Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_compare_op

.copyright.hook

@@ -9,7 +9,7 @@ import subprocess
 import platform
 
 COPYRIGHT = '''
-  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+  Copyright (c) 2016 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.

CMakeLists.txt

@@ -39,7 +39,7 @@ option(WITH_GPU         "Compile PaddlePaddle with NVIDIA GPU"          ${CUDA_F
 option(WITH_AVX         "Compile PaddlePaddle with AVX intrinsics"      ${AVX_FOUND})
 option(WITH_MKL         "Compile PaddlePaddle with MKL support."        ${AVX_FOUND})
 option(WITH_DSO         "Compile PaddlePaddle with dynamic linked CUDA" ON)
-option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        ON)
+option(WITH_TESTING     "Compile PaddlePaddle with unit testing"        OFF)
 option(WITH_SWIG_PY     "Compile PaddlePaddle with inference api"       ON)
 option(WITH_STYLE_CHECK "Compile PaddlePaddle with style check"         ON)
 option(WITH_PYTHON      "Compile PaddlePaddle with python interpreter"  ON)
@@ -137,7 +137,7 @@ include(external/openblas)  # download, build, install openblas
 include(external/mkldnn)    # download, build, install mkldnn
 include(external/swig)      # download, build, install swig
 include(external/warpctc)   # download, build, install warpctc
-include(external/boost)     # download, build, install boost
+include(external/boost)     # download boost
 include(external/any)       # download libn::any
 include(external/eigen)     # download eigen3
 include(external/pybind11)  # download pybind11
@@ -156,6 +156,7 @@ include(rdma)               # set rdma libraries
 include(flags)              # set paddle compile flags
 include(version)            # set PADDLE_VERSION
 include(coveralls)          # set code coverage
+include(inference_lib)      # add paddle fluid inference libraries
 
 
 include_directories("${PADDLE_SOURCE_DIR}")

benchmark/cluster/vgg16/Dockerfile

+#FROM python:2.7.14
+FROM nvidia/cuda:8.0-cudnn5-runtime-ubuntu16.04
+RUN apt-get update && apt-get install -y python
+RUN pip install -U kubernetes opencv-python &&   apt-get update -y &&   apt-get install -y iputils-ping libgtk2.0-dev
+# NOTE: By default CI built wheel packages turn WITH_DISTRIBUTE=OFF,
+#       so we must build one with distribute support to install in this image.
+RUN pip install paddlepaddle
+RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()" | python'
+RUN pip uninstall -y paddlepaddle
+
+# below lines may change a lot for debugging
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
+ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/k8s_tools.py /root
+ADD *.whl /
+RUN pip install /*.whl && rm -f /*.whl && \
+chmod +x /usr/bin/paddle_k8s
+ENV LD_LIBRARY_PATH=/usr/local/lib
+ADD vgg16_fluid.py vgg16_v2.py /workspace/

benchmark/cluster/vgg16/README.md

+# Performance for Distributed vgg16
+
+## Test Result
+
+### Hardware Infomation
+
+- CPU: Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
+- cpu MHz		: 2101.000
+- cache size	: 20480 KB
+
+### Single Node Single Thread
+
+- PServer Count: 10
+- Trainer Count: 20
+- Metrics: samples / sec
+
+| Batch Size | 32 | 64 | 128 | 256 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | 15.44 | 16.32 | 16.74 | 16.79 |
+| PaddlePaddle v2 | 15.97 | 17.04 | 17.60 | 17.83 |
+| TensorFlow | - | - | - | - |
+
+### Different Batch Size
+
+- PServer Count: 10
+- Trainer Count: 20
+- Per trainer CPU Core: 1
+- Metrics: samples / sec
+
+| Batch Size | 32 | 64 | 128 | 256 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | 190.20 | 222.15 | 247.40 | 258.18 |
+| PaddlePaddle v2 | 170.96 | 233.71 | 256.14 | 329.23 |
+| TensorFlow | - | - | - | - |
+
+
+### Accelerate Rate
+
+- Pserver Count: 20
+- Batch Size: 128
+- Metrics: samples / sec
+
+| Trainer Count | 20 | 40 | 80 | 100 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid | 263.29 (78.64%) | 518.80 (77.47%) | 836.26 (62.44%) | 1019.29 (60.89%) |
+| PaddlePaddle v2 (need more tests) | 326.85 (92.85%) | 534.58 (75.93%) | 853.30 (60.60%) | 1041.99 (59.20%) |
+| TensorFlow | - | - | - | - |
+
+### Different Pserver Count
+
+- Trainer Count: 60
+- Batch Size: 128
+- Metrics: samples/ sec
+
+| PServer Count | 3 | 6 |10 | 20 |
+| -- | -- | -- | -- | -- |
+| PaddlePaddle Fluid(should fix in next PR) | 589.1 | 592.6 | 656.4 | 655.8 |
+| PaddlePaddle v2 | 593.4 | 791.3 | 729.7 | 821.7 |
+| TensorFlow | - | - | - | - |
+
+*The performance gap between Fuild and v2 comes from the network interference.*
+
+
+## Steps to Run the Performance Test
+
+1. You must re-compile PaddlePaddle and enable `-DWITH_DISTRIBUTE` to build PaddlePaddle with distributed support.
+1. When the build finishes, copy the output `whl` package located under `build/python/dist` to current directory.
+1. Run `docker build -t [image:tag] .` to build the docker image and run `docker push [image:tag]` to push the image to reponsitory so kubernetes can find it.
+1. Run `kubectl create -f pserver.yaml && kubectl create -f trainer.yaml` to start the job on your kubernetes cluster (you must configure the `kubectl` client before this step).
+1. Run `kubectl get po` to get running pods, and run `kubectl logs [podID]` to fetch the pod log of pservers and trainers.
+
+Check the logs for the distributed training progress and analyze the performance.
+
+## Enable Verbos Logs
+
+Edit `pserver.yaml` and `trainer.yaml` and add an environment variable `GLOG_v=3` and `GLOG_logtostderr=1` to see what happend in detail.

benchmark/cluster/vgg16/fluid_pserver.yaml

+apiVersion: extensions/v1beta1
+kind: ReplicaSet
+metadata:
+  name: vgg16job-pserver
+spec:
+  replicas: 10
+  template:
+    metadata:
+      labels:
+        paddle-job-pserver: vgg16job
+    spec:
+      hostNetwork: true
+      imagePullSecrets:
+      - name: job-registry-secret
+      containers:
+      - name: pserver
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
+        imagePullPolicy: Always
+        ports:
+        - name: jobport-30236
+          containerPort: 30236
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: MKL_NUM_THREADS
+          value: "1"
+        - name: TRAINING_ROLE
+          value: "PSERVER"
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        - name: POD_IP
+          valueFrom:
+            fieldRef:
+              fieldPath: "status.podIP"
+        command: ["paddle_k8s", "start_fluid"]
+        resources:
+          requests:
+            memory: 10Gi
+            cpu: 4
+          limits:
+            memory: 10Gi
+            cpu: 4

benchmark/cluster/vgg16/fluid_trainer.yaml

+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: vgg16job-trainer
+spec:
+  parallelism: 20
+  completions: 20
+  template:
+    metadata:
+      labels:
+        paddle-job: vgg16job
+    spec:
+      imagePullSecrets:
+        - name: job-registry-secret
+      hostNetwork: true
+      containers:
+      - name: trainer
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
+        imagePullPolicy: Always
+        command: ["paddle_k8s", "start_fluid"]
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16job
+        - name: TRAINING_ROLE
+          value: "TRAINER"
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "MKL_NUM_THREADS=1 python /workspace/vgg16_fluid.py --local 0 --batch_size 128"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        - name: POD_IP
+          valueFrom:
+            fieldRef:
+              fieldPath: "status.podIP"
+        resources:
+          requests:
+            memory: 40Gi
+            cpu: 2
+          limits:
+            memory: 40Gi
+            cpu: 2
+      restartPolicy: Never

benchmark/cluster/vgg16/v2_pserver.yaml

+apiVersion: extensions/v1beta1
+kind: ReplicaSet
+metadata:
+  name: vgg16v2job-pserver
+spec:
+  replicas: 10
+  template:
+    metadata:
+      labels:
+        paddle-job-pserver: vgg16v2job
+    spec:
+      hostNetwork: true
+      imagePullSecrets:
+      - name: job-registry-secret
+      containers:
+      - name: pserver
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
+        imagePullPolicy: Always
+        ports:
+        - name: jobport-30236
+          containerPort: 30236
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16v2job
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "python train.py"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "1"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        command: ["paddle_k8s", "start_pserver"]
+        resources:
+          requests:
+            memory: 10Gi
+            cpu: 4
+          limits:
+            memory: 10Gi
+            cpu: 4

benchmark/cluster/vgg16/v2_trainer.yaml

+apiVersion: batch/v1
+kind: Job
+metadata:
+  name: vgg16v2job-trainer
+spec:
+  parallelism: 20
+  completions: 20
+  template:
+    metadata:
+      labels:
+        paddle-job: vgg16v2job
+    spec:
+      imagePullSecrets:
+        - name: job-registry-secret
+      hostNetwork: true
+      containers:
+      - name: trainer
+        image: "registry.baidu.com/paddlepaddle/fluid_benchmark:vgg16"
+        imagePullPolicy: Always
+        command: ["paddle_k8s", "start_trainer", "v2"]
+        env:
+        - name: PADDLE_JOB_NAME
+          value: vgg16v2job
+        - name: BATCH_SIZE
+          value: "256"
+        - name: TRAINERS
+          value: "20"
+        - name: PSERVERS
+          value: "10"
+        - name: TOPOLOGY
+          value: ""
+        - name: ENTRY
+          value: "cd /workspace && MKL_NUM_THREADS=1 python /workspace/vgg16_v2.py"
+        - name: TRAINER_PACKAGE
+          value: "/workspace"
+        - name: PADDLE_INIT_PORT
+          value: "30236"
+        - name: PADDLE_INIT_NICS
+          value: "xgbe0"
+        - name: PADDLE_INIT_TRAINER_COUNT
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM
+          value: "1"
+        - name: PADDLE_INIT_PORTS_NUM_FOR_SPARSE
+          value: "1"
+        - name: PADDLE_INIT_NUM_GRADIENT_SERVERS
+          value: "20"
+        - name: PADDLE_INIT_NUM_PASSES
+          value: "2"
+        - name: PADDLE_INIT_USE_GPU
+          value: "0"
+        - name: LD_LIBRARY_PATH
+          value: "/usr/local/lib:/usr/local/nvidia/lib64"
+        - name: NAMESPACE
+          valueFrom:
+            fieldRef:
+              fieldPath: "metadata.namespace"
+        resources:
+          requests:
+            memory: 40Gi
+            cpu: 2
+          limits:
+            memory: 40Gi
+            cpu: 2
+      restartPolicy: Never

