Как проверить установлена ли cuda windows

Other respondents have already described which commands can be used to check the CUDA version. Here, I’ll describe how to turn the output of those commands into an environment variable of the form «10.2», «11.0», etc.

To recap, you can use

nvcc --version

to find out the CUDA version.
I think this should be your first port of call.
If you have multiple versions of CUDA installed, this command should print out the version for the copy which is highest on your PATH.

The output looks like this:

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:38_PDT_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.TC445_37.28540450_0

We can pass this output through sed to pick out just the MAJOR.MINOR release version number.

CUDA_VERSION=$(nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p')

If nvcc isn’t on your path, you should be able to run it by specifying the full path to the default location of nvcc instead.

/usr/local/cuda/bin/nvcc --version

The output of which is the same as above, and it can be parsed in the same way.

Alternatively, you can find the CUDA version from the version.txt file.

cat /usr/local/cuda/version.txt

The output of which

CUDA Version 10.1.243

can be parsed using sed to pick out just the MAJOR.MINOR release version number.

CUDA_VERSION=$(cat /usr/local/cuda/version.txt | sed 's/.* \([0-9]\+\.[0-9]\+\).*/\1/')

Note that sometimes the version.txt file refers to a different CUDA installation than the nvcc --version. In this scenario, the nvcc version should be the version you’re actually using.

We can combine these three methods together in order to robustly get the CUDA version as follows:

if nvcc --version 2&> /dev/null; then
    # Determine CUDA version using default nvcc binary
    CUDA_VERSION=$(nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');

elif /usr/local/cuda/bin/nvcc --version 2&> /dev/null; then
    # Determine CUDA version using /usr/local/cuda/bin/nvcc binary
    CUDA_VERSION=$(/usr/local/cuda/bin/nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');

elif [ -f "/usr/local/cuda/version.txt" ]; then
    # Determine CUDA version using /usr/local/cuda/version.txt file
    CUDA_VERSION=$(cat /usr/local/cuda/version.txt | sed 's/.* \([0-9]\+\.[0-9]\+\).*/\1/')

else
    CUDA_VERSION=""

fi

This environment variable is useful for downstream installations, such as when pip installing a copy of pytorch that was compiled for the correct CUDA version.

python -m pip install \
    "torch==1.9.0+cu${CUDA_VERSION/./}" \
    "torchvision==0.10.0+cu${CUDA_VERSION/./}" \
    -f https://download.pytorch.org/whl/torch_stable.html

Similarly, you could install the CPU version of pytorch when CUDA is not installed.

if [ "$CUDA_VERSION" = "" ]; then
    MOD="+cpu";
    echo "Warning: Installing CPU-only version of pytorch"
else
    MOD="+cu${CUDA_VERSION/./}";
    echo "Installing pytorch with $MOD"
fi

python -m pip install \
    "torch==1.9.0${MOD}" \
    "torchvision==0.10.0${MOD}" \
    -f https://download.pytorch.org/whl/torch_stable.html

But be careful with this because you can accidentally install a CPU-only version when you meant to have GPU support.
For example, if you run the install script on a server’s login node which doesn’t have GPUs and your jobs will be deployed onto nodes which do have GPUs. In this case, the login node will typically not have CUDA installed.

Checking if CUDA is installed on your Windows 11 system can be crucial if you are working with graphics-intensive applications or using NVIDIA GPU for machine learning tasks. CUDA, short for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to utilize the power of NVIDIA GPUs for general-purpose computing tasks, enabling faster and more efficient processing.

In this blog post, we will explore various methods to check if CUDA is installed on your Windows 11 system. We will guide you through step-by-step instructions for each method, ensuring that you can easily determine whether CUDA is installed and set up correctly on your machine.

Video Tutorial:

The Challenge of Checking If CUDA Is Installed Windows 11

Checking if CUDA is installed on Windows 11 can be challenging for some users, especially those who are not familiar with system configurations and settings. CUDA installation may involve multiple steps, including downloading and installing the necessary CUDA toolkit, configuring environmental variables, and ensuring compatibility with your GPU and drivers.

