Comments (2)
As it says, the GPU card you are using is running out of its graphic memory.
Check if you have multiple jobs on one card. If not, you could turn down the batch size or crop size to reduce the GPU memory usage.
from fasterseg.
Hi, @riazspace
I would like to know if you were able to run. I'm trying with GeForce GTX 1650 but I get a similar error as your screenshot:
I already tried turning down the batch size, crop size and even the number of workers.
Will the GeForce be able to run this work, or do I need a more capable GPU?
from fasterseg.
Related Issues (20)
- I reported an error on my own data set while I was infering HOT 3
- Test Issue HOT 1
- Training with custom data with different resolution to cityscapes dataset HOT 1
- TranA and TrainB is overlapped according to your implementation HOT 5
- NaN values in loss function during Step 2.2 HOT 2
- BaseDataset.__getitem__ always returns random item if file_length is set HOT 1
- CUDA out of memory: GeForce GTX 1650. HOT 1
- Which Nvidia GPU card do you use for training? HOT 2
- What's the idea of `betas2path ` ? HOT 3
- CUDA out of memory HOT 2
- How to estimate the runtime of the four steps depending on epochs and iterations?
- RuntimeError: transform: failed to synchronize: cudaErrorAssert: device-side assert triggered
- error during run_latency.py HOT 3
- Whether the architectures are different after each searching?
- Custom Data Resolution for Training HOT 10
- RuntimeError:CUDA error:API call is not supported in the installed CUDA driver HOT 1
- Setting up on Windows
- Model mismatch occurs when training student network with own data set HOT 2
- A train_fine_val.txt file
- Pretrained model
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from fasterseg.