分类：机器学习

环境: Ubuntu 14.04

export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl

sudo pip install –upgrade $TF_BINARY_URL

python

import tensorflow

出现AttributeError: type object ‘NewBase’ has no attribute ‘is_abstract’

解决方法：

python

import six

print(six.__file__) 查看路径

重新安装

sudo pip uninstall six

sudo pip install six –upgrade –target=”/usr/lib/python2.7/dist-packages”

环境：Ubuntu 14.04

git clone https://github.com/Microsoft/lightlda.git
cd lightlda/

vi build.sh
修改如下：
#git clone https://github.com:Microsoft/multiverso.git
git clone https://github.com/Microsoft/multiverso.git

sh build.sh

cd example

export LD_LIBRARY_PATH=~/lightlda/multiverso/third_party/lib:$LD_LIBRARY_PATH
sh nytimes.sh

环境：Ubuntu 14.04

git clone https://github.com/dmlc/xgboost.git
cd xgboost

编译

make cxx11=1

运行示例

cd demo/binary_classification
../../xgboost mushroom.conf

安装python包

cd ~/xgboost/python-package
sudo python setup.py install

https://archive.ics.uci.edu/ml/datasets.html

环境: Ubuntu 12.04, CUDA 6.0

1. 预先安装软件

pip install -r /u01/caffe/python/requirements.txt
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

# gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip
unzip master.zip
cd gflags-master
mkdir build && cd build
CXXFLAGS=”-fPIC” cmake .. -DGFLAGS_NAMESPACE=google
make && make install

# glog
wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz
tar zxvf glog-0.3.3.tar.gz
cd glog-0.3.3
./configure
make && make install

# lmdb
git clone git://gitorious.org/mdb/mdb.git
cd mdb/libraries/liblmdb
make && make install

2.  配置安装文件

cp Makefile.config.example Makefile.config
vi Makefile.config, 去掉注释（由于虚拟机不支技显卡)
CPU_ONLY := 1

