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93 lines
2.2 KiB
Markdown
93 lines
2.2 KiB
Markdown
## [中文版本请点击这里](./README_CN.md)
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## Train
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#### Requirements
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python==3.6
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cuda==10.1
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cudnn==765
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mxnet-cu101==1.6.0.post0
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pip install easydict mxboard opencv-python tqdm
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[nccl](https://docs.nvidia.com/deeplearning/nccl/install-guide/index.html)
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[openmpi](mxnet/setup-utils/install-mpi.sh)==4.0.0
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[horovod](mxnet/setup-utils/install-horovod.sh)==0.19.2
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#### Failures due to SSH issues
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The host where horovodrun is executed must be able to SSH to all other hosts without any prompts.
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#### Run with horovodrun
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Typically one GPU will be allocated per process, so if a server has 8 GPUs, you will run 8 processes.
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In horovodrun, the number of processes is specified with the -np flag.
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To run on a machine with 8 GPUs:
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```shell script
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horovodrun -np 8 -H localhost:8 bash config.sh
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```
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To run on two machine with 16 GPUs:
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```shell script
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horovodrun -np 16 -H ip1:8,ip2:8 bash config.sh
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```
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#### Run with mpi
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```shell script
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bash run.sh
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```
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## Troubleshooting
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### Horovod installed successfully?
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Run `horovodrun --check` to check the installation of horovod.
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```shell script
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# Horovod v0.19.2:
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#
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# Available Frameworks:
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# [ ] TensorFlow
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# [X] PyTorch
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# [X] MXNet
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#
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# Available Controllers:
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# [X] MPI
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# [X] Gloo
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#
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# Available Tensor Operations:
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# [X] NCCL
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# [ ] DDL
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# [ ] CCL
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# [X] MPI
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# [X] Gloo
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```
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### Mxnet Version!
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Some versions of mxnet with horovod have bug.
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It is recommended to try version **1.5 or 1.6**.
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**The community has found that mxnet1.5.1 cannot install horovod.**
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### Check CUDA version!
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```shell script
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# Make sure your cuda version is same as mxnet, such as mxnet-cu101 (CUDA 10.1)
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/usr/local/cuda/bin/nvcc -V
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# nvcc: NVIDIA (R) Cuda compiler driver
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# Copyright (c) 2005-2019 NVIDIA Corporation
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# Built on Wed_Apr_24_19:10:27_PDT_2019
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# Cuda compilation tools, release 10.1, V10.1.168
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```
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### Block IO
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You can turn on the debug mode to check whether your slow training speed is the cause of IO.
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### Training Speed.
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If you find that your training speed is the io bottleneck, you can mount dataset to RAM,
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using the following command.
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```shell script
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# If your RAM has 256G
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sudo mkdir /train_tmp
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mount -t tmpfs -o size=140G tmpfs /train_tmp
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```
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