Mastering Jupyter Book: Run Notebooks Sequentially

Dec 1, 2025 by Admin 51 views

Hey everyone! Ever found yourself staring at a massive, single Jupyter notebook, thinking, "There has to be a better way to organize this beast!" You're not alone, folks. It's a common dilemma, especially when your notebook project grows into a full-blown practical example, like loading some R data and then performing a ton of intricate operations. The natural instinct, and a really smart one at that, is to break it down into more manageable, shorter pages. This approach makes your work so much easier to digest, both for yourself and for anyone else trying to follow along.

But here's where things can get a little tricky, and frankly, quite frustrating. When you split that grand notebook into several smaller, specialized sections, you often introduce a hidden dependency. Each page now performs just a subset of the original operations, right? The catch is, these subsets aren't always independent; they often rely on the data state or intermediate results generated by the previous section. Think of it like a recipe: you can't frost a cake before you bake it, and you certainly can't bake it before you mix the ingredients! The order matters.

Now, if these notebooks could simply be executed in sequence, one after another, like a perfectly choreographed dance, everything would be dandy. We could easily save the data state at the end of one section, and then the very next notebook could seamlessly load that state and pick up exactly where its predecessor left off. This way, any incremental changes to your data objects — like cleaning a dataset, transforming features, or performing a specific analysis — would be encountered in the correct, logical order. This ensures that your analyses are always building on the right foundation, preventing errors and ensuring reproducible results.

However, the plot thickens when we bring Jupyter Book into the picture, especially when you use the powerful jupyter book build --execute command. While this command is fantastic for automating the execution of your notebooks and baking them directly into your documentation, it has a default behavior that can throw a wrench into our beautifully planned sequential workflow. What often happens is that jupyter book build --execute, in its quest for efficiency, tends to spawn a bunch of processes to parallelize the execution. This parallelism, while speeding up the build for independent notebooks, completely loses our desired sequential execution order. And boom! Suddenly, your data isn't in the right state for each section, leading to confusion, errors, and a lot of head-scratching. This article is all about helping you navigate this challenge, offering practical strategies to ensure your Jupyter Book projects execute just the way you need them to, without sacrificing the benefits of modularity.

Understanding the Jupyter Book Execution Challenge

Alright, let's talk about Jupyter Book and its awesome capabilities. For those unfamiliar, Jupyter Book is an open-source tool that allows you to build publication-quality books and documents from Jupyter Notebooks and Markdown files. It's a game-changer for sharing complex analyses, tutorials, and even textbooks, transforming a collection of raw notebooks into a polished, navigable website. You can include live code, equations, citations, and all sorts of rich content, making it incredibly versatile for researchers, educators, and developers alike. When you run jupyter book build --execute, you're essentially telling Jupyter Book, "Hey, before you compile these notebooks into web pages, go ahead and run all the code cells, so the output is fresh and included in the final document." This is super handy because it ensures your readers see up-to-date results, plots, and tables without having to run the code themselves.

Now, here's the kicker: by default, Jupyter Book, being the smart cookie it is, tries to be as efficient as possible. To speed up the overall build process, it often parallelizes the execution of your notebooks. Imagine you have ten notebooks; instead of running them one by one, it might try to run three or four at the same time, using multiple processor cores. For many projects, especially those where each notebook is largely self-contained or only depends on static input files, this parallel execution is a massive win. It means faster build times, which translates to a quicker feedback loop and more productive development cycles. Who doesn't love a speedy build, right?

However, our situation is a bit different. We're dealing with a scenario where the output, or the data state, from one notebook is absolutely critical as the input for the next. In such cases, the default parallel execution becomes a significant hurdle, not a help. It's like trying to assemble a complex LEGO set by having multiple people randomly grab pieces and build different sections without coordinating. You'd end up with a mess, not a spaceship! The problem isn't that Jupyter Book is broken; it's just optimized for a different common use case. It assumes a level of independence between notebooks that isn't present when you have deeply intertwined, sequential data operations. This design choice, while logical for general purpose, means we, the users with specific sequential needs, have to be a bit more clever in how we structure our projects. Understanding this fundamental behavior of jupyter book build --execute is the first crucial step in figuring out how to work with it, rather than against it, to achieve our desired sequential execution flow without sacrificing the benefits of modularity and a well-structured Jupyter Book project.

