theano扫盲
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2022-03-03 14:37:12
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项目的原因不得不用一下theano,初见theano,简直是老年人写的代码啊
首先安装theano,常规操作
pip install theano
然后才是重点,要在gpu上运行。嫌麻烦不想用gpu?亲测告诉你,theano代码在一颗cpu上的运行速度可能是一颗gpu上的10分之一。
# 要安装Pygpu,这个包不能直接通过pip安装,需要下载源码编译安装。
# 不过编译过程很简单,也很顺畅。是我见过的最顺畅的编译安装了!!!!
# 参照:https://blog.csdn.net/jay463261929/article/details/78933091
$ git clone https://github.com/Theano/libgpuarray.git
$ cd libgpuarray/
$ mkdir build
$ cd build/
$ cmake .. -DCMAKE_BUILD_TYPE=Release
$ make
$ sudo make install
$ cd ..
$ python2 setup.py build
# 否则 install的过程中需要联网安装依赖包 mako
$ pip --trusted-host=pypi.org --trusted-host=files.pythonhosted.org --trusted-host=download.pytorch.org install mako --user
# 安装mpi4py,用于多gpu运行
$ pip2 --trusted-host=pypi.org --trusted-host=files.pythonhosted.org --trusted-host=download.pytorch.org install mpi4py --user
$ sudo python2 setup.py install
$ sudo ldconfig # 创建动态链接库文件
# 测试
$ THEANO_FLAGS=mode=FAST_RUN,device=cuda0,floatX=float32 python2 theano_gpu.py
测试代码参考:https://blog.csdn.net/autocyz/article/details/51674934
(结果输出虽然能看出调用了gpu(或者用nvidia-smi也能看),但是测试代码输出似乎不正确)
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
iters = 1000
rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
print(f.maker.fgraph.toposort())
t0 = time.time()
for i in range(iters):
r = f()
t1 = time.time()
print("Looping %d times took %f seconds" % (iters, t1 - t0))
print("Result is %s" % (r,))
if numpy.any([isinstance(x.op, T.Elemwise) for x in f.maker.fgraph.toposort()]):
print('Used the cpu')
else:
print('Used the gpu')
测试代码输出:
$ THEANO_FLAGS=mode=FAST_RUN,device=cuda3,floatX=float32 python2 test.py
# 输出:
Using cuDNN version 7005 on context None
Mapped name None to device cuda3: TITAN Xp (0000:83:00.0)
[GpuElemwise{exp,no_inplace}(<GpuArrayType<None>(float32, vector)>), HostFromGpu(gpuarray)(GpuElemwise{exp,no_inplace}.0)]
Looping 1000 times took 0.228486 seconds
Result is [ 1.23178029 1.61879349 1.52278066 ..., 2.20771813 2.29967761
1.62323296]
Used the cpu
# 实际是调用了gpu的,但是显示还是cpu
# 为什么会显示成上面这样呢?https://github.com/Theano/Theano/issues/5463
其他测试代码:
# 可能会报错
其他测试一:
$ python2 -c "import theano"
其他测试二:
$ DEVICE=cuda0 python2 -c 'import pygpu;pygpu.test()'
其他测试2可能会报错:
GpuArrayException: Could not load "libnccl.so": libnccl.so: cannot open shared object file: No such file or directory
解决方法:安装nccl,按照官网:https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html
# 按照官网给出的步骤,下载nccl,可能需要登录nvidia账号
# 下载网址:https://developer.nvidia.com/nccl
# 一系列安装过程
$ sudo dpkg -i Downloads/nccl-repo-ubuntu1604-2.5.6-ga-cuda9.0_1-1_amd64.deb
$ sudo apt update
$ sudo apt install libnccl2 libnccl-dev
# 重新测试
$ DEVICE=cuda0 python2 -c 'import pygpu;pygpu.test()'