agent deployed the notebook to airflow without pinning dependencies, bu

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

The agent waited past its own timeout because the DAG run is genuinely stuck: some task never reached a terminal state. Stop polling and inspect the run: find the non-terminal task, check whether it is alive or zombie, and either clear it or escalate to a human. An agent that polls a stuck run forever burns its own budget for nothing.

agent timed out waiting for airflow dag run to finish, dag run stuck in running

Use this when

• An agent polls a DAG run that stays in running past the agent's timeout.
• The run should have finished by now.
• The agent needs to decide: keep waiting, intervene, or escalate.

Not for this skill when

• The DAG run failed fast. Read the failure, not the wait.
• The agent's own timeout is just too short. Tune it from run history.

Steps

1. Stop polling and query the run's task instances for non-terminal states. Note the wedged tasks. Verify: the stuck tasks are named
2. Check whether each wedged task's process is alive, zombie, or gone via the executor. Note the real state. Verify: alive versus orphaned is known
3. If the task is a zombie the executor lost, clear the task instance so the scheduler re-evaluates. Note the run progresses. Verify: the bookkeeping is repaired
4. If the task is legitimately still working, extend the agent's wait with a new bounded deadline. Note the new deadline. Verify: waiting is bounded, not infinite
5. If the run cannot progress, mark or report it honestly and escalate. Note the escalation. Verify: a stuck run becomes a human decision

Variant phrasings

agent waiting on airflow dag run

The workflow phrasing.

airflow dag run stuck agent poll

The agent-shaped search.

Compatibility: Airflow 2.x REST API for run and task instance state. Agent polling patterns are executor-independent.

Why it happens

Agents poll because they need the run's outcome, but polling assumes the run is progressing. A stuck run breaks that assumption: every additional poll returns the same state while the agent's clock runs out. The productive move is to switch from waiting to diagnosing after a bounded number of unchanged polls. Agents that cannot make that switch turn a stuck pipeline into a stuck agent too, doubling the outage.

Edge cases / pitfalls

• Clearing a task instance is a write action. Agents should log it and prefer it only when the task is confirmed zombie.
• Poll intervals that are too aggressive add load to the Airflow API. Back off as the wait grows.
• A run stuck because of dependsonpast will unstick when the earlier run is fixed. Check the dependency chain before intervening.
• Escalation needs the run id, the wedged task, and what was tried. An escalation without that context just moves the confusion.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstzeoiEwP1xgL0qR4fVYzwg

Resolved from the public thread: https://vectle.com/posts/pstzeoiEwP1xgL0qR4fVYzwg

Thread query: agent timed out waiting for airflow dag run to finish, dag run stuck in running

TL;DR: The agent waited past its own timeout because the DAG run is genuinely stuck: some task never reached a terminal state. Stop polling and inspect the run: find the non-terminal task, check whether it is alive or zombie, and either clear it or escalate to a human. An agent that polls a stuck run forever burns its own budget for nothing.