From 2e46c35afc0a0ecd0fab9d5470d1ab10bab14cf5 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Wed, 22 Nov 2017 22:16:38 +0800 Subject: [PATCH] follow comments --- doc/getstarted/basic_usage/index_cn.rst | 108 ------------------ doc/getstarted/basic_usage/index_en.rst | 101 ---------------- doc/getstarted/basic_usage/parameters.png | Bin 44469 -> 0 bytes .../build_from_source_cn.rst | 71 ++++++------ .../build_from_source_en.rst | 43 +++---- .../build_and_install/docker_install_cn.rst | 44 ++++--- .../build_and_install/docker_install_en.rst | 40 ++++--- doc/getstarted/build_and_install/index_cn.rst | 23 +--- doc/getstarted/build_and_install/index_en.rst | 25 +--- .../build_and_install/pip_install_cn.rst | 21 ++-- .../build_and_install/pip_install_en.rst | 16 +-- doc/getstarted/index_cn.rst | 59 +++++++++- doc/getstarted/index_en.rst | 59 +++++++++- 13 files changed, 250 insertions(+), 360 deletions(-) delete mode 100644 doc/getstarted/basic_usage/index_cn.rst delete mode 100644 doc/getstarted/basic_usage/index_en.rst delete mode 100644 doc/getstarted/basic_usage/parameters.png diff --git a/doc/getstarted/basic_usage/index_cn.rst b/doc/getstarted/basic_usage/index_cn.rst deleted file mode 100644 index b473944fc7f..00000000000 --- a/doc/getstarted/basic_usage/index_cn.rst +++ /dev/null @@ -1,108 +0,0 @@ -经典的线性回归任务 -================== - -PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍将向你展示如何利用PaddlePaddle来解决一个经典的线性回归问题。 - -任务简介 --------- - -我们展示如何用PaddlePaddle解决 `单变量的线性回归 `_ 问题。线性回归的输入是一批点 `(x, y)` ，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 `w` 和 `b` 。 - -一个例子是房产估值。我们假设房产的价格（y）是其大小（x）的一个线性函数，那么我们可以通过收集市场上房子的大小和价格，用来估计线性函数的参数w 和 b。 - -准备数据 ------------ - -假设变量 `x` 和 `y` 的真实关系为： `y = 2x + 0.3 + ε`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为线性回归的输入。下面脚本符合PaddlePaddle期待的读取数据的Python程序的模式。 - -.. code-block:: python - - # dataprovider.py - from paddle.trainer.PyDataProvider2 import * - import random - - # 定义输入数据的类型: 2个浮点数 - @provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) - def process(settings, input_file): - for i in xrange(2000): - x = random.random() - yield [x], [2*x+0.3] - -训练模型 ------------ - -为了还原 `y = 2x + 0.3`，我们先从一条随机的直线 `y' = wx + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `y'` 和 `y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。 - -在PaddlePaddle里，该模型的网络配置如下。 - -.. code-block:: python - - # trainer_config.py - from paddle.trainer_config_helpers import * - - # 1. 定义数据来源，调用上面的process函数获得观测数据 - data_file = 'empty.list' - with open(data_file, 'w') as f: f.writelines(' ') - define_py_data_sources2(train_list=data_file, test_list=None, - module='dataprovider', obj='process',args={}) - - # 2. 学习算法。控制如何改变模型参数 w 和 b - settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) - - # 3. 神经网络配置 - x = data_layer(name='x', size=1) - y = data_layer(name='y', size=1) - # 线性计算网络层: ȳ = wx + b - ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) - # 计算误差函数，即 ȳ 和真实 y 之间的距离 - cost = square_error_cost(input= ȳ, label=y) - outputs(cost) - - -这段简短的配置展示了PaddlePaddle的基本用法： - -- 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的 `process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。 - -- 第二部分主要是选择学习算法，它定义了模型参数改变的规则。PaddlePaddle提供了很多优秀的学习算法，这里使用一个基于momentum的随机梯度下降(SGD)算法，该算法每批量(batch)读取12个采样数据进行随机梯度计算来更新更新。 - -- 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络层，所以很多时候你需要做的只是定义正确的网络层并把它们连接起来。这里使用了三种网络单元： - - - **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。 - - **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。 - - **回归误差代价层**：回归误差代价层 `square_error_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。 - -定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令： - -.. code-block:: bash - - paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 - -PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` y = 2x + 0.3 ` - -模型检验 ------------ - -训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用学习的模型对另外一组测试数据进行预测，评价预测的效果。