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d82453fb
编写于
8月 23, 2018
作者:
G
gongweibao
提交者:
GitHub
8月 23, 2018
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电子邮件补丁
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fix typo (#12896)
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doc/fluid/design/dist_train/dist_train_nccl2.md
doc/fluid/design/dist_train/dist_train_nccl2.md
+6
-6
doc/fluid/howto/cluster/nccl2_rdma_training.md
doc/fluid/howto/cluster/nccl2_rdma_training.md
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doc/fluid/design/dist_train/dist_train_nccl2.md
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d82453fb
# Distributed Training with NCCL2
We design a pattern that can enable training with
`ParallelExecutor`
and
us
ing
[
NCCL2
](
https://developer.nvidia.com/nccl
)
as it's collective
us
e
[
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 ha
ve
two running modes:
It ha
s
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 ha
ve
the number of communicators equal to the
distributed. Each trainer ha
s
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
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d82453fb
# 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 bandwi
d
th often becomes the
bottleneck. We introduce a way to use NCCL2 to do such training job to
achieve best performa
n
ce.
## Prepare Hardware
s
with RDMA and Multiple GPUs
## Prepare Hardware with RDMA and Multiple GPUs
I'm using two Linux servers each of them i
s i
nstalled 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 om
mit
section "Install GPU drivers" because we can find it easily
I'll om
it 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 bandwi
d
th.
### 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 d
ir
vers and libs into the docker image (see below section),
1.
Mount RDMA d
ri
vers 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`
.
...
...
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