benchmark/cluster/vgg16/vgg16_fluid.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+# 
+# 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.
+"""VGG16 benchmark in Fluid"""
+from __future__ import print_function
+
+import sys
+import time
+import numpy as np
+import paddle.v2 as paddle
+import paddle.v2.fluid as fluid
+import paddle.v2.fluid.core as core
+import paddle.v2.fluid.profiler as profiler
+import argparse
+import functools
+import os
+
+
+def str2bool(v):
+    if v.lower() in ('yes', 'true', 't', 'y', '1'):
+        return True
+    elif v.lower() in ('no', 'false', 'f', 'n', '0'):
+        return False
+    else:
+        raise argparse.ArgumentTypeError('Boolean value expected.')
+
+
+parser = argparse.ArgumentParser(description=__doc__)
+parser.add_argument(
+    '--batch_size', type=int, default=128, help="Batch size for training.")
+parser.add_argument(
+    '--learning_rate',
+    type=float,
+    default=1e-3,
+    help="Learning rate for training.")
+parser.add_argument('--num_passes', type=int, default=50, help="No. of passes.")
+parser.add_argument(
+    '--device',
+    type=str,
+    default='CPU',
+    choices=['CPU', 'GPU'],
+    help="The device type.")
+parser.add_argument('--device_id', type=int, default=0, help="The device id.")
+parser.add_argument(
+    '--data_format',
+    type=str,
+    default='NCHW',
+    choices=['NCHW', 'NHWC'],
+    help='The data order, now only support NCHW.')
+parser.add_argument(
+    '--data_set',
+    type=str,
+    default='cifar10',
+    choices=['cifar10', 'flowers'],
+    help='Optional dataset for benchmark.')
+parser.add_argument(
+    '--local',
+    type=str2bool,
+    default=True,
+    help='Whether to run as local mode.')
+args = parser.parse_args()
+
+
+def vgg16_bn_drop(input):
+    def conv_block(input, num_filter, groups, dropouts):
+        return fluid.nets.img_conv_group(
+            input=input,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act='relu',
+            conv_with_batchnorm=True,
+            conv_batchnorm_drop_rate=dropouts,
+            pool_type='max')
+
+    conv1 = conv_block(input, 64, 2, [0.3, 0])
+    conv2 = conv_block(conv1, 128, 2, [0.4, 0])
+    conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0])
+    conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
+    conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
+
+    drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
+    fc1 = fluid.layers.fc(input=drop, size=512, act=None)
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
+    drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
+    fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
+    return fc2
+
+
+def main():
+    if args.data_set == "cifar10":
+        classdim = 10
+        if args.data_format == 'NCHW':
+            data_shape = [3, 32, 32]
+        else:
+            data_shape = [32, 32, 3]
+    else:
+        classdim = 102
+        if args.data_format == 'NCHW':
+            data_shape = [3, 224, 224]
+        else:
+            data_shape = [224, 224, 3]
+
+    # Input data
+    images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
+    label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+
+    # Train program
+    net = vgg16_bn_drop(images)
+    predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
+    cost = fluid.layers.cross_entropy(input=predict, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+
+    # Evaluator
+    accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
+
+    # inference program
+    inference_program = fluid.default_main_program().clone()
+    with fluid.program_guard(inference_program):
+        test_target = accuracy.metrics + accuracy.states
+        inference_program = fluid.io.get_inference_program(test_target)
+
+    # Optimization
+    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+    optimize_ops, params_grads = optimizer.minimize(avg_cost)
+
+    # Initialize executor
+    place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(
+        args.device_id)
+    exe = fluid.Executor(place)
+
+    # test
+    def test(exe):
+        accuracy.reset(exe)
+        for batch_id, data in enumerate(test_reader()):
+            img_data = np.array(map(lambda x: x[0].reshape(data_shape),
+                                    data)).astype("float32")
+            y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+            y_data = y_data.reshape([-1, 1])
+
+            exe.run(inference_program,
+                    feed={"pixel": img_data,
+                          "label": y_data})
+
+        return accuracy.eval(exe)
+
+    def train_loop(exe, trainer_prog):
+        iters = 0
+        ts = time.time()
+        for pass_id in range(args.num_passes):
+            # train
+            start_time = time.time()
+            num_samples = 0
+            accuracy.reset(exe)
+            with profiler.profiler("CPU", 'total') as prof:
+                for batch_id, data in enumerate(train_reader()):
+                    ts = time.time()
+                    img_data = np.array(
+                        map(lambda x: x[0].reshape(data_shape), data)).astype(
+                            "float32")
+                    y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+                    y_data = y_data.reshape([-1, 1])
+
+                    loss, acc = exe.run(
+                        trainer_prog,
+                        feed={"pixel": img_data,
+                              "label": y_data},
+                        fetch_list=[avg_cost] + accuracy.metrics)
+                    iters += 1
+                    num_samples += len(data)
+                    print(
+                        "Pass = %d, Iters = %d, Loss = %f, Accuracy = %f, spent %f"
+                        % (pass_id, iters, loss, acc, time.time() - ts)
+                    )  # The accuracy is the accumulation of batches, but not the current batch.
+
+            pass_elapsed = time.time() - start_time
+            pass_train_acc = accuracy.eval(exe)
+            pass_test_acc = test(exe)
+            print(
+                "Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
+                % (pass_id, num_samples / pass_elapsed, pass_train_acc,
+                   pass_test_acc))
+
+    if args.local:
+        # Parameter initialization
+        exe.run(fluid.default_startup_program())
+
+        # data reader
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                else paddle.dataset.flowers.train(),
+                buf_size=5120),
+            batch_size=args.batch_size)
+        test_reader = paddle.batch(
+            paddle.dataset.cifar.test10()
+            if args.data_set == 'cifar10' else paddle.dataset.flowers.test(),
+            batch_size=args.batch_size)
+        train_loop(exe, fluid.default_main_program())
+    else:
+        pserver_ips = os.getenv("PADDLE_INIT_PSERVERS")  # all pserver endpoints
+        eplist = []
+        for ip in pserver_ips.split(","):
+            eplist.append(':'.join([ip, "6174"]))
+        pserver_endpoints = ",".join(eplist)
+        print("pserver endpoints: ", pserver_endpoints)
+        trainers = int(os.getenv("TRAINERS"))  # total trainer count
+        print("trainers total: ", trainers)
+        current_endpoint = os.getenv(
+            "POD_IP") + ":6174"  # current pserver endpoint
+        training_role = os.getenv(
+            "TRAINING_ROLE",
+            "TRAINER")  # get the training role: trainer/pserver
+        t = fluid.DistributeTranspiler()
+        t.transpile(
+            optimize_ops,
+            params_grads,
+            pservers=pserver_endpoints,
+            trainers=trainers)
+
+        if training_role == "PSERVER":
+            if not current_endpoint:
+                print("need env SERVER_ENDPOINT")
+                exit(1)
+            pserver_prog = t.get_pserver_program(current_endpoint)
+            pserver_startup = t.get_startup_program(current_endpoint,
+                                                    pserver_prog)
+            print("starting server side startup")
+            exe.run(pserver_startup)
+            print("starting parameter server...")
+            exe.run(pserver_prog)
+        elif training_role == "TRAINER":
+            # Parameter initialization
+            exe.run(fluid.default_startup_program())
+
+            # data reader
+            train_reader = paddle.batch(
+                paddle.reader.shuffle(
+                    paddle.dataset.cifar.train10() if args.data_set == 'cifar10'
+                    else paddle.dataset.flowers.train(),
+                    buf_size=5120),
+                batch_size=args.batch_size)
+            test_reader = paddle.batch(
+                paddle.dataset.cifar.test10() if args.data_set == 'cifar10' else
+                paddle.dataset.flowers.test(),
+                batch_size=args.batch_size)
+
+            trainer_prog = t.get_trainer_program()
+            feeder = fluid.DataFeeder(feed_list=[images, label], place=place)
+            # TODO(typhoonzero): change trainer startup program to fetch parameters from pserver
+            exe.run(fluid.default_startup_program())
+            train_loop(exe, trainer_prog)
+        else:
+            print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
+
+
+def print_arguments():
+    print('-----------  Configuration Arguments -----------')
+    for arg, value in sorted(vars(args).iteritems()):
+        print('%s: %s' % (arg, value))
+    print('------------------------------------------------')
+
+
+if __name__ == "__main__":
+    print_arguments()
+    main()

benchmark/cluster/vgg16/vgg16_v2.py

+#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+#
+#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.
+
+import gzip
+
+import paddle.v2.dataset.cifar as cifar
+import paddle.v2 as paddle
+import time
+import os
+
+DATA_DIM = 3 * 32 * 32
+CLASS_DIM = 10
+BATCH_SIZE = os.getenv("BATCH_SIZE")
+if BATCH_SIZE:
+    BATCH_SIZE = int(BATCH_SIZE)
+else:
+    BATCH_SIZE = 128
+print "batch_size", BATCH_SIZE
+NODE_COUNT = int(os.getenv("TRAINERS"))
+ts = 0
+
+
+def vgg(input, nums, class_dim):
+    def conv_block(input, num_filter, groups, num_channels=None):
+        return paddle.networks.img_conv_group(
+            input=input,
+            num_channels=num_channels,
+            pool_size=2,
+            pool_stride=2,
+            conv_num_filter=[num_filter] * groups,
+            conv_filter_size=3,
+            conv_act=paddle.activation.Relu(),
+            pool_type=paddle.pooling.Max())
+
+    assert len(nums) == 5
+    # the channel of input feature is 3
+    conv1 = conv_block(input, 64, nums[0], 3)
+    conv2 = conv_block(conv1, 128, nums[1])
+    conv3 = conv_block(conv2, 256, nums[2])
+    conv4 = conv_block(conv3, 512, nums[3])
+    conv5 = conv_block(conv4, 512, nums[4])
+
+    fc_dim = 512
+    fc1 = paddle.layer.fc(input=conv5,
+                          size=fc_dim,
+                          act=paddle.activation.Relu(),
+                          layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    fc2 = paddle.layer.fc(input=fc1,
+                          size=fc_dim,
+                          act=paddle.activation.Relu(),
+                          layer_attr=paddle.attr.Extra(drop_rate=0.5))
+    out = paddle.layer.fc(input=fc2,
+                          size=class_dim,
+                          act=paddle.activation.Softmax())
+    return out
+
+
+def vgg13(input, class_dim):
+    nums = [2, 2, 2, 2, 2]
+    return vgg(input, nums, class_dim)
+
+
+def vgg16(input, class_dim):
+    nums = [2, 2, 3, 3, 3]
+    return vgg(input, nums, class_dim)
+
+
+def vgg19(input, class_dim):
+    nums = [2, 2, 4, 4, 4]
+    return vgg(input, nums, class_dim)
+
+
+def main():
+    global ts
+    paddle.init(use_gpu=False)
+    image = paddle.layer.data(
+        name="image", type=paddle.data_type.dense_vector(DATA_DIM))
+    lbl = paddle.layer.data(
+        name="label", type=paddle.data_type.integer_value(CLASS_DIM))
+
+    extra_layers = None
+    # NOTE: for v2 distributed training need averaging updates.
+    learning_rate = 1e-3 / NODE_COUNT
+    out = vgg16(image, class_dim=CLASS_DIM)
+    cost = paddle.layer.classification_cost(input=out, label=lbl)
+
+    # Create parameters
+    parameters = paddle.parameters.create(cost)
+
+    # Create optimizer
+    optimizer = paddle.optimizer.Momentum(
+        momentum=0.9,
+        regularization=paddle.optimizer.L2Regularization(rate=0.0005 *
+                                                         BATCH_SIZE),
+        learning_rate=learning_rate / BATCH_SIZE,
+        learning_rate_decay_a=0.1,
+        learning_rate_decay_b=128000 * 35,
+        learning_rate_schedule="discexp", )
+
+    train_reader = paddle.batch(
+        paddle.reader.shuffle(
+            cifar.train10(),
+            # To use other data, replace the above line with:
+            # reader.train_reader('train.list'),
+            buf_size=1000),
+        batch_size=BATCH_SIZE)
+    test_reader = paddle.batch(
+        cifar.test10(),
+        # To use other data, replace the above line with:
+        # reader.test_reader('val.list'),
+        batch_size=BATCH_SIZE)
+
+    # Create trainer
+    trainer = paddle.trainer.SGD(cost=cost,
+                                 parameters=parameters,
+                                 update_equation=optimizer,
+                                 extra_layers=extra_layers,
+                                 is_local=False)
+
+    # End batch and end pass event handler
+    def event_handler(event):
+        global ts, ts_pass
+        if isinstance(event, paddle.event.BeginPass):
+            ts_pass = time.time()
+        if isinstance(event, paddle.event.BeginIteration):
+            ts = time.time()
+        if isinstance(event, paddle.event.EndIteration):
+            if event.batch_id % 1 == 0:
+                print "\nPass %d, Batch %d, Cost %f, %s, spent: %f" % (
+                    event.pass_id, event.batch_id, event.cost, event.metrics,
+                    time.time() - ts)
+        if isinstance(event, paddle.event.EndPass):
+            print "Pass %d end, spent: %f" % (event.pass_id,
+                                              time.time() - ts_pass)
+            result = trainer.test(reader=test_reader)
+            print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
+
+    trainer.train(
+        reader=train_reader, num_passes=200, event_handler=event_handler)
+
+
+if __name__ == '__main__':
+    main()

cmake/external/boost.cmake

@@ -15,12 +15,13 @@
 include(ExternalProject)
 
 set(BOOST_PROJECT       "extern_boost")
-set(BOOST_VER           "1.66.0")
-set(BOOST_TAR           "boost_1_66_0")
-set(BOOST_URL           "https://dl.bintray.com/boostorg/release/${BOOST_VER}/source/${BOOST_TAR}.tar.gz")
+set(BOOST_VER           "1.41.0")
+set(BOOST_TAR           "boost_1_41_0")
+set(BOOST_URL           "http://sourceforge.net/projects/boost/files/boost/${BOOST_VER}/${BOOST_TAR}.tar.gz")
 set(BOOST_SOURCES_DIR ${THIRD_PARTY_PATH}/boost)
 set(BOOST_DOWNLOAD_DIR  "${BOOST_SOURCES_DIR}/src/${BOOST_PROJECT}")
 set(BOOST_INCLUDE_DIR "${BOOST_DOWNLOAD_DIR}/${BOOST_TAR}" CACHE PATH "boost include directory." FORCE)
+set_directory_properties(PROPERTIES CLEAN_NO_CUSTOM 1)
 
 include_directories(${BOOST_INCLUDE_DIR})
 

cmake/external/eigen.cmake

@@ -28,9 +28,3 @@ endif()
 add_dependencies(eigen3 extern_eigen3)
 
 LIST(APPEND external_project_dependencies eigen3)
-
-IF(NOT WITH_C_API AND WITH_FLUID)
-    INSTALL(FILES ${EIGEN_INCLUDE_DIR}/Eigen/Core DESTINATION third_party/eigen3/Eigen)
-    INSTALL(DIRECTORY ${EIGEN_INCLUDE_DIR}/Eigen/src DESTINATION third_party/eigen3/Eigen)
-    INSTALL(DIRECTORY ${EIGEN_INCLUDE_DIR}/unsupported/Eigen DESTINATION third_party/eigen3/unsupported)
-ENDIF()

cmake/external/gflags.cmake

@@ -52,7 +52,7 @@ ADD_DEPENDENCIES(gflags extern_gflags)
 
 LIST(APPEND external_project_dependencies gflags)
 
-IF(WITH_C_API OR WITH_FLUID)
+IF(WITH_C_API)
   INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags)
   IF(ANDROID)
     INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI})

cmake/external/glog.cmake

@@ -68,7 +68,7 @@ LINK_LIBRARIES(glog gflags)
 
 LIST(APPEND external_project_dependencies glog)
 
-IF(WITH_C_API OR WITH_FLUID)
+IF(WITH_C_API)
   INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog)
   IF(ANDROID)
     INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI})

cmake/external/protobuf.cmake

@@ -250,7 +250,7 @@ IF(NOT PROTOBUF_FOUND)
     SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY}
         CACHE FILEPATH "protoc library." FORCE)
 
-    IF(WITH_C_API OR WITH_FLUID)
+    IF(WITH_C_API)
         INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
         IF(ANDROID)
             INSTALL(FILES ${PROTOBUF_LITE_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})

cmake/external/warpctc.cmake

@@ -52,6 +52,7 @@ ExternalProject_Add(
                     -DWITH_TORCH=OFF
                     -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON
                     -DBUILD_SHARED=ON
+                    -DBUILD_TESTS=OFF
                     -DCMAKE_POSITION_INDEPENDENT_CODE=ON
                     -DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
                     ${EXTERNAL_OPTIONAL_ARGS}

cmake/generic.cmake

@@ -179,15 +179,24 @@ function(cc_library TARGET_NAME)
   set(oneValueArgs "")
   set(multiValueArgs SRCS DEPS)
   cmake_parse_arguments(cc_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
-  if (cc_library_SRCS)
-    if (cc_library_SHARED OR cc_library_shared) # build *.so
+  if(cc_library_SRCS)
+    if(cc_library_SHARED OR cc_library_shared) # build *.so
       add_library(${TARGET_NAME} SHARED ${cc_library_SRCS})
     else()
       add_library(${TARGET_NAME} STATIC ${cc_library_SRCS})
     endif()
-    if (cc_library_DEPS)
+    if(cc_library_DEPS)
+      # Don't need link libwarpctc.so
+      if("${cc_library_DEPS};" MATCHES "warpctc;")
+        list(REMOVE_ITEM cc_library_DEPS warpctc)
+        add_dependencies(${TARGET_NAME} warpctc)
+      endif()
+      # Support linking flags: --whole-archive (Linux) / -force_load (MacOS)
+      target_circle_link_libraries(${TARGET_NAME} ${cc_library_DEPS})
+      if("${cc_library_DEPS}" MATCHES "ARCHIVE_START")
+        list(REMOVE_ITEM cc_library_DEPS ARCHIVE_START ARCHIVE_END)
+      endif()
       add_dependencies(${TARGET_NAME} ${cc_library_DEPS})
-      target_link_libraries(${TARGET_NAME} ${cc_library_DEPS})
     endif()
     
