title: TensorFlow GPU: is cudnn optional? Couldn't open CUDA library libcudnn.so
body: <p>I installed the tensorflow-0.8.0 GPU version, tensorflow-0.8.0-cp27-none-linux_x86_64.whl. It says it requires CUDA toolkit 7.5 and CuDNN v4.</p>
  
  <pre><code># Ubuntu/Linux 64-bit, GPU enabled. Requires CUDA toolkit 7.5 and CuDNN v4.  For
  # other versions, see "Install from sources" below.
  </code></pre>
  
  <p>However, I accidently forget to install CuDNN v4, but it works OK besides the error message, "Couldn't open CUDA library libcudnn.so". But it works and says, "Creating TensorFlow device (/gpu:0)". </p>
  
  <h3>msg without CuDNN</h3>
  
  <pre><code>I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
  I tensorflow/stream_executor/dso_loader.cc:99] Couldn't open CUDA library libcudnn.so. LD_LIBRARY_PATH: /usr/local/cuda/lib64:
  I tensorflow/stream_executor/cuda/cuda_dnn.cc:1562] Unable to load cuDNN DSO
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
  ('Extracting', 'MNIST_data/train-images-idx3-ubyte.gz')
  /usr/lib/python2.7/gzip.py:268: VisibleDeprecationWarning: converting an array with ndim &gt; 0 to an index will result in an error in the future
    chunk = self.extrabuf[offset: offset + size]
  /home/ubuntu/TensorFlow-Tutorials/input_data.py:42: VisibleDeprecationWarning: converting an array with ndim &gt; 0 to an index will result in an error in the future
    data = data.reshape(num_images, rows, cols, 1)
  ('Extracting', 'MNIST_data/train-labels-idx1-ubyte.gz')
  ('Extracting', 'MNIST_data/t10k-images-idx3-ubyte.gz')
  ('Extracting', 'MNIST_data/t10k-labels-idx1-ubyte.gz')
  I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
  name: GRID K520
  major: 3 minor: 0 memoryClockRate (GHz) 0.797
  pciBusID 0000:00:03.0
  Total memory: 4.00GiB
  Free memory: 3.95GiB
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y
  I tensorflow/core/common_runtime/gpu/gpu_device.cc:755] Creating TensorFlow device (/gpu:0) -&gt; (device: 0, name: GRID K520, pci bus id: 0000:00:03.0)
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1704 get requests, put_count=1321 evicted_count=1000 eviction_rate=0.757002 and unsatisfied allocation rate=0.870305
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 100 to 110
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1704 get requests, put_count=1812 evicted_count=1000 eviction_rate=0.551876 and unsatisfied allocation rate=0.536972
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 256 to 281
  </code></pre>
  
  <p>Later, I installed CuDNN, but I don't see the differences.</p>
  
  <h3>msg with CuDNN</h3>
  
  <pre><code>I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
  ('Extracting', 'MNIST_data/train-images-idx3-ubyte.gz')
  /usr/lib/python2.7/gzip.py:268: VisibleDeprecationWarning: converting an array with ndim &gt; 0 to an index will result in an error in the future
    chunk = self.extrabuf[offset: offset + size]
  /home/ubuntu/TensorFlow-Tutorials/input_data.py:42: VisibleDeprecationWarning: converting an array with ndim &gt; 0 to an index will result in an error in the future
    data = data.reshape(num_images, rows, cols, 1)
  ('Extracting', 'MNIST_data/train-labels-idx1-ubyte.gz')
  ('Extracting', 'MNIST_data/t10k-images-idx3-ubyte.gz')
  ('Extracting', 'MNIST_data/t10k-labels-idx1-ubyte.gz')
  I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
  name: GRID K520
  major: 3 minor: 0 memoryClockRate (GHz) 0.797
  pciBusID 0000:00:03.0
  Total memory: 4.00GiB
  Free memory: 3.95GiB
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y
  I tensorflow/core/common_runtime/gpu/gpu_device.cc:755] Creating TensorFlow device (/gpu:0) -&gt; (device: 0, name: GRID K520, pci bus id: 0000:00:03.0)
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1704 get requests, put_count=1321 evicted_count=1000 eviction_rate=0.757002 and unsatisfied allocation rate=0.870305
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 100 to 110
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:244] PoolAllocator: After 1704 get requests, put_count=1811 evicted_count=1000 eviction_rate=0.552181 and unsatisfied allocation rate=0.537559
  I tensorflow/core/common_runtime/gpu/pool_allocator.cc:256] Raising pool_size_limit_ from 256 to 281
  </code></pre>
  
  <p>So what's differences with/without CuDNN?</p>