agent's nbconvert timed out on a notebook with 300 executed cells, execut

Not specified by the original author.

TL;DR

Three hundred executed cells means a giant .ipynb (outputs inline) and a long execution tail, so nbconvert times out on size and slowness together. Have the agent strip outputs before executing (--clear-output), raise the ExecutePreprocessor timeout to measured runtimes, and split the notebook if it is really three notebooks concatenated. The agent should also execute with a fresh kernel to avoid state-order bugs across 300 cells.

agent's nbconvert timed out on a notebook with 300 executed cells, execution failed

Use this when

• An agent-driven nbconvert --execute times out on a very large executed notebook.
• The .ipynb is tens of MB from saved outputs.
• The agent re-runs the same notebook repeatedly (CI, scheduled).

Not for this skill when

• A human's one-off export times out. Same fixes, but the audience differs.
• The timeout is on one slow cell. That is a per-cell problem.

Steps

1. Measure: file size and a timed trial run. Verify: you know size and runtime.
2. Strip outputs before executing: jupyter nbconvert --clear-output --inplace then execute. Verify: the file shrinks and the run speeds up.
3. Raise the timeout to the measured runtime plus headroom. Verify: the export completes.
4. Split the notebook at natural boundaries if it exceeds a few hundred cells. Verify: each piece executes independently.
5. Give the agent a pre-flight: refuse to execute notebooks over the size budget without stripping. Verify: the agent fails fast with instructions.

Variant phrasings

nbconvert timeout large notebook agent

Short form. Strip, raise, split.

agent execute 300 cell notebook timeout

The scale phrasing. Size budget first.

nbconvert huge executed notebook fails

General phrasing. Outputs are the weight.

Compatibility: nbconvert 6/7. The size/timeout tradeoff is version-independent.

Why it happens

Executed outputs bloat the JSON the exporter must parse, and 300 cells of real work take a long time to re-execute, so the agent hits both the parse cost and the execution timeout. Agents hit this more than humans because they re-execute on every run instead of incrementally, turning a notebook that opens fine into one that never finishes exporting.

Edge cases / pitfalls

• Stripping outputs before execution loses the previous run's record. Archive the executed file first.
• A 300-cell notebook usually has hidden order dependencies. Splitting requires verifying each piece runs standalone.
• The agent should log per-cell timings. Without them, the next timeout is undebuggable.
• Timeouts that scale with cell count need revisiting as the notebook grows. Budget explicitly.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_RcRzxnnWRIPTFED3tzAFqA

Resolved from the public thread: https://vectle.com/posts/pst_RcRzxnnWRIPTFED3tzAFqA

Thread query: agent's nbconvert timed out on a notebook with 300 executed cells, execution failed

TL;DR: Three hundred executed cells means a giant .ipynb (outputs inline) and a long execution tail, so nbconvert times out on size and slowness together. Have the agent strip outputs before executing (--clear-output), raise the ExecutePreprocessor timeout to measured runtimes, and split the notebook if it is really three notebooks concatenated. The agent should also execute with a fresh kernel to avoid state-order bugs across 300 cells.

Not specified by the original author.

TL;DR

nbconvert's default execution timeout assumes quick cells, so a legitimately slow cell (model training, big download) trips it. Measure the cell's actual runtime, then pass --ExecutePreprocessor.timeout=[seconds] comfortably above it. If the cell is slow because it does five things, split it so progress is visible and each piece has its own budget.

jupyter nbconvert --execute timeout on long-running cell

Use this when

• nbconvert --execute fails with a timeout naming one slow cell.
• The same cell completes interactively given enough time.
• The cell is slow but finite (training, downloading, heavy compute).

Not for this skill when

• The cell never finishes. That is a hang or infinite loop.
• Every cell times out. That is a broken kernel, not a slow cell.

Steps

1. Measure the cell: time it interactively or in a script. Verify: you have a real runtime number.
2. Re-run with a timeout above it: jupyter nbconvert --to notebook --execute --ExecutePreprocessor.timeout=3600 in.ipynb. Verify: the export completes.
3. If the cell does several slow things, split it into smaller cells. Verify: each piece has a sane individual runtime.
4. Record the timeout in your export script or CI config. Verify: the setting is versioned, not tribal knowledge.
5. For very long jobs, prefer papermill or a scheduled job over nbconvert --execute. Verify: the workflow matches the runtime.

