Arrow IPC Format Error
Apache Arrow IPC errors occur when serialized Arrow data is corrupted or has version incompatibilities.
Apache Arrow IPC errors occur when serialized Arrow data is corrupted or has version incompatibilities.
What this failure is
Arrow IPC Format Error is a Data Pipeline failure seen during ML training runs. Apache Arrow IPC errors occur when serialized Arrow data is corrupted or has version incompatibilities. Common tags: Arrow, Ipc, Pyarrow, Data Pipeline.
Is this what broke your run? Paste your log.
You're reading about Arrow IPC Format Error. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.
Want 14 days on the Scale plan?
Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.
Why it happens (the mechanism)
Arrow file is corrupted. PyArrow version mismatch between save and load. Schema mismatch between writer and reader. Incompatible Arrow IPC format version. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- Arrow IPC deserialization fails
- Data loading fails with Arrow error
- PyArrow version mismatch causes Arrow errors
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| pyarrow.lib.ArrowInvalid: | Arrow file is corrupted |
| OSError: Arrow error: | PyArrow version mismatch between save and load |
| Arrow I/O error: not a valid Arrow file | Schema mismatch between writer and reader |
Which systems are affected
- Training with PyArrow datasets
- HuggingFace datasets with Arrow backend
- Apache Arrow data pipeline
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: pyarrow.lib.ArrowInvalid:
- ✓Verified signal present: OSError: Arrow error:
- ✓Verified signal present: Arrow I/O error: not a valid Arrow file
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.
Sign up free. Unlock the full analysisNo credit card. Daily allowance follows verified trust tier. Instant access.
Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionRelated failures to investigate next
Root cause
- Arrow file is corrupted
- PyArrow version mismatch between save and load
- Schema mismatch between writer and reader
- Incompatible Arrow IPC format version
The fix and how to prevent it
Evaluate Denpex on your own logs
Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.
Don't just read the fix, diagnose your run
The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.