     # cpplint code style
@@ -224,12 +233,18 @@ function(cc_test TARGET_NAME)
   if(WITH_TESTING)
     set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS)
+    set(multiValueArgs SRCS DEPS ARGS)
     cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_executable(${TARGET_NAME} ${cc_test_SRCS})
-    target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    # Support linking flags: --whole-archive (Linux) / -force_load (MacOS)
+    target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
+    if("${cc_test_DEPS}" MATCHES "ARCHIVE_START")
+      list(REMOVE_ITEM cc_test_DEPS ARCHIVE_START ARCHIVE_END)
+    endif()
     add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
-    add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME} WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
+    add_test(NAME ${TARGET_NAME}
+             COMMAND ${TARGET_NAME} ${cc_test_ARGS}
+             WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
   endif()
 endfunction(cc_test)
 
@@ -457,12 +472,12 @@ endfunction()
 
 function(py_test TARGET_NAME)
   if(WITH_TESTING)
-    set(options STATIC static SHARED shared)
+    set(options "")
     set(oneValueArgs "")
-    set(multiValueArgs SRCS DEPS ARGS)
+    set(multiValueArgs SRCS DEPS ARGS ENVS)
     cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
     add_test(NAME ${TARGET_NAME}
-             COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python
+             COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python ${py_test_ENVS}
              ${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
              WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
   endif()

cmake/inference_lib.cmake

+# make package for paddle fluid shared and static library
+function(copy TARGET)
+    set(options "")
+    set(oneValueArgs "")
+    set(multiValueArgs SRCS DSTS DEPS)
+    cmake_parse_arguments(copy_lib "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    list(LENGTH copy_lib_SRCS copy_lib_SRCS_len)
+    list(LENGTH copy_lib_DSTS copy_lib_DSTS_len)
+    if(NOT ${copy_lib_SRCS_len} EQUAL ${copy_lib_DSTS_len})
+        message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
+    endif()
+    math(EXPR len "${copy_lib_SRCS_len} - 1")
+    
+    add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
+    foreach(index RANGE ${len})
+        list(GET copy_lib_SRCS ${index} src)
+        list(GET copy_lib_DSTS ${index} dst)
+        add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND mkdir -p "${dst}")
+        if(IS_DIRECTORY ${src})
+            add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp -r "${src}" "${dst}")
+        else()
+            add_custom_command(TARGET ${TARGET} PRE_BUILD COMMAND cp "${src}" "${dst}")
+        endif()
+    endforeach()
+endfunction()
+
+# third party
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/eigen3")
+copy(eigen3_lib
+  SRCS ${EIGEN_INCLUDE_DIR}/Eigen/Core ${EIGEN_INCLUDE_DIR}/Eigen/src ${EIGEN_INCLUDE_DIR}/unsupported/Eigen
+  DSTS ${dst_dir}/Eigen ${dst_dir}/Eigen ${dst_dir}/unsupported
+)
+
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/gflags")
+copy(gflags_lib
+  SRCS ${GFLAGS_INCLUDE_DIR} ${GFLAGS_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
+)
+
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/glog")
+copy(glog_lib
+  SRCS ${GLOG_INCLUDE_DIR} ${GLOG_LIBRARIES}
+  DSTS ${dst_dir} ${dst_dir}/lib
+)
+
+IF(NOT PROTOBUF_FOUND)
+    set(dst_dir "${CMAKE_INSTALL_PREFIX}/third_party/install/protobuf")
+    copy(protobuf_lib
+      SRCS ${PROTOBUF_INCLUDE_DIR} ${PROTOBUF_LITE_LIBRARY}
+      DSTS ${dst_dir} ${dst_dir}/lib
+    )
+ENDIF(NOT PROTOBUF_FOUND)
+
+# paddle fluid module
+set(src_dir "${PADDLE_SOURCE_DIR}/paddle")
+set(dst_dir "${CMAKE_INSTALL_PREFIX}/paddle")
+set(module "framework")
+copy(framework_lib DEPS framework_py_proto 
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/framework/framework.pb.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
+)
+
+set(module "memory")
+copy(memory_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/detail/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/detail
+)
+
+set(module "inference")
+copy(inference_lib DEPENDS paddle_fluid_shared
+  SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/inference/libpaddle_fluid.so
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}
+)
+
+set(module "platform")
+copy(platform_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/dynload ${dst_dir}/${module}/details
+)
+
+set(module "string")
+copy(string_lib
+  SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/tinyformat/*.h
+  DSTS ${dst_dir}/${module} ${dst_dir}/${module}/tinyformat
+)
+
+add_custom_target(inference_lib_dist DEPENDS 
+  inference_lib framework_lib memory_lib platform_lib string_lib
+  gflags_lib glog_lib protobuf_lib eigen3_lib)

doc/CMakeLists.txt

@@ -47,3 +47,5 @@ sphinx_add_target(paddle_docs_cn
                   ${SPHINX_CACHE_DIR_CN}
                   ${CMAKE_CURRENT_SOURCE_DIR}
                   ${SPHINX_HTML_DIR_CN})
+
+add_subdirectory(api)

doc/api/CMakeLists.txt

+# configured documentation tools and intermediate build results
+set(BINARY_BUILD_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_build")
+
+# Sphinx cache with pickled ReST documents
+set(SPHINX_CACHE_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/_doctrees")
+
+# HTML output director
+set(SPHINX_HTML_DIR_EN "${CMAKE_CURRENT_BINARY_DIR}/en/html")
+
+configure_file(
+    "${CMAKE_CURRENT_SOURCE_DIR}/../templates/conf.py.en.in"
+    "${BINARY_BUILD_DIR_EN}/conf.py"
+    @ONLY)
+
+sphinx_add_target(paddle_api_docs
+                  html
+                  ${BINARY_BUILD_DIR_EN}
+                  ${SPHINX_CACHE_DIR_EN}
+                  ${CMAKE_CURRENT_SOURCE_DIR}
+                  ${SPHINX_HTML_DIR_EN})

doc/api/v2/config/layer.rst

@@ -87,6 +87,11 @@ roi_pool
 ..  autoclass:: paddle.v2.layer.roi_pool
     :noindex:
 
+pad
+----
+..  autoclass:: paddle.v2.layer.pad
+    :noindex:
+
 Norm Layer
 ==========
 
@@ -133,6 +138,11 @@ grumemory
 ..  autoclass:: paddle.v2.layer.grumemory
     :noindex:
 
+gated_unit
+-----------
+..  autoclass:: paddle.v2.layer.gated_unit
+    :noindex:
+    
 Recurrent Layer Group
 =====================
 
@@ -340,6 +350,11 @@ bilinear_interp
 ..  autoclass:: paddle.v2.layer.bilinear_interp
     :noindex:
 
+dropout
+--------
+..  autoclass:: paddle.v2.layer.dropout
+    :noindex:
+    
 dot_prod
 ---------
 .. autoclass:: paddle.v2.layer.dot_prod
@@ -402,6 +417,11 @@ scale_shift
 ..  autoclass:: paddle.v2.layer.scale_shift
     :noindex:
 
+factorization_machine
+---------------------
+..  autoclass:: paddle.v2.layer.factorization_machine
+    :noindex:
+
 Sampling Layers
 ===============
 
@@ -420,22 +440,6 @@ multiplex
 ..  autoclass:: paddle.v2.layer.multiplex
     :noindex:
 
-Factorization Machine Layer
-============================
-
-factorization_machine
----------------------
-..  autoclass:: paddle.v2.layer.factorization_machine
-    :noindex:
-
-Slicing and Joining Layers
-==========================
-
-pad
-----
-..  autoclass:: paddle.v2.layer.pad
-    :noindex:
-
 ..  _api_v2.layer_costs:
 
 Cost Layers
@@ -526,6 +530,11 @@ multibox_loss
 ..  autoclass:: paddle.v2.layer.multibox_loss
     :noindex:
 
+detection_output
+----------------
+..  autoclass:: paddle.v2.layer.detection_output
+    :noindex:
+    
 Check Layer
 ============
 
@@ -534,31 +543,10 @@ eos
 ..  autoclass:: paddle.v2.layer.eos
     :noindex:
 
-Miscs
-=====
-
-dropout
---------
-..  autoclass:: paddle.v2.layer.dropout
-    :noindex:
-
-Activation with learnable parameter
-===================================
+Activation
+==========
 
 prelu
 --------
 ..  autoclass:: paddle.v2.layer.prelu
     :noindex:
-
-gated_unit
------------
-..  autoclass:: paddle.v2.layer.gated_unit
-    :noindex:
-
-Detection output Layer
-======================
-
-detection_output
-----------------
-..  autoclass:: paddle.v2.layer.detection_output
-    :noindex:

doc/api/v2/data/dataset.rst

@@ -73,3 +73,10 @@ wmt14
 ..  automodule:: paddle.v2.dataset.wmt14
     :members:
     :noindex:
+
+wmt16
++++++
+
+..  automodule:: paddle.v2.dataset.wmt16
+    :members:
+    :noindex:

doc/api/v2/fluid/data_feeder.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-DataFeeder
+data_feeder
 ===========
 
 DataFeeder
------------
-..  automodule:: paddle.v2.fluid.data_feeder
-    :members: DataFeeder
+----------
+
+..  autoclass:: paddle.v2.fluid.data_feeder.DataFeeder
+    :members:
     :noindex:
+

doc/api/v2/fluid/evaluator.rst

-===========
-Evaluator
-===========
-
-Evaluator
------------
-..  automodule:: paddle.v2.fluid.evaluator
-    :members: Evaluator
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+=========
+evaluator
+=========
+
+Accuracy
+--------
+
+..  autoclass:: paddle.v2.fluid.evaluator.Accuracy
+    :members:
     :noindex:
+
+ChunkEvaluator
+--------------
+
+..  autoclass:: paddle.v2.fluid.evaluator.ChunkEvaluator
+    :members:
+    :noindex:
+

doc/api/v2/fluid/executor.rst

-===========
-Executor
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+========
+executor
+========
 
 Executor
+--------
+
+..  autoclass:: paddle.v2.fluid.executor.Executor
+    :members:
+    :noindex:
+
+global_scope
+------------
+
+..  autofunction:: paddle.v2.fluid.executor.global_scope
+    :noindex:
+
+scope_guard
 -----------
-..  automodule:: paddle.v2.fluid.executor
-    :members: Executor
+
+..  autofunction:: paddle.v2.fluid.executor.scope_guard
+    :noindex:
+
+switch_scope
+------------
+
+..  autofunction:: paddle.v2.fluid.executor.switch_scope
     :noindex:
+

doc/api/v2/fluid/gen_doc.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 argparse
+import sys
+import types
+
+import paddle.v2.fluid as fluid
+
+
+def parse_arg():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--submodules', nargs="*")
+    parser.add_argument(
+        'module', type=str, help='Generate the documentation of which module')
+    return parser.parse_args()
+
+
+class DocGenerator(object):
+    def __init__(self, module_name, stream=sys.stdout):
+        self.stream = stream
+        self.module_name = module_name
+        if not hasattr(fluid, module_name):
+            raise ValueError("Cannot find fluid.{0}".format(module_name))
+        else:
+            self.module = getattr(fluid, module_name)
+        self.stream.write('''..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+''')
+
+        self._print_header_(module_name, dot='=', is_title=True)
+
+    def print_submodule(self, submodule_name):
+        submodule = getattr(self.module, submodule_name)
+        if submodule is None:
+            raise ValueError("Cannot find submodule {0}".format(submodule_name))
+        self.print_section(submodule_name)
+
+        for item in submodule.__all__:
+            self.print_item(item)
+
+    def print_current_module(self):
+        for item in self.module.__all__:
+            self.print_item(item)
+
+    def print_section(self, name):
+        self._print_header_(name, dot='=', is_title=False)
+
+    def print_item(self, name):
+        item = getattr(self.module, name)
+        if isinstance(item, types.TypeType):
+            self.print_class(name)
+        elif isinstance(item, types.FunctionType):
+            self.print_method(name)
+        else:
+            raise RuntimeError("Unsupported item {0}".format(name))
+
+    def print_class(self, name):
+        self._print_header_(name, dot='-', is_title=False)
+        self.stream.write('''..  autoclass:: paddle.v2.fluid.{0}.{1}
+    :members:
+    :noindex:
+
+'''.format(self.module_name, name))
+
+    def print_method(self, name):
+        self._print_header_(name, dot='-', is_title=False)
+        self.stream.write('''..  autofunction:: paddle.v2.fluid.{0}.{1}
+    :noindex:
+
+'''.format(self.module_name, name))
+
+    def _print_header_(self, name, dot, is_title):
+        dot_line = dot * len(name)
+        if is_title:
+            self.stream.write(dot_line)
+            self.stream.write('\n')
+        self.stream.write(name)
+        self.stream.write('\n')
+        self.stream.write(dot_line)
+        self.stream.write('\n')
+        self.stream.write('\n')
+
+
+def main():
+    args = parse_arg()
+    gen = DocGenerator(args.module)
+    if args.submodules is None:
+        gen.print_current_module()
+    else:
+        for submodule_name in args.submodules:
+            gen.print_submodule(submodule_name)
+
+
+if __name__ == '__main__':
+    main()

doc/api/v2/fluid/gen_doc.sh

+#!/bin/bash
+python gen_doc.py layers --submodules control_flow device io nn ops tensor > layers.rst
+
+for module in io data_feeder evaluator executor initializer io nets optimizer param_attr profiler regularizer
+do
+  python gen_doc.py ${module} > ${module}.rst
+done

doc/api/v2/fluid/initializer.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-Initializer
+initializer
 ===========
 
+Constant
+--------
 
-
-Initializer
------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: Initializer
-    :noindex:
-
-
-
-ConstantInitializer
--------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: ConstantInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Constant
+    :members:
     :noindex:
 
+Uniform
+-------
 
-
-UniformInitializer
-------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: UniformInitializer
-    :noindex:
-
-
-
-NormalInitializer
------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: NormalInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Uniform
+    :members:
     :noindex:
 