3. 编译，报错如下：

jerry@hq:/u01/caffe$ make
g++ .build_release/tools/convert_imageset.o .build_release/lib/libcaffe.a -o .build_release/tools/convert_imageset.bin -fPIC -DCPU_ONLY -DNDEBUG -O2 -I/usr/include/python2.7 -I/usr/lib/python2.7/dist-packages/numpy/core/include -I/usr/local/include -I.build_release/src -I./src -I./include -Wall -Wno-sign-compare -L/usr/lib -L/usr/local/lib -L/usr/lib -lglog -lgflags -lpthread -lprotobuf -lleveldb -lsnappy -llmdb -lboost_system -lhdf5_hl -lhdf5 -lopencv_core -lopencv_highgui -lopencv_imgproc -lcblas -latlas
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<float>::Update()’:
blob.cpp:(.text._ZN5caffe4BlobIfE6UpdateEv[_ZN5caffe4BlobIfE6UpdateEv]+0x43): undefined reference to `void caffe::caffe_gpu_axpy<float>(int, float, float const*, float*)’
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<float>::asum_data() const’:
blob.cpp:(.text._ZNK5caffe4BlobIfE9asum_dataEv[_ZNK5caffe4BlobIfE9asum_dataEv]+0x3f): undefined reference to `void caffe::caffe_gpu_asum<float>(int, float const*, float*)’
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<float>::asum_diff() const’:
blob.cpp:(.text._ZNK5caffe4BlobIfE9asum_diffEv[_ZNK5caffe4BlobIfE9asum_diffEv]+0x3f): undefined reference to `void caffe::caffe_gpu_asum<float>(int, float const*, float*)’
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<double>::Update()’:
blob.cpp:(.text._ZN5caffe4BlobIdE6UpdateEv[_ZN5caffe4BlobIdE6UpdateEv]+0x43): undefined reference to `void caffe::caffe_gpu_axpy<double>(int, double, double const*, double*)’
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<double>::asum_data() const’:
blob.cpp:(.text._ZNK5caffe4BlobIdE9asum_dataEv[_ZNK5caffe4BlobIdE9asum_dataEv]+0x3f): undefined reference to `void caffe::caffe_gpu_asum<double>(int, double const*, double*)’
.build_release/lib/libcaffe.a(blob.o): In function `caffe::Blob<double>::asum_diff() const’:
blob.cpp:(.text._ZNK5caffe4BlobIdE9asum_diffEv[_ZNK5caffe4BlobIdE9asum_diffEv]+0x3f): undefined reference to `void caffe::caffe_gpu_asum<double>(int, double const*, double*)’
.build_release/lib/libcaffe.a(common.o): In function `caffe::GlobalInit(int*, char***)’:
common.cpp:(.text+0x12a): undefined reference to `gflags::ParseCommandLineFlags(int*, char***, bool)’
.build_release/lib/libcaffe.a(common.o): In function `caffe::Caffe::Caffe()’:
common.cpp:(.text+0x179): undefined reference to `cublasCreate_v2′
common.cpp:(.text+0x1cb): undefined reference to `curandCreateGenerator’
common.cpp:(.text+0x22d): undefined reference to `curandSetPseudoRandomGeneratorSeed’
.build_release/lib/libcaffe.a(common.o): In function `caffe::Caffe::~Caffe()’:
common.cpp:(.text+0x434): undefined reference to `cublasDestroy_v2′
common.cpp:(.text+0x456): undefined reference to `curandDestroyGenerator’
.build_release/lib/libcaffe.a(common.o): In function `caffe::Caffe::DeviceQuery()’:
common.cpp:(.text+0x5f8): undefined reference to `cudaGetDevice’
common.cpp:(.text+0x616): undefined reference to `cudaGetDeviceProperties’
common.cpp:(.text+0xd22): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(common.o): In function `caffe::Caffe::SetDevice(int)’:
common.cpp:(.text+0x1222): undefined reference to `cudaGetDevice’
common.cpp:(.text+0x1247): undefined reference to `cudaSetDevice’
common.cpp:(.text+0x127b): undefined reference to `cublasDestroy_v2′
common.cpp:(.text+0x12a9): undefined reference to `curandDestroyGenerator’
common.cpp:(.text+0x12ce): undefined reference to `cublasCreate_v2′
common.cpp:(.text+0x12fc): undefined reference to `curandCreateGenerator’
common.cpp:(.text+0x1330): undefined reference to `curandSetPseudoRandomGeneratorSeed’
common.cpp:(.text+0x1729): undefined reference to `cudaGetErrorString’
common.cpp:(.text+0x1882): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(common.o): In function `caffe::Caffe::set_random_seed(unsigned int)’:
