CUDA runtime errors can occur due to various reasons:
CUDA driver not installed
CUDA driver version mismatch
Invalid device ordinal or GPU not found
Out-of-memory (OOM) errors due to insufficient GPU memory
Illegal memory access (e.g., accessing memory out of bounds)
Incompatible CUDA versions between libraries and the CUDA Toolkit
Incompatible GPU driver versions with the installed CUDA Toolkit
Compatibility issues between CUDA and other libraries (e.g., cuDNN, NCCL)
First, you need to install the CUDA Toolkit on your system.
Go to the NVIDIA CUDA Toolkit download page.
Select your operating system, architecture, distribution, and version.
Download the CUDA Toolkit installer appropriate for your system.
Run the installer and follow the on-screen instructions to complete the installation.
After installing the CUDA Toolkit, you can verify the installation by checking the version of CUDA installed on your system using the nvcc
command in the terminal:
nvcc --version
Once the CUDA Toolkit is installed, you can use it in your Python code by installing the torch
package using pip:
pip install torch
import torchif not torch.cuda.is_available():print("CUDA driver is not installed.")else:print("CUDA driver is installed.")
If an error occurs or CUDA driver is not available, you may need to re-install or update the CUDA driver for your GPU.
import torchprint(f"CUDA driver version: {torch.version.cuda}")print(f"CUDA runtime version: {torch.version.cuda_runtime}")# Compare the CUDA driver version with the CUDA runtime versionif torch.version.cuda != torch.version.cuda_runtime:print("CUDA driver version mismatch!")
If the CUDA driver version and CUDA runtime version do not match, ensure that you installed compatible versions of the CUDA toolkit and GPU drivers.
import torchif not torch.cuda.is_available():print("No CUDA-enabled devices found.")else:device = torch.device("cuda:0") # Use the first GPUprint(f"Using device: {torch.cuda.get_device_name(device)}")
If no CUDA-enabled devices are found, ensure that you have a compatible GPU and that it is properly connected.
import torchtry:# Code that requires GPU memory# ...except RuntimeError as e:if "out of memory" in str(e):print("Out of GPU memory.")# Try reducing the batch size or modifying the code to use less memoryelse:raise # Re-raise the exception if it's not an OOM error
If the code throws an error, here's what you can do:
If it's an out-of-memory error, try reducing the batch size or modifying the code to use less memory, such as truncating or padding sequences or optimizing memory usage.
If it's not an out-of-memory error, re-raise the exception to investigate and address the underlying cause.
import torch# Get the installed CUDA driver versioncuda_driver_version = torch._C._cuda_getDriverVersion()print(f"Installed CUDA driver version: {cuda_driver_version}")# Compare the installed CUDA driver version with the required driver versionrequired_driver_version = 465 # Replace with your required driver versionif cuda_driver_version < required_driver_version:print("Incompatible GPU driver version!")
If an incompatible CUDA version occurs, ensure that the versions of the libraries you are using(e.g., Torch,Torchvision) are compatible with the installed CUDA Toolkit version.
import torch# Check if cuDNN is availableif torch.backends.cudnn.is_available():print("cuDNN is available.")else:print("cuDNN is not available.")
If cuDNN is not available, ensure that you have installed it correctly. Check the documentation of the library you are using (e.g., Torch) to verify compatibility requirements and installations steps for cuDNN.
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