title: Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1:
body: <p>I'm using a remote server to running my python script, it has NVIDIA's and I want to use it to speed up the processing time.</p>
  <p>I have downloaded the following packages as it's described in this tutorial:</p>
  <pre><code>tensorflow-gpu            2.4.1           h30adc30_0.conda
  tensorflow                2.4.1           gpu_py39h8236f22_0.conda          
  cudatoolkit               10.1.243        nvidia
  cudnn                     7.6.5           cuda10.1_0    anaconda
  </code></pre>
  <p>When I want to verify the installation:</p>
  <p>from tensorflow.python.client import device_lib
  print(device_lib.list_local_devices())</p>
  <p>The following errors are raised:</p>
  <pre><code>2021-09-01 19:44:44.950015: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX
  To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
  2021-09-01 19:44:44.978949: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /app/utils/anaconda3/lib:/rruser/home/anaconda3/lib:
  2021-09-01 19:44:44.979005: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
  2021-09-01 19:44:44.979070: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (klogin1): /proc/driver/nvidia/version does not exist
  2021-09-01 19:44:44.979110: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
  [name: &quot;/device:CPU:0&quot;
  device_type: &quot;CPU&quot;
  memory_limit: 268435456
  locality {
  }
  incarnation: 7297103817937650454
  ]
  </code></pre>