# Free all GPU memory used in between runs

**URL:** <https://discuss.pytorch.org/t/free-all-gpu-memory-used-in-between-runs/168202>\
**Category:** Memory Format\
**Created:** [December 13, 2022, 3:56am UTC](https://discuss.pytorch.org/t/free-all-gpu-memory-used-in-between-runs/168202 "2022-12-13T03:56:47Z")\
**Posts on this page:** 1\
**Showing post:** 2

<div class="post-metadata">

**Author:** ![ptrblck](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/ptrblck/32/1823_2.png) [@ptrblck](https://discuss.pytorch.org/u/ptrblck)\
**Post date:** [December 13, 2022, 5:23am UTC](https://discuss.pytorch.org/t/free-all-gpu-memory-used-in-between-runs/168202/2 "2022-12-13T05:23:02Z")

</div>

Deleting all objects and references pointing to objects allocating GPU memory is the right approach and will free the memory. Calling `empty_cache()` will also clear the cache and free the memory (besides the memory used for the CUDA context).  
Here is a small example:

```python
import torch
import torch.nn as nn

def memory_stats():
    print(torch.cuda.memory_allocated()/1024**2)
    print(torch.cuda.memory_cached()/1024**2)

def allocate():
    x = torch.randn(1024*1024, device='cuda')
    memory_stats()
    
    
memory_stats()
# 0.0
# 0.0

allocate()
# 4.0 # allocated inside the function
# 20.0 # used cache

memory_stats()
# 0.0 # local tensor is free
# 20.0 # cache is still alive

torch.cuda.empty_cache()
memory_stats()
# 0.0
# 0.0 # cache is free again

x = torch.randn(1024, 1024, device='cuda')
memory_stats()
# 4.0
# 20.0

# store referece
y = x

del x # this does not free the memory of x since y still points to it
memory_stats() 
# 4.0  
# 20.0

del y # this allows PyTorch to free the memory and reuse it in the cache
memory_stats()
# 0.0
# 20.0

torch.cuda.empty_cache()
memory_stats() 
# 0.0
# 0.0

```

---

_[View the full topic](https://discuss.pytorch.org/t/free-all-gpu-memory-used-in-between-runs/168202)._