To check if CUDA is installed on Windows 11, you need to navigate through various system settings, command-line tools, and software applications. This process may seem daunting, but with the right methods and guidance, you can easily determine whether CUDA is correctly installed on your system.

Things You Should Prepare for

Before we dive into the methods of checking if CUDA is installed on Windows 11, there are a few things you should prepare for:

1. Windows 11 System: Make sure you have a Windows 11 system up and running. The methods described in this blog post are specific to Windows 11 and may not work on older versions of Windows.

2. NVIDIA GPU: You will need a compatible NVIDIA GPU installed on your Windows 11 system. CUDA is designed to work with NVIDIA GPUs and cannot be installed or utilized without one.

3. Latest GPU Drivers: Ensure that you have the latest drivers installed for your NVIDIA GPU. The CUDA toolkit relies on these drivers to function properly. You can download the latest drivers from the NVIDIA website.

4. CUDA Toolkit: Download and install the CUDA toolkit from the NVIDIA Developer website. The toolkit contains the necessary libraries, binaries, and tools required for CUDA development. Make sure to download the appropriate version for your system and GPU.

Now that you have everything prepared, let’s explore the methods to check if CUDA is installed on your Windows 11 system.

Method 1: How to Check CUDA Version Using NVIDIA Control Panel

Method Overview: The NVIDIA Control Panel is a system utility that allows you to configure various settings for your NVIDIA GPU. It also provides information about the installed GPU drivers and CUDA version.

1. Open the NVIDIA Control Panel by right-clicking on your desktop and selecting «NVIDIA Control Panel» from the context menu.

2. In the NVIDIA Control Panel, navigate to the «System Information» category.

3. Look for the «CUDA – Version» entry under the «Components» section. This will display the installed CUDA version on your system.

Steps:

1. Open the NVIDIA Control Panel by right-clicking on your desktop and selecting «NVIDIA Control Panel» from the context menu.
2. In the NVIDIA Control Panel, navigate to the «System Information» category.
3. Look for the «CUDA – Version» entry under the «Components» section. This will display the installed CUDA version on your system.

Pros:
1. Quick and easy way to check the installed CUDA version.
2. No need for complex commands or technical knowledge.

Cons:
1. Limited to only displaying the installed CUDA version, does not provide detailed information about the CUDA toolkit or its configuration.

Method 2: How to Verify CUDA Installation Using PowerShell

Method Overview: PowerShell is a powerful command-line shell and scripting language that comes built-in with Windows. It provides a convenient way to check if CUDA is installed on your Windows 11 system using simple commands.

1. Open PowerShell by searching for «PowerShell» in the Windows Start menu.

2. Type the following command and press Enter:
«`powershell
nvcc –version
«`

3. The command output will display the installed CUDA version, along with other information about the CUDA toolkit.

Steps:

1. Open PowerShell by searching for «PowerShell» in the Windows Start menu.
2. Type the following command and press Enter:
«`
nvcc –version
«`
3. The command output will display the installed CUDA version, along with other information about the CUDA toolkit.

Pros:
1. PowerShell is a built-in tool in Windows and does not require any additional software installation.
2. Provides detailed information about the installed CUDA version and the CUDA toolkit.

Cons:
1. Requires basic familiarity with command-line tools and syntax.

Method 3: How to Check CUDA Compatibility Using GPU-Z

Method Overview: GPU-Z is a lightweight utility that provides detailed information about your GPU and its capabilities. It can be used to check the compatibility of your NVIDIA GPU with CUDA.

1. Download and install GPU-Z from the official website.

2. Open GPU-Z and navigate to the «CUDA» tab.

3. Look for the «CUDA» entry in the table. If it displays «Supported,» then your GPU is compatible with CUDA.

Steps:

1. Download and install GPU-Z from the official website.
2. Open GPU-Z and navigate to the «CUDA» tab.
3. Look for the «CUDA» entry in the table. If it displays «Supported,» then your GPU is compatible with CUDA.

