add doc

doc/fluid/user_guides/howto/training/multi_node.rst

@@ -7,3 +7,4 @@
 
    cluster_quick_start.rst
    cluster_howto.rst
+   train_on_baidu_cloud_cn.md

doc/fluid/user_guides/howto/training/src/dist_train_demo.py

+#   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))
+

doc/fluid/user_guides/howto/training/train_on_baidu_cloud_cn.md

+# 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]`和`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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