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

2 投票
1 回答
736 浏览
提问于 2025-05-10 21:42

我刚在我的Mac上看完了cifar10 tensorflow教程,这个教程让我对神经网络产生了浓厚的兴趣,所以我在Ubuntu服务器上搭建了环境,想进行大规模的训练。

但是我在服务器上运行cifar10_train.py时总是遇到错误:

tensorflow源代码的路径:

~/python/tensorflow/tensorflow/tensorflow/

tensorflow虚拟环境安装的路径:

~/tensorflow/

命令:

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)

我在虚拟环境中安装了tensorflow,就像在Mac上一样,并且在执行脚本之前正确激活了它。根据很多其他讨论的建议,我已经升级了六次,但仍然遇到同样的错误。

更新 1
在查看了github/tensorflow上的问题讨论后,我发现这是一个与cuda有关的bug。我把这些添加到了我的路径环境中:

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

不过我还是遇到了一个错误,但错误信息缩短成了以下内容:

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
有人建议我通过pip安装protobuf。出于某种奇怪的原因,错误信息又一次发生了变化:

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'

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1 个回答

5

现在,TensorFlow需要安装CUDA工具包7.0和cuDNN。

cuDNN需要一块计算能力为3.0的显卡,而CUDA工具包7.0则需要计算能力为2.0的显卡。

你的9600GT显卡不符合这些要求

要想使用GPU支持来构建或运行TensorFlow,必须安装NVIDIA的CUDA工具包7.0和cuDNN 6.5 V2。

TensorFlow的GPU支持要求显卡的计算能力至少为3.5。

所以,如果你想用TensorFlow的GPU支持,你需要一块计算能力为3.5或更高的显卡,并且要按照步骤正确安装所需的软件。或者,你也可以选择安装不带GPU支持的TensorFlow。

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