+Normal
+------
 
-XavierInitializer
------------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: XavierInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Normal
+    :members:
     :noindex:
 
+Xavier
+------
 
-MSRAInitializer
----------------
-..  automodule:: paddle.v2.fluid.initializer
-    :members: MSRAInitializer
+..  autoclass:: paddle.v2.fluid.initializer.Xavier
+    :members:
     :noindex:
 

doc/api/v2/fluid/io.rst

-===========
-IO
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+==
+io
+==
 
+save_vars
+---------
 
-is_parameter
+..  autofunction:: paddle.v2.fluid.io.save_vars
+    :noindex:
+
+save_params
 -----------
-..  autofunction:: paddle.v2.fluid.io.is_parameter
+
+..  autofunction:: paddle.v2.fluid.io.save_params
+    :noindex:
+
+save_persistables
+-----------------
+
+..  autofunction:: paddle.v2.fluid.io.save_persistables
+    :noindex:
+
+load_vars
+---------
+
+..  autofunction:: paddle.v2.fluid.io.load_vars
+    :noindex:
+
+load_params
+-----------
+
+..  autofunction:: paddle.v2.fluid.io.load_params
     :noindex:
+
+load_persistables
+-----------------
+
+..  autofunction:: paddle.v2.fluid.io.load_persistables
+    :noindex:
+
+save_inference_model
+--------------------
+
+..  autofunction:: paddle.v2.fluid.io.save_inference_model
+    :noindex:
+
+load_inference_model
+--------------------
+
+..  autofunction:: paddle.v2.fluid.io.load_inference_model
+    :noindex:
+
+get_inference_program
+---------------------
+
+..  autofunction:: paddle.v2.fluid.io.get_inference_program
+    :noindex:
+

doc/api/v2/fluid/layers.rst

-==========
-Layers
-==========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+======
+layers
+======
 
-fc
----
-..  autofunction:: paddle.v2.fluid.layers.fc
+control_flow
+============
+
+split_lod_tensor
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
     :noindex:
 
-embedding
----------
-..  autofunction:: paddle.v2.fluid.layers.embedding
+merge_lod_tensor
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
     :noindex:
 
-dynamic_lstm
-------------
-..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
+BlockGuard
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.BlockGuard
+    :members:
     :noindex:
 
-dynamic_gru
------------
-..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
+BlockGuardWithCompletion
+------------------------
+
+..  autoclass:: paddle.v2.fluid.layers.BlockGuardWithCompletion
+    :members:
     :noindex:
 
-data
-----
-..  autofunction:: paddle.v2.fluid.layers.data
+StaticRNNMemoryLink
+-------------------
+
+..  autoclass:: paddle.v2.fluid.layers.StaticRNNMemoryLink
+    :members:
     :noindex:
 
-mean
-----
-..  autofunction:: paddle.v2.fluid.layers.mean
+WhileGuard
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.WhileGuard
+    :members:
     :noindex:
 
-mul
----
-..  autofunction:: paddle.v2.fluid.layers.mul
+While
+-----
+
+..  autoclass:: paddle.v2.fluid.layers.While
+    :members:
     :noindex:
 
-elementwise_add
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_add
+lod_rank_table
+--------------
+
+..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
     :noindex:
 
-elementwise_sub
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_sub
+max_sequence_len
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
     :noindex:
 
-elementwise_mul
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_mul
+topk
+----
+
+..  autofunction:: paddle.v2.fluid.layers.topk
     :noindex:
 
-elementwise_div
----------------
-..  autofunction:: paddle.v2.fluid.layers.elementwise_div
+lod_tensor_to_array
+-------------------
+
+..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
     :noindex:
 
+array_to_lod_tensor
+-------------------
 
-dropout
--------
-..  autofunction:: paddle.v2.fluid.layers.dropout
+..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
     :noindex:
 
+increment
+---------
 
-reshape
---------
-..  autofunction:: paddle.v2.fluid.layers.reshape
+..  autofunction:: paddle.v2.fluid.layers.increment
     :noindex:
 
+array_write
+-----------
 
-sigmoid
----------
-..  autofunction:: paddle.v2.fluid.layers.sigmoid
+..  autofunction:: paddle.v2.fluid.layers.array_write
     :noindex:
 
+create_array
+------------
 
-scale
+..  autofunction:: paddle.v2.fluid.layers.create_array
+    :noindex:
+
+less_than
 ---------
-..  autofunction:: paddle.v2.fluid.layers.scale
+
+..  autofunction:: paddle.v2.fluid.layers.less_than
     :noindex:
 
+array_read
+----------
 
-transpose
+..  autofunction:: paddle.v2.fluid.layers.array_read
+    :noindex:
+
+shrink_memory
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.shrink_memory
+    :noindex:
+
+array_length
+------------
+
+..  autofunction:: paddle.v2.fluid.layers.array_length
+    :noindex:
+
+IfElse
+------
+
+..  autoclass:: paddle.v2.fluid.layers.IfElse
+    :members:
+    :noindex:
+
+DynamicRNN
+----------
+
+..  autoclass:: paddle.v2.fluid.layers.DynamicRNN
+    :members:
+    :noindex:
+
+ConditionalBlock
+----------------
+
+..  autoclass:: paddle.v2.fluid.layers.ConditionalBlock
+    :members:
+    :noindex:
+
+StaticRNN
 ---------
-..  autofunction:: paddle.v2.fluid.layers.transpose
+
+..  autoclass:: paddle.v2.fluid.layers.StaticRNN
+    :members:
     :noindex:
 
+reorder_lod_tensor_by_rank
+--------------------------
 
-sigmoid_cross_entropy_with_logits
----------------------------------
-..  autofunction:: paddle.v2.fluid.layers.esigmoid_cross_entropy_with_logits
+..  autofunction:: paddle.v2.fluid.layers.reorder_lod_tensor_by_rank
     :noindex:
 
+ParallelDo
+----------
 
-cast
+..  autoclass:: paddle.v2.fluid.layers.ParallelDo
+    :members:
+    :noindex:
+
+Print
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.Print
+    :noindex:
+
+device
+======
+
+get_places
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.get_places
+    :noindex:
+
+io
+==
+
+data
 ----
-..  autofunction:: paddle.v2.fluid.layers.cast
+
+..  autofunction:: paddle.v2.fluid.layers.data
     :noindex:
 
+BlockGuardServ
+--------------
 
-concat
--------
-..  autofunction:: paddle.v2.fluid.layers.concat
+..  autoclass:: paddle.v2.fluid.layers.BlockGuardServ
+    :members:
     :noindex:
 
+ListenAndServ
+-------------
 
-sums
+..  autoclass:: paddle.v2.fluid.layers.ListenAndServ
+    :members:
+    :noindex:
+
+Send
 ----
-..  autofunction:: paddle.v2.fluid.layers.sums
+
+..  autofunction:: paddle.v2.fluid.layers.Send
     :noindex:
 
+nn
+==
 
-linear_chain_crf
-----------------
-..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
+fc
+--
+
+..  autofunction:: paddle.v2.fluid.layers.fc
     :noindex:
 
+embedding
+---------
 
-assign
--------
 ..  autofunction:: paddle.v2.fluid.layers.embedding
     :noindex:
 
+dynamic_lstm
+------------
 
-split_lod_tensor
-----------------
-..  autofunction:: paddle.v2.fluid.layers.split_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstm
     :noindex:
 
+dynamic_lstmp
+-------------
 
-merge_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.dynamic_lstmp
+    :noindex:
+
+dynamic_gru
+-----------
+
+..  autofunction:: paddle.v2.fluid.layers.dynamic_gru
+    :noindex:
+
+gru_unit
+--------
+
+..  autofunction:: paddle.v2.fluid.layers.gru_unit
+    :noindex:
+
+linear_chain_crf
 ----------------
-..  autofunction:: paddle.v2.fluid.layers.merge_lod_tensor
+
+..  autofunction:: paddle.v2.fluid.layers.linear_chain_crf
+    :noindex:
+
+crf_decoding
+------------
+
+..  autofunction:: paddle.v2.fluid.layers.crf_decoding
     :noindex:
 
 cos_sim
---------
+-------
+
 ..  autofunction:: paddle.v2.fluid.layers.cos_sim
     :noindex:
 
-
 cross_entropy
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.cross_entropy
     :noindex:
 
-
-
 square_error_cost
 -----------------
+
 ..  autofunction:: paddle.v2.fluid.layers.square_error_cost
     :noindex:
 
-
 accuracy
----------
+--------
+
 ..  autofunction:: paddle.v2.fluid.layers.accuracy
     :noindex:
 
+chunk_eval
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.chunk_eval
+    :noindex:
 
 sequence_conv
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.sequence_conv
     :noindex:
 
-
 conv2d
 ------
+
 ..  autofunction:: paddle.v2.fluid.layers.conv2d
     :noindex:
 
-
 sequence_pool
 -------------
+
 ..  autofunction:: paddle.v2.fluid.layers.sequence_pool
     :noindex:
 
+pool2d
+------
 
-sequence_first_step
--------------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+..  autofunction:: paddle.v2.fluid.layers.pool2d
     :noindex:
 
+batch_norm
+----------
 
-sequence_last_step
+..  autofunction:: paddle.v2.fluid.layers.batch_norm
+    :noindex:
+
+layer_norm
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.layer_norm
+    :noindex:
+
+beam_search_decode
 ------------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+
+..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
     :noindex:
 
+conv2d_transpose
+----------------
 
-pool2d
-------
-..  autofunction:: paddle.v2.fluid.layers.pool2d
+..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
     :noindex:
 
+sequence_expand
+---------------
 
-batch_norm
+..  autofunction:: paddle.v2.fluid.layers.sequence_expand
+    :noindex:
+
+lstm_unit
+---------
+
+..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+    :noindex:
+
+reduce_sum
 ----------
-..  autofunction:: paddle.v2.fluid.layers.batch_norm
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_sum
+    :noindex:
+
+reduce_mean
+-----------
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_mean
     :noindex:
 
+reduce_max
+----------
 
-beam_search_decode
+..  autofunction:: paddle.v2.fluid.layers.reduce_max
+    :noindex:
+
+reduce_min
+----------
+
+..  autofunction:: paddle.v2.fluid.layers.reduce_min
+    :noindex:
+
+sequence_first_step
+-------------------
+
+..  autofunction:: paddle.v2.fluid.layers.sequence_first_step
+    :noindex:
+
+sequence_last_step
 ------------------
-..  autofunction:: paddle.v2.fluid.layers.beam_search_decode
+
+..  autofunction:: paddle.v2.fluid.layers.sequence_last_step
+    :noindex:
+
+dropout
+-------
+
+..  autofunction:: paddle.v2.fluid.layers.dropout
+    :noindex:
+
+split
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.split
     :noindex:
 
+ctc_greedy_decoder
+------------------
 
-lod_rank_table
---------------
-..  autofunction:: paddle.v2.fluid.layers.lod_rank_table
+..  autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder
     :noindex:
 
+edit_distance
+-------------
 
-max_sequence_len
-----------------
-..  autofunction:: paddle.v2.fluid.layers.max_sequence_len
+..  autofunction:: paddle.v2.fluid.layers.edit_distance
     :noindex:
 
+l2_normalize
+------------
 
-topk
------
-..  autofunction:: paddle.v2.fluid.layers.topk
+..  autofunction:: paddle.v2.fluid.layers.l2_normalize
     :noindex:
 
+matmul
+------
 
-lod_tensor_to_array
--------------------
-..  autofunction:: paddle.v2.fluid.layers.lod_tensor_to_array
+..  autofunction:: paddle.v2.fluid.layers.matmul
     :noindex:
 
+warpctc
+-------
 
-
-array_to_lod_tensor
--------------------
-..  autofunction:: paddle.v2.fluid.layers.array_to_lod_tensor
+..  autofunction:: paddle.v2.fluid.layers.warpctc
     :noindex:
 
+sequence_reshape
+----------------
 
+..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
+    :noindex:
 
+transpose
+---------
 
-fill_constant
--------------
-..  autofunction:: paddle.v2.fluid.layers.fill_constant
+..  autofunction:: paddle.v2.fluid.layers.transpose
     :noindex:
 
+im2sequence
+-----------
 
-
-fill_constant_batch_size_like
------------------------------
-..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
+..  autofunction:: paddle.v2.fluid.layers.im2sequence
     :noindex:
 
+nce
+---
 
-ones
-----
-..  autofunction:: paddle.v2.fluid.layers.ones
+..  autofunction:: paddle.v2.fluid.layers.nce
     :noindex:
 
+beam_search
+-----------
 
-zeros
------
-..  autofunction:: paddle.v2.fluid.layers.zeros
+..  autofunction:: paddle.v2.fluid.layers.beam_search
     :noindex:
 
+row_conv
+--------
 
-increment
----------
-..  autofunction:: paddle.v2.fluid.layers.increment
+..  autofunction:: paddle.v2.fluid.layers.row_conv
     :noindex:
 
+multiplex
+---------
 
-array_write
------------
-..  autofunction:: paddle.v2.fluid.layers.array_write
+..  autofunction:: paddle.v2.fluid.layers.multiplex
     :noindex:
 
+ops
+===
 
+mean
+----
 
-create_array
-------------
-..  autofunction:: paddle.v2.fluid.layers.create_array
+..  autofunction:: paddle.v2.fluid.layers.mean
     :noindex:
 
+mul
+---
 
-less_than
----------
-..  autofunction:: paddle.v2.fluid.layers.less_than
+..  autofunction:: paddle.v2.fluid.layers.mul
     :noindex:
 
+reshape
+-------
 
-array_read
-----------
-..  autofunction:: paddle.v2.fluid.layers.array_read
+..  autofunction:: paddle.v2.fluid.layers.reshape
     :noindex:
 
+scale
+-----
 
-shrink_memory
---------------
-..  autofunction:: paddle.v2.fluid.layers.shrink_memory
+..  autofunction:: paddle.v2.fluid.layers.scale
     :noindex:
 
+sigmoid_cross_entropy_with_logits
+---------------------------------
 
-array_length
--------------
-..  autofunction:: paddle.v2.fluid.layers.array_length
+..  autofunction:: paddle.v2.fluid.layers.sigmoid_cross_entropy_with_logits
     :noindex:
 
