NV50_P2P allocation class unimplemented in nvproxy
thundergolfer opened this issue · comments
Description
When running multi-GPU training on A100s applications can attempt to use the unimplemented NV50_P2P allocation class.
This presents as a 'mapping of buffer object failed' error.
torch.distributed.DistBackendError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp:1331, unhandled cuda error (run with NCCL_DEBUG=INFO for details), NCCL version 2.18.1
ncclUnhandledCudaError: Call to CUDA function failed.
Last error:
Cuda failure 'mapping of buffer object failed'I've take a look at the implementation of this allocation class and unfortunately it's non-trivial: https://github.dev/NVIDIA/open-gpu-kernel-modules/blob/4c29105335610933e744f4ab2524ea63fc39edaf/src/common/sdk/nvidia/inc/class/cl503b.h#L57
Opening this as a tracking issue.
Steps to reproduce
Dockerfile
This Dockerfile runs but I wasn't able to use it to reproduce the issue because I couldn't get an on-demand multi-GPU A100 VM in GCP 😓.
FROM nvidia/cuda:12.2.0-devel-ubuntu20.04
RUN apt-get update && apt-get install --yes python3 python3-distutils clang wget vim
RUN wget https://bootstrap.pypa.io/get-pip.py
RUN python3 get-pip.py
RUN python3 -m pip install clang~=10.0.1 # must match version of `clang` installed above.
RUN python3 -m pip install --ignore-installed torch torchvision lightning numpy memory_profiler
COPY <<EOF repro.py
print("Hello from inside container.")
import psutil
current_process = psutil.Process()
parent_process = current_process.parent()
print(f"Processes: {current_process=} {parent_process=}")
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import lightning as L
from memory_profiler import profile
from torchvision.datasets import CIFAR100
from torchvision import transforms
from torchvision import models
from torch.utils.data import DataLoader
class MagixNet(L.LightningModule):
def __init__(self, nbr_cat):
super().__init__()
module = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
module.fc = nn.Linear(2048, nbr_cat)
self.module = module
def forward(self, x):
return self.module(x)
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
def prepare_data():
pipeline = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
train_ds = CIFAR100('data', train=True, download=True, transform=pipeline)
train_dl = DataLoader(train_ds, batch_size=128, num_workers=4)
val_ds = CIFAR100('data', train=False, download=True, transform=pipeline)
val_dl = DataLoader(val_ds, batch_size=128, num_workers=4)
return train_dl, val_dl
torch.set_float32_matmul_precision('medium')
train_dl, val_dl = prepare_data()
model = MagixNet(100)
trainer = L.Trainer(max_epochs=1, strategy="ddp_notebook")
start = time.time()
trainer.fit(model, train_dl, val_dl)
time.sleep(20)
end = time.time()
training_duration = end - start
print(f"Training duration (seconds): {training_duration}")
EOF
ENTRYPOINT ["python3", "repro.py"]On Modal this script reproduces the issue most of the time. I was able to observe the unknown allocation class logline by running this script with debug logs enabled and then jumping onto the worker it ran on to read them.
import modal
stub = modal.Stub("repro-mod-1860")
# TODO: pin these deps for a better repro
DEPS = [
"torch",
"torchvision",
"lightning",
"numpy",
"memory_profiler",
]
runtime = modal.Image.debian_slim().pip_install(*DEPS)
with runtime.run_inside():
print("Hello from inside container.")
import psutil
current_process = psutil.Process()
parent_process = current_process.parent()
print(f"Processes: {current_process=} {parent_process=}")
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import lightning as L
from memory_profiler import profile
from torchvision.datasets import CIFAR100
from torchvision import transforms
from torchvision import models
from torch.utils.data import DataLoader
class MagixNet(L.LightningModule):
def __init__(self, nbr_cat):
super().__init__()
module = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
module.fc = nn.Linear(2048, nbr_cat)
self.module = module
def forward(self, x):
return self.module(x)
def training_step(self, batch, batch_idx):
x, y = batch
y_hat = self(x)
loss = F.cross_entropy(y_hat, y)
return loss
def configure_optimizers(self):
return torch.optim.Adam(self.parameters(), lr=0.02)
def prepare_data():
pipeline = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
train_ds = CIFAR100('data', train=True, download=True, transform=pipeline)
train_dl = DataLoader(train_ds, batch_size=128, num_workers=4)
val_ds = CIFAR100('data', train=False, download=True, transform=pipeline)
val_dl = DataLoader(val_ds, batch_size=128, num_workers=4)
return train_dl, val_dl
@stub.function(image=runtime, gpu=modal.gpu.A100(count=2), timeout=86400)
def train():
torch.set_float32_matmul_precision('medium')
train_dl, val_dl = prepare_data()
model = MagixNet(100)
trainer = L.Trainer(max_epochs=1, strategy="ddp_notebook")
start = time.time()
trainer.fit(model, train_dl, val_dl)
time.sleep(20)
end = time.time()
training_duration = end - start
print(f"Training duration (seconds): {training_duration}")
@stub.local_entrypoint()
def main():
train.remote()runsc version
runsc version release-20230717.0-12-g0244c8c19fb7
spec: 1.1.0-rc.1docker version (if using docker)
No response
uname
No response
runsc debug logs (if available)
Omitted. Logs show unknown allocation class for 0x0000503b, which is NV50_P2P.