The Core Problem: Data State and Sequential Dependencies

Let's really zoom in on the heart of the matter: the data state and sequential dependencies. This isn't just a minor inconvenience; it's a fundamental architectural challenge when you're breaking down a complex, linear workflow into modular pieces. Imagine your original, monolithic Jupyter notebook as a carefully crafted story, where each paragraph builds directly on the previous one. When you split that story into individual chapters, you expect the reader to go through Chapter 1, then Chapter 2, then Chapter 3, because the plot unfolds sequentially. If someone jumps straight to Chapter 3 without reading the first two, they're going to be completely lost. That's essentially what's happening to your data when parallel execution kicks in.

Consider a typical data science workflow: Notebook A is responsible for loading and initial cleaning of raw data. It produces a cleaned_dataframe. Notebook B then takes this cleaned_dataframe, performs feature engineering, and outputs a transformed_dataframe. Finally, Notebook C uses transformed_dataframe to train a machine learning model. See the chain? A -> B -> C. If, by the whims of parallel execution, Notebook B starts running before Notebook A has finished cleaning the data, or if Notebook C tries to train a model before Notebook B has even created the transformed_dataframe, you're going to hit errors. Your scripts will complain about missing variables, incorrect data types, or simply non-existent files. It's a classic case of the cart before the horse, and it completely breaks the integrity of your analytical pipeline.

This concept of incremental data state is vital. Each step in your workflow modifies or enhances the data, producing a new state that the subsequent step expects. You start with df_raw, which becomes df_cleaned after the first notebook. Then, the second notebook transforms df_cleaned into df_features. And so on. When jupyter book build --execute decides to run these in an unpredictable order, the df_features notebook might try to load df_cleaned before it's even been created by its upstream sibling. The result? Frustration, failed builds, and a strong urge to just put everything back into one giant, unwieldy notebook, which defeats the entire purpose of creating a modular, readable project structure.

The original problem statement perfectly encapsulates this dilemma. The user considered a few options, each with its own significant drawbacks. Maintaining several incremental data state directories sounds like a nightmare in terms of version control and complexity. You'd have state_for_section1, state_for_section2, etc., and manually ensure they're always in sync, which is incredibly prone to errors and a huge management burden. Including all the code from the previous sections in the start of each notebook is a non-starter. It introduces massive boilerplate, makes each notebook excessively long, and completely negates the benefit of breaking them into shorter, focused sections. Imagine debugging a problem where a function is defined in Notebook A, copied to B, and then C — changing it means changing it everywhere, which is a recipe for inconsistencies.

And leaving it all as one long document is simply giving up on good software engineering practices. While it solves the execution order problem, it reintroduces the very readability and maintainability issues that prompted the splitting in the first place. Nobody wants to scroll endlessly through hundreds of lines of code to find one specific operation. We want those shorter, focused sections because they're better for readability, easier to debug, and more pleasant for anyone (including our future selves!) to follow. The challenge, then, is finding a way to get the best of both worlds: modularity and guaranteed sequential execution, without falling into these traps.

Exploring Workarounds: Tackling Sequential Notebook Execution

Okay, so we know the problem: Jupyter Book wants to run things in parallel, but our data workflow demands a strict sequence. Since there isn't a direct "run this then that" flag built directly into jupyter book build --execute for defining explicit execution dependencies, we need to get a bit creative with our approach. Don't worry, guys, there are several solid strategies we can employ to regain control over our notebook execution order, ensuring our data state transitions perfectly from one section to the next. Let's dive into some practical workarounds.

Workaround 1: Explicitly Saving and Loading State Between Notebooks

This is perhaps the most straightforward, albeit requiring diligence, and it's something you already hinted at. The idea here is to make the intermediate data explicit. At the end of each notebook, you save the relevant data objects to disk. Then, the next notebook starts by loading those objects. This effectively decouples the execution, as each notebook is then responsible for its own inputs and outputs.

