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Jupyter notebook image should pin TF version #375
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Our notebooks are currently using the nightly build of TF. We should probably pin TF to a specific version. /cc @ojarjur |
We should pin the version of TF in our notebook images. |
Pinning is great, but i wonder how we make this an easily option - we've
seen lots of issues with various versions of frameworks not supporting
particular models, and i want people to be able to swap readily.
…On Sat, Mar 17, 2018 at 2:38 PM Jeremy Lewi ***@***.***> wrote:
We should pin the version of TF in our notebook images.
We should also consider including the TF version in the image name so that
we could potentially build images for different versions of TF.
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Agree. For notebook images we can build different notebook images for different TF versions. Thanks to @ojarjur's changes to the Dockerfile, its already parameterized by the TF version. And thanks to work by @pdmack we can have a drop down box in the Jupyter UI from which users can select the appropriate image to use. So we could have images for different TF versions. |
…471) * Update tensorflow-notebook-image workflow to pin tf version * Build 1.4.1, 1.5.1, 1.6.0 versions for gpu and cpu * Update ksonnet version * Update bazel version Fixes #375 * Use tf-version in image name instead of tag name * Use tornado 4.5.3 * Update notebook image path * Use cudnn 6 for tf1.4
* ingress is now networking.k8s.io/v1beta1 this was introduced in in 1.8 so there shouldn't be any blocker to upgrading our ingresses now https://kubernetes.io/blog/2019/07/18/api-deprecations-in-1-16/ * Most of the ingresses had already been updated * Deployments are now apps/v1 not extensions/v1beta1 * Delete gcp/basic-auth-ingress rather than updating it. * Delete kfdef/generic/istio rather than update it * It doesn't look like this has been modified since it was copied over from the kubeflow/kubeflow repo in 2019. ISTIO configs should be in /istio * Add a test for deprecated Kubernetes resources fixes kubeflow#375 Regenerate tests.
The latest image for jupyter notebook uses tensorflow 1.5-dev version and the TFServing image uses tensorflow 1.4 release.
I ran into an issue where I trained a model using the jupyter notebook, but had compatibility issues while serving that model with TFServing.
We should try to maintain the same tensorflow versions in the various kubeflow images.
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