Comments (3)
From the user level, we do just need some magic commands. But the magic commands are supported from the kernel, e.g. ipython or ir. Thus if we need to add magic commands, we could:
- Add the commands into ipython and implement the corresponding logics in ipython. In this way, we need to maintain a fork of ipython. I think we cannot merge it into ipython mainline.
- Implement a customized kernel. In this way, we can support some more use cases. In the future, maybe we could support model serving and hyperparameter tuning in the kernel.
I think maintaining a fork is not what we want, thus we decided to implement a kernel. But there is a problem in this way: How to run the python code natively, not via kubeflow.
To solve the problem, we use sos-kernel to support multiple kernels in one notebook. We have a demo for it:
You can select kernel in the right top sidebar of the cell. And run different cells using different kernels.
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If you have any idea about it, we can discuss it here. Ciao is experimental now and welcome breaking changes 😄
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/close
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Related Issues (20)
- [feature] Cleanup after job is finished HOT 6
- [UX] non-kubeflow backend HOT 5
- [discussion] Is it too heavy to create a kernel to achieve this?
- Add support for ciao using Incluster k8s config HOT 6
- [chore] Replace gometalinter with golangci-lint HOT 3
- [enhancement] Upgrade TFJob/PyTorchJob to v1beta2 HOT 2
- [feature] Support %tensorboard magic command
- Failed to build ciao image HOT 5
- [enhancement] Upgrade TFJob/PyTorchJob to v1
- [feasibility research] Support Customized UI in Jupyter HOT 1
- About path {kubeconfig} in docker run {kubeconfig} :{kubeconfig} HOT 35
- [bug] zmq4 was installed with ZeroMQ version 4.3.1, but the application links with version 4.2.5 HOT 5
- Kubeflow kernel try to get the pods but failed
- kernel can not started successly if kubeconfig is not set
- Failed to build the image
- Failed to build the image HOT 1
- A Problem HOT 16
- TF_CONFIG environment variable HOT 1
- error msg HOT 2
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