From 0cecbbfcaf6fd5e53ccb533b05284506a8345094 Mon Sep 17 00:00:00 2001 From: Travis CI Date: Thu, 9 Mar 2017 17:34:15 +0000 Subject: [PATCH] Deploy to GitHub Pages: 76749f6895acac3a8d74f127f52a4835220f0054 --- .../docker_install_en.rst.txt | 42 +++++++++---------- .../build_and_install/docker_install_en.html | 16 ++++--- develop/doc/searchindex.js | 2 +- .../docker_install_cn.rst.txt | 32 +++++++++++--- .../build_and_install/docker_install_cn.html | 26 +++++++++--- develop/doc_cn/searchindex.js | 2 +- 6 files changed, 76 insertions(+), 44 deletions(-) diff --git a/develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt b/develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt index a92201c618c..a4f62b28356 100644 --- a/develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt +++ b/develop/doc/_sources/getstarted/build_and_install/docker_install_en.rst.txt @@ -84,27 +84,27 @@ Windows -- in a consistent way. 4. Run PaddlePaddle Book under Docker Container - The Jupyter Notebook is an open-source web application that allows - you to create and share documents that contain live code, equations, - visualizations and explanatory text in a single browser. + The Jupyter Notebook is an open-source web application that allows + you to create and share documents that contain live code, equations, + visualizations and explanatory text in a single browser. - PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. - We already exposed port 8888 for this book. If you want to - dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. + PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. + We already exposed port 8888 for this book. If you want to + dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. - Once you are inside the container, simply issue the command: + Once you are inside the container, simply issue the command: - .. code-block:: bash - - jupyter notebook - - Then, you would back and paste the address into the local browser: + .. code-block:: bash + + jupyter notebook - .. code-block:: text + Then, you would back and paste the address into the local browser: + + .. code-block:: text - http://localhost:8888/ + http://localhost:8888/ - That's all. Enjoy your journey! + That's all. Enjoy your journey! CPU-only and GPU Images ----------------------- @@ -116,21 +116,21 @@ automatically runs the following commands: .. code-block:: bash - docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . - docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . + docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . + docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON . To run the CPU-only image as an interactive container: .. code-block:: bash - docker run -it --rm paddledev/paddle:cpu-latest /bin/bash + docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash or, we can run it as a daemon container .. code-block:: bash - docker run -d -p 2202:22 paddledev/paddle:cpu-latest + docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu and SSH to this container using password :code:`root`: @@ -152,7 +152,7 @@ to install CUDA driver and let Docker knows about it: 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:gpu-latest + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu Non-AVX Images @@ -194,7 +194,7 @@ container: .. code-block:: bash - docker run -d --name paddle-cpu-doc paddle:cpu + docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx diff --git a/develop/doc/getstarted/build_and_install/docker_install_en.html b/develop/doc/getstarted/build_and_install/docker_install_en.html index 60d4470e75b..7054330503f 100644 --- a/develop/doc/getstarted/build_and_install/docker_install_en.html +++ b/develop/doc/getstarted/build_and_install/docker_install_en.html @@ -277,8 +277,7 @@ ctest

  • Run PaddlePaddle Book under Docker Container

    -
    -

    The Jupyter Notebook is an open-source web application that allows +

    The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text in a single browser.

    PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. @@ -293,7 +292,6 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.

    That’s all. Enjoy your journey!

    -
    • @@ -303,16 +301,16 @@ dig deeper into deep learning, PaddlePaddle Book definitely is your best choice. CPU-only one and a CUDA GPU one. We do so by configuring dockerhub.com automatically runs the following commands:

    -
    docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile .
-docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
+
    docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
 
    
 
    
 
    To run the CPU-only image as an interactive container:
    
-
    docker run -it --rm paddledev/paddle:cpu-latest /bin/bash
+
    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
 
    
 
    
 
    or, we can run it as a daemon container
    
-
    docker run -d -p 2202:22 paddledev/paddle:cpu-latest
+
    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
 
    
 
    
 
    and SSH to this container using password root:
    
@@ -328,7 +326,7 @@ from a laptop.
    
 to install CUDA driver and let Docker knows about it:
    
 
    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:gpu-latest
+docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
 
    
 
    
 
    
@@ -359,7 +357,7 @@ for users to browse and understand the C++ source code.
    
