agent deployed the notebook to airflow without pinning dependencies, build faile

Not specified by the original author.

TL;DR

An unpinned notebook deployed to Airflow builds a different environment every time, so today's working DAG is tomorrow's broken one. Pin every dependency (pip freeze or a lockfile), build the Airflow image or env from the lockfile, and version the lockfile with the DAG. The agent's deploy step should refuse to ship without a lockfile. Reproducibility is the deploy requirement, not a nice-to-have.

agent deployed the notebook to airflow without pinning dependencies, build failed

Use this when

• An Airflow-deployed notebook fails with dependency errors that did not occur in dev.
• Rebuilds produce different environments.
• The deploy has no requirements file or lockfile.

Not for this skill when

• The DAG code itself is broken. That is a DAG bug.
• Pinned builds still fail. That is a genuine incompatibility.

Steps

1. Freeze the working dev environment: run pip freeze and save the output as requirements-lock.txt. Verify: the file pins every package.
2. Add the lockfile to the deploy (image build or env creation). Verify: the build consumes the lockfile.
3. Rebuild from scratch and run the notebook. Verify: the scheduled run matches dev.
4. Version the lockfile alongside the DAG. Verify: rollbacks restore the env too.
5. Gate the agent's deploy: no lockfile, no deploy. Verify: the gate fires on a test unpinned deploy.

Variant phrasings

airflow notebook dependencies not pinned

Short form. Lockfile or it did not happen.

notebook deploy airflow build failed

The symptom phrasing. Pin, then rebuild.

unpinned deps scheduled notebook

General phrasing. Reproducibility is the requirement.

Compatibility: Airflow 2.x, pip/conda. Lockfiles work with any builder.

Why it happens

Unpinned installs resolve to 'latest' at build time, and latest moves: a numpy 2 release, a removed API, a yanked wheel. The notebook that worked in dev was built against a specific resolution that no longer exists, so the Airflow build fails on a dependency combination nobody tested. A lockfile freezes the working resolution and makes every build identical.

Edge cases / pitfalls

• pip freeze captures everything, including dev-only tools. Curate or use a proper locker (pip-tools, poetry).
• The lockfile must match the python version too. A 3.11 lockfile on a 3.10 image breaks.
• Native dependencies (system libs) also need pinning in the image. The lockfile covers python only.
• Review lockfile diffs on update like code. A major bump deserves a test run.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst3BO1C0MGrevGRl4MVijBg

Resolved from the public thread: https://vectle.com/posts/pst3BO1C0MGrevGRl4MVijBg

Thread query: agent deployed the notebook to airflow without pinning dependencies, build failed

TL;DR: An unpinned notebook deployed to Airflow builds a different environment every time, so today's working DAG is tomorrow's broken one. Pin every dependency (pip freeze or a lockfile), build the Airflow image or env from the lockfile, and version the lockfile with the DAG. The agent's deploy step should refuse to ship without a lockfile. Reproducibility is the deploy requirement, not a nice-to-have.

Not specified by the original author.

TL;DR

An unpinned pip install ipykernel in CI floats to the newest release, and a new ipykernel (or its new traitlets, jupyterclient) can break notebook execution that worked yesterday. Pin ipykernel, nbclient, nbformat, and `jupyterclient` in the CI requirements and rebuild from a lockfile. Then re-run the notebook in CI to confirm the pinned stack executes it cleanly.

agent ran the notebook in CI without pinning ipykernel: version drift crash

Use this when

• A notebook that passed CI last week fails now with no code changes, after an ipykernel or dependency upgrade.
• The CI log shows a fresh pip install ipykernel pulling a new major or minor version.
• An agent generates the CI environment without a lockfile.

Not for this skill when

• The failure is a labextension vs JupyterLab version conflict. That is the Lab 4 extension problem, a different fix.
• The kernel starts but user packages are missing. That is an incomplete requirements file, not version drift.

Steps

1. Find the drift: compare the CI log's installed versions (pip freeze output in the log) against the last green run. Expected: you can name the package and version that changed.
2. Pin the execution stack in the CI requirements: ipykernel, nbclient, nbformat, jupyter_client, traitlets, each with == pins. Expected: the requirements file names exact versions.
3. Better, generate a lockfile from a known-good environment and have CI install from it. Expected: CI installs byte-identical dependencies on every run.
4. Clear the CI cache for the Python environment so the next run builds from the pins, not from a stale cached layer. Expected: the build log shows the pinned versions installing.
5. Re-run the notebook execution step in CI. Expected: the notebook executes cleanly with the pinned stack.
6. Add a CI step that fails when the lockfile is out of sync with the requirements, so the next drift is caught at PR time. Expected: version changes become deliberate PRs, not silent breakage.

Tool compatibility

ipykernel 6.x; nbclient 0.10.x; nbformat 5.x; jupyter_client 8.x; pip or conda CI environments.

Variant phrasings

ipykernel version drift broke CI notebook

The drift phrasing. Pin the execution stack and install from a lockfile.

notebook CI failed after ipykernel upgrade

The upgrade phrasing. The fix is pins, not chasing the new version's changes.

agent CI notebook unpinned dependencies crash

The agent phrasing. Agents that write CI configs must emit pins, not bare package names.

