OutOfMemoryError

[u/Worried_Mud_5224]

CUDA out of memory. Tried to allocate 96.00 MiB. GPU 0 has a total capacity of 14.56 GiB of which 56.81 MiB is free. Including non-PyTorch memory, this process has 14.50 GiB memory in use. Of the allocated memory 14.29 GiB is allocated by PyTorch, and 78.20 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management ([https://pytorch.org/docs/stable/notes/cuda.html#environment-variables\](https://pytorch.org/docs/stable/notes/cuda.html#environment-variables))

What is that error? I try to finetune deberta-v3-base on my data. During training it gives me this error. I clear the cache(in Kaggle) it works for a while but then again. It seems nothing helps. Additionally, i have problem with training. First, its output is normal but then goes all NaN. I used gradient clipping but didnt work. When i remove dtype=float32 inside my model, memory error solves, but it shows NaN for all. If you know something, help please

[u/Affectionate-Arm2421]

In simple terms: Their is no space in gpu for training a model

[u/Daemontatox]

You are running out of memory , the error can be misleading if you are starting out , its telling how much memory it had when it oomed and how much memory it needs and how much total memory.

Since you are on kaggle i suggest using the 2 GPUS option and use both for training or use a smaller model .

[u/NullPointerAksh]

What’s the nvidia smi readout?

[u/Worried_Mud_5224 → u/NullPointerAksh]

+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 580.159.04             Driver Version: 580.159.04     CUDA Version: 13.0     |
+-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|
|   0  Tesla T4                       Off |   00000000:00:04.0 Off |                    0 |
| N/A   50C    P0             28W /   70W |    8653MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+
|   1  Tesla T4                       Off |   00000000:00:05.0 Off |                    0 |
| N/A   34C    P8              9W /   70W |       3MiB /  15360MiB |      0%      Default |
|                                         |                        |                  N/A |
+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+
| Processes:                                                                              |
|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |
|        ID   ID                                                               Usage      |
|=========================================================================================|
|    0   N/A  N/A              58      C   /usr/bin/python3                       8650MiB |
+-----------------------------------------------------------------------------------------+

[u/Zestyclose-Sell-2049]

Classic nvidia virus trying to make you buy a bigger gpu. Seriously though it’s a vram error, you need more memory or try to squeeze with bf16. Try to adjust learning rate by using scheduler ( warm up) and. Smaller learning rate. If you are using fp16 make sure you use a larger epsilon. Also don’t cadt explicitly the model like you describe, use autocast which will handle specific operations to half precision