 
    As long as we give the Paddle Docker container a name, we can run an
 additional Nginx Docker container to serve the volume from the Paddle
 container:
    
-
    docker run -d --name paddle-cpu-doc paddle:cpu
+
    docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
 docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
 
    
 
    
diff --git a/develop/doc/searchindex.js b/develop/doc/searchindex.js
index 9f1c4320590..80db0b432de 100644
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Dataset","Regression MovieLens Ratting","Semantic Role labeling Tutorial","Sentiment Analysis Tutorial","Text generation Tutorial"],titleterms:{"case":41,"class":34,"function":47,"new":34,"return":24,AWS:42,Abs:14,DNS:42,EFS:42,For:43,KMS:42,Use:[41,43],Using:[27,33],about:0,absactiv:6,access:42,account:42,activ:[6,14],adadelta:18,adadeltaoptim:12,adagrad:18,adagradoptim:12,adam:18,adamax:18,adamaxoptim:12,adamoptim:12,add:42,address:42,addto:16,addto_lay:10,adversari:48,aggreg:[10,16],algorithm:52,analysi:56,api:[1,4],appendix:52,applic:4,approach:36,architectur:[32,52],argument:[24,38,41,52],asset:42,associ:42,async:39,attent:32,attribut:[7,15],auc_evalu:9,avg:19,avgpool:13,avx:27,aws:42,background:25,base:[9,10],baseactiv:6,basepool:19,basepoolingtyp:13,basesgdoptim:12,batch:24,batch_norm:16,batch_norm_lay:10,batch_siz:24,beam_search:[10,16],between:23,bidirect:56,bidirectional_lstm:[11,17],bilinear_interp:16,bilinear_interp_lay:10,bleu:57,block_expand:16,block_expand_lay:10,brelu:14,breluactiv:6,bucket:42,build:[26,28,43],built:36,cach:3,cento:26,check:[10,16,34,37],chines:47,choos:42,chunk_evalu:9,cifar:20,classif:[9,49],classification_error_evalu:9,classification_error_printer_evalu:9,clone:33,cloudform:42,cluster:[37,41,42],code:33,column_sum_evalu:9,command:[40,41,52,57],commit:[33,43],common:39,commun:39,compos:24,concat:16,concat_lay:10,concept:42,config:[4,41,54,55],configur:[21,32,35,37,42,52,54],conll05:20,connect:[10,16],contain:[27,43],content:[36,42],context_project:[10,16],contribut:33,conv:[10,16],conv_oper:[10,16],conv_project:[10,16],conv_shift:16,conv_shift_lay:10,convolut:[49,52],core:42,cos_sim:[10,16],cost:[10,16],cpu:[27,41],creat:[24,33,42,43],creator:24,credenti:42,credit:0,crf:16,crf_decod:16,crf_decoding_lay:10,crf_layer:10,cross_entropi:10,cross_entropy_cost:16,cross_entropy_with_selfnorm:10,cross_entropy_with_selfnorm_cost:16,ctc:16,ctc_error_evalu:9,ctc_layer:10,cudnnavg:19,cudnnmax:19,custom:24,dat:53,data:[10,16,20,24,25,32,42,43,47,48,49,52,54,55,56,57],data_lay:10,datafeed:20,dataprovid:[3,4,39],dataset:[20,53,54,57],datasourc:8,datatyp:20,date:33,debian:29,decayedadagrad:18,decayedadagradoptim:12,decor:24,defin:[42,52,56,57],delet:42,delv:49,demo:42,depend:26,deriv:34,descript:[39,48,53,55],design:[23,24],destroi:42,detail:[39,49],develop:[27,35],devic:41,dictionari:[24,47],differ:41,directori:42,distribut