title: tensorflow-gpu is not working with Blas GEMM launch failed
body: <p>I installed tensorflow-gpu to run my tensorflow code on my GPU. But I can't make it run. It keeps on giving the above mentioned error. Following is my sample code followed by the error stack trace:</p>
  
  <pre><code>import tensorflow as tf
  import numpy as np
  
  def check(W,X):
      return tf.matmul(W,X)
  
  
  def main():
      W = tf.Variable(tf.truncated_normal([2,3], stddev=0.01))
      X = tf.placeholder(tf.float32, [3,2])
      check_handle = check(W,X)
      with tf.Session() as sess:
          tf.initialize_all_variables().run()
          num = sess.run(check_handle, feed_dict = 
              {X:np.reshape(np.arange(6), (3,2))})
          print(num)
  if __name__ == '__main__':
      main()
  </code></pre>
  
  <p>My GPU is pretty good GeForce GTX 1080 Ti with 11 GB vram and there is nothing else significant running on it(just chrome) as you can see in the    nvidia-smi    :</p>
  
  <pre><code>Fri Aug  4 16:34:49 2017       
  +-----------------------------------------------------------------------------+
  | NVIDIA-SMI 381.22                 Driver Version: 381.22                    |
  |-------------------------------+----------------------+----------------------+
  | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
  | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
  |===============================+======================+======================|
  |   0  GeForce GTX 108...  Off  | 0000:07:00.0      On |                  N/A |
  | 30%   55C    P0    79W / 250W |    711MiB / 11169MiB |      0%      Default |
  +-------------------------------+----------------------+----------------------+
  
  +-----------------------------------------------------------------------------+
  | Processes:                                                       GPU Memory |
  |  GPU       PID  Type  Process name                               Usage      |
  |=============================================================================|
  |    0      7650    G   /usr/lib/xorg/Xorg                             380MiB |
  |    0      8233    G   compiz                                         192MiB |
  |    0     24226    G   ...el-token=963C169BB38ADFD67B444D57A299CE0A   136MiB |
  +-----------------------------------------------------------------------------+
  </code></pre>
  
  <p>Following is the error stack trace:</p>
  
  <pre><code>2017-08-04 15:44:21.585091: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
  2017-08-04 15:44:21.585110: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
  2017-08-04 15:44:21.585114: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
  2017-08-04 15:44:21.585118: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
  2017-08-04 15:44:21.585122: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
  2017-08-04 15:44:21.853700: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties: 
  name: GeForce GTX 1080 Ti
  major: 6 minor: 1 memoryClockRate (GHz) 1.582
  pciBusID 0000:07:00.0
  Total memory: 10.91GiB
  Free memory: 9.89GiB
  2017-08-04 15:44:21.853724: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 
  2017-08-04 15:44:21.853728: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0:   Y 
  2017-08-04 15:44:21.853734: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -&gt; (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:07:00.0)
  2017-08-04 15:44:24.948616: E tensorflow/stream_executor/cuda/cuda_blas.cc:365] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
  2017-08-04 15:44:24.948640: W tensorflow/stream_executor/stream.cc:1601] attempting to perform BLAS operation using StreamExecutor without BLAS support
  2017-08-04 15:44:24.948805: W tensorflow/core/framework/op_kernel.cc:1158] Internal: Blas GEMM launch failed : a.shape=(1, 5), b.shape=(5, 10), m=1, n=10, k=5
       [[Node: layer1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_Placeholder_0_0/_11, layer1/weights/read)]]
  Traceback (most recent call last):
    File "test.py", line 51, in &lt;module&gt;
      _, loss_out, res_out = sess.run([train_op, loss, res], feed_dict)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run
      run_metadata_ptr)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run
      feed_dict_string, options, run_metadata)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
      target_list, options, run_metadata)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
      raise type(e)(node_def, op, message)
  tensorflow.python.framework.errors_impl.InternalError: Blas GEMM launch failed : a.shape=(1, 5), b.shape=(5, 10), m=1, n=10, k=5
       [[Node: layer1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_Placeholder_0_0/_11, layer1/weights/read)]]
       [[Node: layer2/MatMul/_17 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_158_layer2/MatMul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
  
  Caused by op u'layer1/MatMul', defined at:
    File "test.py", line 18, in &lt;module&gt;
      pre_activation = tf.matmul(input_ph, weights)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/math_ops.py", line 1816, in matmul
      a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_math_ops.py", line 1217, in _mat_mul
      transpose_b=transpose_b, name=name)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
      op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
      original_op=self._default_original_op, op_def=op_def)
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
      self._traceback = _extract_stack()
  
  InternalError (see above for traceback): Blas GEMM launch failed : a.shape=(1, 5), b.shape=(5, 10), m=1, n=10, k=5
       [[Node: layer1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=false, _device="/job:localhost/replica:0/task:0/gpu:0"](_arg_Placeholder_0_0/_11, layer1/weights/read)]]
       [[Node: layer2/MatMul/_17 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_158_layer2/MatMul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
  </code></pre>
  
  <p>To add to it, my previous installation of tensorflow cpu worked pretty well. Any help is appreciated. Thanks!</p>
  
  <p>Note-  I have cuda-8.0 with cudnn-5.1 installed and their paths added in my bashrc profile .</p>