Pros:
1. Provides information about the compatibility of your GPU with CUDA.
2. User-friendly interface with detailed GPU information.

Cons:
1. Limited to checking GPU compatibility and does not provide information about the installed CUDA version or its configuration.

Method 4: How to Verify CUDA Using Visual Studio Code

Method Overview: Visual Studio Code is a popular integrated development environment (IDE) for software development. It can also be used to verify if CUDA is installed and set up correctly on your Windows 11 system.

1. Install Visual Studio Code from the official website, if you haven’t already.

2. Open Visual Studio Code and create a new file.

3. Copy and paste the following code into the file:
«`cpp
#include
#include

int main() {
int deviceCount;
cudaGetDeviceCount(&deviceCount);

if (deviceCount == 0) {
std::cout << «CUDA is not available.» << std::endl;
} else {
std::cout << «CUDA is available.» << std::endl;
} return 0;
}
«` 4. Save the file with a `.cu` extension, for example, `verify_cuda.cu`. 5. Open the terminal in Visual Studio Code by clicking on «View» in the top menu and selecting «Terminal». 6. Compile and run the CUDA program by running the following command in the terminal:
«`shell
nvcc verify_cuda.cu -o verify_cuda && ./verify_cuda
«` 7. The program output will display either «CUDA is available» or «CUDA is not available» based on the presence of CUDA on your system.

Steps:

1. Install Visual Studio Code from the official website, if you haven’t already.
2. Open Visual Studio Code and create a new file.
3. Copy and paste the provided code into the file.
4. Save the file with a `.cu` extension.
5. Open the terminal in Visual Studio Code.
6. Compile and run the CUDA program using the provided command in the terminal.
7. The program output will display either «CUDA is available» or «CUDA is not available» based on the presence of CUDA on your system.

Pros:
1. Verifies the availability of CUDA using a simple CUDA program.
2. Utilizes an IDE for a more streamlined development experience.

Cons:
1. Requires the installation of Visual Studio Code and basic familiarity with programming concepts.

Why Can’t I Verify CUDA?

While the methods described above should work for most Windows 11 systems, there can be reasons why you are unable to verify CUDA. Here are a few common reasons and their possible fixes:

1. Error: «CUDA is not available» or no GPU devices detected:
– Fix: Ensure that you have a compatible NVIDIA GPU installed on your system and that it is functioning correctly. Install or update the necessary GPU drivers.

2. Error: «nvcc command not found» or «The term ‘nvcc’ is not recognized«:
– Fix: Make sure that the CUDA toolkit is installed correctly and that the CUDA bin directory is added to the system’s PATH environment variable. You may need to restart your system after making changes to the PATH variable.

3. Error: Incompatibility between CUDA version and GPU driver:
– Fix: Ensure that the installed version of CUDA is compatible with your GPU driver. Check for updates to both the CUDA toolkit and the GPU drivers, and install the latest versions if needed.

It is also important to note that some older GPUs may not support the latest versions of CUDA, so compatibility can vary depending on your hardware.

Additional Tips

Here are some additional tips to keep in mind when checking if CUDA is installed on Windows 11:

1. Updating GPU Drivers: Regularly update your NVIDIA GPU drivers to ensure compatibility with the latest CUDA versions.

2. Rebooting System: If you encounter any issues with CUDA detection, try restarting your system after installing or updating CUDA and GPU drivers.

3. Checking CUDA Toolkit Documentation: Refer to the official CUDA documentation for detailed information on installation, troubleshooting, and compatibility.

5 FAQs about Checking If CUDA Is Installed Windows 11

Q1: How can I find the CUDA version on Windows 11?

A: You can find the CUDA version on Windows 11 by using methods like checking the NVIDIA Control Panel, using PowerShell commands, or verifying through Visual Studio Code’s CUDA program. Each method provides the installed CUDA version, along with additional information.

Q2: Is it necessary to have an NVIDIA GPU to use CUDA?

A: Yes, CUDA is designed to work with NVIDIA GPUs specifically. It utilizes the parallel processing power of NVIDIA GPUs to accelerate various computational tasks. Without an NVIDIA GPU, CUDA cannot be installed or utilized.