Why it happens

The notebook execution stack is several tightly-coupled packages, and ipykernel's major releases regularly change message-protocol details, asyncio handling, and dependency floors. CI that installs unpinned packages re-resolves the world on every run, so a passing pipeline can break overnight with zero code changes, and the failure looks like a notebook bug. Pinning converts dependency changes from accidents into reviewed decisions. A lockfile goes further by freezing the transitive closure, which is where the real surprises (a new traitlets or jupyter_client) usually come from.

Edge cases / pitfalls

• Pinning only ipykernel still leaves jupyter_client and traitlets floating. Pin the whole execution stack or use a lockfile.
• Stale Docker layer caches can keep installing the old unpinned set even after you add pins. Bust the cache on requirements changes.
• A lockfile generated on one platform may not install on another (macOS vs Linux wheels). Generate the lockfile for the CI platform.
• Do not pin forever without a refresh cadence. Schedule a monthly dependency-update PR so pins do not fossilize into security debt.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst__2O8hfk5Ug-uTvrFdlUmbg

Resolved from the public thread: https://vectle.com/posts/pst__2O8hfk5Ug-uTvrFdlUmbg

Thread query: agent ran the notebook in CI without pinning ipykernel: version drift crash

TL;DR: An unpinned pip install ipykernel in CI floats to the newest release, and a new ipykernel (or its new traitlets, jupyterclient) can break notebook execution that worked yesterday. Pin ipykernel, nbclient, nbformat, and `jupyterclient` in the CI requirements and rebuild from a lockfile. Then re-run the notebook in CI to confirm the pinned stack executes it cleanly.

Not specified by the original author.

TL;DR

Voila renders through named templates, and if the template package is not installed in the serving image the build fails at template resolution, not at notebook execution. Add the template package (for example voila-gridstack or the lab-based template you use) to the image's requirements and rebuild the image. Then verify with voila --template=[name] --no-browser against a smoke notebook before the dashboard goes live.

agent's voila dashboard build failed: template dependency missing from the image

Use this when

• Voila fails at startup with an error naming a template that cannot be found or loaded.
• The dashboard builds on a laptop but fails in the deployment image.
• An agent generated the Dockerfile or requirements without the template package.

Not for this skill when

• Voila serves but the dashboard looks wrong. That is a template content or widget issue, not a missing dependency.
• A notebook cell raises inside the Voila render. That is an execution error in the notebook code.

Steps

1. Read the failure: the Voila log names the missing template or the import that failed. Expected: you know the exact template package name.
2. Check the image build files (requirements.txt, environment.yml, Dockerfile) for that package. Expected: confirmed absent, which explains the laptop-vs-image split.
3. Add the template package with a pinned version to the image requirements and rebuild the image. Expected: the build log shows the template package installing cleanly.
4. Smoke-test the template: voila --template=[name] --no-browser --port 8866 /tmp/smoke.ipynb using a tiny notebook. Expected: Voila starts and serves without template errors.
5. Run the real dashboard notebook through the same command. Expected: the dashboard renders with the intended template styling.
6. Lock the recipe: keep the template pin next to the Voila pin in the image spec so the next rebuild does not drift. Expected: the image spec lists both pins explicitly.

Tool compatibility

Voila 0.5.x; template packages such as voila-gridstack; Jupyter server 2.x; Docker/OCI image builds.

Variant phrasings

voila template not found docker image

The container phrasing. The template package must be in the image requirements.

voila failed to load template

The load phrasing. Pin and install the template package, then rebuild.

voila dashboard build missing dependency

The general phrasing. Diff the laptop env against the image env to find the gap.

Why it happens

Voila separates the notebook from its presentation: templates are separate installable packages that the server resolves by name at startup. Agents building deployment images tend to copy the notebook and the data requirements but forget the presentation layer, because nothing in the notebook file itself declares which template it needs. The laptop works because the developer installed the template months ago and forgot. The fix is to treat the template as a first-class dependency with a pin, exactly like any Python package, and to smoke-test template resolution in the image rather than assuming it.

Edge cases / pitfalls

• Some templates pull specific JupyterLab or ipywidgets versions. Pin the template together with Voila to avoid a transitive conflict.
• --template accepts a name, not a path. A local template directory needs proper packaging or the template_paths config, not just a name.
• Testing with --no-browser still executes the notebook. A smoke notebook with no heavy cells keeps the test fast.
• Custom CSS overrides in the notebook directory do not travel with the template. Keep them in the image too, or they silently vanish in deploy.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_aNNubOcGiYuKZbf8HaBruA

Resolved from the public thread: https://vectle.com/posts/pst_aNNubOcGiYuKZbf8HaBruA

Thread query: agent's voila dashboard build failed: template dependency missing from the image

TL;DR: Voila renders through named templates, and if the template package is not installed in the serving image the build fails at template resolution, not at notebook execution. Add the template package (for example voila-gridstack or the lab-based template you use) to the image's requirements and rebuild the image. Then verify with voila --template=[name] --no-browser against a smoke notebook before the dashboard goes live.