I am trying to initialize a tensor on Google Colab with GPU enabled.
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
t = torch.tensor([1,2], device=device)
But I am getting this strange error.
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Even by setting that environment variable to 1 seems not showing any further details.
Anyone ever had this issue?
While I tried your code, and it did not give me an error, I can say that usually the best practice to debug CUDA Runtime Errors: device-side assert like yours is to turn collab to CPU and recreate the error. It will give you a more useful traceback error.
Most of the time CUDA Runtime Errors can be the cause of some index mismatching so like you tried to train a network with 10 output nodes on a dataset with 15 labels. And the thing with this CUDA error is once you get this error once, you will recieve it for every operation you do with torch.tensors. This forces you to restart your notebook.
I suggest you restart your notebook, get a more accuracate traceback by moving to CPU, and check the rest of your code especially if you train a model on set of targets somewhere.
To gain a clearer insight into the typical utilization of GPUs in PyTorch applications, I recommend exploring deep learning projects on GitHub. Websites such as repo-rift.com can be particularly useful for this purpose. They allow you to perform text searches with queries like "How does this paper use GPU". This can help you pinpoint the exact usage of CUDA in specific lines of code within extensive repositories.