问题:
Cannot assign a device for operation ‘MatMul‘: Operation was explicitly assigned to /device:GPU:1 but available devices are [ /job:localhost/replica:0/task:0/device:CPU:0, /job:localhost/replica:0/task:0/device:GPU:0 ]. Make sure the device specification refers to a valid device.
解决方法:
1. 卸载tensorflow,安装tensorflow-gpu
pip uninstall tensorflow
pip install tensorflow-gpu
2. 将乘法操作移到gpu以外执行
示例:
# old with tf.Session() as sess: matrix1 = tf.constant([[3., 3.]]) print("matrix1:",matrix1) matrix2 = tf.constant([[2.],[2.]]) print("matrix2:",matrix2) with tf.device("/gpu:1"): # 将乘法运算移到外面执行 product = tf.matmul(matrix1, matrix2) result = sess.run(product) print("result:",result) # new with tf.Session() as sess: matrix1 = tf.constant([[3., 3.]]) print("matrix1:",matrix1) matrix2 = tf.constant([[2.],[2.]]) print("matrix2:",matrix2) product = tf.matmul(matrix1, matrix2) with tf.device("/gpu:1"): result = sess.run(product) print("result:",result)
正确结果:
C:\Users\bin>python d:/PycharmProjects/TFLearn/Unit1/03.py 2018-01-11 22:43:03.490996: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX 2018-01-11 22:43:04.077880: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Found device 0 with properties: name: GeForce GT 740M major: 3 minor: 5 memoryClockRate(GHz): 1.0325 pciBusID: 0000:01:00.0 totalMemory: 1.00GiB freeMemory: 836.07MiB 2018-01-11 22:43:04.078060: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GT 740M, pci bus id: 0000:01:00.0, compute capability: 3.5) matrix1: Tensor("Const:0", shape=(1, 2), dtype=float32) matrix2: Tensor("Const_1:0", shape=(2, 1), dtype=float32) result: [[ 12.]]
问题:
Could not find ‘cudnn64_6.dll‘. TensorFlow requires that this DLL be installed in a directory that is named in your %PATH% environment variable. Note that installing cuDNN is a separate step from installing CUDA, and this DLL is often found in a different directory from the CUDA DLLs. You may install the necessary DLL by downloading cuDNN 6 from this URL: https://developer.nvidia.com/cudnn
解决方法:
1. 安装cudn:
https://developer.nvidia.com/rdp/cudnn-download
https://developer.nvidia.com/cuda-zone
nvidia官网经常更新,请下载对应的版本,cudn和cudnn dll 版本号不一样,比如tensorflow-gpu 1.4对应cndn8,同时需要下载cudnn64_6.dll for cudn8。
2. 下载cudnn64_6,解压到指定cudn目录 C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0
pan.baidu.com/s/1o8mc4Y windows
pan.baidu.com/s/1hs23Hr linux
原文地址:https://www.cnblogs.com/bincoding/p/8270894.html