Merge pull request #9820 from seiriosPlus/quick_help

fix quick start for fluid #9660

Author: seiriosPlus

doc/fluid/getstarted/quickstart_cn.rst

@@ -0,0 +1,45 @@
+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.dataset.uci_housing as uci_housing
+     import paddle.fluid as fluid
+  
+     with fluid.scope_guard(fluid.core.Scope()):
+         # initialize executor with cpu
+         exe = fluid.Executor(place=fluid.CPUPlace())
+         # load inference model 
+         [inference_program, feed_target_names,fetch_targets] =  \
+             fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
+         # run inference
+         result = exe.run(inference_program, 
+                          feed={feed_target_names[0]: uci_housing.predict_reader()}, 
+                          fetch_list=fetch_targets)
+         # print predicted price is $12,273.97 
+         print 'Predicted price: ${:,.2f}'.format(result[0][0][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/fluid/getstarted/quickstart_cn.rst

@@ -0,0 +1,49 @@
+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.dataset.uci_housing as uci_housing
+     import paddle.fluid as fluid
+  
+     with fluid.scope_guard(fluid.core.Scope()):
+         # initialize executor with cpu
+         exe = fluid.Executor(place=fluid.CPUPlace())
+         # load inference model 
+         [inference_program, feed_target_names,fetch_targets] =  \
+             fluid.io.load_inference_model(uci_housing.fluid_model(), exe)
+         # run inference
+         result = exe.run(inference_program, 
+                          feed={feed_target_names[0]: uci_housing.predict_reader()}, 
+                          fetch_list=fetch_targets)
+         # print predicted price is $12,273.97 
+         print 'Predicted price: ${:,.2f}'.format(result[0][0][0] * 1000)
+
+Run :code:`python housing.py` and voila! It should print out a list of predictions
+for the test housing data.

doc/fluid/getstarted/quickstart_en.rst

@@ -19,7 +19,11 @@
 parse training set and test set into paddle reader creators.
 """
 
+import os
+
 import numpy as np
+import tempfile
+import tarfile
 import os
 import paddle.dataset.common
 
@@ -34,8 +38,9 @@
 
 UCI_TRAIN_DATA = None
 UCI_TEST_DATA = None
-URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fit_a_line.tar'
-MD5_MODEL = '52fc3da8ef3937822fcdd87ee05c0c9b'
+
+FLUID_URL_MODEL = 'https://github.com/PaddlePaddle/book/raw/develop/01.fit_a_line/fluid/fit_a_line.fluid.tar'
+FLUID_MD5_MODEL = '6e6dd637ccd5993961f68bfbde46090b'
 
 
 def feature_range(maximums, minimums):
@@ -113,6 +118,29 @@ def reader():
     return reader
 
 
+def fluid_model():
+    parameter_tar = paddle.dataset.common.download(
+        FLUID_URL_MODEL, 'uci_housing', FLUID_MD5_MODEL, 'fit_a_line.fluid.tar')
+
+    tar = tarfile.TarFile(parameter_tar, mode='r')
+    dirpath = tempfile.mkdtemp()
+    tar.extractall(path=dirpath)
+
+    return dirpath
+
+
+def predict_reader():
+    """
+    It returns just one tuple data to do inference.
+
+    :return: one tuple data
+    :rtype: tuple 
+    """
+    global UCI_TEST_DATA
+    load_data(paddle.dataset.common.download(URL, 'uci_housing', MD5))
+    return (UCI_TEST_DATA[0][:-1], )
+
+
 def fetch():
     paddle.dataset.common.download(URL, 'uci_housing', MD5)