common.cpp:(.text+0x1aff): undefined reference to `curandDestroyGenerator’
common.cpp:(.text+0x1b2d): undefined reference to `curandCreateGenerator’
common.cpp:(.text+0x1b5c): undefined reference to `curandSetPseudoRandomGeneratorSeed’
.build_release/lib/libcaffe.a(math_functions.o): In function `void caffe::caffe_copy<double>(int, double const*, double*)’:
math_functions.cpp:(.text._ZN5caffe10caffe_copyIdEEviPKT_PS1_[_ZN5caffe10caffe_copyIdEEviPKT_PS1_]+0x6c): undefined reference to `cudaMemcpy’
math_functions.cpp:(.text._ZN5caffe10caffe_copyIdEEviPKT_PS1_[_ZN5caffe10caffe_copyIdEEviPKT_PS1_]+0x160): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(math_functions.o): In function `void caffe::caffe_copy<int>(int, int const*, int*)’:
math_functions.cpp:(.text._ZN5caffe10caffe_copyIiEEviPKT_PS1_[_ZN5caffe10caffe_copyIiEEviPKT_PS1_]+0x6c): undefined reference to `cudaMemcpy’
math_functions.cpp:(.text._ZN5caffe10caffe_copyIiEEviPKT_PS1_[_ZN5caffe10caffe_copyIiEEviPKT_PS1_]+0x160): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(math_functions.o): In function `void caffe::caffe_copy<unsigned int>(int, unsigned int const*, unsigned int*)’:
math_functions.cpp:(.text._ZN5caffe10caffe_copyIjEEviPKT_PS1_[_ZN5caffe10caffe_copyIjEEviPKT_PS1_]+0x6c): undefined reference to `cudaMemcpy’
math_functions.cpp:(.text._ZN5caffe10caffe_copyIjEEviPKT_PS1_[_ZN5caffe10caffe_copyIjEEviPKT_PS1_]+0x160): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(math_functions.o): In function `void caffe::caffe_copy<float>(int, float const*, float*)’:
math_functions.cpp:(.text._ZN5caffe10caffe_copyIfEEviPKT_PS1_[_ZN5caffe10caffe_copyIfEEviPKT_PS1_]+0x6c): undefined reference to `cudaMemcpy’
math_functions.cpp:(.text._ZN5caffe10caffe_copyIfEEviPKT_PS1_[_ZN5caffe10caffe_copyIfEEviPKT_PS1_]+0x160): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(syncedmem.o): In function `caffe::SyncedMemory::cpu_data()’:
syncedmem.cpp:(.text+0x26): undefined reference to `caffe::caffe_gpu_memcpy(unsigned long, void const*, void*)’
.build_release/lib/libcaffe.a(syncedmem.o): In function `caffe::SyncedMemory::mutable_cpu_data()’:
syncedmem.cpp:(.text+0x136): undefined reference to `caffe::caffe_gpu_memcpy(unsigned long, void const*, void*)’
.build_release/lib/libcaffe.a(syncedmem.o): In function `caffe::SyncedMemory::~SyncedMemory()’:
syncedmem.cpp:(.text+0x1c1): undefined reference to `cudaFree’
syncedmem.cpp:(.text+0x20f): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(syncedmem.o): In function `caffe::SyncedMemory::mutable_gpu_data()’:
syncedmem.cpp:(.text+0x29a): undefined reference to `caffe::caffe_gpu_memcpy(unsigned long, void const*, void*)’
syncedmem.cpp:(.text+0x2b9): undefined reference to `cudaMalloc’
syncedmem.cpp:(.text+0x2e5): undefined reference to `cudaMemset’
syncedmem.cpp:(.text+0x321): undefined reference to `cudaGetErrorString’
syncedmem.cpp:(.text+0x379): undefined reference to `cudaMalloc’
syncedmem.cpp:(.text+0x3c2): undefined reference to `cudaGetErrorString’
syncedmem.cpp:(.text+0x435): undefined reference to `cudaGetErrorString’
.build_release/lib/libcaffe.a(syncedmem.o): In function `caffe::SyncedMemory::gpu_data()’:
syncedmem.cpp:(.text+0x4ca): undefined reference to `caffe::caffe_gpu_memcpy(unsigned long, void const*, void*)’
syncedmem.cpp:(.text+0x4e9): undefined reference to `cudaMalloc’
syncedmem.cpp:(.text+0x515): undefined reference to `cudaMemset’
syncedmem.cpp:(.text+0x549): undefined reference to `cudaMalloc’
syncedmem.cpp:(.text+0x592): undefined reference to `cudaGetErrorString’
syncedmem.cpp:(.text+0x608): undefined reference to `cudaGetErrorString’
syncedmem.cpp:(.text+0x678): undefined reference to `cudaGetErrorString’
collect2: error: ld returned 1 exit status
make: *** [.build_release/tools/convert_imageset.bin] Error 1