+elementwise_add
+---------------
 
-conv2d_transpose
-----------------
-..  autofunction:: paddle.v2.fluid.layers.conv2d_transpose
+..  autofunction:: paddle.v2.fluid.layers.elementwise_add
     :noindex:
 
-
-sequence_expand
+elementwise_div
 ---------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_expand
+
+..  autofunction:: paddle.v2.fluid.layers.elementwise_div
     :noindex:
 
+elementwise_sub
+---------------
 
-gru_unit
---------
-..  autofunction:: paddle.v2.fluid.layers.gru_unit
+..  autofunction:: paddle.v2.fluid.layers.elementwise_sub
     :noindex:
 
+elementwise_mul
+---------------
 
-lstm_unit
----------
-..  autofunction:: paddle.v2.fluid.layers.lstm_unit
+..  autofunction:: paddle.v2.fluid.layers.elementwise_mul
     :noindex:
 
+elementwise_max
+---------------
 
-sequence_softmax
-----------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
+..  autofunction:: paddle.v2.fluid.layers.elementwise_max
     :noindex:
 
+elementwise_min
+---------------
 
-reduce_sum
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_sum
+..  autofunction:: paddle.v2.fluid.layers.elementwise_min
     :noindex:
 
+elementwise_pow
+---------------
 
-reduce_mean
------------
-..  autofunction:: paddle.v2.fluid.layers.reduce_mean
+..  autofunction:: paddle.v2.fluid.layers.elementwise_pow
     :noindex:
 
+clip
+----
 
-reduce_max
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_max
+..  autofunction:: paddle.v2.fluid.layers.clip
     :noindex:
 
+clip_by_norm
+------------
 
-reduce_min
-----------
-..  autofunction:: paddle.v2.fluid.layers.reduce_min
+..  autofunction:: paddle.v2.fluid.layers.clip_by_norm
     :noindex:
 
+sequence_softmax
+----------------
 
-split
------
-..  autofunction:: paddle.v2.fluid.layers.split
+..  autofunction:: paddle.v2.fluid.layers.sequence_softmax
     :noindex:
 
+sigmoid
+-------
 
-matmul
-------
-..  autofunction:: paddle.v2.fluid.layers.matmul
+..  autofunction:: paddle.v2.fluid.layers.sigmoid
     :noindex:
 
 logsigmoid
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.logsigmoid
     :noindex:
 
 exp
 ---
+
 ..  autofunction:: paddle.v2.fluid.layers.exp
     :noindex:
 
 relu
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.relu
     :noindex:
 
 tanh
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.tanh
     :noindex:
 
 tanh_shrink
 -----------
+
 ..  autofunction:: paddle.v2.fluid.layers.tanh_shrink
     :noindex:
 
 softshrink
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.softshrink
     :noindex:
 
 sqrt
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.sqrt
     :noindex:
 
 abs
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.abs
     :noindex:
 
 ceil
 ----
+
 ..  autofunction:: paddle.v2.fluid.layers.ceil
     :noindex:
 
 floor
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.floor
     :noindex:
 
 round
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.round
     :noindex:
 
 reciprocal
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.reciprocal
     :noindex:
 
 log
 ---
+
 ..  autofunction:: paddle.v2.fluid.layers.log
     :noindex:
 
 square
 ------
+
 ..  autofunction:: paddle.v2.fluid.layers.square
     :noindex:
 
 softplus
 --------
+
 ..  autofunction:: paddle.v2.fluid.layers.softplus
     :noindex:
 
 softsign
----------
+--------
+
 ..  autofunction:: paddle.v2.fluid.layers.softsign
     :noindex:
 
 brelu
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.brelu
     :noindex:
 
 leaky_relu
 ----------
+
 ..  autofunction:: paddle.v2.fluid.layers.leaky_relu
     :noindex:
 
 soft_relu
 ---------
+
 ..  autofunction:: paddle.v2.fluid.layers.soft_relu
     :noindex:
 
 elu
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.elu
     :noindex:
 
 relu6
 -----
+
 ..  autofunction:: paddle.v2.fluid.layers.relu6
     :noindex:
 
 pow
-----
+---
+
 ..  autofunction:: paddle.v2.fluid.layers.pow
     :noindex:
 
+stanh
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.stanh
+    :noindex:
+
 hard_shrink
 -----------
+
 ..  autofunction:: paddle.v2.fluid.layers.hard_shrink
     :noindex:
 
 thresholded_relu
 ----------------
+
 ..  autofunction:: paddle.v2.fluid.layers.thresholded_relu
     :noindex:
 
 hard_sigmoid
--------------
+------------
+
 ..  autofunction:: paddle.v2.fluid.layers.hard_sigmoid
     :noindex:
 
 swish
-------
+-----
+
 ..  autofunction:: paddle.v2.fluid.layers.swish
     :noindex:
 
-im2sequence
+tensor
+======
+
+create_tensor
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_tensor
+    :noindex:
+
+create_parameter
+----------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_parameter
+    :noindex:
+
+create_global_var
+-----------------
+
+..  autofunction:: paddle.v2.fluid.layers.create_global_var
+    :noindex:
+
+cast
+----
+
+..  autofunction:: paddle.v2.fluid.layers.cast
+    :noindex:
+
+concat
 ------
-..  autofunction:: paddle.v2.fluid.layers.im2sequence
+
+..  autofunction:: paddle.v2.fluid.layers.concat
     :noindex:
 
-edit_distance
----------------
-..  autofunction:: paddle.v2.fluid.layers.edit_distance_error
+sums
+----
+
+..  autofunction:: paddle.v2.fluid.layers.sums
     :noindex:
 
-ctc_greedy_decoder
----------------
-..  autofunction:: paddle.v2.fluid.layers.ctc_greedy_decoder
+assign
+------
+
+..  autofunction:: paddle.v2.fluid.layers.assign
     :noindex:
 
-l2_normalize
-------------
-..  autofunction:: paddle.v2.fluid.layers.l2_normalize
+fill_constant_batch_size_like
+-----------------------------
+
+..  autofunction:: paddle.v2.fluid.layers.fill_constant_batch_size_like
     :noindex:
 
-sequence_reshape
-----------------
-..  autofunction:: paddle.v2.fluid.layers.sequence_reshape
+fill_constant
+-------------
+
+..  autofunction:: paddle.v2.fluid.layers.fill_constant
     :noindex:
 
-row_conv
---------
-..  autofunction:: paddle.v2.fluid.layers.row_conv
+ones
+----
+
+..  autofunction:: paddle.v2.fluid.layers.ones
+    :noindex:
+
+zeros
+-----
+
+..  autofunction:: paddle.v2.fluid.layers.zeros
     :noindex:
+

doc/api/v2/fluid/nets.rst

-===========
-Nets
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+====
+nets
+====
 
 simple_img_conv_pool
 --------------------
-..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
-    :noindex:
 
-
-img_conv_group
----------------
-..  autofunction:: paddle.v2.fluid.nets.img_conv_group
+..  autofunction:: paddle.v2.fluid.nets.simple_img_conv_pool
     :noindex:
 
-
 sequence_conv_pool
 ------------------
+
 ..  autofunction:: paddle.v2.fluid.nets.sequence_conv_pool
     :noindex:
 
-
 glu
 ---
+
 ..  autofunction:: paddle.v2.fluid.nets.glu
     :noindex:
 
-
 scaled_dot_product_attention
 ----------------------------
+
 ..  autofunction:: paddle.v2.fluid.nets.scaled_dot_product_attention
     :noindex:
 

doc/api/v2/fluid/optimizer.rst

-===========
-Optimizer
-===========
-
-Optimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: Optimizer
-    :noindex:
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+=========
+optimizer
+=========
 
-SGDOptimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: SGDOptimizer
-    :noindex:
+SGD
+---
 
+..  autoclass:: paddle.v2.fluid.optimizer.SGD
+    :members:
+    :noindex:
 
+Momentum
+--------
 
-MomentumOptimizer
------------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: MomentumOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Momentum
+    :members:
     :noindex:
 
+Adagrad
+-------
 
-
-AdagradOptimizer
-----------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdagradOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adagrad
+    :members:
     :noindex:
 
+Adam
+----
 
-AdamOptimizer
--------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdamOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adam
+    :members:
     :noindex:
 
+Adamax
+------
 
-AdamaxOptimizer
------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: AdamaxOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.Adamax
+    :members:
     :noindex:
 
+DecayedAdagrad
+--------------
 
-DecayedAdagradOptimizer
------------------------
-..  automodule:: paddle.v2.fluid.optimizer
-    :members: DecayedAdagradOptimizer
+..  autoclass:: paddle.v2.fluid.optimizer.DecayedAdagrad
+    :members:
     :noindex:
 

doc/api/v2/fluid/param_attr.rst

-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
+==========
+param_attr
+==========
+
 ParamAttr
-===========
+---------
 
+..  autoclass:: paddle.v2.fluid.param_attr.ParamAttr
+    :members:
+    :noindex:
 
+WeightNormParamAttr
+-------------------
 
-ParamAttr
------------
-..  automodule:: paddle.v2.fluid.param_attr
-    :members: ParamAttr
+..  autoclass:: paddle.v2.fluid.param_attr.WeightNormParamAttr
+    :members:
     :noindex:
+

doc/api/v2/fluid/profiler.rst

-===========
-Profiler
-===========
+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
 
+========
+profiler
+========
 
+cuda_profiler
+-------------
 
-Profiler
------------
 ..  autofunction:: paddle.v2.fluid.profiler.cuda_profiler
     :noindex:
+
+reset_profiler
+--------------
+
+..  autofunction:: paddle.v2.fluid.profiler.reset_profiler
+    :noindex:
+
+profiler
+--------
+
+..  autofunction:: paddle.v2.fluid.profiler.profiler
+    :noindex:
+

doc/api/v2/fluid/regularizer.rst

+..  THIS FILE IS GENERATED BY `gen_doc.{py|sh}`
+    !DO NOT EDIT THIS FILE MANUALLY!
+
 ===========
-Regularizer
+regularizer
 ===========
 
-WeightDecayRegularizer
-----------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: WeightDecayRegularizer
-    :noindex:
-
+append_regularization_ops
+-------------------------
 
-L2DecayRegularizer
-------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: L2DecayRegularizer
+..  autofunction:: paddle.v2.fluid.regularizer.append_regularization_ops
     :noindex:
 
+L1Decay
+-------
 
+..  autoclass:: paddle.v2.fluid.regularizer.L1Decay
+    :members:
+    :noindex:
 
-L1DecayRegularizer
--------------------
-..  automodule:: paddle.v2.fluid.regularizer
-    :members: L1DecayRegularizer
+L2Decay
+-------
 
+..  autoclass:: paddle.v2.fluid.regularizer.L2Decay
+    :members:
+    :noindex:
 

doc/getstarted/build_and_install/build_from_source_cn.rst → doc/build_and_install/build_from_source_cn.rst

@@ -115,7 +115,7 @@ PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种B
     "WITH_AVX", "是否编译含有AVX指令集的PaddlePaddle二进制文件", "ON"
     "WITH_PYTHON", "是否内嵌PYTHON解释器", "ON"
     "WITH_STYLE_CHECK", "是否编译时进行代码风格检查", "ON"
-    "WITH_TESTING", "是否开启单元测试", "ON"
+    "WITH_TESTING", "是否开启单元测试", "OFF"
     "WITH_DOC", "是否编译中英文文档", "OFF"
     "WITH_SWIG_PY", "是否编译PYTHON的SWIG接口，该接口可用于预测和定制化训练", "Auto"
     "WITH_GOLANG", "是否编译go语言的可容错parameter server", "ON"

doc/getstarted/build_and_install/build_from_source_en.rst → doc/build_and_install/build_from_source_en.rst

@@ -126,7 +126,7 @@ You can add :code:`-D` argument to pass such options, like:
     "WITH_AVX", "Build with AVX support", "ON"
     "WITH_PYTHON", "Build with integrated Python interpreter", "ON"
     "WITH_STYLE_CHECK", "Check code style when building", "ON"
-    "WITH_TESTING", "Build unit tests", "ON"
+    "WITH_TESTING", "Build unit tests", "OFF"
     "WITH_DOC", "Build documentations", "OFF"
     "WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto"
     "WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON"

doc/getstarted/build_and_install/docker_install_cn.rst → doc/build_and_install/docker_install_cn.rst

@@ -95,6 +95,12 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note
 
      docker run -p 8888:8888 paddlepaddle/book
 
+国内用户可以使用下面的镜像源来加速访问：
+
+  .. code-block: bash
+
+    docker run -p 8888:8888 docker.paddlepaddlehub.com/book
+
 然后在浏览器中输入以下网址：
 
   .. code-block:: text

doc/getstarted/build_and_install/docker_install_en.rst → doc/build_and_install/docker_install_en.rst

@@ -102,6 +102,12 @@ We provide a packaged book image, simply issue the command:
 
      docker run -p 8888:8888 paddlepaddle/book
 
+For users in China, we provide a faster mirror:
+
+  .. code-block: bash
+
+    docker run -p 8888:8888 docker.paddlepaddlehub.com/book
+
 Then, you would back and paste the address into the local browser:
 
   .. code-block:: text

doc/getstarted/build_and_install/index_cn.rst → doc/build_and_install/index_cn.rst

@@ -13,7 +13,7 @@ PaddlePaddle提供pip和Docker的安装方式：
 
    pip_install_cn.rst
    docker_install_cn.rst
-   ../../howto/dev/build_cn.md
+   build_cn.md
 
 编译流程
 ++++++++

doc/getstarted/build_and_install/index_en.rst → doc/build_and_install/index_en.rst

@@ -13,7 +13,7 @@ You can choose either pip or Docker to complete your install:
 
    pip_install_en.rst
    docker_install_en.rst
-   ../../howto/dev/build_en.md
+   build_en.md
 
 
 Build from Source

doc/getstarted/build_and_install/pip_install_cn.rst → doc/build_and_install/pip_install_cn.rst

@@ -39,6 +39,7 @@ PaddlePaddle可以使用常用的Python包管理工具
 
     "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
+    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
     "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"

doc/getstarted/build_and_install/pip_install_en.rst → doc/build_and_install/pip_install_en.rst

@@ -42,6 +42,7 @@ If the links below shows up the login form, just click "Log in as guest" to star
 
     "cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
+    "cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
     "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
     "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"

doc/design/auto_gradient_check.md

-## Auto Gradient Checker Design
+## Auto Gradient Check Design
 
-## Backgraound：
-- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right:
-  1. you should get the right backpropagation formula according to the forward computation.
-  2. you should implement it right in CPP.
-  3. it's difficult to prepare test data.
+## Background：
+- Generally, it is easy to check whether the forward computation of an Operator is correct or not. However, backpropagation is a notoriously difficult algorithm to debug and get right because of the following challenges:
+  1. The formula for backpropagation formula should be correct according to the forward computation.
+  2. The Implementation of the above shoule be correct in CPP.
+  3. It is difficult to prepare an unbiased test data.
 