Let me know if #9828 solves this issue. Do you know if this will repro on T4 as well? Hard to get a hold of A100. But I will try again tomorrow.
I tried running the above mentioned Docker image on an A100 with runsc, it segfaults and crashes with a different error:
Traceback (most recent call last):
File "repro.py", line 65, in <module>
trainer.fit(model, train_dl, val_dl)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 141, in launch
while not process_context.join():
File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 145, in join
raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGSEGV
The boot logs show that a different set of ioctls are not implemented:
$ cat /tmp/logs/runsc.log.20231220-181102.011243.boot.txt | grep "nvproxy: unknown"
W1220 18:11:35.603862 18656 frontend.go:683] [ 15: 15] nvproxy: unknown allocation class 0x0000cb33
W1220 18:11:35.616556 18656 frontend.go:521] [ 15: 15] nvproxy: unknown control command 0x2080182b (paramsSize=20)
What Nvidia driver version is being used at Modal? I was testing on 525.105.17.
0x0000cb33 allocation class is NV_CONFIDENTIAL_COMPUTE, which was only added in 535.43.02.
So I tested again with 535.54.03 driver, and the above mentioned unknown ioctls dissapeared. However, the segfault persists. This does not happen with runc. I can reproduce this segfault on a T4 GPU too.
Here is the relevant logs from the segfault (looks like a null pointer dereference):
Boot logs
D1220 18:48:20.286367 31376 task_run.go:312] [ 15: 32] Unhandled user fault: addr=0 ip=7eb1f7de9ddb access=r-- sig=11 err=bad address
D1220 18:48:20.286470 31376 task_log.go:87] [ 15: 32] Registers:
D1220 18:48:20.286506 31376 task_log.go:94] [ 15: 32] Cs = 0000000000000033
D1220 18:48:20.286513 31376 task_log.go:94] [ 15: 32] Ds = 0000000000000000
D1220 18:48:20.286519 31376 task_log.go:94] [ 15: 32] Eflags = 0000000000010246
D1220 18:48:20.286524 31376 task_log.go:94] [ 15: 32] Es = 0000000000000000
D1220 18:48:20.286529 31376 task_log.go:94] [ 15: 32] Fs = 0000000000000000
D1220 18:48:20.286534 31376 task_log.go:94] [ 15: 32] Fs_base = 00007eb110c00700
D1220 18:48:20.286539 31376 task_log.go:94] [ 15: 32] Gs = 0000000000000000
D1220 18:48:20.286543 31376 task_log.go:94] [ 15: 32] Gs_base = 0000000000000000
D1220 18:48:20.286548 31376 task_log.go:94] [ 15: 32] Orig_rax = ffffffffffffffff
D1220 18:48:20.286552 31376 task_log.go:94] [ 15: 32] R10 = 0000000000000000
D1220 18:48:20.286558 31376 task_log.go:94] [ 15: 32] R11 = 0000000000000000
D1220 18:48:20.286562 31376 task_log.go:94] [ 15: 32] R12 = 0000000000000000
D1220 18:48:20.286566 31376 task_log.go:94] [ 15: 32] R13 = 00007eb0b9d39460
D1220 18:48:20.286570 31376 task_log.go:94] [ 15: 32] R14 = 000000000e39cae0
D1220 18:48:20.286574 31376 task_log.go:94] [ 15: 32] R15 = 0000000000000000
D1220 18:48:20.286579 31376 task_log.go:94] [ 15: 32] R8 = 0000000000000000
D1220 18:48:20.286583 31376 task_log.go:94] [ 15: 32] R9 = 0000000000000001
D1220 18:48:20.286587 31376 task_log.go:94] [ 15: 32] Rax = 0000000000000000
D1220 18:48:20.286591 31376 task_log.go:94] [ 15: 32] Rbp = 0000000000000000
D1220 18:48:20.286595 31376 task_log.go:94] [ 15: 32] Rbx = 00007eb110bdeec0
D1220 18:48:20.286600 31376 task_log.go:94] [ 15: 32] Rcx = 0000000000000001
D1220 18:48:20.286604 31376 task_log.go:94] [ 15: 32] Rdi = 0000000000000000
D1220 18:48:20.286608 31376 task_log.go:94] [ 15: 32] Rdx = 0000000000000000
D1220 18:48:20.286612 31376 task_log.go:94] [ 15: 32] Rip = 00007eb1f7de9ddb
D1220 18:48:20.286616 31376 task_log.go:94] [ 15: 32] Rsi = 00007eb0b9d394f0
D1220 18:48:20.286620 31376 task_log.go:94] [ 15: 32] Rsp = 00007eb110bdee90
D1220 18:48:20.286624 31376 task_log.go:94] [ 15: 32] Ss = 000000000000002b
D1220 18:48:20.286628 31376 task_log.go:111] [ 15: 32] Stack:
D1220 18:48:20.286642 31376 task_log.go:128] [ 15: 32] 7eb110bdee90: c0 ee bd 10 b1 7e 00 00 0c 00 00 00 00 00 00 00