4. 修改Makefile.config, 注释CPU_ONLY := 1, 同时修改CUSTOM_CXX := g++-4.6

sudo apt-get install gcc-4.6 g++-4.6 gcc-4.6-multilib g++-4.6-multilib

修改这两个文件
vi src/caffe/common.cpp
vi tools/caffe.cpp
使用google替代gflags

make clean

make

make pycaffe
g++-4.6 -shared -o python/caffe/_caffe.so python/caffe/_caffe.cpp \\\\
.build_release/lib/libcaffe.a -fPIC -DNDEBUG -O2 -I/usr/include/python2.7 -I/usr/lib/python2.7/dist-packages/numpy/core/include -I/usr/local/include -I.build_release/src -I./src -I./include -I/usr/local/cuda/include -Wall -Wno-sign-compare -L/usr/lib -L/usr/local/lib -L/usr/lib -L/usr/local/cuda/lib64 -L/usr/local/cuda/lib -lcudart -lcublas -lcurand -lglog -lgflags -lpthread -lprotobuf -lleveldb -lsnappy -llmdb -lboost_system -lhdf5_hl -lhdf5 -lopencv_core -lopencv_highgui -lopencv_imgproc -lcblas -latlas -lboost_python -lpython2.7

touch python/caffe/proto/__init__.py
protoc –proto_path=src –python_out=python src/caffe/proto/caffe_pretty_print.proto

protoc –proto_path=src –python_out=python src/caffe/proto/caffe.proto

执行 sudo cp /u01/caffe/python/caffe/ /usr/local/lib/python2.7/dist-packages/ -Rf

环境：Ubuntu 14.04

一直在关注DMLC 这个机器学习项目，最新的一个子项目是虫洞，提供可靠的和可扩展的机器学习工具在不平的计算平台（MPI, Yarn, Sungrid）。将大幅降低安装和部署分布式机器学习应用的门槛。对所有组件提供一致的数据流支持。还提供统一脚本来编译和运行所有组件。使得用户既可以在方便的本地集群运行深盟的任何一个分布式组件。

编译安装如下：

git clone https://github.com/dmlc/wormhole.git

cd wormhole

cp make/config.mk .

vi config.mk

注释HDFS, S3

#USE_HDFS = 1

#USE_S3 = 1

然后编译即可

make

生成两个执行文件：

kmeans.dmlc  xgboost.dmlc

环境: CentOS 6.2

h2o-sparking 是h2o与spark结合的产物，用于机器学习这一方面，它可在spark环境中使用h2o拥有的机器学习包。

安装如下 ：
git clone https://github.com/0xdata/h2o-sparkling.git
cd h2o-sparking
sbt assembly

