Size of gcr.io/kubeflow/tensorflow-notebook-* about kubeflow HOT 9 CLOSED

I also think the size can be reduced. BTW: is it possible to push the images to dockerhub instead of gcr.io?

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@vishh Are we just using the tensorflow Docker images? I don't see any Dcokerfiles for these notebook images inside google/kubeflow.

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No it's not the same, if you do docker history --no-trunc gcr.io/kubeflow/tensorflow-notebook-gpu, you can see it's different from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/Dockerfile.devel-gpu or https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/docker/Dockerfile.gpu

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We'd like to support a bunch of different frameworks e.g.

• TensorFlow
• xgboost
• scikits

Some questions:

• Should we provide one fat image with all these libraries or have multiple images?
• Are there existing, curated images that we can reuse as opposed to building our own?

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@aronchick afaict gcr.io doesn't provide a human friendly URL to pass to people, which I've always found annoying for public images.

@jlewi there's some at http://github.com/jupyter/docker-stacks/ (and PRs welcome!) that do get a fair amount of usage.

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I think it makes sense to have one "fat" image, it it allows us to keep the other images lean.
This image could target being a development environment for data scientists: Jupyter, TensorFlow, TensorBoard and usual python dependencies.

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This is the source for our existing Docker images
https://github.com/GoogleCloudPlatform/container-engine-accelerators/tree/master/example/tensorflow-notebook-image

So everything is public and we should probably move them into Kubeflow.

Using an NVIDIA as the base image for our GPU images makes sense to me.

/cc @flx42

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Ok, let's discuss about the size again when it's on this repo.
But I believe it still makes sense to check with the TensorFlow team if a single common Jupyter image can be created.

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