find cudnn version with code examples

CUDNN is a popular open-source library that is used to accelerate deep learning frameworks such as TensorFlow, PyTorch, and Caffe. It provides highly optimized performance for a wide range of deep learning operations and is designed to run on NVIDIA GPU architectures.

As a machine learning developer, it is important to know the CUDNN version that is installed on your system. This can help you to determine which features are available and can help you to troubleshoot issues that may arise during the training and deployment of your models.

In this article, we will take a look at how to find the CUDNN version installed on your system using code examples. We will cover some common scenarios where you may need to check your CUDNN version, and we will provide examples for both Windows and Linux operating systems.

Scenario 1: You are running TensorFlow and need to know the CUDNN version

If you are running TensorFlow, you might need to check the CUDNN version that is installed on your system. This is important because TensorFlow uses CUDNN to accelerate some of the deep learning operations. To check the CUDNN version in TensorFlow, you can use the following code:

import tensorflow as tf
print(tf.test.is_built_with_cuda())
print(tf.test.is_built_with_cudnn())
print(tf.test.gpu_device_name())

This code will print out some information about your system, including whether or not TensorFlow was built with CUDA and CUDNN, and the name of your GPU device. If TensorFlow was built with CUDNN, it will also print out the version number.

Scenario 2: You are running PyTorch and need to know the CUDNN version

If you are running PyTorch, you may need to know the CUDNN version that is installed on your system. This is important because PyTorch uses CUDNN to accelerate some of the deep learning operations. To check the CUDNN version in PyTorch, you can use the following code:

import torch
print(torch.version.cuda)

This code will print out the version of CUDA that is installed on your system. If PyTorch was built with CUDNN, it will be compatible with a specific version of CUDA. You can find information about compatibility in the PyTorch documentation.

Scenario 3: You want to know the CUDNN version installed on your system

If you want to know the CUDNN version that is installed on your system, you can use the following code:

import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))

This code will print out a list of all the physical GPU devices that are available on your system. If CUDNN is installed, TensorFlow will use it to accelerate the deep learning operations. You can verify that CUDNN is being used by looking for the "cudnn" keyword in the output.

For Linux systems, you can run the following command to check the CUDNN version:

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

This will print out the version of CUDNN that is installed on your system.

Scenario 4: You need to install a specific CUDNN version

If you need to install a specific version of CUDNN, you can download it from the NVIDIA website. The installation process will vary depending on your operating system and deep learning framework.

For TensorFlow on Windows, you can install a specific version of CUDNN by following these steps:

  1. Download the CUDNN archive for your desired version from the NVIDIA website.
  2. Extract the archive to a local directory.
  3. Copy the contents of the "bin" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin"
  4. Copy the contents of the "include" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include"
  5. Copy the contents of the "lib" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64"

Once you have installed the desired version of CUDNN, you can verify that it is being used by your deep learning framework using the code examples provided in this article.

Conclusion

In this article, we have shown you how to find the CUDNN version installed on your system using code examples. We have covered some common scenarios where you may need to know the CUDNN version, and we have provided examples for both Windows and Linux operating systems.

As a machine learning developer, it is important to be familiar with the tools and libraries that you use on a daily basis. Knowing the CUDNN version can help you to optimize your models and troubleshoot issues that may arise during training and deployment.

let's dive a bit deeper into some of the topics we covered.

CUDA and CUDNN

CUDA is a parallel computing platform and application programming interface (API) developed by NVIDIA. It allows developers to leverage the power of NVIDIA GPUs to accelerate computations in a variety of fields, including deep learning. CUDA provides an easy-to-use programming model and a set of libraries that enable developers to write high-performance GPU-accelerated applications.

CUDNN, on the other hand, is a library of primitives for deep neural networks that is optimized for NVIDIA GPUs. It provides highly optimized performance for a wide range of deep learning operations, such as convolution, pooling, normalization, and activation functions. CUDNN is designed to work seamlessly with CUDA and is integrated with popular deep learning frameworks such as TensorFlow, PyTorch, and Caffe.

By using CUDA and CUDNN, deep learning developers can harness the parallel processing power of NVIDIA GPUs to accelerate their models and achieve faster training times. These libraries have become essential tools in the deep learning ecosystem and have contributed to the rapid progress and advancements in the field.