:[23,37,39,42],doc:[23,24],docker:[27,43],document:[27,46],dotmul_oper:[10,16],dotmul_project:[10,16],down:42,download:[26,42,43,47,50,54,57],dropout_lay:[11,17],ec2:42,elast:42,embed:[16,47,52],embedding_lay:10,entri:24,eos:16,eos_lay:10,equat:34,evalu:[9,25,54],evalutaion:57,event:[22,23],exampl:[23,47,48],exercis:49,exp:14,expactiv:6,expand:16,expand_lay:10,extern:42,extract:[47,50,54,57],fc_layer:10,featur:[50,53,54,55],field:54,file:[42,43,52,53,54],find:42,first_seq:[10,16],fork:33,format:52,from:[23,26,28],full_matrix_project:[10,16],fulli:[10,16],gan:48,gate:32,gener:[32,48,57],get:[30,43],get_output:16,get_output_lay:10,github:33,gpu:[27,36,39,41],gradient:34,gradient_printer_evalu:9,group:[10,16,42],gru:[11,17,39],gru_group:[11,17],gru_step:16,gru_step_lay:10,gru_unit:[11,17],grumemori:[10,16],guid:29,hand:36,handler:23,hook:33,how:[24,35,36],hsigmoid:[10,16],huber_cost:[10,16],iam:42,ident:14,identity_project:[10,16],identityactiv:6,imag:[10,11,16,17,27,43,49],imagenet:50,imdb:[20,56],img_cmrnorm:16,img_cmrnorm_lay:10,img_conv:16,img_conv_bn_pool:[11,17],img_conv_group:[11,17],img_conv_lay:10,img_pool:16,img_pool_lay:10,imikolov:20,implement:[24,34,48],infer:[22,52],info:50,ingredi:23,init_hook:3,initi:[41,42],input_typ:3,inspect:42,instal:[26,28,29,42,52],instanc:42,integr:42,interfac:[20,24,50],interpol:16,interpolation_lay:10,introduct:[2,47,50,56,57],isn:24,job:[37,42,43],join:[10,16],keep:33,kei:42,kill:37,kube:42,kubectl:42,kubernet:[42,43],label:55,lambda_cost:[10,16],last_seq:[10,16],lastest:33,launch:37,layer:[10,16,23,34,41],layeroutput:10,layertyp:10,learn:39,line:[40,52],linear:[14,25],linear_comb:16,linear_comb_lay:10,linearactiv:6,list:24,local:[41,42],log:[14,52],logactiv:6,logist:52,lstm:[11,17,39,55,56],lstm_step:16,lstm_step_lay:10,lstmemori:[10,16],lstmemory_group:[11,17],lstmemory_unit:[11,17],map:24,math:[10,16],matrix:39,max:19,maxframe_printer_evalu:9,maxid:16,maxid_lay:10,maxid_printer_evalu:9,maxout:16,maxout_lay:10,maxpool:13,memori:[10,16],meta:54,metric:39,mini:24,minibatch:20,misc:[11,17],mix:[10,16,41],mixed_lay:10,mnist:[20,48],model:[3,4,21,23,25,31,32,37,41,47,48,49,50,51,52,57],modifi:43,momentum:18,momentumoptim:12,movi:[53,54],movielen:[20,53,54],multi_binary_label_cross_entropi:10,multi_binary_label_cross_entropy_cost:16,multipl:24,name:42,nce:16,nce_lay:10,need:[24,36],network:[11,17,32,41,48,49,50,52,54,55],neural:[32,49,52,54,55],neuralnetwork:25,nlp:[11,17,39],non:[3,27],norm:[10,16],nvprof:36,nvvp:36,object:54,observ:[47,50],onli:[24,27],optim:[12,18,35,52],option:[26,47],outlin:38,output:[11,37,42],overview:52,packag:29,pad:16,pad_lay:10,paddl:[24,43],paddlepaddl:[23,27,28,42,46,47,57],pair:42,parallel_nn:41,paramet:[7,15,22,23,39,40,42,47,50],paraphras:47,pass:41,perform:[36,39],pnpair_evalu:9,point:42,pool:[10,13,16,19],pooling_lay:10,power:16,power_lay:10,pre:33,precision_recall_evalu:9