Q3: Can I use CUDA with Windows 10 or older versions?

A: Yes, CUDA is compatible with multiple versions of Windows, including Windows 10 and older versions. However, the methods described in this blog post are specific to Windows 11. You may need to refer to the appropriate documentation for specific instructions related to your Windows version.

Q4: Are there any alternatives to CUDA for GPU computing?

A: Yes, there are alternatives to CUDA for GPU computing, such as OpenCL and Vulkan. These frameworks provide similar functionalities and can be used with a variety of GPUs, including those from AMD. However, CUDA is specifically optimized for NVIDIA GPUs and is widely used in the industry.

Q5: Can I use CUDA without installing the complete CUDA toolkit?

A: No, the CUDA toolkit is required to develop and run CUDA applications. It provides the necessary libraries, compilers, and tools for CUDA development. Installing the complete CUDA toolkit is recommended to ensure proper functionality and compatibility.

In Conclusion

Checking if CUDA is installed on your Windows 11 system is essential for utilizing the power of NVIDIA GPUs in various computational tasks. By following the methods outlined in this blog post, you can easily determine if CUDA is installed and set up correctly on your system.

We explored methods such as checking through the NVIDIA Control Panel, using PowerShell commands, verifying with GPU-Z, and verifying through Visual Studio Code’s CUDA program. Each method provides a different approach but ultimately helps you determine if CUDA is available on your Windows 11 system.

Remember to prepare your system by ensuring you have a compatible NVIDIA GPU, updated GPU drivers, and the CUDA toolkit installed. In case of any issues, refer to the additional tips and FAQs provided to troubleshoot common problems.

Now that you have the knowledge and tools to check if CUDA is installed on Windows 11, you can confidently proceed with your GPU-accelerated computing tasks. Happy CUDA programming!

Here you will learn how to check NVIDIA CUDA version in 3 ways: nvcc from CUDA toolkit, nvidia-smi from NVIDIA driver, and simply checking a file. Using one of these methods, you will be able to see the CUDA version regardless the software you are using, such as PyTorch, TensorFlow, conda (Miniconda/Anaconda) or inside docker.

Prerequisite

You should have NVIDIA driver installed on your system, as well as Nvidia CUDA toolkit, aka, CUDA, before we start. If you haven’t, you can install it by running sudo apt install nvidia-cuda-toolkit.

What is CUDA?

CUDA is a general parallel computing architecture and programming model developed by NVIDIA for its graphics cards (GPUs). Using CUDA, PyTorch or TensorFlow developers will dramatically increase the performance of PyTorch or TensorFlow training models, utilizing GPU resources effectively.

In GPU-accelerated technology, the sequential portion of the task runs on the CPU for optimized single-threaded performance, while the computed-intensive segment, like PyTorch technology, runs parallel via CUDA at thousands of GPU cores. When using CUDA, developers can write a few basic keywords in common languages such as C, C++ , Python, and implement parallelism.

Method 1 — Use nvcc to check CUDA version

If you have installed the cuda-toolkit software either from the official Ubuntu repositories via sudo apt install nvidia-cuda-toolkit, or by downloading and installing it manually from the official NVIDIA website, you will have nvcc in your path (try echo $PATH) and its location will be /usr/bin/nvcc (by running which nvcc).

To check CUDA version with nvcc, run

nvcc --version

You can see similar output in the screenshot below. The last line shows you version of CUDA. The version here is 10.1. Yours may vary, and can be either 10.0, 10.1, 10.2 or even older versions such as 9.0, 9.1 and 9.2. After the screenshot you will find the full text output too.

vh@varhowto-com:~$ nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

What is nvcc?

nvcc is the NVIDIA CUDA Compiler, thus the name. It is the key wrapper for the CUDA compiler suite. For other usage of nvcc, you can use it to compile and link both host and GPU code.

Check out nvcc‘s manpage for more information.

