You ask ChatGPT to crunch a 200MB CSV, fit a model, and chart the residuals. Twenty seconds in, the response cell goes red: “The code execution timed out” or “Sandbox terminated. Retrying…” The model retries once, hits the same wall, and falls back to printing pseudocode instead of running it. Code Interpreter (now branded as Advanced Data Analysis in some surfaces) runs your Python inside a per-message ephemeral container with a hard wall-clock cap — typically 60-120 seconds per cell, plus tight memory and CPU caps. Most timeouts are not “ChatGPT is broken”; they are a script doing too much in one cell, holding the entire dataframe in RAM, or blocking on a network call the sandbox cannot make.
Common causes
Ordered by what we see most often in real sessions.
1. Single cell tries to do everything end-to-end
The model writes one giant cell: load CSV → clean → feature-engineer → train → cross-validate → plot. Even on a 50MB file this routinely passes the 60s sandbox cap. Code Interpreter does not stream partial output, so when the cell dies you lose all intermediate state.
How to spot it: The failing cell has more than ~40 lines of Python, multiple for loops over the dataframe, and at least one fit / cross_val_score call.
2. Network call inside the sandbox
The sandbox is offline by default for most accounts. Code like requests.get("https://api.example.com/...") or yfinance.download(...) does not fail fast — it hangs until the 60s sandbox watchdog kills it. Same for pip install against PyPI on plans where it is restricted.
How to spot it: The cell imports requests, urllib, httpx, yfinance, pandas_datareader, or calls pd.read_csv("http..."). There is no progress output before the timeout.
3. Memory spike kills the container before the timer
Big joins or df.pivot() over a wide table can blow past the ~1-2GB RAM ceiling. The kernel is OOM-killed and ChatGPT surfaces it as a generic timeout, even though no time elapsed.
How to spot it: Cell dies in under 10 seconds with a timeout message. Re-running on a small slice (df.head(1000)) finishes instantly.
4. Pandas chained operations on a wide dataframe
df.apply(lambda row: ...) over 1M+ rows is single-threaded Python. A vectorized version finishes in 2s; the lambda version times out at 60s. The model often writes the lambda version because it is shorter.
How to spot it: .apply(lambda ...) or .iterrows() on a frame with more than ~100k rows.
5. Uploaded file lost between cells, code re-uploads implicitly
Each new conversation turn can spin up a fresh sandbox. If turn 3 references a file uploaded in turn 1, the model may re-read it, re-parse it, and re-build features from scratch on every turn — burning the timeout budget redoing setup. See ChatGPT code execution loses uploaded files between cells for the sandbox-state nuance.
How to spot it: Every cell starts with pd.read_excel("/mnt/data/<file>.xlsx") and a fresh df.head(). Variable state from previous cells is gone.
6. Plot rendering with too many points
matplotlib scatter plots with 500k+ points serialize the figure to PNG on the sandbox CPU. That alone can eat 30s+ before the rest of the cell runs.
How to spot it: Last visible action before timeout is plt.show() or sns.scatterplot. Reducing to 5k points removes the timeout.
Before you start
- Confirm your plan tier — Free has stricter sandbox limits than Plus/Team/Pro/Enterprise.
- Note whether the timeout is deterministic (every time, same cell) or intermittent (sometimes finishes).
- Capture the offending cell’s code before re-prompting; ChatGPT often rewrites the cell on retry and you lose evidence.
- Check the file size of any upload — anything over ~100MB is at risk by default.
Information to collect
- The exact error string (“execution timed out”, “sandbox terminated”, “kernel restarted”).
- Approximate wall-clock time before the timeout fired (use a stopwatch — it tells you OOM vs. wall-clock).
- Dataframe shape (
df.shape) and dtype memory (df.memory_usage(deep=True).sum() / 1e6MB). - Whether the cell does any network I/O or
pip install. - The specific model in use (GPT-5.5, GPT-5.4) — model choice affects how the planner splits work into cells.
Step-by-step fix
Ordered cheapest-first.
Step 1: Force the model to split the job into checkpointed cells
Add to your prompt:
Run this analysis in separate cells, each under 30 seconds:
Cell 1: Load and inspect the file. Print shape and dtypes only.
Cell 2: Clean and downcast. Persist df to /mnt/data/clean.parquet.
Cell 3: Feature engineering. Save features.parquet.
Cell 4: Train and evaluate. Print metrics.
Cell 5: One chart only.
Do not combine cells. Persist intermediate state to /mnt/data/ between cells.This converts one 120s cell into five 20s cells, and intermediate parquet files survive sandbox restarts.
Step 2: Downcast and slice before any heavy step
Before training or pivoting, force the dataframe to a smaller footprint:
import pandas as pd
df = pd.read_csv("/mnt/data/input.csv")
for col in df.select_dtypes("float64"):
df[col] = pd.to_numeric(df[col], downcast="float")
for col in df.select_dtypes("int64"):
df[col] = pd.to_numeric(df[col], downcast="integer")
for col in df.select_dtypes("object"):
if df[col].nunique() / len(df) < 0.5:
df[col] = df[col].astype("category")
print(df.memory_usage(deep=True).sum() / 1e6, "MB")A 1.8GB frame routinely drops to 300MB. The same cell now fits in budget.
