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+# Distributed Training with NCCL2 and RDMA
+
+When doing distributed multi-GPU training, network bandwith often becomes the
+bottle neck. We introduce a way to use NCCL2 to do such training job to
+achieve best performace.
+
+## Prepare Hardwares with RDMA and Multiple GPUs
+
+I'm using two Linux servers each of them is installed with 8 GPUs and
+one 100Gb RDMA card.
+Base environment is:
+
+* OS: CentOS 7.4
+* RDMA device: "Mellanox Technologies MT27700 Family [ConnectX-4]"
+* Kernel version: `4.4.88-1.el7.elrepo.x86_64`
+* Docker version: `1.12.6`
+* Docker storage driver: `overlay2`
+* IP addresses: 192.168.16.30,192.168.16.34
+
+In general, the steps including:
+
+1. Install GPU drivers
+1. Install RDMA drivers
+1. Install "InfiniBand Support"
+1. Use docker to run tests and make sure GPUs and RDMA can work inside
+   the container.
+
+I'll ommit section "Install GPU drivers" because we can find it easily
+somewhere else.
+
+### Install RDMA drivers
+
+For my case, I've got two machines with device
+"Mellanox Technologies MT27700 Family [ConnectX-4]" installed. The OS was
+"CentOS 7.4" and I updated the kernel to version 4.4 so that docker can
+work with latest overlay2 filesystem.
+
+***NOTE: before you start, make sure you have a way to get a console
+of the server other than ssh because we may need to re-configure the
+network device.***
+
+1. Go to http://www.mellanox.com/page/products_dyn?product_family=26,
+   download `MLNX_OFED` software in the bottom of the page, and upload it
+   onto the server.
+1. Run `./mlnxofedinstall --add-kernel-support` in the software package.
+1. Run `/etc/init.d/openibd restart` to make everything work, note that
+   this operation may cause the network goes down if you are using this
+   RDMA device as default network device and use ssh to login the server.
+1. Re-configure the network interface, for example:
+   `ifconfig eth2 192.168.16.30/20 up`, then add routes if needed:
+   `ip route add default via 192.168.16.1 dev eth2`.
+1. Do the same thing on the other node.
+1. Use `ping` to test if the two nodes have typical ICMP connection.
+1. Use either `udaddy` or `ib_write_bw` to test the network connection is
+   ready and have the desired bandwith.
+
+### Prepare Docker Image to Run RDMA Programs
+
+1. Build a docker image using cuda base image like: `nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04` and install paddlepaddle whl
+   package in it.
+1. Start a docker container and mount GPU driver libs into it (you can
+   skip this step if you are using nvidia-docker).
+1. Mount RDMA dirvers and libs into the docker image (see below section),
+   also `udaddy` and `ib_write_bw` if needed.
+1. Mount GPU devices and RDMA devices into the container using `--device`
+   or just use privileged mode `--privileged`.
+1. Start the container using host network mode: `--net=host`
+
+### RDMA Library Files Needed
+
+Usually, `MLNX_OFED` install latest supported libs under
+`/usr/lib64/mlnx_ofed/valgrind`. Other libs also needed to run RDMA programs
+is listed below. These libs must be mounted into the docker container.
+
+* Libs under `/usr/lib64/mlnx_ofed/valgrind`
+  * libibcm.so
+  * libibverbs.so
+  * libmlx4.so
+  * libmlx5.so
+  * libmlx5-rdmav2.so
+  * librdmacm.so
+* Other libs:
+  * libnl-3.so.200
+  * libnl-route-3.so.200
+  * libnuma.so.1
+
+## Start to Run the Training Job
+
+Setting NCCL environment variables to turn NCCL switches on and off:
+
+
+| Env Name | Description |
+| --- | --- |
+| NCCL_SOCKET_IFNAME | The RDMA device, e.g. eth2 |
+| NCCL_P2P_DISABLE | Set to 1 to disable P2P transfer between GPUs |
+| NCCL_IB_DISABLE | Set to 1 to disable using RDMA |
+| NCCL_IB_CUDA_SUPPORT | Set to 1 to enable GPU Direct if supported |
+| NCCL_DEBUG | Set debug level: VERSION, WARN, INFO |
+
+My two servers are: `192.168.16.30,192.168.16.34`, On node 1, Run :
+
+```bash
+PADDLE_TRAINER_ID=0 PADDLE_PORT=48372 PADDLE_WORKERS=192.168.16.30,192.168.16.34 POD_IP=192.168.16.30 stdbuf -oL python vgg16.py
+```
+
+On node 2, Run:
+
+```bash
+PADDLE_TRAINER_ID=1 PADDLE_PORT=48372 PADDLE_WORKERS=192.168.16.30,192.168.16.34 POD_IP=192.168.16.34 stdbuf -oL python vgg16.py
+```