fix typo (#12896)

doc/fluid/design/dist_train/dist_train_nccl2.md

 # Distributed Training with NCCL2
 
 We design a pattern that can enable training with `ParallelExecutor` and
-using [NCCL2](https://developer.nvidia.com/nccl) as it's collective
+use [NCCL2](https://developer.nvidia.com/nccl) as it's collective
 communication library.
 
 In `ParallelExecutor` we can use `AllReduce` or `Reduce` and `Broadcast`
@@ -9,14 +9,14 @@ to do multi GPU training. And if we initialize NCCL2 communicators as
 ranks in a distributed environment, we can simply run the `ParallelExecutor`
 as a distributed program! The only thing that may be different than in
 the single node version is that we need to broadcast the NCCL unique ID
-to all the nodes, and initialize communicators using that ID, so NCCL2
-will know each other as ranks.
+to all the nodes and initialize communicators using that ID, so NCCL2
+can know each other as ranks.
 
 To achieve this feature, we introduce a new operator: `gen_nccl_id` op,
 so we are ***not*** "bind to" running NCCL2 with MPI, we can run it in
-what ever platform you like.
+whatever platform you like.
 
-It have two running modes:
+It has two running modes:
 
 1. Generate and broadcast mode, which should be used on trainer 0;
 1. Listen and fetch mode, which should be used on trainers other than 0.
@@ -29,7 +29,7 @@ initialize NCCL communicator objects.
 <img src="src/ncc2_design.png">
 
 The above figure indicates the general process when training with NCCL2
-distributed. Each trainer have the number of communicators equal to the
+distributed. Each trainer has the number of communicators equal to the
 number of GPUs, but the ranks should match the global ranks number: here
 we have total 8 GPUs, so `nranks==8`, for each trainer, the ranks should
 be from 0 ~ 3 on trainer 0 and 4 ~ 7 on trainer 1.

doc/fluid/howto/cluster/nccl2_rdma_training.md

 # 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.
+When doing distributed multi-GPU training, network bandwidth often becomes the
+bottleneck. We introduce a way to use NCCL2 to do such training job to
+achieve best performance.
 
-## Prepare Hardwares with RDMA and Multiple GPUs
+## Prepare Hardware with RDMA and Multiple GPUs
 
-I'm using two Linux servers each of them is installed with 8 GPUs and
+I'm using two Linux servers each of them installed with 8 GPUs and
 one 100Gb RDMA card.
 Base environment is:
 
@@ -25,7 +25,7 @@ In general, the steps including:
 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
+I'll omit the section "Install GPU drivers" because we can find it easily
 somewhere else.
 
 ### Install RDMA drivers
@@ -33,7 +33,7 @@ somewhere else.
 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.
+work with the 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
@@ -45,14 +45,14 @@ network device.***
 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.
+   RDMA device as default network device and use ssh to log in 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.
+   ready and have the desired bandwidth.
 
 ### Prepare Docker Image to Run RDMA Programs
 
@@ -60,7 +60,7 @@ network device.***
    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),
+1. Mount RDMA drivers 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`.