D1220 18:48:20.286649 31376 task_log.go:128] [ 15: 32] 7eb110bdeea0: 00 00 00 00 00 00 00 00 de 37 48 55 b1 7e 00 00
D1220 18:48:20.286655 31376 task_log.go:128] [ 15: 32] 7eb110bdeeb0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286661 31376 task_log.go:128] [ 15: 32] 7eb110bdeec0: 34 30 38 34 66 34 35 65 37 33 61 61 00 00 00 00
D1220 18:48:20.286667 31376 task_log.go:128] [ 15: 32] 7eb110bdeed0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286672 31376 task_log.go:128] [ 15: 32] 7eb110bdeee0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286678 31376 task_log.go:128] [ 15: 32] 7eb110bdeef0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286683 31376 task_log.go:128] [ 15: 32] 7eb110bdef00: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286688 31376 task_log.go:128] [ 15: 32] 7eb110bdef10: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286693 31376 task_log.go:128] [ 15: 32] 7eb110bdef20: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286698 31376 task_log.go:128] [ 15: 32] 7eb110bdef30: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286703 31376 task_log.go:128] [ 15: 32] 7eb110bdef40: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286708 31376 task_log.go:128] [ 15: 32] 7eb110bdef50: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286713 31376 task_log.go:128] [ 15: 32] 7eb110bdef60: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286718 31376 task_log.go:128] [ 15: 32] 7eb110bdef70: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286724 31376 task_log.go:128] [ 15: 32] 7eb110bdef80: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286729 31376 task_log.go:128] [ 15: 32] 7eb110bdef90: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286734 31376 task_log.go:128] [ 15: 32] 7eb110bdefa0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286740 31376 task_log.go:128] [ 15: 32] 7eb110bdefb0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286745 31376 task_log.go:128] [ 15: 32] 7eb110bdefc0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286749 31376 task_log.go:128] [ 15: 32] 7eb110bdefd0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286755 31376 task_log.go:128] [ 15: 32] 7eb110bdefe0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286760 31376 task_log.go:128] [ 15: 32] 7eb110bdeff0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286767 31376 task_log.go:128] [ 15: 32] 7eb110bdf000: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286772 31376 task_log.go:128] [ 15: 32] 7eb110bdf010: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286777 31376 task_log.go:128] [ 15: 32] 7eb110bdf020: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286782 31376 task_log.go:128] [ 15: 32] 7eb110bdf030: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286787 31376 task_log.go:128] [ 15: 32] 7eb110bdf040: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286792 31376 task_log.go:128] [ 15: 32] 7eb110bdf050: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286797 31376 task_log.go:128] [ 15: 32] 7eb110bdf060: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286802 31376 task_log.go:128] [ 15: 32] 7eb110bdf070: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286806 31376 task_log.go:128] [ 15: 32] 7eb110bdf080: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286811 31376 task_log.go:128] [ 15: 32] 7eb110bdf090: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286816 31376 task_log.go:128] [ 15: 32] 7eb110bdf0a0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286824 31376 task_log.go:128] [ 15: 32] 7eb110bdf0b0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286829 31376 task_log.go:128] [ 15: 32] 7eb110bdf0c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286834 31376 task_log.go:128] [ 15: 32] 7eb110bdf0d0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286839 31376 task_log.go:128] [ 15: 32] 7eb110bdf0e0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286844 31376 task_log.go:128] [ 15: 32] 7eb110bdf0f0: 00 00 00 00 00 00 00 00 bc 8c 2b ba b1 7e 00 00
