Project-MONAI / Project-MONAI/tutorials
AutoRunner GPU usage
Nobody has claimed this yet.
- Dominant language
- Jupyter Notebook
- Stars
- 2.5k
- Forks
- 803
- Avg merge
- 6d 22h
- Merged PRs (30d)
- 3
Description
Describe the bug
AutoRunner spikes CPU usage and ends with SIGKILL9. Pytorch recognizes GPU and other scripts utilize GPU.
To Reproduce
Steps to reproduce the behavior:
- run autorunner on HPC GPU instance
- run data analysis
- run algorithm generation
- fail occurs on Step 3: Model training, validation, and inference
Expected behavior
GPU utilized and no fail on run
Screenshots
If applicable, add screenshots to help explain your problem.
Environment (please complete the following information):
-
OS --> ubuntu 20.04 (also tried 22.04)
-
Python version --> 3.8 (also tried 3.10.6)
-
MONAI version [e.g. git commit hash] --> 1.1.0+21.g4b464e7b
-
GPU models and configuration -->
-
exouser@auto3dseg:~/Documents$ nvidia-smi
Fri Jan 13 19:19:07 2023
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.60.13 Driver Version: 525.60.13 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| 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 GRID A100X-40C On | 00000000:04:00.0 Off | 0 |
| N/A N/A P0 N/A / N/A | 0MiB / 40960MiB | 0% Default |
| | | Disabled |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
Error:
exouser@auto3dseg:~/Documents$ python -m monai.apps.auto3dseg AutoRunner run --input='./task.yaml'
2023-01-13 15:12:06,572 - INFO - AutoRunner using work directory ./work_dir
2023-01-13 15:12:06,574 - INFO - Loading input config ./task.yaml
2023-01-13 15:12:06,604 - INFO - The output_dir is not specified. /home/exouser/Documents/work_dir/ensemble_output will be used to save ensemble predictions
2023-01-13 15:12:06,604 - INFO - Skipping data analysis...
2023-01-13 15:12:06,604 - INFO - Skipping algorithm generation...
2023-01-13 15:12:06,616 - INFO - Launching: python /home/exouser/Documents/work_dir/dints_0/scripts/search.py run --config_file='/home/exouser/Documents/work_dir/dints_0/configs/hyper_parameters_search.yaml','/home/exouser/Documents/work_dir/dints_0/configs/network.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_infer.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_validate.yaml','/home/exouser/Documents/work_dir/dints_0/configs/network_search.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_train.yaml','/home/exouser/Documents/work_dir/dints_0/configs/hyper_parameters.yaml'
[info] number of GPUs: 1
[info] world_size: 1
train_files_w: 64
train_files_a: 64
val_files: 33
Traceback (most recent call last):
File "/usr/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/usr/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/exouser/Documents/MONAI/monai/apps/auto3dseg/main.py", line 24, in
fire.Fire(
File "/home/exouser/.local/lib/python3.8/site-packages/fire/core.py", line 141, in Fire
component_trace = _Fire(component, args, parsed_flag_args, context, name)
File "/home/exouser/.local/lib/python3.8/site-packages/fire/core.py", line 475, in _Fire
component, remaining_args = _CallAndUpdateTrace(
File "/home/exouser/.local/lib/python3.8/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
component = fn(*varargs, **kwargs)
File "/home/exouser/Documents/MONAI/monai/apps/auto3dseg/auto_runner.py", line 685, in run
self._train_algo_in_sequence(history)
File "/home/exouser/Documents/MONAI/monai/apps/auto3dseg/auto_runner.py", line 557, in _train_algo_in_sequence
algo.train(self.train_params)
File "/home/exouser/Documents/work_dir/algorithm_templates/dints/scripts/algo.py", line 398, in train
self._run_cmd(cmd_search, devices_info)
File "/home/exouser/Documents/MONAI/monai/apps/auto3dseg/bundle_gen.py", line 191, in _run_cmd
normal_out = subprocess.run(cmd.split(), env=ps_environ, check=True)
File "/usr/lib/python3.8/subprocess.py", line 516, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['python', '/home/exouser/Documents/work_dir/dints_0/scripts/search.py', 'run', "--config_file='/home/exouser/Documents/work_dir/dints_0/configs/hyper_parameters_search.yaml','/home/exouser/Documents/work_dir/dints_0/configs/network.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_infer.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_validate.yaml','/home/exouser/Documents/work_dir/dints_0/configs/network_search.yaml','/home/exouser/Documents/work_dir/dints_0/configs/transforms_train.yaml','/home/exouser/Documents/work_dir/dints_0/configs/hyper_parameters.yaml'"]' died with <Signals.SIGKILL: 9>.
CPU/GPU traces from start till crash:
Contributor guide
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 monai/apps/auto3dseg/auto_runner.py at _train_algo_in_sequence and bundle_gen.py at _run_cmd, then inspect the generated work_dir/dints_0/scripts/search.py command from the traceback. Reproduce the Step 3 training run while monitoring CPU and GPU usage. Done means AutoRunner completes training, validation, and inference without SIGKILL and uses the available GPU.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100