Can the notebook generated from papermill be outputted with a live running kernel?

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When papermill generates a notebook, a .ipynb file is created in the output path that says it is not running in the jupyter home page. I would prefer that when the notebook has finished executing, it remains running with a live kernel so I can interact with any variables inside of it. Instead now I have to re-run the cells to get the variables that were generated in the notebook. This is especially cumbersome for any time intensive notebooks.

I am generating the notebooks using execute_notebook function.

My feeling is that this is not possible because while the new notebook is being executed it never shows "Running" in my jupyter homepage. Is what I am asking for even possible with papermill or is there another way of achieving this that is scalable?

Keeping the kernel running sounds indeed useful, and I too could not find support for this in Papermill documentation.

It appears that the kernel may not run with any user interface, e.g., any local port that you can browse to, so that even if it remained running after execution, you would not be able to interact with it anyway.

However, it seems that you do not need to re-run anything in the saved notebooks to recover already computed variables, as you can simply use papermill.read_notebook, no?

[PDF] papermill Documentation, When generating parameters for python (when running on 3.6+) the NotebookCollection abstractions which are now living in scrapbook and the kernel but not impact input/output paths. papermill --cwd foo bar/input_nb.ipynb bar/ Output and input paths can be automatically passed to notebooks with� Can the notebook generated from papermill be outputted with a live running kernel? 1 What is the best way to copy a pandas dataframe from one jupyter notebook to another

You could implement this by following the extending papermill docs to implement a custom engine which links to a live kernel, or leaves the kernel up post-execution. This would require a little bit of custom code to avoid nbconvert from stopping the kernel and/or to have the target kernel passed into papermill's execute function. Possible, but not out of the box.

[PDF] Notebooks as Functions with papermill., # Linear notebooks with dummy parameters can test integrations pm. execute_notebook('s3://commuter/templates/spark.ipynb',. 's3://commuter/tests/ runs/{run_id}/� Papermill takes a source notebook, applies parameters to the sourcenotebook, executes the (4)notebook with the specified kernel, and saves theoutput in the destination notebook. The NOTEBOOK_PATH and OUTPUT_PATH can now be replaced by `-` representingstdout and stderr, or by the presence of pipe inputs / outputs.

As far as I am aware there are several options for that. Papermill used to allow recording variables in the notebook using papermill.record(), which has been deprecated; I believe you can get an older version and still use it.

Another option they suggest is to use scrapbook. You can find more about it here.

You can also use %store magic: Share data between IPython Notebooks

Finally you can simply write into flat files either by using python's context manager functionality:

with open('<dir>', 'w') as file:
    file.write(<var_of_choice>)

import json    
with open(<out_path>, "a+") as file:
    json.dump(<var_of_choice>, file)

If your notebooks load a lot of data it may be sub-optimal to leave kernels running.

jupyter-notebook, Can the notebook generated from papermill be outputted with a live running kernel? 发表于 2019-04-02 03:37:22. 活跃于 2019-05-31 12:11:46. 查看215 次. Bases: papermill.engines.Engine. A notebook engine representing an nbclient process. This can execute a notebook document and update the nb_man.nb object with the results. classmethod execute_managed_notebook (nb_man, kernel_name, log_output=False, stdout_file=None, stderr_file=None, start_timeout=60, execution_timeout=None, **kwargs) ¶

Introduction to Papermill. How to transform your Jupyter Notebook , <process output>`with `papermill - -` being implied by the pipes will read a With a specific kernel, we can run our notebooks on defined The weather forecast report created with our workflow for the city of Sao Paulo, BR. You can now programmatically execute a workflow without having to copy and paste from notebook to notebook manually. Papermill takes an opinionated approach to notebook parameterization and execution based on our experiences using notebooks at scale in data pipelines.

Advanced Jupyter Notebook Tutorial – Dataquest, This free Jupyter Notebooks tutorial has will help you get the best out of Jupyter. Exploring topics like logging, macros, running external code, and Jupyter extensions. Magics are handy commands built into the IPython kernel that make it easier to In the output of %lsmagic above, you may have noticed a number of cell� Yes and no. The notebook file does not – and cannot – store all the necessary information especially while the kernel is still running (for example mapping from message-id to handlers. You need an extra store (that can be in server RAM) and has a richer representation than ipynb.

Executing notebooks — nbconvert 6.0.0a5 documentation, Jupyter notebooks are often saved with output cells that have been cleared. Executing notebooks can be very helpful, for example, to run all notebooks in When not specified or when using nbconvert <4.2, the default Python kernel is chosen. This allows rendering of the live widgets on for instance nbviewer, or when� Papermill takes a source notebook, applies parameters to the source notebook, executes the notebook with the specified kernel, and saves the output in the destination notebook. The NOTEBOOK_PATH and OUTPUT_PATH can now be replaced by ` - ` representing stdout and stderr, or by the presence of pipe inputs / outputs.