Method 2 — Check CUDA version by nvidia-smi from NVIDIA Linux driver

The second way to check CUDA version is to run nvidia-smi, which comes from downloading the NVIDIA driver, specifically the NVIDIA-utils package. You can install either Nvidia driver from the official repositories of Ubuntu, or from the NVIDIA website.

$ which nvidia-smi
/usr/bin/nvidia-smi
$ dpkg -S /usr/bin/nvidia-smi
nvidia-utils-440: /usr/bin/nvidia-smi

To check CUDA version with nvidia-smi, directly run

nvidia-smi

You can see similar output in the screenshot below. The version is at the top right of the output. Here’s my version is CUDA 10.2. You may have 10.0, 10.1 or even the older version 9.0 or 9.1 or 9.2 installed.

Importantly, except for CUDA version. There are more details in the nvidia-smi output, driver version (440.100), GPU name, GPU fan percentage, power consumption/capability, memory usage, can also be found here. You can also find the processes which use the GPU at the moment. This is helpful if you want to see if your model or system is using GPU such as PyTorch or TensorFlow.

Here is the full text output:

vh@varhowto-com:~$ nvidia-smi
Tue Jul 07 10:07:26 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 440.100 Driver Version: 440.100 CUDA Version: 10.2 |
|-------------------------------+----------------------+----------------------+
| 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 1070 Off | 00000000:01:00.0 On | N/A |
| 31% 48C P0 35W / 151W | 2807MiB / 8116MiB | 1% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1582 G /usr/lib/xorg/Xorg 262MiB |
| 0 2481 G /usr/lib/xorg/Xorg 1646MiB |
| 0 2686 G /usr/bin/gnome-shell 563MiB |
| 0 3244 G …AAAAAAAAAAAACAAAAAAAAAA= --shared-files 319MiB |
+-----------------------------------------------------------------------------+

What is nvidia-smi?

nvidia-smi (NVSMI) is NVIDIA System Management Interface program. It is also known as NVSMI. nvidia-smi provides monitoring and maintenance capabilities for all of tje Fermi’s Tesla, Quadro, GRID and GeForce NVIDIA GPUs and higher architecture families. For most functions, GeForce Titan Series products are supported with only little detail given for the rest of the Geforce range.

NVSMI is also a cross-platform application that supports both common NVIDIA driver-supported Linux distros and 64-bit versions of Windows starting with Windows Server 2008 R2. Metrics may be used directly by users via stdout, or stored via CSV and XML formats for scripting purposes.

For more information, check out the man page of nvidia-smi.

Method 3 — cat /usr/local/cuda/version.txt

cat /usr/local/cuda/version.txt

Note that if you install Nvidia driver and CUDA from Ubuntu 20.04’s own official repository this approach may not work.

linux view cuda version

CUDA is generally installed under the path /usr/local/cuda/, there is a version.txt file under this path, which records the version information of CUDA

cat /usr/local/cuda/version.txt

How to check the CUDA version of windows

What is CUDA?

CUDA (Compute Unified Device Architecture) is a graphics card manufacturerNVIDIAIntroduced computing platform. CUDA is a universalParallel ComputingArchitecture that enablesGPUAble to solve complex calculation problems. It contains CUDAInstruction set architecture（ISA) And the parallel computing engine inside the GPU. (The above explanation comes from Baidu entry)

Knowing what CUDA is, what is my CUDA version number?

(1) Open the control panel and click the NVIDIA control panel:

Click Help on the NVIDIA control panel, and click System Information in the lower left corner:

Click on the component: Your CUDA information is displayed here! ! !

(2) The second way is:

Open CMD, enter nvcc —version

D:\>nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:32:27_Pacific_Daylight_Time_2019
Cuda compilation tools, release 10.2, V10.2.89

If it is reported that nvcc is not an internal or external command, nor an executable program or batch file, then the path is not added to the path. The path of the general file isC:\Program Files\NVIDIA GPU Computing Toolkit\CUDA

Source: https://www.cnblogs.com/xym4869/p/12173382.html
https://blog.csdn.net/qq_38295511/article/details/89223169