标签归档:Torch安装

从零开始搭建深度学习服务器: 深度学习工具安装(Theano + MXNet)

Deep Learning Specialization on Coursera

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

以下是相关深度学习工具包的安装,包括Theano, MXNet

4. Theano

Theano虽然官宣不在更新,但是它的价值依然很大,很多早期深度学习工具的底层依然依赖的是它。在Ubuntu下安装Theano有两种模式,一种是通过Conda安装,Theano的官方安装文档给得是这个方式;另外一种是pip安装模式,官方文档没有给出很好的描述,我参考了网上其他的文章,安装过程中遇到了几个小问题,不过顺利解决。首先安装相关的依赖:

sudo apt-get install python-numpy python-scipy python-dev python-pip python-nose g++ libopenblas-dev git

这个时候可以先尝试用pip的方式安装Theano:

pip install Theano

测试时会遇到类似找不到pygpu模块的提示,而这个模块,是无法用pip安装的,必须通过Theano提供的libgpuarray编译,官方安装文档也给了专门的说明

git clone https://github.com/Theano/libgpuarray.git
cd libgpuarray/
mkdir Build
cd Build/
cmake .. -DCMAKE_BUILD_TYPE=Release
make
sudo make install
cd ..
sudo pip install Cython(如果提示cython没有安装需要先安装Cython)
sudo python setup.py build
sudo python setup.py install
sudo ldconfig

还有最后一步,配置文件

vim ~/.theanorc

[global]
floatX=float32
device=cuda
[cuda]
root=/usr/local/cuda
[nvcc]
flags=-D_FORCE_INLINES

然后可以试一下在ipython中导入Theano是否ok:

Python 2.7.13 (default, Jan 19 2017, 14:48:08) 
Type "copyright", "credits" or "license" for more information.
 
IPython 5.1.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.
 
In [1]: import theano
Using cuDNN version 6021 on context None
Mapped name None to device cuda: GeForce GTX 1080 Ti (0000:05:00.0)

5. MXNet

MXNet的安装还是比较方便的,按照MXNet官方的安装指南,我是在Ubuntu17.04的环境下用virtualenv安装的:

Python2.x的安装方式如下:

如果没有安装python环境和virtualenv,可以先安装:
sudo apt-get update
sudo apt-get install -y python-dev python-virtualenv

然后用virtualenv生成MXNet的虚拟环境:
virtualenv --system-site-packages venv
source venv/bin/activate

要升级pip到最新版(不清楚是为什么):
pip install --upgrade pip

目前MXNet的最新版是1.0:
pip install mxnet-cu80==1.0.0

如果需要可视化训练过程,则可以选择安装graphviz:
sudo apt-get install graphviz
pip install graphviz

最后测试一下MXNet在GPU环境下是否生效:

Python 2.7.13 (default, Nov 23 2017, 15:37:09) 
[GCC 6.3.0 20170406] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/textminer/mxnet/venv/local/lib/python2.7/site-packages/mxnet/__init__.py", line 25, in <module>
    from . import engine
  File "/home/textminer/mxnet/venv/local/lib/python2.7/site-packages/mxnet/engine.py", line 23, in <module>
    from .base import _LIB, check_call
  File "/home/textminer/mxnet/venv/local/lib/python2.7/site-packages/mxnet/base.py", line 111, in <module>
    _LIB = _load_lib()
  File "/home/textminer/mxnet/venv/local/lib/python2.7/site-packages/mxnet/base.py", line 103, in _load_lib
    lib = ctypes.CDLL(lib_path[0], ctypes.RTLD_LOCAL)
  File "/usr/lib/python2.7/ctypes/__init__.py", line 362, in __init__
    self._handle = _dlopen(self._name, mode)
OSError: libgfortran.so.3: cannot open shared object file: No such file or directory

报了如上libgfortran.so.3的错误,google了一下,需要安装gfortran:

sudo apt-get install gfortran

再次测试,就没有问题了:

(venv) textminer@textminer:~/mxnet$ python
Python 2.7.13 (default, Nov 23 2017, 15:37:09) 
[GCC 6.3.0 20170406] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import mxnet as mx
>>> a = mx.nd.ones((2,3), mx.gpu())
>>> b = a * 2 + 1
>>> b.asnumpy()
array([[ 3.,  3.,  3.],
       [ 3.,  3.,  3.]], dtype=float32)

Python3.x下的安装基本上过程相同。

注:原创文章,转载请注明出处及保留链接“我爱自然语言处理”:http://www.52nlp.cn

本文链接地址:从零开始搭建深度学习服务器: 深度学习工具安装(Theano + MXNet) http://www.52nlp.cn/?p=10058

从零开始搭建深度学习服务器: 深度学习工具安装(TensorFlow + PyTorch + Torch)

