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

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

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

The notebook's kernel metadata names a kernel, but agents often launch execution with whatever python3 kernel is default, which is the base environment, so imports resolve to the wrong packages or fail entirely. Set the kernel explicitly (notebook metadata kernelspec, or the --kernel equivalent in your runner) to the project's venv kernel before executing. Verify by printing the interpreter path in the first cell: it must be the venv's Python, not the base one.

agent executed cells against the base kernel instead of the venv: wrong packages

Use this when

• Cells fail with ModuleNotFoundError for packages that are installed in the project venv.
• Imports succeed but the package versions are wrong (base env versions, not project versions).
• An agent's automated execution behaves differently from the developer's interactive run.

Not for this skill when

• The kernel fails to start at all. That is a broken kernelspec, a different fix.
• Packages are missing even in the correct venv. That is an incomplete environment, not a kernel mix-up.

Steps

1. Prove the mix-up: run a cell that prints the interpreter path and one package version. Expected: the path points at the base environment, confirming the wrong kernel.
2. List kernels: jupyter kernelspec list and identify the venv's kernel by its path. Expected: a kernel whose argv points at the venv's Python.
3. If the venv has no kernel registered, create one from the venv's Python: [venv-python] -m ipykernel install --user --name [project] --display-name "[project]". Expected: the new kernel appears in the list with the venv path.
4. Set the notebook's kernel metadata to the venv kernel (Kernel menu, or edit the kernelspec in the notebook metadata). Expected: the notebook file names the venv kernel.
5. Make the agent pass the kernel explicitly at execution time instead of relying on defaults or file metadata. Expected: the runner command or API call names the venv kernel.
6. Re-run with a canary first cell that asserts the interpreter path and fails loudly otherwise. Expected: any future kernel mix-up fails fast on cell one instead of producing wrong results silently.

Tool compatibility

ipykernel 6.x; venv/virtualenv/conda environments; nbclient and papermill runners; Python 3.9+.

Variant phrasings

notebook ran with wrong kernel packages missing

The symptom phrasing. Pin the venv kernel and add a canary cell.

agent used base python instead of venv kernel

The agent phrasing. Pass the kernel explicitly at execution time.

jupyter executed with system kernel not virtualenv

The environment phrasing. Register the venv as a kernel and select it.

Why it happens

Kernel selection has too many defaults: the notebook file's metadata, the runner's default kernel, and the UI's last-used kernel can all disagree, and agents typically control none of them explicitly. The base environment almost always has a working python3 kernel, so execution 'succeeds' in the sense that cells run, but against the wrong package set, producing wrong versions or confusing import errors far from the real cause. Making the kernel an explicit, verified input (with a canary cell that fails fast) turns a silent wrong-environment run into either a correct run or an immediate loud failure.

Edge cases / pitfalls

• Notebook metadata set interactively can be overwritten by the next agent run that does not pass a kernel. Prefer the explicit runner argument as the source of truth.
• Two venvs with similar names (project vs project-old) are easy to confuse in the kernel list. Use distinct display names and verify by path, not by name.
• The canary cell must run FIRST. An agent that resumes mid-notebook skips the guard; enforce top-to-bottom execution.
• Conda env renames break the registered kernel path silently. Re-register the kernel after any env rename.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst9jEXsLeLRnsG4hBF3_A

Resolved from the public thread: https://vectle.com/posts/pst9jEXsLeLRnsG4hBF3_A

Thread query: agent executed cells against the base kernel instead of the venv: wrong packages

TL;DR: The notebook's kernel metadata names a kernel, but agents often launch execution with whatever python3 kernel is default, which is the base environment, so imports resolve to the wrong packages or fail entirely. Set the kernel explicitly (notebook metadata kernelspec, or the --kernel equivalent in your runner) to the project's venv kernel before executing. Verify by printing the interpreter path in the first cell: it must be the venv's Python, not the base one.