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Run Jupyter notebooks as scripts

Jupyter notebooks are designed almost exclusively for interactive use, but many people want to use them for heavy-duty computational usage. nbscript is designed to process notebooks as scripts and provide the most common script features: clear start and end, arguments (argv), (stdin) and stdout, and so on.

We also take the perspective of batch processing, so also have a wrapper that allows you to submit notebooks as Slurm scripts (similar sbatch with a Python as the interpreter).

Notebooks are very good for interactive work, but for large computation interactive just isn't an efficient use of resources. For other expensive resources that can't be shared (GPUs for example), interactive work even for development can be a bit questionable. A proper course of action would be to create proper programs to run separate from notebooks... but sometimes people prefer to stay in notebooks.

Many other modules (see references below) try to allow notebooks to be run, but we take the viewpoint that the traditional UNIX script interface is good and notebooks should be made like scripts: nbscript notebook.ipynb should behave similarly to python This also allows us to provide a logical pathway to non-notebook programs.

Quick examples of invocation


  • nbscript input.ipynb [argv]: runs, prints results as asciidoc to stdout. Within the script, from nbscript import argv to get the argv.

  • nbscript --save input.ipynb: runs, saves to input.out.ipynb

  • nbscript --save --timestamp input.ipynb: runs, saves to input.out.TIMESTAMP.ipynb


  • snotebook [slurm opts] input.ipynb: submits to slurm with sbatch, using the --save option like you see above. Slurm output is in input.out.ipynb.log.

  • snotebook [slurm opts] --- --timestamp input.ipynb: like above, but adds --timestamp option like you see above.


nbscript is still in development, so not all of this functionality exists yet. In general, nbscript notebook.ipynb should have as similar an interface as python

Run a notebook from the command line:

  • nbscript nb.ipynb arg1 arg2 .... Within the notebook, you can access the arguments by import nbscript ; nbscript.argv (these are currently transferred via environment variables). Note that argv[0] is the notebook name if it is known, otherwise None.

  • By default, only the output of the cells is printed to stdout. Options may used to save the notebook to a file in any of nbconvert's supported output formats.

You may also run a notebook via IPython extensions:

  • %nbscript nb.ipynb [arg1 arg2 ...]. By default the output isn't substituted back in, because we couldn't do much with that. Instead, it is saved to a HTML file with the output and errors. If you don't give an output name, the output is timestamped.

    • Currently not implemented, use !nbscript instead.
  • nbscript sets the NBSCRIPT_RUNNING environment variable, and if this is already set it won't run again. That way, you can have a notebook execute itself with the %nbscriptmagic function.

Interface within notebooks:

  • import nbscript ; nbscript.is_active() will return True if the current Python interpreter is being run via nbscript.

  • import nbscript ; nbscript.argv is the argv in analogy to sys.argv. (json-encoded in the environment variable NB_ARGV).

    • One would use argparse with nbscript.argv, in particularly parser.parse_args(args=nbscript.argv[1:]).
  • Other environment variables: NB_NAME is the notebook name (note that there is no way for Jupyter kernels to know the currently executing notebook name, this seems to be intentional because it's a protocol layer violation).

  • nbscript sets the environment variable NBSCRIPT_RUNNING before it executes a notebook, and if this is already set then it will do nothing if it tries to execute again (print an error message and exit). This is so that an notebook can nbscript-execute itself without recursive execution. This behavior is up for debate.

Submit a notebook via Slurm

  • snotebook nb.ipynb arg1 arg2. This is similar to sbatch arg1 arg2 - it will search for #SBATCH lines and process them (stopping the search after the first cell that has any).

  • Similar to the %nbscript magic function, there is the %snotebook magic function.

Saving output state:

  • When a notebook is run non-interactively, it would be useful to save the state so that the output variables can be re-loaded and played with. To do that, we should find some way to serialize the state and re-load it. It would be nice if nbscript could automate this, but perhaps that makes things too fragile.

  • The dill module is supposed to be able to serialize most Python objects (but starts failing at some complex machine learning pipeline objects). One can try to serialize the state at the end of the execution.

  • In the future, we want to add support for automatically serializing the final state after the notebook is run in parallel to the html output. This would allow one to re-load the state to continue post-processing. For now, though, we recommend you explicitly save whatever is important (this is probably more reliable anyway).

Server extension

Nbscript provides a server extension which allows you to submit requests for synchronous/asynchronous batch jobs. For example, you can use the URL to run a test command. By default, this runs nbscript --save --timestamp {path}

To enable the extension:

jupyter serverextension enable nbscript.server_extensions.batch

Currently, there is not much documentation - the code should be easy to understand. Currently, configuration is hard-coded, see the code. Return value is a json object (which you can figure out from the code).

See also

There are many commands to execute notebooks, but most of these do not see the notebooks as a first-class script, but as an interactive thing which happens to be run. that would have run, arguments, stdout, etc.

  • seems to be one of the most similar projects to this. Cells are tagged as containing parameters, which means that they can be overridden from the command line. It still takes the view that this is mainly a notebook.

  • nbconvert is the default way for executing notebooks. There is no default way to pass arguments and output formats are designed to look like notebooks.

  • is a pretty basic script similar to nbconvert --execute it seems (deprecated in favor of nbconvert --execute).

  • Several tools called nbrun, some of which are deprecated in favor of nbconvert.

  • also dynamically replaces values in cells.

  • is IPython magic functions for interacting with Slurm. It doesn't do anything special about the notebook format. The %%sbatch magic submits a cell as a Slurm shell script and probably the %srun magic runs a command line. This makes a logical companion to nbscript and is perhaps better than an interface we might make.

  • and many more...

All of these accomplish the same thing but have different (a few) or no (most) ways of passing parameters.

Development status and maintnance

Currently this is a usable alpha - the main invocations work, but get too creative and expect problems! There are tests to verify the important stuff works, though.

Maintainer: Richard Darst, Aalto University. Feedback and improvements encouraged.


Run and process notebooks as you would shell scripts







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