Deep Learning Specialization on Coursera

这个系列写了好几篇文章,这是相关文章的索引,仅供参考:

以下是相关深度学习工具包的安装,包括Tensorflow, PyTorch, Torch等:

1. TensorFlow:

首先安装libcupti-dev

sudo apt-get install libcupti-dev

然后用 virtualenv 方式安装 Tensorflow(当前是1.4版本)

sudo apt-get install python-pip python-dev python-virtualenv 
mkdir tensorflow
cd tensorflow
virtualenv --system-site-packages venv
source venv/bin/activate
pip install --upgrade tensorflow-gpu

测试GPU:

Python 2.7.12 (default, Nov 19 2016, 06:48:10) 
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
...
2017-10-24 20:37:24.290049: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties: 
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6575
pciBusID 0000:01:00.0
Total memory: 10.91GiB
Free memory: 10.52GiB
...
2017-10-24 20:37:24.387363: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 1 with properties: 
name: GeForce GTX 1080 Ti
major: 6 minor: 1 memoryClockRate (GHz) 1.6575
pciBusID 0000:02:00.0
Total memory: 10.91GiB
Free memory: 10.76GiB
2017-10-24 20:37:24.388168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0 1 
2017-10-24 20:37:24.388176: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0:   Y Y 
2017-10-24 20:37:24.388179: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 1:   Y Y 
2017-10-24 20:37:24.388186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)
2017-10-24 20:37:24.388189: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0
2017-10-24 20:37:24.449867: I tensorflow/core/common_runtime/direct_session.cc:300] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0
/job:localhost/replica:0/task:0/gpu:1 -> device: 1, name: GeForce GTX 1080 Ti, pci bus id: 0000:02:00.0
>>> 

2. PyTorch:

首先在PyTorch的官网下载对应的pip安装文件:

然后用virtualenv的方式安装,非常方便:

mkdir pytorch
cd pytorch/
virtualenv venv
source venv/bin/activate
pip install /path/to/torch-0.2.0.post3-cp27-cp27mu-manylinux1_x86_64.whl 
pip install torchvision 

3. Torch

首先按照Torch官方的方法进行安装:http://torch.ch/docs/getting-started.html

git clone https://github.com/torch/distro.git ~/torch --recursive
cd ~/torch; bash install-deps;
./install.sh

如无意外,可以顺利安装,如果遇到了如下两个问题,可按下述方法修改:

1) 执行./install.sh时出现Moses>=1.错误

Missing dependencies for nn:moses >= 1.,有时候执行./install.sh时,会出现这个问题。

用这个方法解决:

sudo apt install luarocks
sudo luarocks install moses

2) install.sh 过程中提示“error -- unsupported GNU version! gcc versions later than 5 are not supported!”

ubuntu17.04自带gcc 6.x 版本,所以降级安装gcc 4.9版本解决问题:

sudo apt-get install g++-4.9  
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20  
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20 

成功执行安装脚本后后提示:

Do you want to automatically prepend the Torch install location
to PATH and LD_LIBRARY_PATH in your /home/yourpath/.bashrc? (yes/no)
[yes] >>>
yes

安装脚本会自动将torch的安装路径写入到 .bashrc里,然后输入 th试试:

如果你想用Lua5.2替代LuaJIT的方式安装Torch(If you want to install torch with Lua 5.2 instead of LuaJIT, simply run),可按如下方式安装:

git clone https://github.com/torch/distro.git torch --recursive
cd torch

# clean old torch installation
./clean.sh

在 ~/.bashrec中设置lua的环境:
TORCH_LUA_VERSION=LUA52
并执行 source ~/.bashrc, 然后运行:

./install.sh

遇到第一个问题:

cmake: not found

安装cmake解决:
sudo apt-get install cmake

第二个问题:
readline.c:8:31: fatal error: readline/readline.h: 没有那个文件或目录

安装libreadine-dev解决:
sudo apt-get install libreadline-dev

第三个问题:安装过程依然提示“error -- unsupported GNU version! gcc versions later than 5 are not supported!”

ubuntu17.04自带gcc 6.x 版本,所以降级安装gcc 4.9版本解决问题:

sudo apt-get install g++-4.9
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20

安装完毕依然会提示:

Not updating your shell profile.
You might want to
add the following lines to your shell profile:

. /home/textminer/torch/torch/install/bin/torch-activate

在 ~/.profile 文件末尾加上这行 ". /home/textminer/torch/torch/install/bin/torch-activate " 并执行 source ~/.profile,然后输入 th试试。

注:原创文章,转载请注明出处及保留链接“我爱自然语言处理”:http://www.52nlp.cn

本文链接地址:从零开始搭建深度学习服务器: 深度学习工具安装(TensorFlow + PyTorch + Torch) http://www.52nlp.cn/?p=10008