I’m asking for help here as well because I feel that the CUDA errors (see below) occurred with multiple scripts that were working on a machine with NVIDIA RTX 3090 x2 and may be more like issues from PyTorch, CUDA, other dependencies, or NVIDIA RTX 3090 Ti.
I built my own dual GPU machine and wanted to train some random model (resnet152), using torchvision, to make sure the machine is ready for running experiments with PyTorch.
However, vision/references/classification/train.py
did not complete the training sessions due to various CUDA errors.
(Note that I did not modify any code in the repository, and the commit version is beb4bb706b5e13009cb5d5586505c6d2896d184a)
Errors
Using vision/references/classification/train.py
, I attempted to train a model in three different ways, which all turned out to fail.
torchrun
didn’t help me identify at which line of train.py the training failed, but the last two attempts show it failed at loss.backward()
with different types of errors.
1. Distributed training mode (with torchrun)
This is the first attempt:
torchrun --nproc_per_node=2 train.py --model='resnet152' --data-path /home/yoshitomo/datasets/ilsvrc2012/ --print-freq 10000
After a few minutes, it failed and returned “Signal 11 (SIGSEGV) received by PID 74466”
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
| distributed init (rank 0): env://
| distributed init (rank 1): env://
Namespace(data_path='/home/yoshitomo/datasets/ilsvrc2012/', model='resnet152', device='cuda', batch_size=32, epochs=90, workers=16, opt='sgd', lr=0.1, momentum=0.9, weight_decay=0.0001, norm_weight_decay=None, bias_weight_decay=None, transformer_embedding_decay=None, label_smoothing=0.0, mixup_alpha=0.0, cutmix_alpha=0.0, lr_scheduler='steplr', lr_warmup_epochs=0, lr_warmup_method='constant', lr_warmup_decay=0.01, lr_step_size=30, lr_gamma=0.1, lr_min=0.0, print_freq=10000, output_dir='.', resume='', start_epoch=0, cache_dataset=False, sync_bn=False, test_only=False, auto_augment=None, ra_magnitude=9, augmix_severity=3, random_erase=0.0, amp=False, world_size=2, dist_url='env://', model_ema=False, model_ema_steps=32, model_ema_decay=0.99998, use_deterministic_algorithms=False, interpolation='bilinear', val_resize_size=256, val_crop_size=224, train_crop_size=224, clip_grad_norm=None, ra_sampler=False, ra_reps=3, weights=None, rank=0, gpu=0, distributed=True, dist_backend='nccl')
Loading data
Loading training data
Took 1.1964967250823975
Loading validation data
Creating data loaders
Creating model
Start training
Epoch: [0] [ 0/20019] eta: 13:36:49 lr: 0.1 img/s: 19.59049709064079 loss: 7.1077 (7.1077) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 2.4482 data: 0.8147 max mem: 8343
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 74467 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -11) local_rank: 0 (pid: 74466) of binary: /home/yoshitomo/anaconda3/bin/python
Traceback (most recent call last):
File "/home/yoshitomo/anaconda3/bin/torchrun", line 33, in <module>
sys.exit(load_entry_point('torch==1.13.1', 'console_scripts', 'torchrun')())
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/elastic/multiprocessing/errors/__init__.py", line 346, in wrapper
return f(*args, **kwargs)
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/run.py", line 762, in main
run(args)
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/run.py", line 753, in run
elastic_launch(
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
=======================================================
train.py FAILED
-------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
-------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-03-06_22:42:17
host : my_pc
rank : 0 (local_rank: 0)
exitcode : -11 (pid: 74466)
error_file: <N/A>
traceback : Signal 11 (SIGSEGV) received by PID 74466
=======================================================
2. Distributed training mode (without torchrun)
I gave it another try, but without torchrun:
python3 -m torch.distributed.launch --nproc_per_node=2 --use_env train.py --world-size 2 --model resnet152 --data-path /home/yoshitomo/datasets/ilsvrc2012/ --print-freq 1000
This time it returned “Signal 6 (SIGABRT) received by PID 75526”
/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launch.py:180: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions
warnings.warn(
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as ne
eded.
