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توضیحات

Jupyter D1 Server for Callisto

ویژگی	مقدار
سیستم عامل	-
نام فایل	callisto-jupyter-d1-0.0.9
نام	callisto-jupyter-d1
نسخه کتابخانه	0.0.9
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Oak City Labs
ایمیل نویسنده	team@oakcity.io
آدرس صفحه اصلی	https://callistoapp.com
آدرس اینترنتی	https://pypi.org/project/callisto-jupyter-d1/
مجوز	-

# jupyter_d1 Jupiter Discovery One server ## Info ### Install `pip install git+https://github.com/OakCityLabs/jupyter_d1.git@branch_name#egg=jupyter_d1` Once installed, install kernel definitions with `jupyter_d1_install_kernels` ### Start Server `start_jupyter_d1 --port=8000 --secret_token=user_provided_secret --root_dir=/path/to/working/directory` ### Run tests (Requires pytest, async-asgi-testclient) `jupyter_d1_test` ### How Stuff Works The Jupyter D1 server sits between a REST client and a Jupyter Kernel. Kernels don't understand notebooks. They just execute commands they receive. Jupyter D1 manages the state of the notebook, keeping track of which cell is being executed and updating the cells results with the kernel finishes processing. In the traditional JavaScript Jupyter client, all of this state is held in the web app. This makes it impossible to share that state with another client. In Jupyter D1, this state is held in the D1 server and a client can connect at any time and ask for the current state. A client may also ask that new code is executed. The D1 server relays that to the active kernel. Kernel results are used to update the current model of the notebook in the D1 server and broadcast to clients to update UIs. Clients should connect to the notebook websocket stream where they will receive a broadcast of all the updates to the notebook cells. Clients can send regular REST requests to initiate action, like executing a cell or changing a cell's source code. A websocket is also avaible for the raw stream of kernel messages, but that's not intended for client consumption in the long term. Kernel messages are more of a debugging tool. ### See Makefile for convenience commands make save_env save current development environment using conda to environment.yml make load_env create a development environment using conda from environment.yml (deletes current env definition) make show_env list available conda environments make lint run the linter make test run pytest make ci_test run the linter and then run pytest make run_debug run the 'uvicorn' server in debug mode make docs open the docs in your browser (mac only, requires server running locally) make redoc open the alternate docs in your browser (mac only, requires server running locally) ### Documentation * Run the server with `make run_debug` * Open the Swagger UI docs URL with `make docs` [link](http://localhost:8000/docs) * Open the ReDoc docs URL with `make docs` [link](http://localhost:8000/redoc) ### Stream listeners Run `./ws_listener.py` to echo out messages from the websocket broadcast channels. Two options are: * `./ws_listener.py --feed stream` -- Listen to the stream of kernel messages direct from the Jupyter kernel * `./ws_listener.py --feed notebook` -- List to the jupyter_d1 stream of updates to notebook cells ### Paw See the included Paw file `jupyter_d1.paw` for some examples. A simple test sequence that might represent what a client would do: * Open the simple notebook * Execute a cell by ID -- set x * Execute a cell by ID -- increment x This loads a notebook, executes a cell which assigns a value to `x` and then executes another cell that increments the value of `x`. It's useful to have the stream listeners active to see the results. ### Testing * `make test` will pytest and report coverage * Just running `pytest` from the top level directory will run all the tests * Can specify a specific test with `pytest tests/test_notebooks_websocket.py::TestNotebookWebSocket::test_notebook` * VS Code should understand the tests and run them from the editor, but sometimes that seems flakey. * If VS Code has trouble finding tests, run `pytest --collect-only` at the cli to check for errors. ### WebDAV server * The FastAPI app instantiates a [WebDAV server](https://wsgidav.readthedocs.io/en/latest/index.html) mounted under `/dav`. * Currently, this exposes `/tmp` on the local machine. * Can be mounted via a WebDAV [client](https://en.wikipedia.org/wiki/WebDAV). * Eventually, this will provide clients file level access to the Jupyter working directory. * Current username is `user` with password `password`. * There is a read-only user called `readonly_user` but permission levels aren't implemented, so this user has full access. ### Static typing * Jupyter_D1 uses static typing for compatibility with FastAPI * The python runtime ignores type information at the moment. * In VS Code, you should enable the `mypy` linter to check typing. * [Mypy Cheatsheet](https://mypy.readthedocs.io/en/stable/cheat_sheet_py3.html) ### Authenication with shared secret * Use the shared secret from the server command line to get an access token * curl -H "Authorization: <SHARED_SECRET>" http://127.0.0.1:<port_number>/login/access-token * returns {"token":{"access_token":"**ACCESS_TOKEN**","token_type":"bearer"}} * Use the returned access token for subsequent requests * curl -H "Authorization: Bearer <**ACCESS_TOKEN**>" http://127.0.0.1:<port_number>/notebooks

نیازمندی

مقدار	نام
<5,>=4.0.1	wsgidav
<1,>=0.52.0	fastapi
<1,>=0.3.2	asyncblink
<1,>=0.11.3	uvicorn
>=10.0	websockets
>=0.4.0	httptools
<1,>=0.13.0	python-dotenv
<6,>=5.0.4	nbformat
<4,>=3.1.0	python-jose
<2,>=1.0.0	jupyter
<8,>=7	jupyter-client
<7,>=6.1.0	jupyter-console
<5,>=4.6.3	jupyter-core
<3,>=2.4.0	jupyter-kernel-gateway
>=3.2	zsh-jupyter-kernel
>=0.7.2	bash-kernel
<9,>=8.0.4	pynvml
<1,>=0.0.4	callisto-python
<2,>=1.13.8	jupytext
<1,>=0.0.5	python-multipart
==5.10.2	importlib-resources
<6,>=5.7.2	psutil
<1,>=0.0.1	callisto-watchdog

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl callisto-jupyter-d1-0.0.9:

pip install callisto-jupyter-d1-0.0.9.whl

نصب پکیج tar.gz callisto-jupyter-d1-0.0.9:

pip install callisto-jupyter-d1-0.0.9.tar.gz