-- Auto gradient checking gets a numerical gradient by forward Operator and use it as a reference of the backward Operator's result. It has several advantages:
-  1. numerical gradient checker only need forward operator.
-  2. user only need to prepare the input data for forward Operator.
+- Auto gradient checking gets a numerical gradient using forward Operator and uses it as a reference for the backward Operator's result. It has several advantages:
+  1. Numerical gradient checker only needs the forward operator.
+  2. The user only needs to prepare the input data for forward Operator and not worry about the backward Operator.
 
 ## Mathematical Theory
-The following two document from Stanford has a detailed explanation of how to get numerical gradient and why it's useful.
+The following documents from Stanford have a detailed explanation of how to compute the numerical gradient and why it is useful.
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)
 
 
-## Numeric Gradient Implementation
+## Numerical Gradient Implementation
 ### Python Interface
 ```python
 def get_numerical_gradient(op,
@@ -27,73 +27,76 @@ def get_numerical_gradient(op,
                          delta=0.005,
                          local_scope=None):
     """
-    Get Numeric Gradient for an operator's input.
+    Get Numerical Gradient for the input of an operator.
 
-    :param op: C++ operator instance, could be an network
+    :param op: C++ operator instance, could be an network.
     :param input_values: The input variables. Should be an dictionary, whose key is
-    variable name, and value is numpy array.
+    variable name, and value is a numpy array.
     :param output_name: The final output variable name.
-    :param input_to_check: The input variable with respect to which to compute the gradient.
-    :param delta: The perturbation value for numeric gradient method. The
-    smaller delta is, the more accurate result will get. But if that delta is
-     too small, it will suffer from numerical stability problem.
+    :param input_to_check: The input variable with respect to which the gradient has to be computed.
+    :param delta: The perturbation value for numerical gradient method. The
+    smaller the delta, the more accurate the result. But if the delta is too
+    small, it will suffer from the numerical stability problem.
     :param local_scope: The local scope used for get_numeric_gradient.
     :return: The gradient array in numpy format.
     """
 ```
 
-### Explaination:
+### Explanation:
 
-- Why need `output_name`
-  - An Operator may have multiple Output, one can get independent gradient from each Output. So caller should specify the name of the output variable.
+- Why do we need an `output_name`
+  - An Operator may have multiple Outputs, one can compute an independent gradient from each Output. So the caller should specify the name of the output variable.
 
-- Why need `input_to_check`
-  - One operator may have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numeric Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times.
+- Why do we need `input_to_check`
+  - One operator can have multiple inputs. Gradient Op can calculate the gradient of these inputs at the same time. But Numerical Gradient needs to calculate them one by one. So `get_numeric_gradient` is designed to calculate the gradient for one input. If you need to compute multiple inputs, you can call `get_numeric_gradient` multiple times each with a different input.
 
 
 ### Core Algorithm Implementation
 
 
 ```python
-    # we only compute gradient of one element a time.
+    # we only compute the gradient of one element a time.
     # we use a for loop to compute the gradient of each element.
     for i in xrange(tensor_size):
-        # get one input element by its index i.
-        origin = tensor_to_check.get_float_element(i)
+        # get one input element using the index i.
+        original = tensor_to_check.get_float_element(i)
 
-        # add delta to it, run op and then get the new value of the result tensor.
-        x_pos = origin + delta
+        # add delta to it, run the forward op and then
+        # get the new value of the result tensor.
+        x_pos = original + delta
         tensor_to_check.set_float_element(i, x_pos)
         y_pos = get_output()
 
-        # plus delta to this element, run op and get the new value of the result tensor.
-        x_neg = origin - delta
+        # Subtract delta from this element, run the op again
+        # and get the new value of the result tensor.
+        x_neg = original - delta
         tensor_to_check.set_float_element(i, x_neg)
         y_neg = get_output()
 
         # restore old value
-        tensor_to_check.set_float_element(i, origin)
+        tensor_to_check.set_float_element(i, original)
 
-        # compute the gradient of this element and store it into a numpy array.
+        # compute the gradient of this element and store
+        # it into a numpy array.
         gradient_flat[i] = (y_pos - y_neg) / delta / 2
 
     # reshape the gradient result to the shape of the source tensor.
     return gradient_flat.reshape(tensor_to_check.get_dims())
 ```
 
-## Auto Graident Checker Framework
+## Auto Gradient Check Framework
 
 Each Operator Kernel has three kinds of Gradient:
 
 1. Numerical gradient
 2. CPU kernel gradient
-3. GPU kernel gradient (if supported)
+3. GPU kernel gradient (if supported by the device)
 
-The numerical gradient only relies on forward Operator. So we use the numerical gradient as the reference value. And the gradient checking is performed in the following three steps:
+The numerical gradient only relies on the forward Operator, so we use the numerical gradient as the reference value. The gradient checking is performed in the following three steps:
 
-1. calculate the numerical gradient
-2. calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient
-3. calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient (if supported)
+1. Calculate the numerical gradient
+2. Calculate CPU kernel gradient with the backward Operator and compare it with the numerical gradient.
+3. Calculate GPU kernel gradient with the backward Operator and compare it with the numeric gradient. (if supported)
 
 #### Python Interface
 
@@ -109,26 +112,27 @@ The numerical gradient only relies on forward Operator. So we use the numerical
         """
         :param forward_op: used to create backward_op
         :param input_vars: numpy value of input variable. The following
-            computation will use these variables.
-        :param inputs_to_check: the input variable with respect to which to compute the gradient.
+          computation will use these variables.
+        :param inputs_to_check: the input variable with respect to which the
+          gradient will be computed.
         :param output_name: The final output variable name.
         :param max_relative_error: The relative tolerance parameter.
-        :param no_grad_set: used when create backward ops
+        :param no_grad_set: used to create backward ops
         :param only_cpu: only compute and check gradient on cpu kernel.
         :return:
         """
 ```
 
-### How to check if two numpy array is close enough?
-if `abs_numerical_grad` is nearly zero, then use abs error for numerical_grad
+### How to check if two numpy arrays are close enough?
+if `abs_numerical_grad` is nearly zero, then use absolute error for numerical_grad.
 
 ```python
 numerical_grad = ...
 operator_grad = numpy.array(scope.find_var(grad_var_name(name)).get_tensor())
 
 abs_numerical_grad = numpy.abs(numerical_grad)
-# if abs_numerical_grad is nearly zero, then use abs error for numeric_grad, not relative
-# error.
+# if abs_numerical_grad is nearly zero, then use abs error for
+# numeric_grad, instead of relative error.
 abs_numerical_grad[abs_numerical_grad < 1e-3] = 1
 
 diff_mat = numpy.abs(abs_numerical_grad - operator_grad) / abs_numerical_grad
@@ -137,10 +141,10 @@ max_diff = numpy.max(diff_mat)
 
 
 #### Notes：
-The Input data for auto gradient checker should be reasonable to avoid numerical  stability problem.
+The Input data for auto gradient checker should be reasonable to avoid numerical stability problem.
 
 
-#### Refs:
+#### References:
 
 - [Gradient checking and advanced optimization(en)](http://deeplearning.stanford.edu/wiki/index.php/Gradient_checking_and_advanced_optimization)
 - [Gradient checking and advanced optimization(cn)](http://ufldl.stanford.edu/wiki/index.php/%E6%A2%AF%E5%BA%A6%E6%A3%80%E9%AA%8C%E4%B8%8E%E9%AB%98%E7%BA%A7%E4%BC%98%E5%8C%96)

doc/design/cpp_data_feeding.md

+# C++ Data Feeding
+
+In training with Paddle V2 API, data feeding wholly dependents on Python code. To get rid of the Python environment and achieve the goal of "wrapping the whole training by a while loop op" in Paddle Fluid, a C++ data feeding mechanism is required. 
+
+In this document we show the fundamental design of C++ data feeding process, which includes the data reading, shuffling and batching.
+
+## Reader
+
+A new concept named 'Reader' is introduced. `Reader` is a series of inherited classes which can be hold by our `Variable` and they are used to read or process file data.
+
+
+### `ReaderBase`
+
+`ReaderBase` is the abstract base class of all readers. It defines the all readers' interfaces.
+
+```cpp
+class ReaderBase {
+ public:
+  explicit ReaderBase(const std::vector<DDim>& shapes) : shapes_(shapes) {
+    PADDLE_ENFORCE(!shapes_.empty());
+  }
+  // Read the next batch of data. (A 'batch' can be only one instance)
+  virtual void ReadNext(std::vector<LoDTensor>* out) = 0;
+  // Show whether the next bacth exists.
+  virtual bool HasNext() const = 0;
+  
+  // Reinitialize the reader and read the file from the begin.
+  virtual void ReInit() = 0;
+  
+  // Get a certain read in data's shape.
+  DDim shape(size_t idx) const;
+  // Get shapes of all read in data.
+  std::vector<DDim> shapes() const { return shapes_; }
+  // Set shapes of read in data.
+  void set_shapes(const std::vector<DDim>& shapes) { shapes_ = shapes; }
+
+  virtual ~ReaderBase() {}
+
+ protected:
+  std::vector<DDim> shapes_;
+};
+```
+
+### `FileReader` and `DecoratedReader`
+
+These two classes are derived from the `ReaderBase` and will further be derived by respective specific readers. That is to say, in our design, there are two kinds of readers: file readers and decorated readers. A file reader reads from a file of some specific format, and yield only one instance of data at a time. e.g. RecordIO reader, jpg reader, .... A decorated reader takes another reader(both file reader and decorated reader are OK) as its 'underlying reader'. It gets data from its underlying reader, does some process on them(shuffling, or batching), then yields processed data. The output data of a decorated reader can be a single instance or a batch. `ShuffleReader` and `BatchReader` are both decorated readers.
+
+All the readers share exactly the same interfaces defined in `ReaderBase`. So they can be decorated for more than one time: We can **shuffle** a reader's outputs and then **batch** the shuffle outputs. The interface consistency also allows related ops use readers without knowing what they are exactly.
+
+
+### `ReaderHolder`
+
+Different readers belong to different class types. It leads to a problem: How can we drop them into `Variable`s and fetch them out by a unified method? For example, if a Variable holds a `BatchReader`, we can not get it by the following code:
+
+```cpp
+var->Get<ReaderBase>("batch_reader");
+```
+
+we have to write:
+
+```cpp
+var->Get<BatchReader>("batch_reader");
+```
+
+This requires each time getting a reader from a variable we must know the reader's type exactly. It is nearly impossible.
+
+To solve this problem, we introduce `ReaderHolder` as a wrapper. It acts as an empty decorator of `ReaderBase`, which erases reader's type. With `ReaderHolder` we are able to fetch all types of readers by `var->Get<ReaderHolder>("...")` and regard the obtained object as a reader.
+
+## Related Operators
+
+To create and invoke readers, some now ops are introduced:
+
+### `CreateReaderOp`
+
+Each reader has its creating op. File readers' creating ops have no input and yield the created file reader as its output. Decorated readers' creating ops take the underlying readers as inputs and then yield new decorated readers.
+
+### `ReadOp`
+
+A reader is only a Variable. It cannot trigger the reading process by itself. So we add the `ReadOp` to execute it. A `ReadOp` takes a reader Variable as its input. Each time it runs, it invokes the reader‘s `ReadNext()` function and gets a new batch of data(or only one instance of data, if we use file reader directly). The output data of a reader are in the form of `std::vector<LoDTenosr>`, so the `ReadOp` also needs to split the vector and move LoDTensors to their respective output Variables.

doc/design/csp.md

@@ -42,7 +42,7 @@ The type *channel* is conceptually the blocking queue.  In Go, its implemented i
 
 The `select` operation has been in OS kernels long before Go language.  All Unix kernels implement system calls *poll* and *select*.  They monitor multiple file descriptors to see if I/O is possible on any of them.  This takes O(N) time.  Since Linux 2.6, a new system call, *epoll*, can do the same in O(1) time.  In BSD systems, there is a similar system call *kqueue*.  Go's Linux implementation uses epoll.
 
-It might be a good idea to implement Fluid's select using epoll too.  In this design doc, we start from the O(N) way, so we could focus on Python binding and the syntax.
+It might be a good idea to implement Fluid's select using epoll too.  In this design doc, we start from the O(N) way so that we could focus on Python binding and the syntax.
 