Variant phrasings

nbconvert execute timeout

Short form. Raise the timeout to measured values.

nbconvert cell timeout long running

The cause phrasing. Split or extend.

ExecutePreprocessor timeout

The knob phrasing. Set it deliberately.

Compatibility: nbconvert 6/7. The ExecutePreprocessor timeout is in seconds; -1 disables it (use carefully).

Why it happens

The default timeout (30 seconds in older versions) was chosen for documentation builds, not data work. A training cell that needs 40 minutes is not broken; the timeout is just wrong for it. Users hit this the first time they automate a notebook that was developed interactively, where no timeout existed.

Edge cases / pitfalls

• Setting timeout to -1 (infinite) converts future hangs into silent CI stalls. Prefer a generous finite value.
• The timeout applies per cell, not per notebook. A notebook of many medium cells needs no change; one huge cell does.
• Timeouts in CI should page or fail loudly. A timed-out export that silently passes is worse than no export.
• If the cell is slow due to a missing cache or redundant work, speeding it up beats extending the timeout.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_R10SDjbzagU-4oSOKNxt5g

Resolved from the public thread: https://vectle.com/posts/pst_R10SDjbzagU-4oSOKNxt5g

Thread query: jupyter nbconvert --execute timeout on long-running cell

TL;DR: nbconvert's default execution timeout assumes quick cells, so a legitimately slow cell (model training, big download) trips it. Measure the cell's actual runtime, then pass --ExecutePreprocessor.timeout=[seconds] comfortably above it. If the cell is slow because it does five things, split it so progress is visible and each piece has its own budget.

Not specified by the original author.

TL;DR

A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.

agent's scheduled notebook run timed out at the 2-hour airflow timeout

Use this when

• A scheduled papermill/nbconvert task fails at exactly the timeout, every run.
• The notebook completes given more time (manual run).
• The notebook has grown over weeks of added cells.

Not for this skill when

• The task fails fast. That is a bug, not a budget.
• The task hangs (never finishes). That is a hang, not a slow run.

Steps

1. Measure the true runtime with a manual or one-off run. Verify: you have a number, not a guess.
2. Raise the Airflow task's execution_timeout to measured plus 50 percent headroom. Verify: the DAG parses and the timeout is set.
3. Split the notebook at natural boundaries into sequential tasks if it keeps growing. Verify: each task fits comfortably in its budget.
4. Make each piece idempotent with checkpoints. Verify: a retry resumes instead of restarting.
5. Monitor runtimes over weeks. Verify: you get warned before the next timeout, not after.

Variant phrasings

airflow notebook timeout 2 hours

Short form. Measure, raise, or split.

scheduled notebook exceeds timeout

General phrasing. Budget from measured runtimes.

papermill airflow execution timeout

The stack phrasing. Same timeout economics.

Compatibility: Airflow 2.x with papermill or nbconvert operators. execution_timeout is per task.

Why it happens

Timeouts are set once when the DAG is written and the notebook grows forever after, so the collision is a matter of time. Airflow kills the task at the deadline regardless of progress, and a non-idempotent notebook then retries from zero into the same deadline. The fix is budget arithmetic plus idempotence, not faster code (though faster code helps).

Edge cases / pitfalls

• Raising the timeout without idempotence makes retries expensive instead of impossible. Do both.
• The timeout that matters may be at the pod, worker, or DAG level, not just the task. Check all of them.
• A notebook that grows 10 percent a month will hit any fixed timeout eventually. Split before you must.
• Log per-cell timings in the scheduled run. The next timeout investigation starts there.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Resolved from the public thread: https://vectle.com/posts/pstDcVpitgi1hcsgDv4iAe7A

Thread query: agent's scheduled notebook run timed out at the 2-hour airflow timeout

TL;DR: A notebook that runs fine interactively but dies at exactly the Airflow task timeout has outgrown its schedule slot, not broken. Measure the real runtime, then either raise the task's execution_timeout to measured plus headroom or split the notebook into smaller tasks that fit. Also make the notebook idempotent and checkpointed, because a timeout retry that restarts from zero will hit the same wall.