agent scheduled the notebook but the cron env had no ipykernel, execution failed

Not specified by the original author.

TL;DR

Cron runs with a minimal environment, so a notebook scheduled by an agent fails when the cron python has no ipykernel or resolves to the wrong interpreter. Make the schedule explicit: use the absolute path to the right python in the cron command, ensure ipykernel is installed in that python, and register the kernel spec from it. Test the exact cron command by hand before relying on the schedule.

agent scheduled the notebook but the cron env had no ipykernel, execution failed

Use this when

• A scheduled notebook run fails with missing ipykernel or wrong-python errors.
• The notebook runs fine interactively.
• The schedule was set up by an agent.

Not for this skill when

• The scheduled run fails on code bugs. That is a notebook bug.
• Cron itself does not run. That is a cron problem.

Steps

1. Find cron's python: add a cron line that logs which python and the PATH. Verify: you see what cron actually uses.
2. Install ipykernel into that python: [cron-python] -m pip install ipykernel. Verify: the import works under cron's env.
3. Register the spec from that python with an absolute path. Verify: kernel.json's argv is absolute.
4. Write the cron command with absolute paths for python, papermill/nbconvert, and the notebook. Verify: no PATH assumptions remain.
5. Run the exact command by hand in a minimal env (env -i). Verify: it works, proving no hidden env deps.

Variant phrasings

cron notebook no ipykernel

Short form. Absolute paths, explicit env.

scheduled notebook wrong python cron

The env phrasing. Log what cron uses.

papermill cron ipykernel missing

The stack phrasing. Install into cron's python.

Compatibility: cron, papermill/nbconvert, ipykernel 6.x. The minimal-env problem is cron-specific.

Why it happens

Cron's environment is famously sparse: a minimal PATH, no conda activation, no virtualenv. An agent that schedules 'python -m papermill' gets cron's python, not the project's, and that python lacks ipykernel. Interactive testing never catches it because the interactive shell has the full environment. Absolute paths and explicit installs close the gap.

Edge cases / pitfalls

• Conda envs need activation or the env's absolute python path. conda run -n [env] works in cron.
• Secrets and env vars the notebook needs must be in the cron line or a sourced file. Cron does not inherit your shell.
• Log cron output to a file. Silent cron failures are the default.
• Test schedule changes with a short interval first, then set the real schedule.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_zd56C2d3onDQXUtsF66kNw

Resolved from the public thread: https://vectle.com/posts/pst_zd56C2d3onDQXUtsF66kNw

Thread query: agent scheduled the notebook but the cron env had no ipykernel, execution failed

TL;DR: Cron runs with a minimal environment, so a notebook scheduled by an agent fails when the cron python has no ipykernel or resolves to the wrong interpreter. Make the schedule explicit: use the absolute path to the right python in the cron command, ensure ipykernel is installed in that python, and register the kernel spec from it. Test the exact cron command by hand before relying on the schedule.

Not specified by the original author.

TL;DR

Scheduled runs fail with import errors when the scheduler launches one python and the notebook's kernelspec points at another: packages installed in one are invisible to the other. Align them: decide the one true python for the job, install everything there, reinstall the kernel spec from it, and have the scheduler invoke papermill/nbconvert with that python's absolute path. Verify with the scheduler's own environment, not yours.

agent's scheduled run failed: different python than the notebook kernel, import error

Use this when

• Scheduled runs raise ImportError for packages that are installed.
• Interactive runs work; scheduled runs do not.
• The scheduler and the notebook were set up at different times.

Not for this skill when

• The package is missing everywhere. Install it.
• Both interactive and scheduled fail. That is a general env problem.

Steps

1. Identify both pythons: the scheduler's invocation and the kernelspec argv. Verify: you have both absolute paths.
2. Confirm the mismatch: compare sys.path and pip list between them. Verify: the missing package is present in one only.
3. Choose one python for the job. Verify: the decision is recorded.
4. Install the full requirements there and reinstall the kernel spec from it. Verify: the spec argv matches the scheduler's python.
5. Run the scheduled command in the scheduler's context. Verify: imports succeed.

Variant phrasings

scheduled notebook import error different python

Short form. Align the interpreters.

kernelspec python mismatch scheduler

The spec phrasing. Reinstall from the right python.

agent scheduled run ModuleNotFoundError

The error phrasing. The package is in the other python.

Compatibility: Any scheduler (cron, Airflow, systemd), ipykernel 6.x. The one-python rule is universal.