,predict:[5,49,50,54,55,56],prefetch:24,prepar:[25,32,37,42,47,48,49,54,56,57],preprocess:[47,49,52,54,57],prerequisit:37,pretrain:[47,57],print:9,privat:42,problem:25,profil:36,provid:[3,24,52,54,55],pull:33,push:33,pydataprovider2:3,python:[5,24,34,50,52,54],quick:52,randomnumb:39,rank:9,rank_cost:[10,16],rat:54,rate:53,reader:[20,23,24],recurr:[10,11,16,17,32,52],recurrent_group:[10,16],recurrent_lay:10,refer:[3,36,55,56],region:42,regress:[25,52,54],relu:14,reluactiv:6,render:42,repeat:16,repeat_lay:10,request:33,requir:[26,33],reshap:[10,16],resnet:50,result:[37,43,57],revis:[33,47],rmsprop:18,rmspropoptim:12,rnn:[31,32,39],role:55,rotat:16,rotate_lay:10,route53:42,run:[37,43,55],sampl:[10,16],sampling_id:16,sampling_id_lay:10,scale:16,scaling_lay:10,scaling_project:[10,16],script:43,secur:42,selective_fc:16,selective_fc_lay:10,semant:55,sentiment:[20,56],seq_concat:16,seq_concat_lay:10,seq_reshap:16,seq_reshape_lay:10,seqtext_printer_evalu:9,sequenc:32,sequence_conv_pool:[11,17],sequencesoftmax:14,sequencesoftmaxactiv:6,sequenti:3,server:[39,42],servic:42,set:[12,40],setup:[26,42],sgd:39,share:23,shuffl:24,sigmoid:14,sigmoidactiv:6,simpl:[25,32],simple_attent:[11,17],simple_gru:[11,17],simple_img_conv_pool:[11,17],simple_lstm:[11,17],singl:24,slice:[10,16],slope_intercept:16,slope_intercept_lay:10,softmax:14,softmaxactiv:6,softrelu:14,softreluactiv:6,sourc:[26,28],span:26,spars:41,specifi:[41,47],split:54,spp:16,spp_layer:10,squar:14,squareactiv:6,squarerootn:19,squarerootnpool:13,stack:56,standard:52,stanh:14,stanhactiv:6,start:[23,30,42,43,52],startup:43,structur:48,suffici:24,sum:19,sum_cost:[10,16],sum_evalu:9,sum_to_one_norm:16,sum_to_one_norm_lay:10,summar:23,summari:52,sumpool:13,system:42,table_project:[10,16],take:24,tanh:14,tanhactiv:6,tear:42,templat:42,tensor:16,tensor_lay:10,test:[34,39,41,54,55,56],text:57,text_conv_pool:[11,17],timer:36,tip:36,toi:48,tool:36,train:[22,23,24,25,37,39,41,42,43,47,48,49,52,54,55,56,57],trainer:[22,42,54],tran:16,trans_full_matrix_project:[10,16],trans_lay:10,transfer:52,tune:[36,39],tutori:[47,49,51,55,56,57],ubuntu:26,uci_h:20,unit:[34,39],updat:[23,33,42],usag:[24,35],use:24,user:[47,53,54,56,57],util:9,value_printer_evalu:9,vector:39,verifi:42,version:33,vgg_16_network:[11,17],visual:50,volum:42,vpc:42,warp_ctc:16,warp_ctc_lay:10,what:36,why:[24,36],wmt14:20,word:[47,52],workflow:57,workspac:37,wrapper:34,write:[34,52],yaml:43,your:33,zoo:[50,51]}})
\ No newline at end of file
diff --git a/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt b/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt
index 78f518cfe49..cf7dddd073c 100644
--- a/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt
+++ b/develop/doc_cn/_sources/getstarted/build_and_install/docker_install_cn.rst.txt
@@ -56,6 +56,26 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do
       cd /paddle/build
       ctest
 