在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。 - -PaddlePaddle将每个模型参数作为一个numpy数组单独存为一个文件，所以可以利用如下方法读取模型的参数。 - -.. code-block:: python - - import numpy as np - import os - - def load(file_name): - with open(file_name, 'rb') as f: - f.read(16) # skip header for float type. - return np.fromfile(f, dtype=np.float32) - - print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) - # w=1.999743, b=0.300137 - -.. image:: ./parameters.png - :align: center - :scale: 80 % - -从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型一致。 - -这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入、模型训练和最后的结果验证。 diff --git a/doc/getstarted/basic_usage/index_en.rst b/doc/getstarted/basic_usage/index_en.rst deleted file mode 100644 index 2cc438ebbe0..00000000000 --- a/doc/getstarted/basic_usage/index_en.rst +++ /dev/null @@ -1,101 +0,0 @@ -Simple Linear Regression -======================== - -PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on. - -Problem Background ------------------- - -Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression `_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets. - -Prepare the Data ------------------ - -Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types. - - .. code-block:: python - - # dataprovider.py - from paddle.trainer.PyDataProvider2 import * - import random - - # define data types of input: 2 real numbers - @provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False) - def process(settings, input_file): - for i in xrange(2000): - x = random.random() - yield [x], [2*x+0.3] - -Train a NeuralNetwork ----------------------- - -To recover this relationship between ``X`` and ``Y``, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line ``Y' = wX + b`` , then we gradually adapt ``w`` and ``b`` to minimize the difference between ``Y'`` and ``Y``. Here is what it looks like in PaddlePaddle: - - .. code-block:: python - - # trainer_config.py - from paddle.trainer_config_helpers import * - - # 1. read data. Suppose you saved above python code as dataprovider.py - data_file = 'empty.list' - with open(data_file, 'w') as f: f.writelines(' ') - define_py_data_sources2(train_list=data_file, test_list=None, - module='dataprovider', obj='process',args={}) - - # 2. learning algorithm - settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer()) - - # 3. Network configuration - x = data_layer(name='x', size=1) - y = data_layer(name='y', size=1) - y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b')) - cost = square_error_cost(input=y_predict, label=y) - outputs(cost) - -Some of the most fundamental usages of PaddlePaddle are demonstrated: - -- The first part shows how to feed data into PaddlePaddle. In general cases, PaddlePaddle reads raw data from a list of files, and then do some user-defined process to get real input. In this case, we only need to create a placeholder file since we are generating synthetic data on the fly. - -- The second part describes learning algorithm. It defines in what ways adjustments are made to model parameters. PaddlePaddle provides a rich set of optimizers, but a simple momentum based optimizer will suffice here, and it processes 12 data points each time. - -- Finally, the network configuration. It usually is as simple as "stacking" layers. Three kinds of layers are used in this configuration: - - **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for ``X`` and ``Y``. - - **FC Layer**: FC layer is short for Fully Connected Layer, which connects all the input units to current layer and does the actual computation specified as activation function. Computation layers like this are the fundamental building blocks of a deeper model. - - **Cost Layer**: in training phase, cost layers are usually the last layers of the network. They measure the performance of current model, and provide guidence to adjust parameters. - -Now that everything is ready, you can train the network with a simple command line call: - - .. code-block:: bash - - paddle train --config=trainer_config.py --save_dir=./output --num_passes=30 - - -This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path ``./output``. