My model has 195465 trainable parameters and when I start my training loop with batch_size = 1 the loop works. But when I try to increase the batch_size to even 2 then the cuda goes out of memory.
I tried to check status of my gpu using this block of code
device = torch.device(‘cuda’ if torch.cuda.is_available() else ‘cpu’)
print(‘Using device:’, device)
print()
#Additional Info when using cuda
if device.type == 'cuda':
    print(torch.cuda.get_device_name(0))
    print('Memory Usage:')
    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')
    print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')

which gave the following output:-
Using device: cuda

GeForce GTX 1050
Memory Usage:
Allocated: 2.9 GB
Cached:    2.9 GB

Can anyone help me understand the above output and why is this happening?