Question 1

How do I pass a list or dictionary as a parameter in Papermill?

Accepted Answer

Use YAML input via the -y, -f, or -b flags for structured data. For example, -y allows nested YAML syntax to define arrays and dictionaries directly in the command line or files.

Question 2

Papermill vs. nbconvert: which should I use for automating notebooks?

Accepted Answer

Papermill is for parameterizing and executing notebooks with dynamic inputs, ideal for data pipelines. nbconvert focuses on converting notebooks to static formats like HTML or PDF; use it for reporting without parameter changes.

Question 3

Can Papermill run notebooks in different Python environments or with conda?

Accepted Answer

Papermill executes notebooks using the current Jupyter kernel, so you must pre-configure kernels for each environment. It doesn't manage conda environments natively, requiring manual kernel setup.

Question 4

How to handle errors when a notebook fails during Papermill execution?

Accepted Answer

Papermill raises exceptions on failures, but for robust error handling, wrap execute_notebook in try-catch blocks or use orchestration tools like Apache Airflow to manage retries, logging, and alerts.

Question 5

Does Papermill work with JupyterHub or cloud notebook platforms?

Accepted Answer

Yes, but you need to ensure notebook paths and kernels are accessible. Papermill can execute via API in JupyterHub, though configuration might require additional setup for authentication and file storage.

Question 6

How to schedule Papermill notebook runs automatically?

Accepted Answer

Use cron jobs for CLI execution or integrate the Python API into workflow schedulers like Apache Airflow, Prefect, or Luigi, leveraging their scheduling features to trigger parameterized runs.

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