torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

[SHITIANYU-hue]

may i know if this issue is my memory is not enough?

ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 1622990) of binary: /home/changquan/anaconda3/envs/lang/bin/python
Traceback (most recent call last):
File "/home/changquan/anaconda3/envs/lang/bin/torchrun", line 8, in
sys.exit(main())
File "/home/changquan/anaconda3/envs/lang/lib/python3.8/site-packages/torch/distributed/elastic/multiprocessing/errors/init.py", line 346, in wrapper
return f(*args, **kwargs) File "/home/changquan/anaconda3/envs/lang/lib/python3.8/site-packages/torch/distributed/run.py", line 794, in main
run(args)
File "/home/changquan/anaconda3/envs/lang/lib/python3.8/site-packages/torch/distributed/run.py", line 785, in run
elastic_launch(
File "/home/changquan/anaconda3/envs/lang/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 134, in call
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/changquan/anaconda3/envs/lang/lib/python3.8/site-packages/torch/distributed/launcher/api.py", line 250, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

[Facico]

This error may pop up whenever the program interrupts abnormally, and there are so many factors that determine this error. You might be able to add a TORCH_DISTRIBUTED_DEBUG=DETAIL in front of the command to show a more detailed error message.