 ### Type Channel
 
@@ -71,14 +71,14 @@ ch1 := make(chan int, 100)  // a channel that can buffer 100 ints.
 In Fluid, we should be able to do the same:
 
 ```python
-ch  = fluid.make_chan(dtype=INT)
-ch1 = fluid.make_chan(dtype=INT, 100)
+ch  = fluid.make_channel(dtype=INT)
+ch1 = fluid.make_channel(dtype=INT, 100)
 ```
 
 In addition to that, we want channels that can hold more complex element types, e.g., Tensors of float16:
 
 ```python
-ch = fluid.make_chan(dtype=Tensor, etype=float16)
+ch = fluid.make_channel(dtype=Tensor, etype=float16)
 ```
 
 or Tensors of Tensors of float16 etc.
@@ -87,8 +87,136 @@ The point here is that we need a consistent way to compose types, like in C++ we
 
 ### Send and Recv
 
+Go's CSP implementation depends on data type *channel*. There are two types of channels:
+
+1. The unblocked channel, or buffered channel, is a blocking queue with a non-zero sized buffer. The sending to buffered channel blocks if the buffer is full, and the receive operation blocks if the buffer is empty.
+1. blocked channel, or unbuffered channel, is a blocking queue with no buffer.  Both sending and receiving block with unbuffered channels.
+
+There are four types of actions with a channel:
+
+1. Create a channel
+
+   ```go
+   ch := make(chan int) // this is an unbuffered channel
+   ch := make(chan int, 100) // this is a buffered channel of 100 ints.
+   ```
+
+1. Send
+
+   ```go
+   ch <- 111
+   ```
+
+1. Recv
+
+   ```go
+   y, ok <- ch
+   ```
+
+1. Close
+
+   ```go
+   close(ch)
+   ```
+   
+   Please be aware that a closed channel is not a nil channel, which is `var ch chan int`.
+   
+There are some [axioms with channels](https://dave.cheney.net/2014/03/19/channel-axioms):
+
+1. A send to a nil channel blocks forever
+
+1. A receive from a nil channel blocks forever
+
+1. A send to a closed channel panics
+
+1. A receive from a closed channel returns the residual values and then zeros.
+
+In Fluid, we have [buffered channels](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/buffered_channel.h) and [unbuffered channels](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/details/unbuffered_channel.h)
+
+The following program illustrates the Python syntax for accessing Fluid buffers.
+
+```python
+import fluid
+
+buffer_size = 10
+ch = fluid.make_channel(dtype=INT, buffer_size)
+
+# Now write three elements to the channel
+with fluid.while(steps=buffer_size):
+  fluid.send(ch, step)
+
+fluid.close_channel(ch)
+
+with fluid.while(steps=buffer_size):
+  fluid.print(fluid.recv(ch))
+```
+
+The following example shows that to avoid the always-blocking behavior of unbuffered channels, we need to use Fluid's goroutines.
+
+```python
+import fluid
+
+ch = fluid.make_channel(dtype=INT)
+
+with fluid.go():
+  fluid.send(ch)
+
+y = fluid.recv(ch)
+
+fluid.close_channel(ch)
+```
+
 ### Select
 
+In Go, the `select` statement lets a goroutine wait on multiple communication operations. A `select` blocks until one of its cases can run, then it executes that case. It chooses one at random if multiple are ready.
+
+```go
+
+ch1  := make(chan int)       
+ch2  := make(chan int, 100)
+
+x := 0
+
+for {
+    select {
+    case ch1 <- x:
+      x := x + 1
+    case y <- ch2:
+      fmt.Println("Received on channel")
+    default:
+      fmt.Println("Default")
+    }
+  }
+
+```
+
+In Fluid, we should be able to do the same:
+
+```python
+ch1  = fluid.make_chan(dtype=INT)
+ch2 = fluid.make_chan(dtype=INT, 100)
+
+sel = fluid.select()
+
+with sel.case(ch1, 'w', X):
+    fluid.layers.increment(X)
+
+with sel.case(ch2, 'r', Y):
+    fluid.print("Received on Channel")
+
+with sel.default():
+    fluid.print("Default")
+
+```
+
+In the above code snippet, `X` and `Y` are variables. Now let us look at each of these statements one by one.
+
+- `sel.case(ch1, 'w', X)` : This specifies that we are writing to `ch1` and we want to write the integer in variable `X` to the channel. The character `w` is used here to make the syntax familiar to write syntax in Python I/O.
+
+- `sel.case(ch2, 'r', Y)` : This specifies that we would like to read the result from `ch2` into variable `Y`. The character `r` is used here to make the syntax familiar to read syntax in Python I/O.
+
+- `sel.default()` : This is equivalent to the default in Go `select`. If none of the channels are ready for read or write, then the fluid code in the default block will be executed.
+
 ## Example Programs
 
 ### 1. RPC between Trainers and Parameter Servers

doc/design/dist_refactor/distributed_architecture.md

@@ -152,12 +152,12 @@ for data in train_reader():
 `JobDesc` object describe the distributed job resource specification to run on
 Cluster environment.
 
-<img src="src/remote_executor.png"/>
+<img src="src/remote_executor.png" width="500" align="center" />
 
 `RemoteExecutor.run` sends the `ProgramDesc` and
 [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/develop/doc/autoscale/README.md#training-job-resource)
 to a server in the cluster which executes `RemoteExecutor.listen`. This server is responsible
-to start the final Kubernetes Jobs to run the different role of `ProgramDesc`.
+to start the final Kubernetes Jobs to run the different role of `ProgramDesc` from `ConfigMap`.
 
 
 ### Placement Algorithm

doc/design/speech/README.MD → doc/design/speech/deep_speech_2.md

@@ -140,7 +140,19 @@ TODO by Assignees
 
 ### Beam Search with CTC and LM
 
-TODO by Assignees
+<div align="center">
+<img src="image/beam_search.png" width=600><br/>
+Figure 2. Algorithm for CTC Beam Search Decoder.
+</div>
+
+- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] as shown in Figure 2, with two important modifications for the ambiguous parts: 
+   - 1) in the iterative computation of probabilities, the assignment operation is changed to accumulation for one prefix may comes from different paths; 
+   - 2) the if condition ```if l^+ not in A_prev then``` after probabilities' computation is deprecated for it is hard to understand and seems unnecessary.
+- An **external scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a white space appended in English decoding and any character appended in Mandarin decoding.
+- Such external scorer consists of language model, word count or any other custom scorers.
+- The **language model** is built from Task 5, with parameters should be carefully tuned to achieve minimum WER/CER (c.f. Task 7)
+- This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. 
+ 
 
 ## Future Work
 
@@ -153,3 +165,4 @@ TODO by Assignees
 
 1. Dario Amodei, etc., [Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin](http://proceedings.mlr.press/v48/amodei16.pdf). ICML 2016.
 2. Dario Amodei, etc., [Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595). 	arXiv:1512.02595.
+3. Awni Y. Hannun, etc. [First-Pass Large Vocabulary Continuous Speech Recognition using Bi-Directional Recurrent DNNs](https://arxiv.org/abs/1408.2873). arXiv:1408.2873

doc/design/support_new_device.md

@@ -2,9 +2,9 @@
 
 ## Background
 
-Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries flexibly and efficiently.
+Deep learning has a high demand for computing resources. New high-performance devices and computing libraries are appearing very frequently. Deep learning frameworks have to integrate these high-performance devices and computing libraries in a flexible and efficient manner.
 
-On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example,Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
+On one hand, hardware and computing libraries usually do not have a one-to-one correspondence. For example, Intel CPUs support Eigen and MKL computing libraries while Nvidia GPUs support Eigen and cuDNN computing libraries. We have to implement operator specific kernels for each computing library.
 
 On the other hand, users usually do not want to care about the low-level hardware and computing libraries when writing a neural network configuration. In Fluid, `Layer` is exposed in `Python`, and `Operator` is exposed in `C++`. Both `Layer` and `Operator` are hardware independent.
 
@@ -17,7 +17,7 @@ For a general overview of fluid, please refer to the [overview doc](https://gith
 
 There are mainly three parts that we have to consider while integrating a new device/library:
 
-- Place and DeviceContext: indicates the device id and manages hardware resources
+- Place and DeviceContext: indicate the device id and manage hardware resources
 
 - Memory and Tensor: malloc/free data on certain device
 
@@ -25,10 +25,10 @@ There are mainly three parts that we have to consider while integrating a new de
 
 ### Place and DeviceContext
 
-Please remind that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
+Please note that device and computing library are not one-to-one corresponding. A device can have a lot of computing libraries and a computing library can also support several devices.
 
 #### Place
-Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add corresponding `DevicePlace`.
+Fluid uses class [Place](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/platform/place.h#L55) to represent the device memory where data is located. If we add another device, we have to add the corresponding `DevicePlace`.
 
 ```
         |   CPUPlace
@@ -144,7 +144,7 @@ class Tensor {
 };
 ```
 
-`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configure its shape, and then call `mutuable_data` to allocate the actual memory.
+`Placeholder` is used to delay memory allocation; that is, we can first define a tensor, using `Resize` to configurate its shape, and then call `mutuable_data` to allocate the actual memory.
 
 ```cpp
 paddle::framework::Tensor t;
@@ -163,7 +163,7 @@ Fluid implements computing units based on different DeviceContexts. Some computi
 
 Let's take [MaxOutFunctor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/math/maxouting.h#L27) as an example:
 
-The interface is defined in header file.
+The interface is defined in the header file.
 
 ```
 template <typename DeviceContext, typename T>
@@ -174,7 +174,7 @@ class MaxOutFunctor {
 };
 ```
 
-CPU implemention is in .cc file
+CPU implementation is in .cc file
 
 ```
 template <typename T>
@@ -188,7 +188,7 @@ class MaxOutFunctor<platform::CPUDeviceContext, T> {
 };
 ```
 
-CUDA implemention is in .cu file
+CUDA implementation is in .cu file
 
 ```
 template <typename T>
@@ -203,9 +203,9 @@ class MaxOutFunctor<platform::CUDADeviceContext, T> {
 ```
 
 
-We get computing handle from a concrete DeviceContext, and make compution on tensors.
+We first obtain the computing handle from a concrete DeviceContext and then compute on tensors.
 
-The implemention of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
+The implementation of `OpKernel` is similar to math functors, the extra thing we need to do is to register the OpKernel in a global map.
 
 Fluid provides different register interfaces in op_registry.h
 
@@ -231,7 +231,7 @@ REGISTER_OP_CUDA_KERNEL(
 
 ## Advanced topics: How to switch between different Device/Library
 
-Generally, we will impelement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not sutibale on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run at GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
+Generally, we will implement OpKernel for all Device/Library of an Operator. We can easily train a Convolutional Neural Network in GPU. However, some OpKernel is not suitable on a specific Device. For example, crf operator can only run on CPU, whereas most other operators can run on GPU. To achieve high performance in such circumstance, we have to switch between different Device/Library.
 
 
 For more details, please refer to following docs:

doc/design/switch.md

+### Design Doc: Switch
+
+### Background
+
+Many programming languages provide `switch` as a generalization of `if-elif-else`.  We want to add it to Fluid.
+
+The following example shows the usage of `fluid.switch`.
+
+```python
+a = fluid.Var(10)
+b = fluid.Var(0)
+
+with switch() as switch:
+    with switch.case(fluid.less_equal(a, 10)):
+        fluid.print("Case 1")
+    with switch.case(fluid.larger(a, 0)):
+        fluid.print("Case 2")
+    with switch.default():
+        fluid.print("Case 3")
+```
+
+### The Semantics
+
+1. A `switch` control-flow checks cases one-by-one.
+1. The condition of each case is a boolean value, which is a scalar, and differs from the `fluid.if_else` control-flow, which condition could be a vector of boolean values.
+1. It runs the first matched case, or the default case if there is one.
+1. Once it matches a case, it runs the corresponding branch and only that branch.  It's like there is a C's `break` keyword at the end of each case.
+
+The above program should print and print only "Case 1".
+
+The implementation of the backward pass of the `switch` control-flow is easier than the backward of the `if_else`, because `switch` runs at most one branch, whereas `if-else` could run more than one branches.

doc/dev/contribute_to_paddle_en.md

+../../CONTRIBUTING.md
\ No newline at end of file

doc/dev/index_cn.rst

+开发标准
+========
+
+..  toctree::
+  :maxdepth: 1
+
+  contribute_to_paddle_cn.md
+  write_docs_cn.rst

doc/dev/index_en.rst

+Development
+------------
+
+..  toctree::
+  :maxdepth: 1
+
+  new_layer_en.rst
+  contribute_to_paddle_en.md
+  write_docs_en.rst

doc/howto/dev/write_docs_cn.rst → doc/dev/write_docs_cn.rst

-##################
-如何贡献/修改文档
-##################
+#############
+如何贡献文档
+#############
 
 PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
 也可以利用PaddlePaddle 工具来编译文档，这个情况下所有的文件会存在整理过的的文件目录 .ppo_workspace/content 下

doc/howto/dev/write_docs_en.rst → doc/dev/write_docs_en.rst

-##################
+########################
 Contribute Documentation
-##################
+########################
 
 PaddlePaddle supports English documentation ``doc`` and Chinese documentation ``doc_cn``.
 Both are compiled by `cmake`_ and `sphinx`_ , the compiled documentations will be stored under ``doc`` and ``doc_cn`` directories.

doc/getstarted/concepts/use_concepts_cn.rst

@@ -4,7 +4,7 @@
 
 PaddlePaddle是源于百度的一个深度学习平台。PaddlePaddle为深度学习研究人员提供了丰富的API，可以轻松地完成神经网络配置，模型训练等任务。
 这里将介绍PaddlePaddle的基本使用概念，并且展示了如何利用PaddlePaddle来解决一个经典的线性回归问题。
-在使用该文档之前，请参考 `安装文档 <../build_and_install/index_cn.html>`_ 完成PaddlePaddle的安装。
+在使用该文档之前，请参考 `安装文档 <../../build_and_install/index_cn.html>`_ 完成PaddlePaddle的安装。
 
 
 配置网络

doc/getstarted/index_cn.rst

 新手入门
 ============
 
-.. _quick_install:
-
-快速安装
-++++++++
-
-PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
-执行下面的命令完成快速安装，版本为cpu_avx_openblas：
-
-  .. code-block:: bash
-
-     pip install paddlepaddle
-
-如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
-
-  .. code-block:: bash
-
-     pip install paddlepaddle-gpu
-
-更详细的安装和编译方法参考：
-
-..  toctree::
-  :maxdepth: 1
-
-  build_and_install/index_cn.rst
-
-.. _quick_start:
-
-快速开始
-++++++++
-
-创建一个 housing.py 并粘贴此Python代码：
-
-  .. code-block:: python
-
-     import paddle.v2 as paddle
-
-     # Initialize PaddlePaddle.
-     paddle.init(use_gpu=False, trainer_count=1)
-
-     # Configure the neural network.
-     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
-     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-
-     # Infer using provided test data.
-     probs = paddle.infer(
-         output_layer=y_predict,
-         parameters=paddle.dataset.uci_housing.model(),
-         input=[item for item in paddle.dataset.uci_housing.test()()])
-
-     for i in xrange(len(probs)):
-         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
-
-执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
-
 ..  toctree::
   :maxdepth: 1
 
+  quickstart_cn.rst
   concepts/use_concepts_cn.rst

doc/getstarted/index_en.rst

 GET STARTED
 ============
 
-.. _quick_install:
-
-Quick Install
-----------------------
-
-You can use pip to install PaddlePaddle with a single command, supports
-CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
-Simply run the following command to install, the version is cpu_avx_openblas:
-
-  .. code-block:: bash
-
-     pip install paddlepaddle
-
-If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
-
-  .. code-block:: bash
-
-     pip install paddlepaddle-gpu
-
-For more details about installation and build:
-
 ..  toctree::
   :maxdepth: 1
 
-  build_and_install/index_en.rst
-
-
-.. _quick_start:
-
-Quick Start
-++++++++
-
-Create a new file called housing.py, and paste this Python
-code:
-
-
-  .. code-block:: python
-
-     import paddle.v2 as paddle
-
-     # Initialize PaddlePaddle.
-     paddle.init(use_gpu=False, trainer_count=1)
-
-     # Configure the neural network.
-     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
-     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
-
-     # Infer using provided test data.
-     probs = paddle.infer(
-         output_layer=y_predict,
-         parameters=paddle.dataset.uci_housing.model(),
-         input=[item for item in paddle.dataset.uci_housing.test()()])
-
-     for i in xrange(len(probs)):
-         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
-
-Run :code:`python housing.py` and voila! It should print out a list of predictions
-for the test housing data.
+  quickstart_en.rst

doc/getstarted/quickstart_cn.rst

+快速开始
+========
+
+快速安装
+--------
+
+PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
+执行下面的命令完成快速安装，版本为cpu_avx_openblas：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+如果需要安装支持GPU的版本（cuda7.5_cudnn5_avx_openblas），需要执行：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+更详细的安装和编译方法参考：:ref:`install_steps` 。
+
+快速使用
+--------
+
+创建一个 housing.py 并粘贴此Python代码：
+
+  .. code-block:: python
+
+     import paddle.v2 as paddle
+
+     # Initialize PaddlePaddle.
+     paddle.init(use_gpu=False, trainer_count=1)
+
+     # Configure the neural network.
+     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+     # Infer using provided test data.
+     probs = paddle.infer(
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
+
+     for i in xrange(len(probs)):
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+
+执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。

doc/getstarted/quickstart_en.rst

+Quick Start
+============
+
+Quick Install
+-------------
+
+You can use pip to install PaddlePaddle with a single command, supports
+CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
+Simply run the following command to install, the version is cpu_avx_openblas:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle
+
+If you need to install GPU version (cuda7.5_cudnn5_avx_openblas), run:
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+
+For more details about installation and build: :ref:`install_steps` .
+
+Quick Use
+---------
+
+Create a new file called housing.py, and paste this Python
+code:
+
+
+  .. code-block:: python
+
+     import paddle.v2 as paddle
+
+     # Initialize PaddlePaddle.
+     paddle.init(use_gpu=False, trainer_count=1)
+
+     # Configure the neural network.
+     x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
+     y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
+
+     # Infer using provided test data.
+     probs = paddle.infer(
+         output_layer=y_predict,
+         parameters=paddle.dataset.uci_housing.model(),
+         input=[item for item in paddle.dataset.uci_housing.test()()])
+
+     for i in xrange(len(probs)):
+         print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
+
+Run :code:`python housing.py` and voila! It should print out a list of predictions
+for the test housing data.

doc/howto/usage/capi/compile_paddle_lib_cn.md → doc/howto/capi/compile_paddle_lib_cn.md

-## 编译 PaddlePaddle 预测库
+## 安装与编译C-API预测库
 
 ### 概述
 

doc/howto/usage/capi/index_cn.rst → doc/howto/capi/index_cn.rst

-PaddlePaddle C-API
+C-API预测库
 ==================
 
 ..  toctree::

doc/howto/usage/capi/workflow_of_capi_cn.md → doc/howto/capi/workflow_of_capi_cn.md

-## C-API 使用流程
+## C-API使用流程
 
 这篇文档介绍 PaddlePaddle C-API 整体使用流程。
 

doc/howto/usage/cluster/cluster_train_cn.md → doc/howto/cluster/cmd_argument_cn.md

-# 分布式训练
-
-
-## 概述
-
-本文将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
-
-<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
-
-- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
-- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
-- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
-
-这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
-
-在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
-
-
-## 环境准备
-
-1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
-1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/index_cn.html)的多种安装方式。我们推荐使用[Docker](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/build_and_install/docker_install_cn.html)安装方式来快速安装PaddlePaddle。
-
-安装完成之后，执行下面的命令可以查看已经安装的版本（docker安装方式可以进入docker容器执行：`docker run -it paddlepaddle/paddle:[tag] /bin/bash`）：
-```bash
-$ paddle version
-PaddlePaddle 0.10.0, compiled with
-    with_avx: ON
-    with_gpu: OFF
-    with_double: OFF
-    with_python: ON
-    with_rdma: OFF
-    with_timer: OFF
-```
+## 启动参数说明
 
-下面以`doc/howto/usage/cluster/src/word2vec`中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。
+下面以`doc/howto/cluster/src/word2vec`中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。
 
-## 启动参数说明
 ### 启动参数服务器
 执行以下的命令启动一个参数服务器并等待和计算节点的数据交互
 ```bash
@@ -92,11 +58,11 @@ paddle.init(
 参数说明
 