+4. 在Docker容器中运行PaddlePaddle书籍
+
+   Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
+
+   PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
+   如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。
+   
+   当您进入容器内之后，只用运行以下命令：
+
+   .. code-block:: bash
+		   
+      jupyter notebook
+
+   然后在浏览器中输入以下网址：
+      
+   .. code-block:: text
+
+      http://localhost:8888/
+
+   就这么简单，享受您的旅程！
 
 纯CPU和GPU的docker镜像
 ----------------------
@@ -64,20 +84,20 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do
 
 .. code-block:: bash
 
-   docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile .
-   docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
+   docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+   docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
 
 以交互容器方式运行纯CPU的镜像：
 
 .. code-block:: bash
 
-    docker run -it --rm paddledev/paddle:cpu-latest /bin/bash
+    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
 
 或者，可以以后台进程方式运行容器：
 
 .. code-block:: bash
 
-    docker run -d -p 2202:22 paddledev/paddle:cpu-latest
+    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
 
 然后用密码 :code:`root` SSH进入容器：
 
@@ -94,7 +114,7 @@ SSH方式的一个优点是我们可以从多个终端进入容器。比如，
 
     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:gpu-latest
+    docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
 
 
 非AVX镜像
@@ -128,7 +148,7 @@ Paddle的Docker镜像带有一个通过 `woboq code browser
 
 .. code-block:: bash
 
-   docker run -d --name paddle-cpu-doc paddle:cpu
+   docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
    docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
 
 接着我们就能够打开浏览器在 http://localhost:8088/paddle/ 浏览代码。
diff --git a/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html b/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html
index aef9cbcd04a..ea7e67d4639 100644
--- a/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html
+++ b/develop/doc_cn/getstarted/build_and_install/docker_install_cn.html
@@ -260,21 +260,35 @@ ctest
 
    
 
    
 
+
  • 在Docker容器中运行PaddlePaddle书籍
    
+
    Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
    
+
    PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。
+如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。
    
+
    当您进入容器内之后，只用运行以下命令：
    
+
    jupyter notebook
+
    
+
    
+
    然后在浏览器中输入以下网址：
    
+
    http://localhost:8888/
+
    
+
    
+
    就这么简单，享受您的旅程！
    
+
    • 
 
 
    
 
    
 
    纯CPU和GPU的docker镜像
    
 
    对于每一个PaddlePaddle版本，我们都会发布两个Docker镜像：纯CPU的和GPU的。我们通过设置 dockerhub.com 自动运行以下两个命令：
    
-
    docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile .
-docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
+
    docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
+docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON .
 
    
 
    
 
    以交互容器方式运行纯CPU的镜像：
    
-
    docker run -it --rm paddledev/paddle:cpu-latest /bin/bash
+
    docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
 
    
 
    
 
    或者，可以以后台进程方式运行容器：
    
-
    docker run -d -p 2202:22 paddledev/paddle:cpu-latest
+
    docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
 
    
 
    
 
    然后用密码 root SSH进入容器：
    
@@ -285,7 +299,7 @@ docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu .
 
    以上方法在GPU镜像里也能用－只是请不要忘记按装CUDA驱动，以及告诉Docker：
    
 
    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:gpu-latest
+docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu
 
    
 
    
 
    
@@ -308,7 +322,7 @@ docker build --build-arg WITH_AVX=文档
 
    Paddle的Docker镜像带有一个通过 woboq code browser 生成的HTML版本的C++源代码，便于用户浏览C++源码。
    
 
    只要在Docker里启动PaddlePaddle的时候给它一个名字，就可以再运行另一个Nginx Docker镜像来服务HTML代码：
    
-
    docker run -d --name paddle-cpu-doc paddle:cpu
+
    docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu
 docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
 
    
 
    
diff --git a/develop/doc_cn/searchindex.js b/develop/doc_cn/searchindex.js
index f175c4f2db1..b628cb0f3ac 100644
--- a/develop/doc_cn/searchindex.js
+++ b/develop/doc_cn/searchindex.js
@@ -1 +1 @@
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