How to change batch size for evaluation?

[tanatomoe]

Hello,
When trying to evaluate the model I get the following error:

ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 887) of binary: /opt/conda/bin/python
Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/opt/conda/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/run.py", line 755, in run
    )(*cmd_args)
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/opt/conda/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 247, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
./tools/test.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-01-16_18:10:10
  host      : adas-server
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 887)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

As far as I know, this error happens due to insufficient memory in a computer. Right now, I have around 45 GBs of memory available and am not able to increase it for the time being, but I read it is possible to avoid it by changing the batch size. Unfortunately, I cannot seem to figure out how exactly to do that.
Any help would be greatly appreciated!