run inside the container (for example, an Apache or NGINX web server).
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To make this easy, we use the [s6-overlay](https://github.com/just-containers/s6-overlay).
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The [s6-overlay](https://github.com/just-containers/s6-overlay) differs from other init systems like [tini](https://github.com/krallin/tini).
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While `tini` was created to handle a single process running in a container as PID 1, the `s6-overlay` is built to manage multiple processes and allows the creator of the image to determine which process failures should silently restart, and which should cause the container to exit.
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#### Create Scripts
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Scripts that need to run during the startup of the container can be placed in `/etc/cont-init.d/`, and are executed in ascending alphanumeric order.
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An example of a startup script can be found in [`./rstudio/s6/cont-init.d/02-rstudio-env-fix`](./rstudio/s6/cont-init.d/02-rstudio-env-fix).
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This script uses the [with-contenv](https://github.com/just-containers/s6-overlay#container-environment) helper so that environment variables (passed to container) are available in the script.
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The purpose of this script is to snapshot any `KUBERNETES_*` environment variables into the `Renviron.site` at pod startup, as without these variables `kubectl` does not work.
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#### Create Services
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Extra services to be monitored by `s6-overlay` should be placed in their own folder under `/etc/services.d/` containing a script called `run` and optionally a finishing script `finish`.
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For more information about the `run` and `finish` scripts, please see the [s6-overlay documentation](https://github.com/just-containers/s6-overlay#writing-a-service-script).
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An example of a service can be found in the `run` script of [jupyter/s6/services.d/jupyterlab](jupyter/s6/services.d/jupyterlab) which is used to start JupyterLab itself.
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#### Run Services As Root
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There may be cases when you need to run a service as root, to do this, you can change the Dockerfile to have `USER root` at the end, and then use `s6-setuidgid` to run the user-facing services as `$NB_USER`.
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> __NOTE:__
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> Our example images run `s6-overlay` as `$NB_USER` (not `root`), meaning any files or scripts related to `s6-overlay` must be owned by the `$NB_USER` user to successfully run.
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## Troubleshooting
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### Jupyter
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#### Kernel stuck in `connecting` state:
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This is a problem that occurs from time to time and is not a Kubeflow problem, but rather a browser.
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It can be identified by looking in the browser error console, which will show errors regarding the websocket not connecting.
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To solve the problem, please restart your browser or try using a different browser.
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