Project-MONAI / Project-MONAI/tutorials
ValueError: Default process group has not been initialized in maisi diffusion train
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Description
Executing Cell 19--------------------------------------
INFO:notebook:Training the model...
INFO:training:Using cuda:0 of 1
INFO:training:[config] ckpt_folder -> ./temp_work_dir/./models.
INFO:training:[config] data_root -> ./temp_work_dir/./embeddings.
INFO:training:[config] data_list -> ./temp_work_dir/sim_datalist.json.
INFO:training:[config] lr -> 0.0001.
INFO:training:[config] num_epochs -> 2.
INFO:training:[config] num_train_timesteps -> 1000.
INFO:training:num_files_train: 2
Traceback (most recent call last):
File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/opt/toolkit/tutorials/monai/generation/maisi/scripts/diff_model_train.py", line 434, in <module>
diff_model_train(args.env_config, args.model_config, args.model_def)
File "/opt/toolkit/tutorials/monai/generation/maisi/scripts/diff_model_train.py", line 355, in diff_model_train
data=train_files, shuffle=True, num_partitions=dist.get_world_size(), even_divisible=True
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 2002, in get_world_size
return _get_group_size(group)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 987, in _get_group_size
default_pg = _get_default_group()
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/distributed_c10d.py", line 1151, in _get_default_group
raise ValueError(
ValueError: Default process group has not been initialized, please make sure to call init_process_group.
E0930 01:33:38.255000 140127265727104 torch/distributed/elastic/multiprocessing/api.py:863] failed (exitcode: 1) local_rank: 0 (pid: 383) of binary: /usr/bin/python
Traceback (most recent call last):
File "/usr/local/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch==2.5.0a0+872d972e41.nv24.8.1', 'console_scripts', 'torchrun')())
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 355, in wrapper
return f(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 919, in main
run(args)
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/run.py", line 910, in run
elastic_launch(
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 138, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/usr/local/lib/python3.10/dist-packages/torch/distributed/launcher/api.py", line 269, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
scripts.diff_model_train FAILED
------------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2024-09-30_01:33:38
host : ipp2-0112.ipp2u1.colossus.nvidia.com
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 383)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with scripts/diff_model_train.py at line 355, where dist.get_world_size() is called, and inspect how the training process is launched from the notebook and via torchrun. Reproduce Cell 19 with the shown configuration and trace process-group setup; done means the Maisi diffusion training run proceeds without the reported initialization error.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100