Comments (7)
Would you provide us more information about your environment? Are you building byteps using our dockerfile? If not, what is your gcc version?
This is related to the OpenMP version. We have tested gcc 4.9 and above, which includes OpenMP 4.0. I guess we'll need a flag to detect gcc version and choose omp clause accordingly.
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byteps/common/cpu_reducer.cc:64:26: error: expected '#pragma omp' clause before 'simd'
Mind to share your environment, e.g. OS, compiler version, CUDA version etc.?
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gcc version 4.8.5 20150623 (Red Hat 4.8.5-36) (GCC)
CUDA: NVIDIA_CUDA-9.0
from byteps.
Could you yum install devtoolset-7?
from byteps.
Could you yum install devtoolset-7?
It works, thank you
from byteps.
Okay. Good to know. In general, we recommend starting with our dockerfiles, so that you will have less version issues.
If not, you must have gcc>=4.9.
We may add a compilation flag to disable OpenMP in the future, since it is not always needed by BytePS.
from byteps.
Could you yum install devtoolset-7?
It works, thank you
Good to know. I will close for now. Let us know if you have any other issues!
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Related Issues (20)
- Stuck in the bps.init(). HOT 7
- Is it right to do allreduce immediately for non-zero ranks in bytescheduler? HOT 2
- 啥时候支持sparse模型?
- 有计划支持纯cpu吗?我们worker也用cpu机器的 HOT 2
- benchmark with cross barrier error
- Successfully installed BytePS but cannot import byteps.torch or byteps.tensorflow HOT 2
- Running multiple workers on a single GPU machine
- Release BytePS docker image support for TF2
- 安装报错 HOT 1
- Communication failure in MXNet with BytePS HOT 3
- support for fault tolerance and straggler mitigation
- broadcast and is_initialized api are not supported with pytorch.
- Supported environment
- 安装问题
- Mistakes of Workload calculation HOT 5
- How does the tensorflow scheduler plugin used in the tf_benchmark_cnn.py HOT 1
- segmentation fault while launching the worker HOT 1
- Is there any benchmark comparison with Megatron-LM ?
- 支持的cuda和pytorch版本
- install failed
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