*****************************************
| distributed init (rank 1): env://
| distributed init (rank 0): env://
Namespace(data_path='/home/yoshitomo/datasets/ilsvrc2012/', model='resnet152', device='cuda', batch_size=32, epochs=90, workers=16, opt='sgd', lr=0.1, momentum=0.9, weight_decay=0.0001, norm_weight_decay=None, bias_weight_decay=None, transformer_embedding_decay=None, label_smoothing=0.0, mixup_alpha=0.0, cutmix_alpha=0.0, lr_scheduler='steplr', lr_warmup_epochs=0, lr_warmup_method='constant', lr_warmup_decay=0.01, lr_step_size=30, lr_gamma=0.1, lr_min=0.0, print_freq=1000, output_dir='.', resume='', start_epoch=0, cache_dataset=False, sync_bn=False, test_only=False, auto_augment=None, ra_magnitude=9, augmix_severity=3, random_erase=0.0, amp=False, world_size=2, dist_url='env://', model_ema=False, model_ema_steps=32, model_ema_decay=0.99998, use_deterministic_algorithms=False, interpolation='bilinear', val_resize_size=256, val_crop_size=224, train_crop_size=224, clip_grad_norm=None, ra_sampler=False, ra_reps=3, weights=None, rank=0, gpu=0, distributed=True, dist_backend='nccl')
Loading data
Loading training data
Took 1.1878349781036377
Loading validation data
Creating data loaders
Creating model
Start training
Epoch: [0] [ 0/20019] eta: 13:32:42 lr: 0.1 img/s: 19.396707388591327 loss: 7.1465 (7.1465) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 2.4358 data: 0.7860 max mem: 8343
Epoch: [0] [ 1000/20019] eta: 0:58:49 lr: 0.1 img/s: 173.86594400494326 loss: 6.8894 (6.9586) acc1: 0.0000 (0.1467) acc5: 0.0000 (0.6119) time: 0.1842 data: 0.0000 max mem: 8343
Epoch: [0] [ 2000/20019] eta: 0:55:36 lr: 0.1 img/s: 173.47313783347485 loss: 6.7749 (6.9015) acc1: 0.0000 (0.1515) acc5: 0.0000 (0.8027) time: 0.1854 data: 0.0000 max mem: 8343
Traceback (most recent call last):
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 515, in <module>
main(args)
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 357, in main
train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema, scaler)
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 42, in train_one_epoch
loss.backward()
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: cuDNN error: CUDNN_STATUS_MAPPING_ERROR
[W CUDAGuardImpl.h:124] Warning: CUDA warning: unspecified launch failure (function destroyEvent)
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 75527 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: -6) local_rank: 0 (pid: 75526) of binary: /home/yoshitomo/anaconda3/bin/python3
Traceback (most recent call last):
File "/home/yoshitomo/anaconda3/lib/python3.9/runpy.py", line 197, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/yoshitomo/anaconda3/lib/python3.9/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launch.py", line 195, in <module>
main()
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launch.py", line 191, in main
launch(args)
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launch.py", line 176, in launch
run(args)
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/run.py", line 753, in run
elastic_launch(
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 132, in __call__
return launch_agent(self._config, self._entrypoint, list(args))
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/distributed/launcher/api.py", line 246, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
======================================================
train.py FAILED
------------------------------------------------------
Failures:
<NO_OTHER_FAILURES>
------------------------------------------------------
Root Cause (first observed failure):
[0]:
time : 2023-03-06_23:03:47
host : my_pc
rank : 0 (local_rank: 0)
exitcode : -6 (pid: 75526)
error_file: <N/A>
traceback : Signal 6 (SIGABRT) received by PID 75526
======================================================
3. Non-distributed training mode
I also tried to train the same model, using only one GPU
CUDA_VISIBLE_DEVICES=0 python3 train.py --model resnet152 --data-path /home/yoshitomo/datasets/ilsvrc2012/ --print-freq 10000
