Comments (2)
from kungfu.
I recently had a discussion with my friend in Google. It seems like people are moving to use Group Normalisation or other Normalisation approaches instead of using Batch Norm because Batch Norm is indeed difficult synchronise in a distributed setting. Also, Batch Norm is difficult to be applied in a RL scenario because it out-weight the samples in an online trajectory.
from kungfu.
Related Issues (20)
- bert demo question HOT 4
- panic error HOT 3
- When using the config-server, if you call allgather api, it will block.
- After remove the worker from the cluster, it is better to set the rank id to -1. HOT 3
- Elastic hook can't support training from checkpoint.
- Inconsistency detected by ld.so
- failed to establish connection to the newly runner HOT 5
- the kungfu-job is hang when it scale down HOT 2
- kungfu job is hang in a inconsistent version when i scale down/up mutiple times HOT 14
- Performance drops when TensorFlow experimental XLA JIT is enabled. HOT 6
- [doc] request parameters doc when the -init-version=-1 HOT 5
- Support for share-memory channels? HOT 1
- use a dedicated thread for NCCL operations
- Access to Adaptive Batch Size Policy
- Is Windows supported?
- Error from pytoch demo HOT 1
- code loss HOT 2
- A question about Horovod central coordinator in the paper of KungFu HOT 2
- With PairAveragingOptimizer, is it possible that two workers in different iterations average their models? HOT 1
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from kungfu.