@ptrblck

What kind of GPUs are you using?

I am using a titan xp I believe.
---- running ----
is cuda available: True
device: cuda
/home/miranda9/data/dataset_LS_fully_connected_NN_with_BN_nb_tasks200_data_per_task1000_l_4_nb_h_layes3_out1_H15/meta_set_fully_connected_NN_with_BN_std1_0.01_std2_1.0_noise_std0.1nb_h_layes3_out1_H15
['fc1_l1', 'fc2_l1', 'fc3_l1']
number of workers = 0
--> args.meta_batch_size = 200
--> args.iters = 5
--> args.nb_inner_train_steps = 1
  0%|                                                                                                                                                                                                                                                                                                                                                                                                     | 0/5 [00:00<?, ?it/s]it = 0
Segmentation fault
(automl-meta-learning) miranda9~/automl-meta-learning/results_plots $ nvidia-smi
Thu Jan  7 15:59:51 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.36.06    Driver Version: 450.36.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| 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  TITAN Xp            Off  | 00000000:02:00.0 Off |                  N/A |
| 53%   84C    P2   253W / 250W |   9209MiB / 12196MiB |     84%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  TITAN Xp            Off  | 00000000:03:00.0 Off |                  N/A |
| 26%   43C    P0    63W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  TITAN Xp            Off  | 00000000:82:00.0 Off |                  N/A |
| 51%   82C    P2   182W / 250W |  11931MiB / 12196MiB |     71%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   3  TITAN Xp            Off  | 00000000:83:00.0 Off |                  N/A |
| 26%   38C    P0    62W / 250W |      0MiB / 12196MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     10994      C   python                           9207MiB |
|    2   N/A  N/A     30964      C   python                          11929MiB |
+-----------------------------------------------------------------------------+
(automl-meta-learning) miranda9~/automl-meta-learning/results_plots $ 

I don’t get it…what is causing the issue in the first place? I know segmentation faults are due to accessing or doing something illegal/not allowed with the memory. So is the error due to GPU memory or normal RAM or what is going on?

related link I found (not sure if it will help): ubuntu - Error in `python': free(): invalid pointer: 0x00007fc3c90dc98e - Stack Overflow 2

fyi this is the library causing my error:

  
      github.com/moskomule/anatome
  
  
    
  
	  
  

  
    
      cca code for gpu code not working
    

    
      
        opened 
        
          
        
        Dec 2, 2020
      
      

        
          closed 
        
          
        
        Dec 28, 2020
      
        

      
        
          
          brando90
        
      
    
  



  small example:
import torch
import torch.nn as nn
from anatome import SimilarityHook
from collections import OrderedDict
#
Din, Dout = 1, 1
mdl1 = nn.Sequential(OrderedDict([
 ('fc1_l1', nn.Linear(Din, Dout)),
...





  
  
    
    
  
  


when I try to compute CCA with gpu it seems.