Finding CUDNN Version

As we mentioned earlier, finding the CUDNN version installed on your system is important for several reasons. Here are a few more details on why you might need to know the CUDNN version:

  1. Compatibility: Deep learning frameworks such as TensorFlow and PyTorch require specific versions of CUDNN that are compatible with the version of CUDA installed on your system. Knowing the CUDNN version can help you to ensure that your framework is compatible with your GPU setup.

  2. Troubleshooting: If you encounter any issues or errors during training or deployment of your deep learning models, the CUDNN version installed on your system can be a helpful piece of information in troubleshooting the problem.

  3. Feature support: Different versions of CUDNN may offer different features or optimizations for specific deep learning operations. Knowing the CUDNN version can help you to determine which features are available for your models.

In summary, finding the CUDNN version installed on your system is important for compatibility, troubleshooting, and taking advantage of features offered by different versions.

Installing CUDNN

If you need to install a specific version of CUDNN on your system, the first step is to download the archive file from the NVIDIA website. Once downloaded, the installation process will vary depending on your operating system and deep learning framework.

For example, if you are using TensorFlow on Windows, you can install a specific version of CUDNN by following the steps we outlined earlier:

  1. Download the CUDNN archive for your desired version from the NVIDIA website.
  2. Extract the archive to a local directory.
  3. Copy the contents of the "bin" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin"
  4. Copy the contents of the "include" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include"
  5. Copy the contents of the "lib" directory to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64"

Once you've installed the desired version of CUDNN, you can verify that it is being used by your deep learning framework using the code examples we provided earlier.

Conclusion

In conclusion, CUDA and CUDNN are essential tools for deep learning developers who want to take advantage of the power of NVIDIA GPUs. Finding the CUDNN version installed on your system is important for compatibility, troubleshooting, and taking advantage of different features. If you need to install a specific version of CUDNN, the process will vary depending on your operating system and deep learning framework, but the general steps are similar. Always make sure to consult the documentation for your specific environment and follow best practices when installing and configuring these libraries.

Popular questions

  1. Why is it important to know the CUDNN version installed on my system?

Knowing the CUDNN version installed on your system is important for compatibility with deep learning frameworks, troubleshooting issues in your models, and taking advantage of different features present in different versions.

  1. How can I find the CUDNN version installed on my system using TensorFlow?

One way to find the CUDNN version installed on your system using TensorFlow is by using the following code:

import tensorflow as tf
print(tf.test.is_built_with_cudnn())

This code will return a boolean value indicating whether TensorFlow is built with CUDNN or not. If it is, you can verify the version number by executing:

print(tf.__version__)
  1. How can I find the CUDNN version installed on my system using PyTorch?

You can find the CUDNN version installed on your system using PyTorch by executing the following code:

import torch
print(torch.version.cuda)

This code will print the version of CUDA that you have installed. If PyTorch was built with CUDNN, it will be compatible with a specific version of CUDA, which can be found in the PyTorch documentation.

  1. How can I find the CUDNN version installed on my Linux system?

On Linux systems, you can find the CUDNN version installed on your system by executing the following command on the terminal:

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

This command will output information about the installed CUDNN version.

  1. How can I install a specific version of CUDNN on my system?

To install a specific version of CUDNN on your system, you can follow the steps outlined in the documentation of your deep learning framework for your specific operating system. Typically, it involves downloading the archive file from the NVIDIA website, extracting it to a directory, and copying specific files to certain directories where they can be accessed by the deep learning framework. For instance, if you want to install a specific version of CUDNN for TensorFlow on Windows, you can follow the steps we provided earlier in this article.

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As a senior DevOps Engineer, I possess extensive experience in cloud-native technologies. With my knowledge of the latest DevOps tools and technologies, I can assist your organization in growing and thriving. I am passionate about learning about modern technologies on a daily basis. My area of expertise includes, but is not limited to, Linux, Solaris, and Windows Servers, as well as Docker, K8s (AKS), Jenkins, Azure DevOps, AWS, Azure, Git, GitHub, Terraform, Ansible, Prometheus, Grafana, and Bash.

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