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess. - - -Evaluate the Model -------------------- - -Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: ``w=2, b=0.3``, thus a better option is to check out model parameters directly. - -In PaddlePaddle, training is just to get a collection of model parameters, which are ``w`` and ``b`` in this case. Each parameter is saved in an individual file in the popular ``numpy`` array format. Here is the code that reads parameters from last pass. - - .. code-block:: python - - import numpy as np - import os - - def load(file_name): - with open(file_name, 'rb') as f: - f.read(16) # skip header for float type. - return np.fromfile(f, dtype=np.float32) - - print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b')) - # w=1.999743, b=0.300137 - - .. image:: parameters.png - :align: center - -Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. 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zlQ#;936%aFTq+O^(Sl@NEcw7c4MA+FLl9Qtmi!wY@f-IF*aT*P3IDH#AmRh6k1<{O j{~Ea`=`_ 以及GCC, G++作为编译 git clone https://github.com/PaddlePaddle/Paddle.git cd Paddle - # 如果使用Docker编译环境，执行下面的命令 - docker run -it -v $PWD:/paddle -e "WITH_GPU=ON" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh + # 如果使用Docker编译环境，执行下面的命令编译CPU-Only的二进制 + docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh # 如果不使用Docker编译环境，执行下面的命令 mkdir build cd build - cmake -DWITH_GPU=ON -DWITH_TESTING=OFF .. + cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF .. make @@ -56,64 +56,57 @@ PaddlePaddle编译需要使用到下面的依赖（包含但不限于），其 编译选项 ---------------- -PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种BLAS库等。用户可在调用cmake的时候设置它们，详细的cmake使用方法可以参考 `官方文档 `_ 。 +PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种BLAS库等。 +用户可在调用cmake的时候设置它们，详细的cmake使用方法可以参考 +`官方文档 `_ 。 -.. _build_options_bool: - -Bool型的编译选项 ----------------- - -用户可在cmake的命令行中，通过使用 ``-D`` 命令设置该类编译选项，例如 +在cmake的命令行中，通过使用 ``-D`` 命令设置该类编译选项，例如： .. code-block:: bash cmake .. -DWITH_GPU=OFF -.. csv-table:: Bool型的编译选项 +.. csv-table:: 编译选项说明 :header: "选项", "说明", "默认值" :widths: 1, 7, 2 - "WITH_GPU", "是否支持GPU。", "是" - "WITH_DOUBLE", "是否使用双精度浮点数。", "否" - "WITH_DSO", "是否运行时动态加载CUDA动态库，而非静态加载CUDA动态库。", "是" - "WITH_AVX", "是否编译含有AVX指令集的PaddlePaddle二进制文件", "是" - "WITH_PYTHON", "是否内嵌PYTHON解释器。", "是" - "WITH_STYLE_CHECK", "是否编译时进行代码风格检查", "是" - "WITH_TESTING", "是否开启单元测试", "是" - "WITH_DOC", "是否编译中英文文档", "否" - "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", "自动" - "WITH_GOLANG", "是否编译go语言的可容错parameter server", "是" - -.. _build_options_blas: - -BLAS/CUDA/Cudnn的编译选项 --------------------------- + "WITH_GPU", "是否支持GPU", "ON" + "WITH_C_API", "是否仅编译CAPI", "OFF" + "WITH_DOUBLE", "是否使用双精度浮点数", "OFF" + "WITH_DSO", "是否运行时动态加载CUDA动态库，而非静态加载CUDA动态库。", "ON" + "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_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" + "WITH_MKL", "是否使用MKL数学库，如果为否则是用OpenBLAS", "ON" + BLAS +++++ -PaddlePaddle支持以下任意一种BLAS库：`MKL `_ ，`ATLAS `_ ，`OpenBlAS `_ 和 `REFERENCE BLAS `_ 。 +PaddlePaddle支持 `MKL `_ 和 +`OpenBlAS `_ 两种BLAS库。默认使用MKL。如果使用MKL并且机器含有AVX2指令集， +还会下载MKL-DNN数学库，详细参考 `这里 `_ 。 -.. csv-table:: BLAS路径相关的编译选项 - :header: "编译选项", "描述", "注意" - :widths: 1, 2, 7 - - "MKL_ROOT", "${MKL_ROOT}/include下需要包含mkl.h，${MKL_ROOT}/lib目录下需要包含mkl_core，mkl_sequential和mkl_intel_lp64三个库。" - "ATLAS_ROOT", "${ATLAS_ROOT}/include下需要包含cblas.h，${ATLAS_ROOT}/lib下需要包含cblas和atlas两个库。" - "OPENBLAS_ROOT", "${OPENBLAS_ROOT}/include下需要包含cblas.h，${OPENBLAS_ROOT}/lib下需要包含openblas库。" - "REFERENCE_CBLAS_ROOT", "${REFERENCE_CBLAS_ROOT}/include下需要包含cblas.h，${REFERENCE_CBLAS_ROOT}/lib下需要包含cblas库。" +如果关闭MKL，则会使用OpenBLAS作为BLAS库。 -CUDA/Cudnn +CUDA/cuDNN +++++++++++ -PaddlePaddle可以使用cudnn v2之后的任何一个版本来编译运行，但尽量请保持编译和运行使用的cudnn是同一个版本。 我们推荐使用最新版本的cudnn v5.1。 +PaddlePaddle在编译时/运行时会自动找到系统中安装的CUDA和cuDNN库进行编译和执行。 + +PaddlePaddle可以使用cuDNN v5.1之后的任何一个版本来编译运行，但尽量请保持编译和运行使用的cuDNN是同一个版本。 +我们推荐使用最新版本的cuDNN。 编译选项的设置 ++++++++++++++ -PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/Cudnn库。cmake编译时，首先在系统路径(/usr/lib\:/usr/local/lib)中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如 +PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径(/usr/lib\:/usr/local/lib)中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如 .. code-block:: bash - cmake .. -DMKL_ROOT=/opt/mkl/ -DCUDNN_ROOT=/opt/cudnnv5 + cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5 注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（``rm -rf``）后，再指定。 diff --git a/doc/getstarted/build_and_install/build_from_source_en.rst b/doc/getstarted/build_and_install/build_from_source_en.rst index 80dfb8c468b..02d5ab3bb81 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.rst +++ b/doc/getstarted/build_and_install/build_from_source_en.rst @@ -16,12 +16,12 @@ Then run: git clone https://github.com/PaddlePaddle/Paddle.git cd Paddle - # run the following command if you are using docker - docker run -it -v $PWD:/paddle -e "WITH_GPU=ON" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh + # run the following command to build CPU-Only binaries if you are using docker + docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh # else run these commands mkdir build cd build - cmake -DWITH_GPU=ON -DWITH_TESTING=OFF .. + cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF .. make When the compile finishes, you can get the output whl package under @@ -78,6 +78,7 @@ You can add :code:`-D` argument to pass such options, like: :widths: 1, 7, 2 "WITH_GPU", "Build with GPU support", "ON" + "WITH_C_API", "Build only CAPI", "OFF" "WITH_DOUBLE", "Build with double precision", "OFF" "WITH_DSO", "Dynamically load CUDA libraries", "ON" "WITH_AVX", "Build with AVX support", "ON" @@ -87,34 +88,26 @@ You can add :code:`-D` argument to pass such options, like: "WITH_DOC", "Build documentaions", "OFF" "WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto" "WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON" + "WITH_MKL", "Use MKL as BLAS library, else use OpenBLAS", "ON" -.. _build_options_blas: -BLAS/CUDA/Cudnn Options --------------------------- BLAS +++++ -You can build PaddlePaddle with any of the below BLAS libraries: -`MKL `_ , -`ATLAS `_ , -`OpenBlAS `_ and -`REFERENCE BLAS `_ . - -.. csv-table:: BLAS Options - :header: "Option", "Description" - :widths: 1, 7 - - "MKL_ROOT", "${MKL_ROOT}/include must have mkl.h, ${MKL_ROOT}/lib must have mkl_core, mkl_sequential and mkl_intel_lp64 libs." - "ATLAS_ROOT", "${ATLAS_ROOT}/include must have cblas.h，${ATLAS_ROOT}/lib must have cblas and atlas libs" - "OPENBLAS_ROOT", "${OPENBLAS_ROOT}/include must have cblas.h，${OPENBLAS_ROOT}/lib must have OpenBlas libs." - "REFERENCE_CBLAS_ROOT", "${REFERENCE_CBLAS_ROOT}/include must have cblas.h，${REFERENCE_CBLAS_ROOT}/lib must have cblas lib." - -CUDA/Cudnn +PaddlePaddle supports `MKL `_ and +`OpenBlAS `_ as BLAS library。By default it uses MKL. +If you are using MKL and your machine supports AVX2, MKL-DNN will also be downloaded +and used, for more `details `_ . + +If you choose not to use MKL, then OpenBlAS will be used. + +CUDA/cuDNN +++++++++++ -PaddlePaddle can build with any version later than Cudnn v2, and we intend to -keep on with latest cudnn versions. Be sure to run with the same version of cudnn +PaddlePaddle will automatically find CUDA and cuDNN when compiling and running. + +PaddlePaddle can build with any version later than cuDNN v5.1, and we intend to +keep on with latest cuDNN versions. Be sure to run with the same version of cuDNN you built. Pass Compile Options @@ -127,7 +120,7 @@ passed to cmake, i.e. .. code-block:: bash - cmake .. -DMKL_ROOT=/opt/mkl/ -DCUDNN_ROOT=/opt/cudnnv5 + cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5 **NOTE: These options only take effect when running cmake for the first time, you need to clean the cmake cache or clean the build directory if you want to change it.