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Suggested doc improvements based on first-time user experience

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#129 1 comentario 1 reacción 0 asignados Ver en GitHub

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Evaluación

Dificultad
3/5
Tiempo estimado
1-2 días
Aptitud para principiantes
38/100
Tipo de issue
Documentación
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
docker, helm, kubernetes, python
Área
cloud, devops, documentation

Línea de trabajo

Comienza con la README del chart y su guía de inicio rápido; después, revisa el Dockerfile, environment.yml y la configuración de la imagen documentados que se mencionan en el issue. Actualiza la documentación para explicar las limitaciones del chart, la configuración correcta de GatewayCluster, el flujo de trabajo para imágenes personalizadas y la fijación de versiones. Se considera completado cuando un usuario de GKE que lo utiliza por primera vez puede seguir la guía sin depender de las discusiones externas enlazadas.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Thank you for this fantastic daskhub chart. Myself and @saschahofmann has just set this up on Google Kubernetes Engine. I thought it would be helpful to share our experience, gotchas and potentially suggest improvements to the docs.

Making the decision between charts

We started with the simpler "dask" helm chart.

However it didn't really suit our need for a few reasons:

  • The jupyter notebook has no persistent disk storage
  • We wanted the ability to control the number of workers, without making updates via helm

It would have been helpful to have these limitations listed on the README, especially the persistence one which I'd imagine most people would expect (I guess will #78 fix it though).

Setting up daskhub

Setting up the initial "daskhub" worked fine, out of the box. The one part that is a bit flaky for us is the "launch cluster" button on the left sidebar. Sometimes it launches a local cluster, rather than via dask-gateway - we haven't worked out why yet.

Small thing - the quickstart does not quite work for us:

>>> from dask_gateway import GatewayCluster
>>> cluster = gateway.new_cluster()
>>> client = cluster.get_client()

Instead we ran:

from dask_gateway import GatewayCluster
# This line is missing
gateway = GatewayCluster()
cluster = gateway.new_cluster()
client = cluster.get_client()

Customising the image 🏗️

For our use case, we needed to build a custom image.

The default tag for daskhub is pangeo/base-notebook:{XXX}. Unfortunately, googling that led us to the pangeo stacks Github and webpage and spent time trying to get the "ONBUILD" images to work.

Thankfully we eventually stumbled on this comment, and led us to the correct repo. The "ONBUILD" trick is neat, just took us time to get our head around.

Anyone else stuck doing the same thing, here is what worked for us:

  • Create a local copy of the pangeo/base-notebook directory here. You minimally need the Dockerfile, apt.txt, environment.yml, postBuild and start.
  • Modify the apt.txt with system packages, and environment.yml with your extra conda packages
  • Docker build and push
  • Set this to the jupyterhub.singleuser.image.{name, tag, pullPolicy}

For the dask custom image, we just used daskgateway/dask-gateway we just did:

# Dockerfile
FROM daskgateway/dask-gateway:0.9.0
COPY ./environment.yml ./environment.yml
RUN conda env update -f environment.yml

# environment.yml
name: base
dependencies:
  - # extra packages...

And set this to dask-gateway.gateway.backend.image.{name, tag, pullPolicy}

Making sure to tag specific versions 🤦

A few days ago, we discovered inexplicably that we could no longer build new custom images that worked on the cluster. Turns out we hadn't pinned our images, and dask-gateway 0.9.0 was released.

Obviously completely our fault, but wanted to note that it's essential to have consistent versions for:

  • The helm chart
  • The pangeo/base-notebook in your jupyterhub.singleuser Dockerfile
  • The daskgateway/dask-gateway in your Dockerfile

Otherwise things will break in hard to debug ways. A particularly nasty example was using dask dataframe, where we had pandas 1.1.* on the client and 1.0.* on the workers.

We'd be happy to submit a PR to amend the docs or maybe a separate guide if that's useful, please let us know.

Once again, thanks for building this!

Lenguaje dominante
YAML
Estrellas
100
Forks
92
Sin PR fusionados en 30 d

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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