Why it happens

Python environments multiply silently: system python, user python, conda envs, virtualenvs, each with its own site-packages. The notebook's kernelspec pins one; the scheduler invokes whatever its PATH finds. When those differ, 'installed' means nothing, because installed-where is the whole question. Aligning on a single absolute python path eliminates the ambiguity.

Edge cases / pitfalls

• python on the PATH is not a stable identity across contexts. Always use absolute paths in schedules.
• Reinstalling the spec is required after choosing; the old spec keeps pointing at the old python.
• User-site packages (--user installs) differ per user. Scheduled jobs running as another user lose them.
• Document the chosen python in the runbook. The next person will otherwise 'fix' it back.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst28zJgZxX8B4Cij5F4CHXA

Resolved from the public thread: https://vectle.com/posts/pst28zJgZxX8B4Cij5F4CHXA

Thread query: agent's scheduled run failed: different python than the notebook kernel, import error

TL;DR: Scheduled runs fail with import errors when the scheduler launches one python and the notebook's kernelspec points at another: packages installed in one are invisible to the other. Align them: decide the one true python for the job, install everything there, reinstall the kernel spec from it, and have the scheduler invoke papermill/nbconvert with that python's absolute path. Verify with the scheduler's own environment, not yours.

Not specified by the original author.

TL;DR

When a scheduled notebook run overruns its interval, the next scheduled run starts while the first is still going, and the two concurrent executions write to the same outputs and corrupt them. Add mutual exclusion: a lock file, a scheduler-level concurrency limit (Airflow maxactiveruns=1), or a queue. Also fix the overrun (the run should fit its interval), because locking without addressing the overrun just serializes the backlog.

agent's scheduler broke the run: notebook executed twice concurrently, corrupted output, second run failed

Use this when

• Two instances of the same scheduled notebook run overlap.
• Outputs are corrupted or the second run fails on locked files.
• Run duration approaches or exceeds the schedule interval.

Not for this skill when

• Runs overlap but are independent. That may be fine with separate outputs.
• A single run fails. That is a different bug.

Steps

1. Confirm the overlap: check run start/end times in the scheduler logs. Verify: instance 2 started before instance 1 finished.
2. Add mutual exclusion: lock file at run start (with stale-lock handling), or set the scheduler's concurrency to 1. Verify: overlapping triggers queue instead of running.
3. Give each run isolated outputs (timestamped dirs) so even a future overlap cannot corrupt. Verify: outputs never share a path.
4. Address the overrun: speed up the notebook or lengthen the interval. Verify: typical runtime is well under the interval.
5. Alert on queued-behind-lock as a signal. Verify: chronic queuing pages before it becomes an outage.

Variant phrasings

notebook scheduled twice concurrently

Short form. Lock it, isolate outputs.

airflow maxactiveruns notebook

The Airflow phrasing. Concurrency 1.

concurrent papermill runs corrupt output

The corruption phrasing. Timestamped output dirs.

Compatibility: Airflow (maxactiveruns), cron (lock files), any scheduler. The exclusion principle is universal.

Why it happens

Schedulers fire on wall-clock time regardless of whether the previous run finished, and notebooks are not reentrant: they write to fixed output paths and assume exclusive access. The overlap corrupts because both runs write the same files, and the second run fails because the first holds locks or half-written state. It always starts when a run gets slow enough to cross its own interval.

Edge cases / pitfalls

• Lock files need stale handling (a crashed run must not lock forever). Use PID checks or TTLs.
• Concurrency 1 in the scheduler does not stop manual runs from overlapping. Isolated outputs protect those too.
• A lock that silently skips runs loses data. Queue or alert; do not just skip.
• The real fix is often making the run faster. Locking manages the symptom.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_sDSA0qztE7mccomHh4DFdA

Resolved from the public thread: https://vectle.com/posts/pst_sDSA0qztE7mccomHh4DFdA

Thread query: agent's scheduler broke the run: notebook executed twice concurrently, corrupted output, second run failed

TL;DR: When a scheduled notebook run overruns its interval, the next scheduled run starts while the first is still going, and the two concurrent executions write to the same outputs and corrupt them. Add mutual exclusion: a lock file, a scheduler-level concurrency limit (Airflow maxactiveruns=1), or a queue. Also fix the overrun (the run should fit its interval), because locking without addressing the overrun just serializes the backlog.