When we are working with GPU accelerated frameworks like PyTorch we tend to face errors related to CUDA which can be a frustrating experience. One such error is RuntimeError: cuda error: cublas_status_not_initialized when calling cublascreate(handle).
Knowing the exact cause of error is quite difficult but we can narrow it down to places where we can check. The error majorly occurs during deep learning tasks. Understanding some fundamental concepts around CUDA, cuBLAS, and GPU resource management can provide a clearer way to solve the error.
Let’s break down the error and try to figure out the solution.
What is a cuBLAS ?
cuBLAS stands for CUDA Basic Linear Algebra Subprograms. It is a GPU accelerated library used to perform highly optimized linear algebra operations such as matrix multiplication.
It is very important in many machine learning and scientific computing applications where we have heavy matrix related operations. When we see this error, it means that there was an issue in initializing the critical library for GPU computations.
Why does the Error Occur ?
Causes and solution for "RuntimeError: cuda error: cublas_status_not_initialized" in PyTorch
CUDA Driver and Toolkit Mismatch
One of the common reasons for the error is due to incompatibility between the CUDA driver and toolkit installed on our system. CUDA’s full form is compute unified Device architecture which is a parallel computing platform and application programming interface (API) model created by NVIDIA for general computing on GPUs.
CUDA usually comes with two components:
- CUDA driver: This is the low level interface that directly interacts with the hardware.
- CUDA ToolKit: It contains libraries, tools and headers necessary for compiling and running CUDA applications.
Incompatibilities between the versions of the CUDA driver and toolkit can cause issues when libraries like cuBLAS are initialized. For example, if our PyTorch installation expects a specific version of the toolkit but the installed toolkit or driver doesn’t match, it might lead to the error.
Solution:
We need to check for the CUDA version using the following command.
nvcc --version
We must verify the compatibility between our CUDA version and the version of PyTorch by going to the official PyTorch website.
If we have a mismatch we should consider updating our CUDA drivers or PyTorch installation.
Out of GPU Memory
I have personally faced many errors due to out of GPU memory in PyTorch. Modern deep learning models or large language models are large and memory hungry.
If our GPU runs out of memory, it may fail to initialize necessary libraries such as cuBLAS. This is common when we are running multiple applications or if we have a large batch size.
Solution:Free up GPU memory. Restart your system to release GPU memory and clear any unused memory in your script. In short, we need to optimize the GPU memory usage in code.
To clear unused memory in our script we should run the following command.
torch.cuda.empty_cache()
Use smaller batch sizes. Reducing the batch size in our model can help us reduce memory usage which can allow cuBLAS initialized successfully.
Monitoring GPU memory usage can also be used to identify when we are hitting the limit. We can use nvidia-smi to track memory usage.
Environment issues
When we have incorrectly configured the CUDA environment we may face initialization failures. CUDA requires the correct environment variables to locate necessary libraries. If we have missing or misconfigured variables then the cuBLAS library might fail to initialize.
Solution:We should see to it that our CUDA environment variables are correctly set. The variables like CUDA_HOME and LD_LIBRARY_PATH on Linux or Path on Windows are pointing to the correct directories where your CUDA libraries are located.
If you are using CUDA_HOME, you can set CUDA_HOME like this:
export CUDA_HOME=/usr/local/cudaAnd you can check if the path set is correct by using the following command.
$CUDA_HOME
Incompatible PyTorch and CUDA Version
We need to check whether we have compatible PyTorch and CUDA versions in our working setup. When one of them is updated we may have backward compatibility issues.
Solution:
Installing a compatible version of Pytorch and CUDA is the solution to the problem. For example, if you are using CUDA 11.3, install the necessary compatible PyTorch version:
pip install torch==1.10.0+cu113
You can further check for the compatible versions on the PyTorch website.
GPU Driver Issue
If you have recently updated the GPU driver and getting an error means the GPU drivers are not compatible with current setup. This can prevent cuBLAS library from initializing.
Solution:
We need to make sure we have the latest NVIDIA GPU drivers installed. We can make sure that we have the latest driver for the GPU model by visiting the NVIDIA website.
After updating we must restart our system and check if error still exists.
Restart the Kernel or System
In some scenarios the issues may be related to temporary glitch in the system. This can happen when we have processes that are consuming GPU resources or interfering with Library initialization.
Solution:The best thing to do when your system behaves differently is to restart the system. This makes sure all the processes are terminated and our GPU resources are freed up which may solve the issue of library initialization.
Tips for avoiding RuntimeError: cuda error: cublas_status_not_initialized in future.
We need to make sure that our dependencies are updated. We must have the latest and compatible version of cuDNN, PyTorch, and CUDA in our project setup.
Check for PyTorch Logs as they contain more information about the error. We get hints from additional messages in log and which can help us to solve the error.
Keep an eye on GPU utilization using the nvidia-smi tool. This can help us to get hidden insights of GPU consumption.
Conclusion:
The RuntimeError: cuda error: cublas_status_not_initialized when calling cublascreate(handle) error is a common issue when working with GPU accelerated libraries like PyTorch.
The checks you can follow to solve the error.
- Incompatible CUDA versions
- Out of GPU memory conditions
- Misconfigured environment setting
- Outdated GPU drivers
You can also read about our other blog on Layer normalization vs Batch normalization and Notimplementederror: cannot copy out of meta tensor; no data! .
