# FailedPreconditionError: Parameter server at /job:ps/replica:0/task:0 is stale. Expected model version 5 but got 4. [Op:ApplyGradientDescent]

- **ID:** `tensorflow/distributed-training-stale-parameter-server`
- **Domain:** tensorflow
- **Category:** runtime_error
- **Error Code:** `DPSV`
- **Verification:** ai_generated
- **Fix Rate:** 80%

## Root Cause

In asynchronous distributed training, a parameter server has not updated to the latest model version due to network delays or worker crashes, causing a version mismatch during gradient application.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| tensorflow==2.9.0 | active | — | — |
| tensorflow==2.11.0 | active | — | — |
| tensorflow==2.13.0 | active | — | — |

## Workarounds

1. **Switch to synchronous training using `tf.distribute.MultiWorkerMirroredStrategy` to ensure all parameter servers are consistent before applying gradients.** (90% success)
   ```
   Switch to synchronous training using `tf.distribute.MultiWorkerMirroredStrategy` to ensure all parameter servers are consistent before applying gradients.
   ```
2. **Reduce the staleness tolerance by setting `tf.config.optimizer.set_jit(True)` and increasing the timeout for parameter server updates in the cluster configuration.** (70% success)
   ```
   Reduce the staleness tolerance by setting `tf.config.optimizer.set_jit(True)` and increasing the timeout for parameter server updates in the cluster configuration.
   ```

## Dead Ends

- **** — Increasing the number of workers amplifies the synchronization issue because more workers compete for updates. (85% fail)
- **** — Using a larger batch size does not address the version staleness; it only changes gradient accumulation. (90% fail)