jupyter nbconvert -stdout -to markdown quickstart.ipynb 2>/dev/null | We can pipe the output of pygmentize to a pager like less to scroll through and search within the notebook. Pygments is a dependency of nbconvert, so if you’ve followed to here you’ll already have it installed.Īs we’re piping text to pygmentize on standard input, there’s no filename from which to determine the language of the input so we specify it using the -l flag. We’ll pipe the output of nbconvert to pygmentize, a command line interface to the Pygments syntax highlighting library.
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This is already more readable than the raw JSON document, but we can improve the readability of the code cells by applying syntax highlighting. If you’re using an alternative such as fish, change as appropriate. This redirection syntax is for Bourne-like shells such as Bash. jupyter nbconvert -stdout -to markdown quickstart.ipynb 2>/dev/null Nbconvert writes the header Converting notebook quickstart.ipynb to markdown to standard error and the notebook body to standard output, so we can filter out the header by redirecting standard error. One bigĭifference between NumPy and JAX is how you generate random numbers.
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We'll be generating random data in the following examples. **JAX is NumPy on the CPU, GPU, and TPU, with great automaticĭifferentiation for high-performance machine learning research.** Converting notebook quickstart.ipynb to markdown $ jupyter nbconvert -stdout -to markdown quickstart.ipynb We can use nbconvert to convert the notebook to Markdown, printing the output to standard output. By design, Markdown syntax is unobtrusive and readable in plain text without rendering, so it’s a good choice for our output format.Īs an example, we’ll use a notebook that contains both prose and code cells taken from the documentation for JAX. nbconvert is a program to convert notebooks to rich formats including HTML and PDF, as well as plain text such as Markdown. Instead of printing the notebook file directly to the terminal, we’ll convert it to another format first. Characters such as " are backslash-escaped, making copying code from the output laborious. Notebooks can contain images and other binary data, which if viewed with a pager will print unintelligible Base64 encoded data to your terminal. A notebook is a structured JSON document conforming to a schema. This isn’t the case for Jupyter notebooks. As such it’s enough to print the raw file contents to the terminal, for example with cat. Viewing other literate programming formats such as R Markdown and Pweave at the command line is trivial: these use lightweight markup languages that don’t need special rendering to be legible. This is convenient when logged into a remote machine via SSH, and the process of configuring SSH to forward a port, starting a Jupyter server, and navigating to it in a web browser is a chore to view a notebook for a few seconds. While the standard tools for interacting with notebooks are web applications, it’s often useful to be able to view notebooks at the command line. The Jupyter notebook is a literate programming environment that has become ubiquitous in scientific computing.
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Pulling, pushing, and merging code will be handled just as they would be for any other Python project. Two different people can now work on these. Every saved change to a Python cell in this Jupyter notebook will now be reflected in the.
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No issues! Version Control the Python ScriptĪdd the.