 - use_gpu： **可选，默认False**，是否启用GPU训练
-- trainer_count：**必选，默认1**，当前训练任务trainer总个数
+- trainer_count：**必选，默认1**，当前trainer的线程数目
 - port：**必选，默认7164**，连接到pserver的端口
 - ports_num：**必选，默认1**，连接到pserver的端口个数
 - ports_num_for_sparse：**必选，默认0**，和pserver之间用于稀疏类型参数通信的端口个数
-- num_gradient_servers：**必选，默认1**，当前训练任务pserver总数
+- num_gradient_servers：**必选，默认1**，当前训练任务trainer总数
 - trainer_id：**必选，默认0**，每个trainer的唯一ID，从0开始的整数
 - pservers：**必选，默认127.0.0.1**，当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开
 
@@ -167,22 +133,3 @@ test.txt-00002
 
 - `train_data_dir`：包含训练数据的目录，可以是从分布式存储挂载过来的，也可以是在任务启动前下载到本地的。
 - `test_data_dir`：包含测试数据集的目录。
-
-## 使用分布式计算平台或工具
-
-PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
-- [Kubernetes](http://kubernetes.io) Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
-- [OpenMPI](https://www.open-mpi.org) 成熟的高性能并行计算框架。
-- [Fabric](http://www.fabfile.org) 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。
-
-对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在[cluster_train_v2](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2)找到。
-
-在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
-
-## 在不同集群中运行
-
-  - [fabric集群](fabric_cn.md)
-  - [openmpi集群](openmpi_cn.md)
-  - [kubernetes单机](k8s_cn.md)
-  - [kubernetes distributed分布式](k8s_distributed_cn.md)
-  - [AWS上运行kubernetes集群训练](k8s_aws_cn.md)

doc/howto/usage/cluster/cluster_train_en.md → doc/howto/cluster/cmd_argument_en.md

-# Distributed Training
-
-## Introduction
-
-In this article, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
-
-<img src="https://user-images.githubusercontent.com/13348433/31772146-41523d84-b511-11e7-8a12-a69fd136c283.png" width="500">
-
-- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
-- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
-- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
-
-PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
-
-When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
-
-## Preparations
-1. Prepare your computer cluster. It's normally a bunch of Linux servers connected by LAN. Each server will be assigned a unique IP address. The computers in the cluster can be called "nodes".
-2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html) document. We strongly recommend using [Docker installation](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/docker_install_en.html).
-
-After installation, you can check the version by typing the below command (run a docker container  if using docker: `docker run -it paddlepaddle/paddle:[tag] /bin/bash`):
-
-```bash
-$ paddle version
-PaddlePaddle 0.10.0rc, compiled with
-    with_avx: ON
-    with_gpu: OFF
-    with_double: OFF
-    with_python: ON
-    with_rdma: OFF
-    with_timer: OFF
-```
-
-We'll take `doc/howto/usage/cluster/src/word2vec` as an example to introduce distributed training using PaddlePaddle v2 API.
-
 ## Command-line arguments
 
+We'll take `doc/howto/cluster/src/word2vec` as an example to introduce distributed training using PaddlePaddle v2 API.
+
 ### Starting parameter server
 
 Type the below command to start a parameter server which will wait for trainers to connect:
@@ -95,11 +62,11 @@ paddle.init(
 Parameter Description
 
 - use_gpu: **optional, default False**, set to "True" to enable GPU training.
-- trainer_count: **required, default 1**, total count of trainers in the training job.
+- trainer_count: **required, default 1**, number of threads in current trainer.
 - port: **required, default 7164**, port to connect to parameter server.
 - ports_num: **required, default 1**, number of ports for communication.
 - ports_num_for_sparse: **required, default 0**, number of ports for sparse type caculation.
-- num_gradient_servers: **required, default 1**, total number of gradient server.
+- num_gradient_servers: **required, default 1**, number of trainers in current job.
 - trainer_id: **required, default 0**, ID for every trainer, start from 0.
 - pservers: **required, default 127.0.0.1**, list of IPs of parameter servers, separated by ",".
 
@@ -171,21 +138,3 @@ Your workspace may looks like:
 
 - `train_data_dir`: containing training data. Mount from storage service or copy trainning data to here.
 - `test_data_dir`: containing testing data.
-
-## Use cluster platforms or cluster management tools
-
-PaddlePaddle supports running jobs on several platforms including:
-- [Kubernetes](http://kubernetes.io) open-source system for automating deployment, scaling, and management of containerized applications from Google.
-- [OpenMPI](https://www.open-mpi.org) Mature high performance parallel computing framework.
-- [Fabric](http://www.fabfile.org) A cluster management tool. Write scripts to submit jobs or manage the cluster.
-
-We'll introduce cluster job management on these platforms. The examples can be found under [cluster_train_v2](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2).
-
-These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
-
-## Use different clusters
-
-  - [fabric](fabric_en.md)
-  - [openmpi](openmpi_en.md)
-  - [kubernetes](k8s_en.md)
-  - [kubernetes on AWS](k8s_aws_en.md)

doc/howto/usage/cluster/fluid_cluster_train_en.md → doc/howto/cluster/fluid_cluster_train_en.md

@@ -16,6 +16,12 @@ PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes
 
 PaddlePaddle build and installation guide can be found  [here](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html).
 
+In addition to above, the `cmake` command should be run with the option `WITH_DISTRIBUTE` set to on. An example bare minimum `cmake` command would look as follows:
+
+``` bash
+cmake .. -DWITH_DOC=OFF -DWITH_GPU=OFF -DWITH_DISTRIBUTE=ON -DWITH_SWIG_PY=ON -DWITH_PYTHON=ON
+```
+
 ### Update the training script
 
 #### Non-cluster training script
@@ -119,7 +125,14 @@ for pass_id in range(100):
 
 ### E2E demo
 
-Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py). In parameter server node run the following in the command line:
+Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py).
+First `cd` into the folder that contains the `python` files. In this case:
+
+```bash
+cd /paddle/python/paddle/v2/fluid/tests/book_distribute
+```
+
+In parameter server node run the following in the command line:
 
 ``` bash
 PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py

doc/howto/cluster/index_cn.rst

+分布式训练
+==========
+
+本节将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
+
+.. image:: src/ps_cn.png
+   :width: 500
+
+- 数据分片（Data shard): 用于训练神经网络的数据，被切分成多个部分，每个部分分别给每个trainer使用。
+- 计算节点（Trainer）: 每个trainer启动后读取切分好的一部分数据，开始神经网络的“前馈”和“后馈”计算，并和参数服务器通信。在完成一定量数据的训练后，上传计算得出的梯度（gradients），然后下载优化更新后的神经网络参数（parameters）。
+- 参数服务器（Parameter server）:每个参数服务器只保存整个神经网络所有参数的一部分。参数服务器接收从计算节点上传的梯度，并完成参数优化更新，再将更新后的参数下发到每个计算节点。
+
+这样，通过计算节点和参数服务器的分布式协作，可以完成神经网络的SGD方法的训练。PaddlePaddle可以同时支持同步随机梯度下降（SGD）和异步随机梯度下降。
+
+在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
+
+..  toctree::
+  :maxdepth: 1
+
+  preparations_cn.md
+  cmd_argument_cn.md
+  multi_cluster/index_cn.rst

doc/howto/cluster/index_en.rst

+Distributed Training
+====================
+
+In this section, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
+
+.. image:: src/ps_en.png
+   :width: 500
+
+- Data shard: training data will be split into multiple partitions, trainers use the partitions of the whole dataset to do the training job.
+- Trainer: each trainer reads the data shard, and train the neural network. Then the trainer will upload calculated "gradients" to parameter servers, and wait for parameters to be optimized on the parameter server side. When that finishes, the trainer download optimized parameters and continues its training.
+- Parameter server: every parameter server stores part of the whole neural network model data. They will do optimization calculations when gradients are uploaded from trainers, and then send updated parameters to trainers.
+
+PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and asynchronous SGD.
+
+When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
+
+..  toctree::
+  :maxdepth: 1
+
+  preparations_en.md
+  cmd_argument_en.md
+  multi_cluster/index_en.rst

doc/howto/cluster/multi_cluster/index_cn.rst

+在不同集群中运行
+================
+
+PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
+- `Kubernetes <http://kubernetes.io>`_ Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
+- `OpenMPI <https://www.open-mpi.org>`_ 成熟的高性能并行计算框架。
+- `Fabric <http://www.fabfile.org>`_ 集群管理工具。可以使用`Fabric`编写集群任务提交和管理脚本。
+
+对于不同的集群平台，会分别介绍集群作业的启动和停止方法。这些例子都可以在 `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ 找到。
+
+在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
+
+..  toctree::
+  :maxdepth: 1
+
+  fabric_cn.md
+  openmpi_cn.md
+  k8s_cn.md
+  k8s_distributed_cn.md
+  k8s_aws_cn.md

doc/howto/cluster/multi_cluster/index_en.rst

+Use different clusters
+======================
+
+PaddlePaddle supports running jobs on several platforms including:
+- `Kubernetes <http://kubernetes.io>`_ open-source system for automating deployment, scaling, and management of containerized applications from Google.
+- `OpenMPI <https://www.open-mpi.org>`_ Mature high performance parallel computing framework.
+- `Fabric <http://www.fabfile.org>`_ A cluster management tool. Write scripts to submit jobs or manage the cluster.
+
+We'll introduce cluster job management on these platforms. The examples can be found under `cluster_train_v2 <https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/scripts/cluster_train_v2>`_ .
+
+These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
+
+..  toctree::
+  :maxdepth: 1
+
+  fabric_en.md
+  openmpi_en.md
+  k8s_en.md
+  k8s_aws_en.md