CUDA out of memory. Tried to allocate 196.00 MiB (GPU 0; 2.00 GiB total capacity; 359.38 MiB already allocated; 192.29 MiB free; 152.37 MiB cached)

My batch size is 1, my dataset is very small. I have set PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True and export 'PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512' I also tried the 'restart pc' method.

If this works: https://user-images.githubusercontent.com/29906369/153782097-2b59756b-0197-4ff3-b1d1-1bfc3f6f9a0e.jpeg where should I write this? Can you also provide more detailed instructions for the stupid? =)

I am having a similar issue. I am using the pytorch dataloader. SaysI should have over 5 Gb free but it gives 0 bytes free.

In my case I just lowered the batch size number from 8 to 4. It worked and the error of "cuda out of memory" was solved.

It is because of mini-batch of data does not fit on to GPU memory. Just decrease the batch size. When I set batch size = 256 for cifar10 dataset I got the same error; Then I set the batch size = 128, it is solved.

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I too am running into the same errors. My model was working earlier with the exact setup, but now it's giving this error after I modified some seemingly unrelated code.

I have trained my model in a cluster of servers and the error unpredictably happened to one of my servers. Also such wired error only happens in one of my training strategies. And the only difference is that I modify the code during data augmentation, and make the data preprocess more complicated than others. But I am not sure how to solve this problem.

Can I ask why the numbers in the error don't add up?! I (like all of you) get: Tried to allocate 20.00 MiB (GPU 0; 1.95 GiB total capacity; 763.17 MiB already allocated; 6.31 MiB free; 28.83 MiB cached) To me it means the following should be approximately true: 1.95 (GB total) - 20 (MiB needed) == 763.17 (MiB already used) + 6.31 (MiB free) + 28.83 (MiB cached) But it is not. What is that I am getting wrong?

I have the same error... RuntimeError: CUDA out of memory. Tried to allocate 312.00 MiB (GPU 0; 10.91 GiB total capacity; 1.07 GiB already allocated; 109.62 MiB free; 15.21 MiB cached)

RuntimeError: CUDA out of memory. Tried to allocate 11.00 MiB (GPU 0; 6.00 GiB total capacity; 448.58 MiB already allocated; 0 bytes free; 942.00 KiB cached)

2024-02-03 20:39:53,872 - Inpaint Anything - ERROR - Allocation on device 0 would exceed allowed memory. (out of memory) Currently allocated : 3.26 GiB Requested : 2.64 GiB Device limit : 8.00 GiB Free (according to CUDA): 0 bytes PyTorch limit (set by user-supplied memory fraction) 怎么解决的老是出现 这个问题

9 day fortnight working pattern 37.5 hours

I am getting after 3h of training this very strange CUDA Out of Memory error message: RuntimeError: CUDA out of memory. Tried to allocate 12.50 MiB (GPU 0; 10.92 GiB total capacity; 8.57 MiB already allocated; 9.28 GiB free; 4.68 MiB cached). According to the message, I have the required space but it does not allocate the memory.

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces) ERROR: Exception in ASGI application Traceback (most recent call last): File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/uvicorn/protocols/websockets/websockets_impl.py", line 240, in run_asgi result = await self.app(self.scope, self.asgi_receive, self.asgi_send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 69, in call return await self.app(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/fastapi/applications.py", line 1054, in call await super().call(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/applications.py", line 123, in call await self.middleware_stack(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/middleware/errors.py", line 151, in call await self.app(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/middleware/cors.py", line 77, in call await self.app(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 65, in call await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app raise exc File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/routing.py", line 756, in call await self.middleware_stack(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/routing.py", line 776, in app await route.handle(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/routing.py", line 373, in handle await self.app(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/routing.py", line 96, in app await wrap_app_handling_exceptions(app, session)(scope, receive, send) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app raise exc File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app await app(scope, receive, sender) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/routing.py", line 94, in app await func(session) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/fastapi/routing.py", line 348, in app await dependant.call(**values) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/routes.py", line 571, in join_queue session_info = await asyncio.wait_for( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/asyncio/tasks.py", line 445, in wait_for return fut.result() File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/websockets.py", line 158, in receive_json self._raise_on_disconnect(message) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/starlette/websockets.py", line 130, in _raise_on_disconnect raise WebSocketDisconnect(message["code"], message.get("reason")) starlette.websockets.WebSocketDisconnect: (1006, None) Traceback (most recent call last): File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/queueing.py", line 407, in call_prediction output = await route_utils.call_process_api( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/route_utils.py", line 226, in call_process_api output = await app.get_blocks().process_api( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/blocks.py", line 1550, in process_api result = await self.call_function( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/blocks.py", line 1185, in call_function prediction = await anyio.to_thread.run_sync( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 2144, in run_sync_in_worker_thread return await future File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/anyio/_backends/_asyncio.py", line 851, in run result = context.run(func, *args) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/gradio/utils.py", line 661, in wrapper response = f(*args, **kwargs) File "/home/workstation/Track-Anything/ap.py", line 120, in get_frames_from_video model.samcontroler.sam_controler.set_image(video_state["origin_images"][0]) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/home/workstation/Track-Anything/tools/base_segmenter.py", line 38, in set_image self.predictor.set_image(image) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/predictor.py", line 60, in set_image self.set_torch_image(input_image_torch, image.shape[:2]) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(*args, **kwargs) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/predictor.py", line 89, in set_torch_image self.features = self.model.image_encoder(input_image) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/modeling/image_encoder.py", line 112, in forward x = blk(x) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/modeling/image_encoder.py", line 174, in forward x = self.attn(x) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1194, in _call_impl return forward_call(*input, **kwargs) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/modeling/image_encoder.py", line 234, in forward attn = add_decomposed_rel_pos(attn, q, self.rel_pos_h, self.rel_pos_w, (H, W), (H, W)) File "/home/workstation/anaconda3/envs/tracking/lib/python3.10/site-packages/segment_anything/modeling/image_encoder.py", line 358, in add_decomposed_rel_pos attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :] torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 7.78 GiB total capacity; 4.98 GiB already allocated; 920.94 MiB free; 5.06 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF facing same error while running trackanything, can anyone help?

