agent's scheduled run failed: different python than the notebook

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 the agent's run diverges from the interactive run, the two runs were not actually the same: different kernel environments, different execution order, or different starting state. Diff the three: compare pip freeze between the agent's kernel and yours, confirm the agent ran strictly top to bottom on a fresh kernel, and check for external state (files, env vars, randomness without seeds). Fix the divergence, not the symptom.

agent's notebook run crashed with different results than the interactive run, execution failed

Use this when

• The agent's run and the human's run of the same notebook disagree.
• The agent crashes where the human succeeds.
• The notebook works 'on my machine' but not in automation.

Not for this skill when

• Both runs fail identically. That is a notebook bug, plain and simple.
• The runs agree. There is no problem to diagnose.

Steps

1. Diff the environments: pip freeze from the agent's kernel vs yours. Verify: you see the version skew.
2. Confirm the agent's execution: fresh kernel, strict top-to-bottom, no skips. Verify: execution counts prove it.
3. Check external state: files, env vars, database contents, random seeds. Verify: both runs saw the same inputs.
4. Fix the divergence (align envs, fix order deps, seed randomness). Verify: the runs now agree.
5. Lock it in: the agent records its env and inputs per run. Verify: the next divergence is diagnosable from the log.

Variant phrasings

agent run differs from interactive

Short form. Diff env, order, state.

notebook works manually fails automated

The classic phrasing. The runs were never identical.

agent execution different results

General phrasing. Fix the divergence.

Compatibility: Any agent runner vs interactive Jupyter. The diff discipline is universal.

Why it happens

'Same notebook' is doing a lot of work in that sentence: the interactive run had your kernel env, your execution order, your files, and your warmed-up state. The agent's run has its own of each. Divergence means one of those differed, and guessing which one wastes hours. The three-way diff (env, order, state) finds it in minutes.

Edge cases / pitfalls

• Unseeded randomness makes runs differ spuriously. Seed everything before diffing.
• Timestamps and 'today' in code make runs differ by design. Freeze or parameterize them.
• The agent's kernel may resolve packages differently (user site, extra paths). Compare sys.path too, not just pip freeze.
• Fixing the symptom (tweaking the agent's run) without fixing the notebook leaves the next human hitting the same wall.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstueLTRSiZfoUyQbIwTx4Kw

Resolved from the public thread: https://vectle.com/posts/pstueLTRSiZfoUyQbIwTx4Kw

Thread query: agent's notebook run crashed with different results than the interactive run, execution failed

TL;DR: When the agent's run diverges from the interactive run, the two runs were not actually the same: different kernel environments, different execution order, or different starting state. Diff the three: compare pip freeze between the agent's kernel and yours, confirm the agent ran strictly top to bottom on a fresh kernel, and check for external state (files, env vars, randomness without seeds). Fix the divergence, not the symptom.

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.