python - 使用CUDA GeForce9600GT在Ubuntu服务器上执行tensorflow

Question

我刚刚通过我的mac上的cifar10 tensorflow教程.这个教程激起了我对NN的热情,所以我设置了一个Ubuntu Server,以便进行大规模的训练.

但是我无法在我的服务器上运行cifar10_train.py.我总是遇到这个错误:

tensorflow源代码的路径:

~/python/tensorflow/tensorflow/tensorflow/

tensorflow virtualenv安装的路径:

~/tensorflow/

CMD:

source ~/tensorflow/bin/activate #activate virtualenv
python/tensorflow/tensorflow/tensorflow/models/image/cifar10/cifar10_train.py #the raw source code of tensorflow is in ~/python/tensorflow/tensorflow/tensorflow

错误:

Traceback (most recent call last):
  File "python/tensorflow/tensorflow/tensorflow/models/image/cifar10/cifar10_train.py", line 28, in <module>
    import tensorflow.python.platform
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in <module>
    from tensorflow.python import *
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 22, in <module>
    from tensorflow.python.client.client_lib import *
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/client_lib.py", line 35, in <module>
    from tensorflow.python.client.session import InteractiveSession
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 11, in <module>
    from tensorflow.python import pywrap_tensorflow as tf_session
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 28, in <module>
    _pywrap_tensorflow = swig_import_helper()
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow', fp, pathname, description)

我在virtualenv中安装了tensorflow就像在我的mac上一样,并在执行脚本之前正确激活它.正如许多其他线程建议我已经升级了六个,但是我仍然遇到了同样的错误.

更新1 在github/tensorflow上发出问题线程之后我注意到这是一个涉及cuda的bug.我将这些添加到我的路径环境中:

export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64"
export CUDA_HOME=/usr/local/cuda

我仍然遇到错误,但它已缩短为以下内容:

Traceback (most recent call last):
File "cifar10_train.py", line 28, in <module>
import tensorflow.python.platform
ImportError: No module named tensorflow.python.platform

更新2正如有人建议的那样,我通过pip安装protobuf.由于一些奇怪的原因,错误再次发生了变化:

Traceback (most recent call last):
  File "cifar10_train.py", line 28, in <module>
    import tensorflow.python.platform
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/__init__.py", line 4, in <module>
    from tensorflow.python import *
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/__init__.py", line 13, in <module>
    from tensorflow.core.framework.graph_pb2 import *
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/core/framework/graph_pb2.py", line 16, in <module>
    from tensorflow.core.framework import attr_value_pb2 as tensorflow_dot_core_dot_framework_dot_attr__value__pb2
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/core/framework/attr_value_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__pb2
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_pb2.py", line 16, in <module>
    from tensorflow.core.framework import tensor_shape_pb2 as tensorflow_dot_core_dot_framework_dot_tensor__shape__pb2
  File "/home/it13095/tensorflow/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py", line 22, in <module>
    serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02 \x03(\x0b\x32 .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01 \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
TypeError: __init__() got an unexpected keyword argument 'syntax'

Answer 1

TensorFlow目前需要CUDA工具包7.0和cuDNN.

cuDNN需要cc 3.0 GPU,CUDA工具包7.0需要cc 2.0 GPU.

您的9600GT不满足这些要求:

为了在GPU支持下构建或运行TensorFlow,需要安装来自NVIDIA的Cuda Toolkit 7.0和CUDNN 6.5 V2.

TensorFlow GPU支持需要具有NVidia Compute Capability> = 3.5的GPU卡.

因此,如果您想在支持GPU的情况下使用TensorFlow,则需要cc3.5或更高版本的GPU,并按照步骤正确安装所需的支持软件.或者,您可以在没有GPU支持的情况下安装TensorFlow.