Step 3: Replace apply(lambda) with vectorized operations
Ask the model:
Rewrite this cell with no .apply() and no .iterrows(). Use vectorized
pandas / numpy ops only. If a step cannot be vectorized, do it on
df.sample(50_000) and explain the trade-off.This single rewrite often turns a 90-second timeout into a 3-second success.
Step 4: Cap matplotlib point count
For any scatter on a large frame:
sample = df.sample(min(5000, len(df)), random_state=0)
plt.scatter(sample["x"], sample["y"], s=4, alpha=0.4)
plt.show()Or switch to hexbin / 2dhist which aggregates before rendering. Render time goes from 30s to under 1s.
Step 5: Persist intermediate state to /mnt/data/
The sandbox CWD /mnt/data/ survives most sandbox restarts within the same conversation. After each heavy step:
df_clean.to_parquet("/mnt/data/clean.parquet")
features.to_parquet("/mnt/data/features.parquet")Next cell starts with df = pd.read_parquet(...) — sub-second. A timeout in cell 4 no longer wipes cells 1-3.
Step 6: Move network calls out of the sandbox
If the script needs external data, do not ask Code Interpreter to fetch it. Instead:
- Download the data yourself (locally / with curl).
- Upload the file to the chat.
- Tell the model to read from
/mnt/data/<file>only.
For paywalled or auth-required sources, see ChatGPT browsing tool blocked by paywall sites — the same boundary applies to Code Interpreter.
Step 7: If everything fits but still times out, restart the sandbox
In a new turn:
Reset the Python sandbox. Confirm with print("ok") in a fresh cell, then
re-load /mnt/data/clean.parquet.Stale kernel state (hung threads, leaked file handles from earlier turns) is a real source of slowdowns that a fresh kernel fixes instantly.
Verify
- The previously failing cell now completes with output visible inline.
- Total wall-clock for the analysis is under your sandbox budget with headroom (target: each cell under 30s).
- Re-running the same prompt in a new conversation reproduces success — proves it is not a flaky kernel.
- Memory usage after downcast is under 500MB for a dataframe that previously needed 1.5GB+.
Long-term prevention
- For any file over 50MB, start the conversation by asking the model to print
shape,dtypes, and a downcast plan before touching the data. - Keep the rule “one analytical action per cell” in your prompt template. Loads, cleans, joins, models, plots are all separate cells.
- Always persist to parquet after any step that took more than 10 seconds; treat the sandbox as ephemeral.
- For repeated workflows, prefer a local Jupyter / Colab notebook and use ChatGPT only for code generation and review.
- Never let the model write
requests.get(...)inside the sandbox; pre-download or skip the call. - When using Advanced Data Analysis with very large files, split the upload first (
split -n l/4 big.csv) and process chunks separately.
Common pitfalls
- Telling the model “make it faster” without a concrete budget — it produces marginal tweaks instead of restructuring.
- Re-running the same failing prompt 5 times and burning your message cap; see ChatGPT message cap reached for the cost.
- Assuming
pip install <pkg>is free; on some plans it triggers a sandbox refresh that costs 10-20s. - Asking for a chart with 1M raw points “for accuracy” — the eye cannot distinguish 5k points from 1M, but the sandbox can.
- Forgetting that
/mnt/data/is wiped when you start a brand-new conversation. Files survive within a thread, not across threads. - Trusting a “completed successfully” message when no plot or table actually rendered — re-check the output cell.
FAQ
Q: Is the 60-second timeout the same on all plans?
No. Free is the tightest, Plus / Team are looser, Pro and Enterprise have the highest caps but still finite. The sandbox specs are not publicly documented and change frequently; treat any single cell over 30 seconds as risky regardless of tier.
Q: Can I increase the timeout?
Not directly. You restructure: smaller files, downcast, vectorize, split cells, persist parquet. The only knob that helps a lot is moving to a Pro/Enterprise plan, which raises the ceiling but does not remove it.
Q: My cell finishes locally in 3 seconds but times out in ChatGPT — why?
The sandbox has fewer CPU cores than your laptop, no GPU, and slower disk. Operations dominated by single-threaded Python (lambdas, loops) hit a much lower ceiling. Vectorize and the gap usually disappears.
Q: Does Advanced Data Analysis have the same limits as Code Interpreter?
Yes — they are the same feature with different branding across surfaces. The sandbox, packages, and timeout behavior are identical.
Q: My uploaded file vanished mid-conversation. Did the timeout cause it?
No, that is a separate sandbox-restart issue. See ChatGPT file disappears from conversation — re-upload and persist to parquet before any heavy step.
Tags: #ChatGPT #code-interpreter #timeout #python #Troubleshooting