Support running kernels in multiple locations (locally or remote) simultaneously.#1601
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Support running kernels in multiple locations (locally or remote) simultaneously.#1601ojarjur wants to merge 5 commits into
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The only differences between GatewaySessionManager and the underlying SessionManager type were: 1. Calling `get_kernel` inside the `kernel_cull` method to determine if the kernel exists. The underlying `SessionManager` class was instead checking if the specified kernel_id was *in* the MultiKernelManager. However, the MultiKernelManager class implements `get_kernel` by indexing into itself, so we could make the base SessionManager class also call `get_kernel` and it would be made compatible with `GatewayMappingKernelManager` while still remaining compatible with any other subclass of MultiKernelManager. 2. Specifying that the `kernel_manager` was of type `GatewayMappingKernelManager`. This was unnecessary because the kernel_manager_class was already being set to that inside of `serverapp.py`, and the `test_gateway.py` test has been updated to verify that the GatewayMappingKernelManager class is in fact being used.
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This PR extends the Jupyter server to support running kernels using multiple separate MappingKernelManagers simultaneously.
For example, you can run both local kernels and Kernel Gateway kernels at the same time without having to reconfigure (and restart) the server.
The topology for how these separate managers are plugged together is configurable, with the configuration being done by specifying a
RoutingProviderclass.This has been discussed multiple times both in issues on the jupyter_server repo and in community meetings.
The most recent and up-to-date such discussion is here.
That issue was opened to give a single place for folks to talk about the overall proposal and to raise any objections or concerns they might have about it.
No such objections have been raised, so I am proceeding with a PR to actually implement the proposal.