How to Check CUDNN Installed Version for Deep Learning Compatibility

Verifying the installed version of cuDNN is crucial for ensuring compatibility and optimal performance in deep learning applications. This step helps confirm that your deep learning environment is correctly set up, allowing frameworks like TensorFlow and PyTorch to leverage GPU acceleration effectively.

Checking cuDNN Version via Terminal

1. Open Terminal:

   • On Linux: Press Ctrl + Alt + T.
   • On macOS: Press Cmd + Space, type “Terminal”, and press Enter.

2. Run Command:

   cat /usr/local/cuda/include/cudnn_version.h | grep CUDNN_MAJOR -A 2
   

This command will display the installed cuDNN version.

Checking cuDNN Version on Windows

1. Right-click the cuDNN library file.
2. Select “Properties.”
3. Go to the “Details” tab.
4. Find the version information under “File version” or “Product version”.

Using nvcc Command to Verify cuDNN

To check the cuDNN version using the nvcc --version command, follow these steps:

1. Open a terminal or command prompt.
2. Run the command:

   nvcc --version
   

3. Interpret the output:
   • The output will display the CUDA version, not the cuDNN version directly.
   • Example output:

     nvcc: NVIDIA (R) Cuda compiler driver
     Copyright (c) 2005-2023 NVIDIA Corporation
     Built on Fri_Jan_22_19:32:24_PST_2023
     Cuda compilation tools, release 12.1, V12.1.105
     Build cuda_12.1.r12.1/compiler.30521435_0
     

To find the cuDNN version, you need to check the compatibility of your CUDA version with cuDNN versions. Refer to the cuDNN support matrix on NVIDIA’s documentation.

Verifying cuDNN with TensorFlow

Sure, here are the steps to verify the cuDNN version using TensorFlow:

1. Install TensorFlow:

   pip install tensorflow
   

2. Check cuDNN Version:

   import tensorflow as tf
   from tensorflow.python.platform import build_info as tf_build_info
   
   print("cuDNN version:", tf_build_info.cudnn_version_number)
   

3. Run a Simple TensorFlow Script:

   import tensorflow as tf
   
   # Create a simple computation graph
   a = tf.constant(2.0)
   b = tf.constant(3.0)
   c = a + b
   
   # Run the computation
   with tf.Session() as sess:
       result = sess.run(c)
       print("Result of the computation:", result)
   

These steps will help you verify the cuDNN version and confirm that TensorFlow is correctly installed and working.

Verifying cuDNN with PyTorch

Here’s a basic PyTorch script to check the cuDNN version:

import torch

# Check if CUDA is available
if torch.cuda.is_available():
    cudnn_version = torch.backends.cudnn.version()
    print(f"cuDNN version: {cudnn_version}")
else:
    print("CUDA is not available.")

Run this script, and it will print the cuDNN version if CUDA is available.

Verifying the Installed Version of cuDNN

Verifying the installed version of cuDNN is crucial for ensuring compatibility and optimal performance in deep learning applications.

It helps confirm that your deep learning environment is correctly set up, allowing frameworks like TensorFlow and PyTorch to leverage GPU acceleration effectively.

Regular checks are necessary to maintain system compatibility and prevent potential issues with model training and deployment.