Comments (11)
I created a plugin template in https://github.com/DanielSchiavini/titanoboa-jupyterlab
Update dec 5: trying to figure out how to add a comm target from the frontend. The kernel is not directly available like before. Alternatevely we could implement some other 2 way communication via ipython or tornado, but that's much more cumbersome.
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We definitely don't need any extra dependencies, this was an easy way to proof the concept.
About Collab, I have no idea, and I wasn't able to find the restrictions quickly today. I can investigate more.
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also debugger (cf. https://jupyterlab.readthedocs.io/en/stable/user/debugger.html) and kernel for vyper could be done
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Is the idea here to simply port the existing code into a proper plugin, or do we want to add/change the functionality as well?
If so it would be great to know what the new functions we want implement are (maybe in separate issues?)
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i think first port into a plugin, and then we can talk about adding additional functionality. the real issue that i'm trying to address here is that the way of jerry-rigging the required libraries that works for jupyter notebook does not work for jupyter labs.
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I got it working with API calls and shared memory, no websockets needed frontend and backend and the signer. There's still bugs to fix and tests to write, but this solution with shared memory is feasible and relatively simple.
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I got it working with API calls and shared memory, no websockets needed frontend and backend and the signer. There's still bugs to fix and tests to write, but this solution with shared memory is feasible and relatively simple.
very slick! i think i have a couple reservations about using shared_memory_dict
-- it's another dependency, and it also seems insecure because it uses pickle for instance (but that seems easy to get around by using shared_memory directly instead) and pass via json, or use something builtin to the mp library -- https://docs.python.org/3/library/multiprocessing.html#sharing-state-between-processes
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btw, does this technique work with google colab somehow?
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I replaced the shared dict dependency with a SharedMemory object for each of the expected callbacks. DanielSchiavini/titanoboa-jupyterlab@2e86d6b
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The integration tests are failing in Github for some stupid reason (they pass locally).
I've managed to reproduce the issue locally, and track down an exception thru act, docker and jupyterlab to this line. It produces ["TypeError: Cannot read properties of undefined (reading 'get')","TypeError: Cannot read properties of undefined (reading 'get')"]
which is about the most generic error in the planet and it can come from literally anywhere. And the error details are right there being ignored in the line. 😠
I'm trying to intercept the call somehow but it's not easy inside Github actions 😞
Of course we could ignore the included tests, but the pipelines are vital to easily produce a npm+pip package.
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I have 2 PRs with the implementation ready for review:
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Related Issues (20)
- cache blueprint bytecode in VyperBlueprint HOT 2
- Implement RPC via browser wallet HOT 1
- gnosis safe implementation HOT 3
- Set chain_id via BrowserRPC HOT 1
- Patch encoder/decoder to convert ABI addresses
- sign typed data using browsersigner HOT 1
- Remove Address inheritance from string HOT 1
- Notify user of tx fail in boa call output HOT 1
- Empty, nested revert messages contain unexpected characters HOT 2
- Add types for better integrating with mypy and other checkers HOT 3
- Allow using boa with Vyper master branch HOT 1
- Feature Request: Pydantic support for smart contracts HOT 1
- use python import hooks to provide native import experience HOT 1
- docs: boa.fuzz
- feature: `boa.env.expect_event()`
- Jupyter plugin uses too much space HOT 2
- code coverage crashses with segfault HOT 4
- ImportError: cannot import name 'get_interface_codes' from 'vyper.cli.vyper_compile' HOT 2
- docs: custom precompiles HOT 1
- Gas profiler does not give a name to getter functions for public variables HOT 3
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