I have java background and I am new to Python, so my assumption was that if we close the process then memory should be released. but I find out that it is not true.
My question is:
Why PyTorch does not free the memory after execution is completed.
forPyGCL is my conda env on a shared server, and the below table shows that it is using the memory  5457MiB  Why it does not free the memory, I can’t even run the next experiments. because now it says that memory is full. This problem wasted my 3 weeks and then I came to know that this is the problem that memory is not free after the execution.
torch.cuda.empty_cache() is use less in this case.
So i had to kill the process, but still getting this error.

RuntimeError: CUDA out of memory. Tried to allocate 1.02 GiB (GPU 0; 23.70 GiB total capacity; 5.20 GiB already allocated; 573.56 MiB free; 5.23 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

Thu Dec 23 16:51:39 2021       
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.27.04    Driver Version: 460.27.04    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| 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  GeForce RTX 3090    Off  | 00000000:02:00.0 Off |                  N/A |
| 43%   49C    P2   140W / 350W |  18322MiB / 24268MiB |     21%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  GeForce RTX 3090    Off  | 00000000:03:00.0 Off |                  N/A |
| 60%   60C    P2   321W / 350W |  22951MiB / 24268MiB |     99%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   2  GeForce RTX 3090    Off  | 00000000:82:00.0 Off |                  N/A |
| 61%   61C    P2   325W / 350W |  23130MiB / 24268MiB |     98%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   3  GeForce RTX 3090    Off  | 00000000:83:00.0 Off |                  N/A |
| 30%   39C    P2   105W / 350W |  19942MiB / 24268MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
                                                                               
+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A   1800637      C   .../envs/forPyGCL/bin/python     1827MiB |
|    0   N/A  N/A   3955522      C   python                          16493MiB |
|    1   N/A  N/A   3931490      C   ...drsum-torch1.8/bin/python    22949MiB |
|    2   N/A  N/A   1800637      C   .../envs/forPyGCL/bin/python     5457MiB |
|    2   N/A  N/A   3931491      C   ...drsum-torch1.8/bin/python    17671MiB |
|    3   N/A  N/A   1800637      C   .../envs/forPyGCL/bin/python     1825MiB |
|    3   N/A  N/A   1852217      C   python                          18115MiB |
+-----------------------------------------------------------------------------+