Comments (2)
dataplayer12
commented on July 19, 2024
You can use pytorch ngc containers to make sure your software environment is properly setup. Also, while running the container make sure that the container and driver are compatible and that CUDA is not running in compatibility mode. I had some issues too when I bought the newly released 3090 back in the day. I'm not sure if these will fix your problems, but they worked for me with a new GPU in 2020.
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ptrblck
commented on July 19, 2024
for some reason, running pytorch codes on it crashes the whole gpu and causes the machine to reboot.
This sounds like a PSU issue and I would recommend replacing it (even if only temporarily) with another (larger) one.
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Related Issues (20)
- unable to download annoations from the main readme.md
- using vit as image backbone HOT 1
- Keyerror: image_id (training detr on custom dataset
- How to train with a custom dataset on mac m2?
- continuously growing memory
- Question about object queries. HOT 4
- I want to train the DETR model on a CPU. How can I make it possible on a small computer, 8gb RAM HOT 3
- Why positional encoding is added to different role in encoder and decoder. HOT 1
- 🐛 Bug: Architecture diagram in README.md renders incorrectly when using dark mode
- continue training with chekckpoint
- How to finetune DETR for semantic segmentation task?
- I do not understand what the mask meaning in "samlpes"
- Process finished with exit code 137 (interrupted by signal 9: SIGKILL)Please read & provide the following
- Very low performance for segmentation task.
- box_cxcywh_to_xyxy
- ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -9) local_rank: 6 (pid: 257736) of binary: /home/public/anaconda3/envs/DL/bin/python
- Average Precision of each class for best epoch and then it's mean HOT 1
- the mAP is chage
- I think there are some errors in the posted code HOT 6
- Queries for images with low number of objects HOT 2
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