For information, my preprocessing relies on torch.multiprocessing.Queue and an iterator over the lines of my source data to preprocess the data on the fly.

7.78 times 36formula

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It works for me! @torch.no_grad() also works. Thanks so much! Could you help me, in what specific place should I write this line? I would be very grateful

7.78 times 36example

I don't know if my scenario is relatable to the original issue, but I resolved my problem (the OOM error in the previous message went away) by breaking up the nn.Sequential layers in my model, e.g.

9 day fortnight 37.5 hours NHS

RuntimeError: CUDA out of memory. Tried to allocate 2.89 GiB (GPU 1; 12.00 GiB total capacity; 8.66 GiB already allocated; 2.05 GiB free; 8.67 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

warnings.warn(old_gpu_warn % (d, name, major, capability[1])) [05.22.19|12:02:41] Parameters: {'base_lr': 0.1, 'ignore_weights': [], 'model': 'net.st_gcn.Model', 'eval_interval': 5, 'weight_decay': 0.0001, 'work_dir': './work_dir', 'save_interval': 10, 'model_args': {'in_channels': 3, 'dropout': 0.5, 'num_class': 60, 'edge_importance_weighting': True, 'graph_args': {'strategy': 'spatial', 'layout': 'ntu-rgb+d'}}, 'debug': False, 'pavi_log': False, 'save_result': False, 'config': 'config/st_gcn/ntu-xsub/train.yaml', 'optimizer': 'SGD', 'weights': None, 'num_epoch': 80, 'batch_size': 64, 'show_topk': [1, 5], 'test_batch_size': 64, 'step': [10, 50], 'use_gpu': True, 'phase': 'train', 'print_log': True, 'log_interval': 100, 'feeder': 'feeder.feeder.Feeder', 'start_epoch': 0, 'nesterov': True, 'device': [0], 'save_log': True, 'test_feeder_args': {'data_path': './data/NTU-RGB-D/xsub/val_data.npy', 'label_path': './data/NTU-RGB-D/xsub/val_label.pkl'}, 'train_feeder_args': {'data_path': './data/NTU-RGB-D/xsub/train_data.npy', 'debug': False, 'label_path': './data/NTU-RGB-D/xsub/train_label.pkl'}, 'num_worker': 4}

I was facing the problem while training, I tried reducing the batch-size, it didn't work. But I noticed while changing my optimizer from Adam to SGD, it works.