, but it still returns RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED
Not using distributed mode
Namespace(data_path='/home/yoshitomo/datasets/ilsvrc2012/', model='resnet152', device='cuda', batch_size=32, epochs=90, workers=16, opt='sgd', lr=0.1, momentum=0.9, weight_decay=0.0001, norm_weight_decay=None, bias_weight_decay=None, transformer_embedding_decay=None, label_smoothing=0.0, mixup_alpha=0.0, cutmix_alpha=0.0, lr_scheduler='steplr', lr_warmup_epochs=0, lr_warmup_method='constant', lr_warmup_decay=0.01, lr_step_size=30, lr_gamma=0.1, lr_min=0.0, print_freq=10000, output_dir='.', resume='', start_epoch=0, cache_dataset=False, sync_bn=False, test_only=False, auto_augment=None, ra_magnitude=9, augmix_severity=3, random_erase=0.0, amp=False, world_size=1, dist_url='env://', model_ema=False, model_ema_steps=32, model_ema_decay=0.99998, use_deterministic_algorithms=False, interpolation='bilinear', val_resize_size=256, val_crop_size=224, train_crop_size=224, clip_grad_norm=None, ra_sampler=False, ra_reps=3, weights=None, distributed=False)
Loading data
Loading training data
Took 1.1773681640625
Loading validation data
Creating data loaders
Creating model
Start training
Epoch: [0] [ 0/40037] eta: 21:54:13 lr: 0.1 img/s: 22.003122653665447 loss: 7.0583 (7.0583) acc1: 0.0000 (0.0000) acc5: 0.0000 (0.0000) time: 1.9695 data: 0.5152 max mem: 8113
Traceback (most recent call last):
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 515, in <module>
main(args)
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 357, in main
train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema, scaler)
File "/home/yoshitomo/workspace/vision/references/classification/train.py", line 42, in train_one_epoch
loss.backward()
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/_tensor.py", line 488, in backward
torch.autograd.backward(
File "/home/yoshitomo/anaconda3/lib/python3.9/site-packages/torch/autograd/__init__.py", line 197, in backward
Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA error: CUBLAS_STATUS_EXECUTION_FAILED when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc)`
Versions
Environment
- NVIDIA RTX 3090 Ti x2
- Ubuntu 22 LTS
- NVIDIA-SMI 515.86.01
- Driver Version: 515.86.01
- CUDA Version: 11.7
I installed torch and torchvision with the following command:
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
Versions
Here is the output of python3 collect_env.py
Collecting environment information...
PyTorch version: 1.13.1
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35
Python version: 3.9.16 (main, Mar 1 2023, 18:22:10) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090 Ti
GPU 1: NVIDIA GeForce RTX 3090 Ti
Nvidia driver version: 515.86.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 24
On-line CPU(s) list: 0-23
Vendor ID: GenuineIntel
Model name: 12th Gen Intel(R) Core(TM) i9-12900KS
CPU family: 6
Model: 151
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 2
CPU max MHz: 5300.0000
CPU min MHz: 800.0000
BogoMIPS: 6835.20
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 640 KiB (16 instances)
L1i cache: 768 KiB (16 instances)
L2 cache: 14 MiB (10 instances)
L3 cache: 30 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-23
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.23.5
[pip3] numpydoc==1.5.0
[pip3] torch==1.13.1
[pip3] torchaudio==0.13.1
[pip3] torchvision==0.14.1
[conda] blas 1.0 mkl
[conda] ffmpeg 4.3 hf484d3e_0 pytorch
[conda] mkl 2021.4.0 h06a4308_640
[conda] mkl-service 2.4.0 py39h7f8727e_0
[conda] mkl_fft 1.3.1 py39hd3c417c_0
[conda] mkl_random 1.2.2 py39h51133e4_0
[conda] numpy 1.23.5 py39h14f4228_0
[conda] numpy-base 1.23.5 py39h31eccc5_0
[conda] numpydoc 1.5.0 py39h06a4308_0
[conda] pytorch 1.13.1 py3.9_cuda11.7_cudnn8.5.0_0 pytorch
[conda] pytorch-cuda 11.7 h67b0de4_1 pytorch
[conda] pytorch-mutex 1.0 cuda pytorch
[conda] torchaudio 0.13.1 py39_cu117 pytorch
[conda] torchvision 0.14.1 py39_cu117 pytorch
I also tried different NVIDIA drivers available as “Additional Drivers” and corresponding CUDA versions, but it didn’t resolve the issue.
Thank you for your help