How to Do It:
- Saving Data: For Python, popular choices include pickle (for native Python objects), feather or parquet (excellent for DataFrames due to speed and efficiency), csv (universal, but less efficient for large data), or hdf5 (for more complex hierarchical data). If you're using R, you'd typically use saveRDS() to save single R objects or save() for multiple objects, and readRDS() or load() to retrieve them. The key is to choose a format that preserves your data structure and types as accurately as possible. For instance, after Notebook A cleans your data, you might have a line like cleaned_df.to_feather('data/processed/stage1_cleaned_data.feather').
- Loading Data: The very first cell (or an early one) of Notebook B would then be cleaned_df = pd.read_feather('data/processed/stage1_cleaned_data.feather'). This ensures Notebook B always starts with the correct, pre-processed data from Notebook A, regardless of when jupyter book build --execute decides to kick off Notebook B's process.
Pros: This approach makes each notebook more self-contained once its inputs are loaded, improving debuggability. It also means you can re-run a single notebook without needing to re-run all preceding ones, as long as its input files haven't changed. It explicitly manages dependencies, which is good practice.
Cons: You're adding file I/O operations, which can incur a performance overhead if your data is massive. More importantly, it requires careful management of these saved files. If Notebook A changes, you must re-execute it to update stage1_cleaned_data.feather before running Notebook B, otherwise Notebook B will be working with stale data. This can lead to subtle bugs if you're not diligent. You'll also need a clear directory structure (e.g., data/raw, data/processed, data/final) to keep things organized.

Workaround 2: External Orchestration and Pre-computation

This strategy involves completely sidestepping jupyter book build --execute for the actual execution part. Instead, you pre-execute all your notebooks in the correct sequence before you even call jupyter book build. Think of it as a separate, controlled execution phase.

How to Do It:

Scripted Execution: You can write a simple shell script (.sh), a Python script, or even use a Makefile to define the exact order of execution. Inside this script, you'd use tools like papermill or nbclient to programmatically execute your notebooks. For example:

papermill notebook_A.ipynb executed_notebook_A.ipynb
papermill notebook_B.ipynb executed_notebook_B.ipynb
papermill notebook_C.ipynb executed_notebook_C.ipynb
jupyter book build . # Now build the book from the *executed* notebooks

Alternatively, a Python script could look something like:

import nbclient
from pathlib import Path

notebooks = ['notebook_A.ipynb', 'notebook_B.ipynb', 'notebook_C.ipynb']

for nb_path in notebooks:
    print(f"Executing {nb_path}...")
    with Path(nb_path).open() as f:
        nb_node = nbclient.read(f, as_version=4)
    # Execute the notebook
    executor = nbclient.NotebookClient(nb_node)
    executor.execute()
    # Save the executed notebook back (optional, but often useful)
    with Path(nb_path).open('w') as f:
        nbclient.write(nb_node, f)

# Then you'd run jupyter book build manually or via subprocess
# subprocess.run(['jupyter', 'book', 'build', '.'])

Disable Jupyter Book Execution: Once your external script has run all the notebooks in order, you would then run jupyter book build . without the --execute flag. You might even set execute_notebooks: 'off' in your _config.yml globally, and rely entirely on the pre-executed notebooks, assuming they are saved back to their original .ipynb files, complete with outputs.

Pros: This gives you absolute control over the execution order. You can easily define dependencies, pass parameters between notebooks (if using papermill), and integrate this into a larger CI/CD pipeline. It's robust and predictable.
Cons: It adds an extra layer of complexity to your build process, requiring a separate script or workflow. You need to ensure the executed notebooks are saved with their outputs, or Jupyter Book won't have anything to render. This might also mean your source control gets updated with executed notebooks, which some folks prefer to avoid, but it's a trade-off for sequential reliability.

Workaround 3: Consolidating Dependent Code into a Single Execution Unit (Refined)

This isn't about copying all code everywhere, but rather identifying truly shared, foundational code and structuring it intelligently. If you have several initial setup steps that are absolutely critical for all subsequent notebooks, you can abstract them.

How to Do It:
- Python Modules: Extract common functions, classes, and even initial data loading/cleaning steps into a standard Python .py module. For example, my_project/utils.py. Then, each of your notebooks can simply import my_project.utils and call the necessary functions. This way, the code lives in one place, is easily testable, and doesn't get copied around.
- Dedicated Setup Notebook: Create one initial "00-setup.ipynb" notebook that performs all the heavy lifting of data loading, initial cleaning, and perhaps even saves multiple intermediate data artifacts (using the methods from Workaround 1). Subsequent notebooks then only load the specific artifacts they need. This keeps the setup isolated but ensures consistent initial state.
Pros: Reduces boilerplate significantly, promotes code reuse, makes your individual notebooks cleaner and more focused. Debugging is easier as the core logic is centralized.
Cons: Requires thinking about your code structure more like a software project than just a series of scripts. Some data operations might still necessitate sequential execution of notebooks if the intermediate data products are too complex to manage via simple file saving.

Workaround 4: Rethinking Your Data Workflow and Intermediate Outputs

This is a more architectural recommendation, encouraging you to think about your data flow like a robust data pipeline. Instead of just passing "state," think about producing well-defined "intermediate data products" at each stage.