CUDA out of memory. Tried to allocate 196.50 MiB (GPU 0; 15.75 GiB total capacity; 7.09 GiB already allocated; 20.62 MiB free; 72.48 MiB cached)

[05.22.19|12:02:41] Training epoch: 0 Traceback (most recent call last): File "main1.py", line 31, in p.start() File "F:\Suresh\st-gcn\processor\processor.py", line 113, in start self.train() File "F:\Suresh\st-gcn\processor\recognition.py", line 91, in train output = self.model(data) File "C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 489, in call result = self.forward(*input, **kwargs) File "F:\Suresh\st-gcn\net\st_gcn.py", line 82, in forward x, _ = gcn(x, self.A * importance) File "C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 489, in call result = self.forward(*input, **kwargs) File "F:\Suresh\st-gcn\net\st_gcn.py", line 194, in forward x, A = self.gcn(x, A) File "C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 489, in call result = self.forward(*input, **kwargs) File "F:\Suresh\st-gcn\net\utils\tgcn.py", line 60, in forward x = self.conv(x) File "C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\nn\modules\module.py", line 489, in call result = self.forward(*input, **kwargs) File "C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\nn\modules\conv.py", line 320, in forward self.padding, self.dilation, self.groups) RuntimeError: CUDA out of memory. Tried to allocate 1.37 GiB (GPU 0; 12.00 GiB total capacity; 8.28 GiB already allocated; 652.75 MiB free; 664.38 MiB cached)

9 day fortnight working pattern 35 hours

RuntimeError: CUDA out of memory. Tried to allocate 32.75 MiB (GPU 0; 4.93 GiB total capacity; 3.85 GiB already allocated; 29.69 MiB free; 332.48 MiB cached)

Tell me where to write "torch c.no_grad():"? This question has already been answered, but I did not understand due to the language barrier. Can you give more details, maybe with a photo?

Hi, I also have the same problem and I can't solve it. I can't use Animatediff because it then gives me an error. Yet I have 32 GB of DDR4 3600mhz RAM - RTX 4070 OC and Ryzen 7 5800x CPU.

The same issue to me Dear, did you get the solution? (base) F:\Suresh\st-gcn>python main1.py recognition -c config/st_gcn/ntu-xsub/train.yaml --device 0 --work_dir ./work_dir C:\Users\cudalab10\Anaconda3\lib\site-packages\torch\cuda_init_.py:117: UserWarning: Found GPU0 TITAN Xp which is of cuda capability 1.1. PyTorch no longer supports this GPU because it is too old.

I was facing the problem while training, I tried reducing the batch-size, it didn't work. But I noticed while changing my optimizer from Adam to SGD, it works.

I can not reproduce the problem anymore, thus I will close the issue. The problem disappeared when I stopped storing the preprocessed data in RAM.

I am also having this issue. How to solve it??? RuntimeError: CUDA out of memory. Tried to allocate 18.00 MiB (GPU 0; 4.00 GiB total capacity; 2.94 GiB already allocated; 10.22 MiB free; 18.77 MiB cached)

9 day fortnight calculator

I am trying to reproduce the fine tuning steps for phi3 described here https://github.com/microsoft/Phi-3CookBook/blob/main/code/04.Finetuning/Phi_3_Inference_Finetuning.ipynb

7.78 times 36calculator

I am currently training a lightweight model on very large amount of textual data (about 70GiB of text). For that I am using a machine on a cluster ('grele' of the grid5000 cluster network).

From my previous experience with this problem, either you do not free the CUDA memory or you try to put too much data on CUDA. By not freeing the CUDA memory, I mean you potentially still have references to tensors in CUDA that you do not use anymore. Those would prevent the allocated memory from being freed by deleting the tensors.

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacity of 6.00 GiB of which 4.52 GiB is free. Of the allocated memory 520.00 MiB is allocated by PyTorch, and 0 bytes is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

RuntimeError Traceback (most recent call last) in 22 23 data, inputs = states_inputs ---> 24 data, inputs = Variable(data).float().to(device), Variable(inputs).float().to(device) 25 print(data.device) 26 enc_out = encoder(data)

It works for me! @torch.no_grad() also works. Thanks so much! Could you help me, in what specific place should I write this line? I would be very grateful

I was facing the problem while training, I tried reducing the batch-size, it didn't work. But I noticed while changing my optimizer from Adam to SGD, it works.

I tried reducing the batch size and it worked. The confusing part is the error msg that the cached memory is larger than the to be allocated memory.

RuntimeError: CUDA out of memory. Tried to allocate 1.34 GiB (GPU 0; 22.41 GiB total capacity; 11.42 GiB already allocated; 59.19 MiB free; 912.00 KiB cached)

I am using 8 V100 to train the model. The confusing part is that there is still 3.03GB cached and it cannot be allocated for 11.88MB.

i think it might be related to less ram in your system. My ram reached its full capacity before reducing drastially and showing the same error mentioned above. i use 3060 12GB vram and 16 GB ram.

Same issue here RuntimeError: CUDA out of memory. Tried to allocate 54.00 MiB (GPU 0; 11.00 GiB total capacity; 7.89 GiB already allocated; 7.74 MiB free; 478.37 MiB cached)

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I am having a similar issue, my batch size is already very small. But I want highlight a detail that many people missed in this post.