提交 6ad374ae 编写于 作者: T typhoonzero

add doc

上级 0ce08a41
......@@ -7,3 +7,4 @@
cluster_quick_start.rst
cluster_howto.rst
train_on_baidu_cloud_cn.md
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid.core as core
import math
import os
import sys
import numpy
import paddle
import paddle.fluid as fluid
BATCH_SIZE = 64
PASS_NUM = 1
def loss_net(hidden, label):
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
acc = fluid.layers.accuracy(input=prediction, label=label)
return prediction, avg_loss, acc
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
return loss_net(conv_pool_2, label)
def train(use_cuda, role, endpoints, current_endpoint, trainer_id, trainers):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
prediction, avg_loss, acc = conv_net(img, label)
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
t = fluid.DistributeTranspiler()
t.transpile(trainer_id, pservers=endpoints, trainers=trainers)
if role == "pserver":
prog = t.get_pserver_program(current_endpoint)
startup = t.get_startup_program(current_endpoint, pserver_program=prog)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(startup)
exe.run(prog)
elif role == "trainer":
prog = t.get_trainer_program()
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
acc_np, avg_loss_np = exe.run(prog,
feed=feeder.feed(data),
fetch_list=[acc, avg_loss])
if (batch_id + 1) % 10 == 0:
print(
'PassID {0:1}, BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_np.mean()), float(acc_np.mean())))
if __name__ == '__main__':
if len(sys.argv) != 6:
print("Usage: python %s role endpoints current_endpoint trainer_id trainers" % sys.argv[0])
exit(0)
role, endpoints, current_endpoint, trainer_id, trainers = \
sys.argv[1:]
train(True, role, endpoints, current_endpoint, int(trainer_id), int(trainers))
# 在百度云上启动Fluid分布式训练
PaddlePaddle Fluid分布式训练,可以不依赖集群系统(比如MPI,Kubernetes)也可以启动分布式训练。
本章节将会以[百度云](https://cloud.baidu.com/)为实例,说明如何在云端环境,甚至云端GPU环境启动
大规模分布式任务。
### 创建集群模板
登录到百度云控制台,选择BCC服务,点击“创建实例”。选择地域,注意,只有一些地域有GPU服务器可选,选择合适
的地域之后,并选择对应型号然后创建一个空的服务器,如下图:
<img src="src/create_gpu_machine.png" width="500">
* 在操作系统选项中,可以根据需要选择对应的版本,注意根据实际情况选择CUDA版本,这里我们选择CUDA-9.2。
* 示例中选择机器付费方式为后付费,表示随着机器的释放,收费也会对应停止,对运行一次性任务会比较划算。
在机器创建成功之后,执行下面的命令安装paddlepaddle GPU版本和相关依赖。
```bash
apt-get update && apt-get install -y python python-pip python-opencv
# 注:百度云cuda-9.2镜像默认没有安装cudnn和nccl2,需要手动安装,如果自行安装,需要从官网下载
wget -q "http://paddle-train-on-cloud.cdn.bcebos.com/libcudnn7_7.2.1.38-1+cuda9.2_amd64.deb"
wget -q "http://paddle-train-on-cloud.cdn.bcebos.com/nccl_2.2.13-1+cuda9.0_x86_64.txz"
dpkg -i libcudnn7_7.2.1.38-1+cuda9.2_amd64.deb
ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so
unxz nccl_2.2.13-1+cuda9.0_x86_64.txz
tar xf nccl_2.2.13-1+cuda9.0_x86_64.tar
cp -r nccl_2.2.13-1+cuda9.0_x86_64/lib/* /usr/lib
# 注:可以选择是否使用下面的pip镜像加速下载
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple matplotlib==2.2.3
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple paddlepaddle-gpu==0.15.0.post97
```
完成安装后,使用下面的测试程序,测试当前机器是否可以正确运行GPU训练程序,如果遇到报错,请根据报错提示修复
运行环境问题。为了方便启动GPU集群,测试程序执行成功之后,选择当前服务器,然后选择“创建自定义镜像”,后续
创建GPU集群时即可选择配置好的镜像。
