Project-MONAI / Project-MONAI/tutorials
AutoRunner GPU usage
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Descripción
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:
Guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
Línea de trabajo
Comienza con monai/apps/auto3dseg/auto_runner.py en _train_algo_in_sequence y bundle_gen.py en _run_cmd; después, inspecciona el comando de work_dir/dints_0/scripts/search.py generado que aparece en el traceback. Reproduce la ejecución de entrenamiento del Paso 3 mientras monitorizas el uso de la CPU y la GPU. Se considera terminado cuando AutoRunner completa el entrenamiento, la validación y la inferencia sin SIGKILL y utiliza la GPU disponible.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- python, pytorch
- Área
- machine-learning
- Tipo de issue
- Error
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Necesita aclaración
- Aptitud para principiantes
- 35/100