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jmvizcainoio
cc18bf7adf
add information new variables in readme.md Signed-off-by: Juan Manuel <juanmanuel.vizcaino@iomed.es>
112 lines
4.7 KiB
Markdown
112 lines
4.7 KiB
Markdown
# Dask Helm Chart
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Dask allows distributed computation in Python.
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- https://dask.pydata.org
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- http://jupyter.org/
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## Chart Details
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This chart will deploy the following:
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- 1 x Dask scheduler with port 8786 (scheduler) and 80 (Web UI) exposed on an external LoadBalancer
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- 3 x Dask workers that connect to the scheduler
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- 1 x Jupyter notebook (optional) with port 80 exposed on an external LoadBalancer
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- All using Kubernetes Deployments
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## Installing the Chart
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To install the chart with the release name `my-release`:
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```bash
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$ helm install --name my-release stable/dask
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```
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## Configuration
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The following tables list the configurable parameters of the Dask chart and their default values.
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### Dask scheduler
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| Parameter | Description | Default |
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| -------------------------- | -------------------------| -----------------|
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| `scheduler.name` | Dask scheduler name | `scheduler` |
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| `scheduler.image` | Container image name | `daskdev/dask` |
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| `scheduler.imageTag` | Container image tag | `1.1.0` |
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| `scheduler.replicas` | k8s deployment replicas | `1` |
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| `scheduler.tolerations` | Tolerations | `[]` |
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| `scheduler.nodeSelector` | nodeSelector | `{}` |
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| `scheduler.affinity` | Container affinity | `{}` |
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### Dask webUI
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| Parameter | Description | Default |
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|-----------------------|-------------------|-----------|
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| `webUI.name` | Dask webui name | `webui` |
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| `webUI.servicePort` | k8s service port | `80` |
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### Dask worker
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| Parameter | Description | Default |
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| ----------------------- | ---------------------------------| ---------------|
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| `worker.name` | Dask worker name | `worker` |
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| `worker.image` | Container image name | `daskdev/dask` |
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| `worker.imageTag` | Container image tag | `1.1.0` |
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| `worker.replicas` | k8s hpa and deployment replicas | `3` |
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| `worker.resources` | Container resources | `{}` |
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| `worker.tolerations` | Tolerations | `[]` |
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| `worker.nodeSelector` | nodeSelector | `{}` |
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| `worker.affinity` | Container affinity | `{}` |
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### jupyter
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| Parameter | Description | Default |
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|-------------------------|----------------------------------|--------------------------|
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| `jupyter.name` | Jupyter name | `jupyter` |
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| `jupyter.enabled` | Include optional Jupyter server | `true` |
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| `jupyter.image` | Container image name | `daskdev/dask-notebook` |
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| `jupyter.imageTag` | Container image tag | `1.1.0` |
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| `jupyter.replicas` | k8s deployment replicas | `1` |
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| `jupyter.servicePort` | k8s service port | `80` |
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| `jupyter.resources` | Container resources | `{}` |
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| `jupyter.tolerations` | Tolerations | `[]` |
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| `jupyter.nodeSelector` | nodeSelector | `{}` |
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| `jupyter.affinity` | Container affinity | `{}` |
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Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`.
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Alternatively, a YAML file that specifies the values for the parameters can be provided while installing the chart. For example,
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```bash
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$ helm install --name my-release -f values.yaml stable/dask
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```
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> **Tip**: You can use the default [values.yaml](values.yaml)
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### Customizing Python Environment
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The default `daskdev/dask` images have a standard Miniconda installation along
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with some common packages like NumPy and Pandas. You can install custom packages
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with either Conda or Pip using optional environment variables. This happens
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when your container starts up. Consider the following config.yaml file as an
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example:
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```yaml
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jupyter:
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env:
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- EXTRA_PIP_PACKAGES: s3fs git+https://github.com/user/repo.git --upgrade
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- EXTRA_CONDA_PACKAGES: scipy matplotlib -c conda-forge
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worker:
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env:
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- EXTRA_PIP_PACKAGES: s3fs git+https://github.com/user/repo.git --upgrade
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- EXTRA_CONDA_PACKAGES: scipy -c conda-forge
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```
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Note that the Jupyter and Dask worker environments should have matching
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software environments, at least where a user is likely to distribute that
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functionality.
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