How to Do It:
- Explicit Stages: Define clear stages in your data processing (e.g., Raw -> Cleaned -> Enriched -> Modeled). Each notebook (or a small group of notebooks) is responsible for transforming data from one stage to the next.
- Structured Storage: Save all intermediate data products to a clearly defined directory structure (e.g., data/01_raw, data/02_cleaned, data/03_features). Use versioned filenames (e.g., cleaned_data_v1.feather).
- Data Version Control (DVC): For truly complex projects, consider tools like DVC (Data Version Control). DVC allows you to version your data files and define dependencies between them, much like Git versions code. This means if an upstream data file changes, DVC knows which downstream steps need to be re-executed. While DVC doesn't directly control Jupyter Book's --execute parallelism, it provides a strong framework for managing the data dependencies that lie underneath.
Pros: Highly robust, reproducible, and scalable. Makes your entire data pipeline transparent and auditable. Great for collaborative projects. Decouples code execution from data management.
Cons: Adds a layer of complexity to your project setup. Has a learning curve if you're new to data pipeline concepts or tools like DVC.

Workaround 5: Investigating Jupyter Book's Configuration Options (and Limitations)

It's always a good idea to double-check Jupyter Book's native configuration. While a direct sequential_execution: true flag isn't available for build --execute, understanding the existing options is crucial.

_config.yml Execution Settings: In your _config.yml, you'll find settings under execute. The main ones are execute_notebooks, which can be auto, force, cache, or off. These control if and when notebooks are executed, but not the order of execution among parallel processes.
- execute_notebooks: 'force' will run all notebooks every time, regardless of cache, but still in parallel.
- execute_notebooks: 'cache' will re-run only changed notebooks, which can save time but still doesn't guarantee sequence for dependent notebooks that might not have changed but whose inputs have.
- Setting execute_notebooks: 'off' and relying on pre-executed notebooks (as in Workaround 2) is the most robust way to guarantee order if you're not saving and loading. Alternatively, you can set execute_notebooks: 'off' globally and then enable execute: true on individual notebooks where you do want Jupyter Book to run them, although this doesn't solve the sequential dependency for those specific executed notebooks either.
Current Limitations: As of current stable versions, Jupyter Book itself doesn't offer a built-in mechanism to specify explicit execution order or dependencies for its parallel --execute mode directly within the _config.yml or individual notebook metadata. This is why external orchestration or robust data state management becomes so critical for these specific use cases.

Each of these workarounds has its place, and the best solution for your project might be a combination of several. The key is to consciously manage the flow of data and execution rather than hoping parallel processes will magically align with your sequential needs.

Best Practices for Managing Complex Jupyter Book Projects

Beyond just tackling the sequential execution challenge, adopting some general best practices can significantly improve the maintainability, reproducibility, and overall quality of your Jupyter Book projects. Trust me, folks, a little foresight here saves a lot of headache down the road!

1. Embrace Modularity (Wisely): You're already on the right track by breaking down large notebooks. Continue this by aiming for notebooks that do one thing well. A notebook that loads data, cleans it, performs analysis, and then builds a model is doing too much. Instead, think: "This notebook loads and cleans." "This one performs feature engineering." "This one trains the model." This focused approach, combined with the workarounds we discussed for managing state, makes each component easier to understand, test, and debug. Don't be afraid to create helper Python modules for functions that are used across multiple notebooks, further enhancing modularity and reusability.

2. Clearly Define Inputs and Outputs: For every single notebook in your project, you should have a crystal-clear understanding of what it expects as input and what it produces as output. This isn't just for you; it's for anyone else (including your future self!) trying to understand your workflow. Document these expectations explicitly, perhaps in a markdown cell at the beginning of each notebook or in a README file for your data/processed directory. If a notebook depends on stage1_cleaned_data.feather, make that dependency obvious. This practice naturally forces you to consider the data flow and helps identify implicit dependencies.

3. Version Control for Everything: This one is non-negotiable. Use Git for all your code, notebooks, and configuration files. This means every change is tracked, allowing you to revert to previous versions if something goes wrong. For your data, especially intermediate data products, consider integrating a Data Version Control (DVC) system. While Git is great for code, it's not designed for large binary files. DVC works with Git to track changes in your data, ensuring that your entire project, from raw data to final analysis, is versioned and reproducible. This is crucial for collaborative environments and for proving the integrity of your results over time.