运行测试：
[cloudera@localhost h2o-sparkling]$ sbt -mem 500 “run –local”
[info] Loading project definition from /home/cloudera/h2o-sparkling/project
[info] Set current project to h2o-sparkling-demo (in build file:/home/cloudera/h2o-sparkling/)
[info] Running water.sparkling.demo.SparklingDemo –local
03:41:11.030 main      INFO WATER: —– H2O started —–
03:41:11.046 main      INFO WATER: Build git branch: (unknown)
03:41:11.047 main      INFO WATER: Build git hash: (unknown)
03:41:11.047 main      INFO WATER: Build git describe: (unknown)
03:41:11.047 main      INFO WATER: Build project version: (unknown)
03:41:11.047 main      INFO WATER: Built by: ‘(unknown)’
03:41:11.047 main      INFO WATER: Built on: ‘(unknown)’
03:41:11.048 main      INFO WATER: Java availableProcessors: 1
03:41:11.077 main      INFO WATER: Java heap totalMemory: 3.87 gb
03:41:11.077 main      INFO WATER: Java heap maxMemory: 3.87 gb
03:41:11.078 main      INFO WATER: Java version: Java 1.6.0_31 (from Sun Microsystems Inc.)
03:41:11.078 main      INFO WATER: OS   version: Linux 2.6.32-220.23.1.el6.x86_64 (amd64)
03:41:11.381 main      INFO WATER: Machine physical memory: 4.83 gb
03:41:11.393 main      INFO WATER: ICE root: ‘/tmp/h2o-cloudera’
03:41:11.438 main      INFO WATER: Possible IP Address: eth1 (eth1), 192.168.56.101
03:41:11.439 main      INFO WATER: Possible IP Address: eth0 (eth0), 10.0.2.15
03:41:11.439 main      INFO WATER: Possible IP Address: lo (lo), 127.0.0.1
03:41:11.669 main      WARN WATER: Multiple local IPs detected:
+                                    /192.168.56.101  /10.0.2.15
+                                  Attempting to determine correct address…
+                                  Using /10.0.2.15
03:41:11.929 main      INFO WATER: Internal communication uses port: 54322
+                                  Listening for HTTP and REST traffic on  http://10.0.2.15:54321/
03:41:12.912 main      INFO WATER: H2O cloud name: ‘cloudera’
03:41:12.913 main      INFO WATER: (v(unknown)) ‘cloudera’ on /10.0.2.15:54321, discovery address /230.63.2.255:58943
03:41:12.913 main      INFO WATER: If you have trouble connecting, try SSH tunneling from your local machine (e.g., via port 55555):
+                                    1. Open a terminal and run ‘ssh -L 55555:localhost:54321 cloudera@10.0.2.15’
+                                    2. Point your browser to http://localhost:55555
03:41:12.954 main      INFO WATER: Cloud of size 1 formed [/10.0.2.15:54321 (00:00:00.000)]
03:41:12.954 main      INFO WATER: Log dir: ‘/tmp/h2o-cloudera/h2ologs’
prostate
03:41:20.369 main      INFO WATER: Running demo with following configuration: DemoConf(prostate,true,RDDExtractor@file,true)
03:41:20.409 main      INFO WATER: Demo configuration: DemoConf(prostate,true,RDDExtractor@file,true)
03:41:21.830 main      INFO WATER: Data : data/prostate.csv
03:41:21.831 main      INFO WATER: Table: prostate_table
03:41:21.831 main      INFO WATER: Query: SELECT * FROM prostate_table WHERE capsule=1
03:41:21.831 main      INFO WATER: Spark: LOCAL
03:41:21.901 main      INFO WATER: Creating LOCAL Spark context.
03:41:34.616 main      INFO WATER: RDD result has: 153 rows
03:41:34.752 main      INFO WATER: Going to write RDD into /tmp/rdd_null_6.csv
03:41:36.099 FJ-0-1    INFO WATER: Parse result for rdd_data_6 (153 rows):
03:41:36.136 FJ-0-1    INFO WATER:     C1:              numeric        min(6.000000)      max(378.000000)
03:41:36.140 FJ-0-1    INFO WATER:     C2:              numeric        min(1.000000)        max(1.000000)                    constant
03:41:36.146 FJ-0-1    INFO WATER:     C3:              numeric       min(47.000000)       max(79.000000)
03:41:36.152 FJ-0-1    INFO WATER:     C4:              numeric        min(0.000000)        max(2.000000)
03:41:36.158 FJ-0-1    INFO WATER:     C5:              numeric        min(1.000000)        max(4.000000)
03:41:36.161 FJ-0-1    INFO WATER:     C6:              numeric        min(1.000000)        max(2.000000)
03:41:36.165 FJ-0-1    INFO WATER:     C7:              numeric        min(1.400000)      max(139.700000)
03:41:36.169 FJ-0-1    INFO WATER:     C8:              numeric        min(0.000000)       max(73.400000)
03:41:36.176 FJ-0-1    INFO WATER:     C9:              numeric        min(5.000000)        max(9.000000)
03:41:37.457 main      INFO WATER: Extracted frame from Spark:
03:41:37.474 main      INFO WATER: {id,capsule,age,race,dpros,dcaps,psa,vol,gleason}, 2.8 KB
+                                  Chunk starts: {0,83,}
+                                  Rows: 153
03:41:37.482 #ti-UDP-R INFO WATER: Orderly shutdown command from /10.0.2.15:54321
[success] Total time: 44 s, completed Aug 4, 2014 3:41:37 AM