<img src="src/create_image.png" width="500">
* 测试程序:
```python
from __future__ import print_function
import paddle.fluid.core as core
import math
import os
import sys
import numpy
import paddle
import paddle.fluid as fluid
BATCH_SIZE = 64
PASS_NUM = 1
def loss_net(hidden, label):
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
acc = fluid.layers.accuracy(input=prediction, label=label)
return prediction, avg_loss, acc
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
return loss_net(conv_pool_2, label)
def train(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
prediction, avg_loss, acc = conv_net(img, label)
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
acc_np, avg_loss_np = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[acc, avg_loss])
if (batch_id + 1) % 10 == 0:
print(
'PassID {0:1}, BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_np.mean()), float(acc_np.mean())))
if __name__ == '__main__':
train(True)
```
### 创建集群
完成创建镜像之后,可以使用这个配置好的镜像创建一个GPU集群,根据您的实际需求创建足够数量的GPU服务器,
作为示例,这里启动2台GPU服务器,包括上一步创建的服务器,所以这里再启动一台新的服务器。
点击“创建实例”,在相同地域选择同样配置的GPU服务器,注意选择刚才创建的镜像作为操作系统。
<img src="src/create_more_nodes.png" width="500">
### 编写集群任务启动脚本
为了方便在更多的GPU服务器上启动分布式训练任务,我们将使用[fabric](http://www.fabfile.org/)
作为集群任务启动管理工具,您可以选择其他熟悉的集群框架,比如MPI, Kubernetes,本示例演示的方法
仅针对简单集群环境,而且服务器之间可以互相ssh登录。
安装fabric,需要执行:
```bash
pip install fabric
```
假设我们创建了2台GPU服务器,ip分别是`172.16.0.5,172.16.0.6`,然后在第一台服务器上,
先创建训练程序文件`dist_train_demo.py`,从[这里](./src/dist_train_demo.py)下载代码。
然后编写`fabfile.py`脚本,用于控制在不同服务器上启动训练任务的parameter server和trainer:
```python
from fabric import Group, task
endpoints = "172.16.0.5:6173,172.16.0.6:6173"
port = "6173"
pservers = 2
trainers = 2
hosts = []
eps = []
for ep in endpoints.split(","):
eps.append(ep)
hosts.append(ep.split(":")[0])
def start_server(c):
current_endpoint = "%s:%s" % (c.host, port)
trainer_id = hosts.index(c.host)
cmd = "python /root/work/dist_train_demo.py pserver %s %s %d %d &> /root/work/server.log.%s &" % (
endpoints, current_endpoint, trainer_id, trainers, c.host)
c.run(cmd)
def start_trainer(c):
current_endpoint = "%s:%s" % (c.host, port)
trainer_id = hosts.index(c.host)
cmd = "python /root/work/dist_train_demo.py trainer %s %s %d %d &> /root/work/trainer.log.%s &" % (
endpoints, current_endpoint, trainer_id, trainers, c.host)
c.run(cmd)
@task
def start(c):
c.connect_kwargs.password = "work@paddle123"
c.run("mkdir -p /root/work")
c.put("dist_train_demo.py", "/root/work")
start_server(c)
start_trainer(c)
@task
def tail_log(c):
c.connect_kwargs.password = "work@paddle123"
c.run("tail /root/work/trainer.log.%s" % c.host)
```
保存上述代码到`fabfile.py`之后,执行
```bash
fab -H 172.16.0.5,172.16.0.6 start
```
就可以开始一个分布式训练任务。这个任务会在两台GPU服务器分别启动2个pserver进程和2个trainer进程开始训练。
### 获取分布式训练结果
示例任务会在`/root/work`下记录日志,分别为`pserver.log.[IP]``trainer.log.[IP]`的形式,可以手动在
服务器上查看这些日志文件观察结果,也可以使用fabric获取所有节点的日志信息,比如:
```bash
fab -H 172.16.0.5,172.16.0.6 tail-log
```
### 关闭集群
任务执行完成后,不要忘记释放掉GPU集群资源,勾选选择需要释放的服务器,选择“释放”,则会关闭机器并释放资源。
如果需要执行新的任务,可以直接使用之前保存的镜像,启动新的集群,并参照前面的步骤开始训练。
<img src="src/release.png" width="500">
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