4. Test, Test, Test: Just like any software project, your analytical pipeline needs testing. This can range from simple unit tests for your utility functions to integration tests that ensure your notebooks run correctly in sequence and produce the expected outputs. Tools like nbval can integrate with pytest to test the output of your notebooks. Automated testing ensures that changes in one part of your workflow don't silently break another, providing confidence in your results and making it much safer to refactor or update components.

5. Comprehensive Documentation: Your Jupyter Book is documentation, but don't stop there. Beyond the executable content, provide higher-level overviews. Explain the overall architecture of your project, the purpose of each major notebook, and how they fit together. Explicitly state the execution order required for your project to run successfully. Use comments liberally within your code. The goal is to make your project as accessible and understandable as possible, reducing the cognitive load for new contributors or for when you revisit it after a long break.

6. Manage Your Environment: Reproducibility isn't just about code and data; it's also about the software environment. Use conda, pipenv, or venv to define and manage your project's dependencies (Python versions, libraries, R packages, etc.). Generate a requirements.txt (for pip) or environment.yml (for conda) file that lists all necessary packages and their versions. This ensures that anyone (or any machine) running your project will use the exact same software stack, eliminating the dreaded "it works on my machine" problem. This step is critical for ensuring that your sequentially executed notebooks always behave consistently.

By incorporating these best practices, you're not just solving the sequential execution problem; you're building a robust, sustainable, and truly reproducible Jupyter Book project that stands the test of time. It's about thinking strategically about your workflow and setting up your project for long-term success, ensuring both humans and machines can understand and rely on your work.

Looking Ahead: Future of Jupyter Book and Sequential Execution

It's pretty clear from discussions like ours that the challenge of managing sequential execution in a parallel-first environment like jupyter book build --execute is a common pain point for many users. While the current version of Jupyter Book doesn't offer a direct, built-in flag for specifying complex execution dependencies, the open-source world is constantly evolving. The developers and community behind Jupyter Book are incredibly active, and user feedback, like the original post, plays a crucial role in shaping future features.

There's always a possibility that future releases of Jupyter Book, or perhaps new extensions, might introduce more sophisticated execution control mechanisms. We could potentially see features that allow users to define a directed acyclic graph (DAG) of notebook dependencies directly within _config.yml or through enhanced notebook metadata. Imagine a system where you could simply tag Notebook B as requires: notebook_A.ipynb, and the build system would then automatically ensure Notebook A completes before Notebook B starts, even in a parallel environment. This kind of feature would be a game-changer for projects with intricate sequential workflows, removing the need for many of the manual workarounds we've discussed.

For now, the best path forward is to continue engaging with the Jupyter Book community. Participate in discussions on GitHub, contribute to feature requests, and share your own workarounds and experiences. The collective wisdom of the community often leads to innovative solutions and ultimately influences the development roadmap. Who knows, maybe one day our desire for explicit sequential execution will become a seamless, built-in option, making our lives even easier!

Conclusion

Phew! We've covered a lot of ground, haven't we? The journey from a single, sprawling Jupyter notebook to a beautifully structured, multi-page Jupyter Book is a rewarding one. It enhances readability, promotes modularity, and makes your work far more accessible. However, as we've seen, this journey introduces a unique challenge: ensuring that notebooks with sequential data dependencies execute in the correct order when Jupyter Book's build process defaults to parallel execution.

While there isn't a magic button that says "run this in sequence, please!" directly within Jupyter Book's core functionality for build --execute, that doesn't mean we're out of luck. Far from it! We've explored a range of powerful workarounds, from explicitly saving and loading data states between notebooks, to orchestrating your entire execution workflow externally with tools like papermill or nbclient.

We also discussed the importance of architectural strategies, such as refactoring common code into Python modules and thinking about your data as a series of well-defined intermediate products. And let's not forget the crucial role of general best practices: embracing wise modularity, clearly defining inputs/outputs, rigorous version control, thorough testing, comprehensive documentation, and robust environment management. These practices don't just solve immediate problems; they lay the foundation for sustainable, reproducible, and truly high-quality projects.

Ultimately, mastering sequential execution in Jupyter Book is about being intentional. It means understanding the default parallel behavior and proactively implementing strategies to manage your data flow and execution order. By combining smart data engineering with thoughtful project organization, you can absolutely achieve a modular, readable Jupyter Book that executes exactly as you intend, delivering reliable results every single time. So go forth, build those awesome Jupyter Books, and keep creating amazing, well-structured content!