** diff --git a/doc/getstarted/build_and_install/docker_install_cn.rst b/doc/getstarted/build_and_install/docker_install_cn.rst index 03a83627930..c03352562e2 100644 --- a/doc/getstarted/build_and_install/docker_install_cn.rst +++ b/doc/getstarted/build_and_install/docker_install_cn.rst @@ -30,29 +30,39 @@ 下载GPU版本的Docker镜像： .. code-block:: bash + docker pull paddlepaddle/paddle:latest-gpu docker pull docker.paddlepaddle.org/paddle:latest-gpu -下载指定版本的Docker镜像，可以从 - `DockerHub网站 `_ - 获取可选的tag，并执行下面的命令： +选择下载使用不同的BLAS库的Docker镜像： + + .. code-block:: bash + + # 默认是使用MKL的镜像 + docker pull paddlepaddle/paddle + # 使用OpenBLAS的镜像 + docker pull paddlepaddle/paddle:latest-openblas + +下载指定版本的Docker镜像，可以从 `DockerHub网站 `_ 获取可选的tag，并执行下面的命令： .. code-block:: bash + docker pull paddlepaddle/paddle:[tag] # 比如： docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu - .. _docker_run: 在Docker中执行PaddlePaddle训练程序 ------------------------------ -假设您已经在当前目录编写了一个PaddlePaddle的程序 :code:`train.py`（可以参考 +假设您已经在当前目录（比如在/home/work）编写了一个PaddlePaddle的程序 :code:`train.py`（可以参考 `PaddlePaddleBook `_ 编写），就可以使用下面的命令开始执行训练： .. code-block:: bash + + cd /home/work docker run -it -v $PWD:/work paddlepaddle/paddle /work/train.py 上述命令中， :code:`-it` 参数说明容器已交互式运行； :code:`-v $PWD:/work` @@ -74,22 +84,22 @@ 使用Docker启动PaddlePaddle Book教程 ------------------------------ -使用Docker可以快速在本地启动一个包含了PaddlePaddle官方Book教程的Jupiter Notebook，可以通过网页浏览。 +使用Docker可以快速在本地启动一个包含了PaddlePaddle官方Book教程的Jupyter Notebook，可以通过网页浏览。 PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Notebook。 如果您想要更深入了解deep learning，PaddlePaddle Book一定是您最好的选择。 大家可以通过它阅读教程，或者制作和分享带有代码、公式、图表、文字的交互式文档。 我们提供可以直接运行PaddlePaddle Book的Docker镜像，直接运行： -.. code-block:: bash + .. code-block:: bash - docker run -p 8888:8888 paddlepaddle/book + docker run -p 8888:8888 paddlepaddle/book 然后在浏览器中输入以下网址： -.. code-block:: text + .. code-block:: text - http://localhost:8888/ + http://localhost:8888/ 就这么简单，享受您的旅程！ @@ -102,19 +112,19 @@ PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Note `nvidia-docker `_ 来运行镜像。 请不要忘记提前在物理机上安装GPU最新驱动。 -.. code-block:: bash + .. code-block:: bash - nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash + nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash **注: 如果没有安装nvidia-docker，可以尝试以下的方法，将CUDA库和Linux设备挂载到Docker容器内：** -.. code-block:: bash + .. code-block:: bash - export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" - export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') - docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu + export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" + export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu -关于AVX： +**关于AVX：** AVX是一种CPU指令集，可以加速PaddlePaddle的计算。最新的PaddlePaddle Docker镜像默认 是开启AVX编译的，所以，如果您的电脑不支持AVX，需要单独 diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index 4ee55380f09..8cdb0031bd1 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -31,14 +31,26 @@ For users in China, we provide a faster mirror: Download GPU version images: .. code-block:: bash + docker pull paddlepaddle/paddle:latest-gpu docker pull docker.paddlepaddle.org/paddle:latest-gpu +Choose between different BLAS version: + + .. code-block:: bash + + # image using MKL by default + docker pull paddlepaddle/paddle + # image using OpenBLAS + docker pull paddlepaddle/paddle:latest-openblas + + If you want to use legacy versions, choose a tag from `DockerHub `_ and run: .. code-block:: bash + docker pull paddlepaddle/paddle:[tag] # i.e. docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu @@ -49,11 +61,13 @@ Launch your training program in Docker ------------------------------ Assume that you have already written a PaddlePaddle program -named :code:`train.py` (refer to +named :code:`train.py` under directory :code:`/home/work` (refer to `PaddlePaddleBook `_ for more samples), then run the following command: .. code-block:: bash + + cd /home/work docker run -it -v $PWD:/work paddlepaddle/paddle /work/train.py In the above command, :code:`-it` means run the container interactively; @@ -77,22 +91,22 @@ interactively: PaddlePaddle Book ------------------ -You can create a container serving PaddlePaddle Book using Jupiter Notebook in +You can create a container serving PaddlePaddle Book using Jupyter Notebook in one minute using Docker. PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.If you want to dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. We provide a packaged book image, simply issue the command: -.. code-block:: bash + .. code-block:: bash - docker run -p 8888:8888 paddlepaddle/book + docker run -p 8888:8888 paddlepaddle/book Then, you would back and paste the address into the local browser: -.. code-block:: text + .. code-block:: text - http://localhost:8888/ + http://localhost:8888/ That's all. Enjoy your journey! @@ -106,19 +120,19 @@ We recommend using to run GPU training jobs. Please ensure you have latest GPU driver installed before move on. -.. code-block:: bash + .. code-block:: bash - nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash + nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash **NOTE: If you don't have nvidia-docker installed, try the following method to mount CUDA libs and devices into the container.** -.. code-block:: bash + .. code-block:: bash - export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" - export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') - docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu + export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')" + export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}') + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu -About AVX: +**About AVX:** AVX is a kind of CPU instruction can accelerate PaddlePaddle's calculations. The latest PaddlePaddle Docker image turns AVX on by default, so, if your diff --git a/doc/getstarted/build_and_install/index_cn.rst b/doc/getstarted/build_and_install/index_cn.rst index e68d677412d..88c5142ddee 100644 --- a/doc/getstarted/build_and_install/index_cn.rst +++ b/doc/getstarted/build_and_install/index_cn.rst @@ -1,24 +1,6 @@ 安装与编译 ========== -.. _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： - - .. code-block:: bash - - pip install paddlepaddle - -如果需要安装支持GPU的版本，需要执行： - - .. code-block:: bash - - pip install paddlepaddle-gpu - .. _install_steps: 安装流程 @@ -44,3 +26,8 @@ PaddlePaddle提供pip和Docker的安装方式： :maxdepth: 1 build_from_source_cn.rst + +常见问题解答 +++++++++++ + +`常见问题解答 `_ diff --git a/doc/getstarted/build_and_install/index_en.rst b/doc/getstarted/build_and_install/index_en.rst index bf8e01a35c0..c8b60d03578 100644 --- a/doc/getstarted/build_and_install/index_en.rst +++ b/doc/getstarted/build_and_install/index_en.rst @@ -1,26 +1,6 @@ Install and Build ================= -.. _quick_install: - -Quick Install ----------------------- - -You can use pip to install PaddlePaddle using 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: - - .. code-block:: bash - - pip install paddlepaddle - -If you need to install GPU version, run: - - .. code-block:: bash - - pip install paddlepaddle-gpu - - .. _install_steps: Install Steps @@ -46,3 +26,8 @@ Build from Source :maxdepth: 1 build_from_source_en.md + +FAQ +++++++++++ + +`FAQ `_ diff --git a/doc/getstarted/build_and_install/pip_install_cn.rst b/doc/getstarted/build_and_install/pip_install_cn.rst index e4bba7b21ab..88c3d89856e 100644 --- a/doc/getstarted/build_and_install/pip_install_cn.rst +++ b/doc/getstarted/build_and_install/pip_install_cn.rst @@ -24,15 +24,18 @@ PaddlePaddle可以使用常用的Python包管理工具 pip install paddlepaddle-gpu -如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包并安装，在下面的链接中，使用guest登陆，然后点击Artifact标签，可以找到最新的whl安装包： - -- `CPU版本 `_ - -- `GPU CUDA-7.5 CUDNN-5版本 `_ - -- `GPU CUDA-8.0 CUDNN-5版本 `_ - -- `GPU CUDA-8.0 CUDNN-7版本 `_ +如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装， +您可以从下面的表格中找到需要的版本： + +.. csv-table:: 各个版本最新的whl包 + :header: "版本说明", "cp27-cp27mu", "cp27-cp27mu", "C-API" + :widths: 1, 3, 3, 3 + + "cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cpu_avx_openblas", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "-" + "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" .. _pip_dependency: diff --git a/doc/getstarted/build_and_install/pip_install_en.rst b/doc/getstarted/build_and_install/pip_install_en.rst index b9fa6dd9edf..5d18defd522 100644 --- a/doc/getstarted/build_and_install/pip_install_en.rst +++ b/doc/getstarted/build_and_install/pip_install_en.rst @@ -30,13 +30,15 @@ you can download the latest whl package from our CI system. Access the below links, log in as guest, then click at the "Artifact" tab, you'll find the download link of whl packages. -- `CPU Only Version `_ - -- `GPU CUDA-7.5 CUDNN-5 Version `_ - -- `GPU CUDA-8.0 CUDNN-5 Version `_ - -- `GPU CUDA-8.0 CUDNN-7 Version `_ +.. csv-table:: whl package of each version + :header: "version", "cp27-cp27mu", "cp27-cp27mu", "C-API" + :widths: 1, 3, 3, 3 + + "cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cpu_avx_openblas", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "-" + "cuda7.5_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cuda8.0_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" + "cuda8.0_cudnn7_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl `_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl `_", "`paddle.tgz `_" .. _pip_dependency: diff --git a/doc/getstarted/index_cn.rst b/doc/getstarted/index_cn.rst index aa418c657a4..660ad578afe 100644 --- a/doc/getstarted/index_cn.rst +++ b/doc/getstarted/index_cn.rst @@ -1,10 +1,65 @@ 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： + + .. code-block:: bash + + pip install paddlepaddle + +如果需要安装支持GPU的版本，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 - concepts/use_concepts_cn.rst -- `深度学习入门课程 `_ +.. _quick_start: + +快速开始 +++++++++ + +下载 `房价模型文件 `_ + +创建一个 housing.py 并粘贴此Python代码 (请确保fit_a_line.tar 是在正确的路径上) + + .. 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()) + + with open('fit_a_line.tar', 'r') as f: + parameters = paddle.parameters.Parameters.from_tar(f) + + # Infer using provided test data. + probs = paddle.infer( + output_layer=y_predict, parameters=parameters, + 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 + + concepts/use_concepts_cn.rst diff --git a/doc/getstarted/index_en.rst b/doc/getstarted/index_en.rst index be3253e3d41..845506cea7d 100644 --- a/doc/getstarted/index_en.rst +++ b/doc/getstarted/index_en.rst @@ -1,9 +1,66 @@ GET STARTED ============ +.. _quick_install: + +Quick Install +---------------------- + +You can use pip to install PaddlePaddle using 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: + + .. code-block:: bash + + pip install paddlepaddle + +If you need to install GPU version, run: + + .. code-block:: bash + + pip install paddlepaddle-gpu + +For more details about installation and build: + .. toctree:: :maxdepth: 1 build_and_install/index_en.rst -- `Deep Learning 101 `_ + +.. _quick_start: + +Quick Start +++++++++ + +Download the `trained housing prices model `_ + +Now, create a new file called housing.py, and paste this Python +code (make sure to set the right path based on the location of fit_a_line.tar +on your computer): + + + .. 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()) + + with open('fit_a_line.tar', 'r') as f: + parameters = paddle.parameters.Parameters.from_tar(f) + + # Infer using provided test data. + probs = paddle.infer( + output_layer=y_predict, parameters=parameters, + 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. -- GitLab