DPSV tensorflow runtime_error ai_generated true

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

Also available as: JSON · Markdown · 中文
80%Fix Rate
83%Confidence
1Evidence
2023-05-12First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow==2.9.0 active — — —
tensorflow==2.11.0 active — — —
tensorflow==2.13.0 active — — —

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.

generic

中文

在异步分布式训练中，参数服务器由于网络延迟或工作节点崩溃未更新到最新模型版本，导致梯度应用时版本不匹配。

Official Documentation

https://www.tensorflow.org/guide/distributed_training

Workarounds

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

中文步骤

  1. 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.

Dead Ends

Common approaches that don't work:

  1. 85% fail

    Increasing the number of workers amplifies the synchronization issue because more workers compete for updates.

  2. 90% fail

    Using a larger batch size does not address the version staleness; it only changes gradient accumulation.