D1220 18:48:20.286849 31376 task_log.go:128] [ 15: 32] 7eb110bdf100: 00 00 00 00 00 00 00 00 e7 03 00 00 00 00 00 00
D1220 18:48:20.286854 31376 task_log.go:128] [ 15: 32] 7eb110bdf110: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286859 31376 task_log.go:128] [ 15: 32] 7eb110bdf120: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286864 31376 task_log.go:128] [ 15: 32] 7eb110bdf130: 60 0b 00 c4 b0 7e 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286868 31376 task_log.go:128] [ 15: 32] 7eb110bdf140: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286873 31376 task_log.go:128] [ 15: 32] 7eb110bdf150: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286878 31376 task_log.go:128] [ 15: 32] 7eb110bdf160: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286883 31376 task_log.go:128] [ 15: 32] 7eb110bdf170: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286888 31376 task_log.go:128] [ 15: 32] 7eb110bdf180: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286892 31376 task_log.go:128] [ 15: 32] 7eb110bdf190: 00 00 00 00 00 00 00 00 49 a0 56 55 b1 7e 00 00
D1220 18:48:20.286897 31376 task_log.go:128] [ 15: 32] 7eb110bdf1a0: 00 00 00 00 00 00 00 00 d7 1a 52 55 b1 7e 00 00
D1220 18:48:20.286902 31376 task_log.go:128] [ 15: 32] 7eb110bdf1b0: 80 00 00 00 00 00 00 00 77 f3 4f 55 b1 7e 00 00
D1220 18:48:20.286907 31376 task_log.go:128] [ 15: 32] 7eb110bdf1c0: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286912 31376 task_log.go:128] [ 15: 32] 7eb110bdf1d0: 00 00 00 00 00 00 00 00 80 14 f9 0d 00 00 00 00
D1220 18:48:20.286917 31376 task_log.go:128] [ 15: 32] 7eb110bdf1e0: a0 c7 ed 00 00 00 00 00 64 94 d3 b9 b0 7e 00 00
D1220 18:48:20.286923 31376 task_log.go:128] [ 15: 32] 7eb110bdf1f0: 80 00 00 00 00 00 00 00 a0 c6 53 55 b1 7e 00 00
D1220 18:48:20.286927 31376 task_log.go:128] [ 15: 32] 7eb110bdf200: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286932 31376 task_log.go:128] [ 15: 32] 7eb110bdf210: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286937 31376 task_log.go:128] [ 15: 32] 7eb110bdf220: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286942 31376 task_log.go:128] [ 15: 32] 7eb110bdf230: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286947 31376 task_log.go:128] [ 15: 32] 7eb110bdf240: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286951 31376 task_log.go:128] [ 15: 32] 7eb110bdf250: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286957 31376 task_log.go:128] [ 15: 32] 7eb110bdf260: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286962 31376 task_log.go:128] [ 15: 32] 7eb110bdf270: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286968 31376 task_log.go:128] [ 15: 32] 7eb110bdf280: 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
D1220 18:48:20.286972 31376 task_log.go:149] [ 15: 32] Code:
D1220 18:48:20.286978 31376 task_log.go:167] [ 15: 32] 7eb1f7de9d90: 4d 00 00 02 00 00 e9 50 ff ff ff 0f 1f 44 00 00
D1220 18:48:20.286984 31376 task_log.go:167] [ 15: 32] 7eb1f7de9da0: 48 8b 1d c9 a0 16 00 8b 7d 70 64 44 8b 23 e8 cd
D1220 18:48:20.286989 31376 task_log.go:167] [ 15: 32] 7eb1f7de9db0: df 08 00 64 44 89 23 85 c0 75 d4 e9 2b ff ff ff
D1220 18:48:20.286998 31376 task_log.go:167] [ 15: 32] 7eb1f7de9dc0: e8 9b bd 00 00 e9 fd fe ff ff e8 c1 de 0a 00 90
D1220 18:48:20.287003 31376 task_log.go:167] [ 15: 32] 7eb1f7de9dd0: f3 0f 1e fa 41 54 55 48 89 fd 53 8b 07 f6 c4 20
D1220 18:48:20.287008 31376 task_log.go:167] [ 15: 32] 7eb1f7de9de0: 0f 85 ea 00 00 00 89 c2 81 e2 00 80 00 00 0f 84
D1220 18:48:20.287012 31376 task_log.go:167] [ 15: 32] 7eb1f7de9df0: e9 00 00 00 c1 e0 1a c1 f8 1f 41 89 c4 85 d2 75
D1220 18:48:20.287017 31376 task_log.go:167] [ 15: 32] 7eb1f7de9e00: 11 48 8b bd 88 00 00 00 83 6f 04 01 0f 84 3e 01
D1220 18:48:20.287021 31376 task_log.go:71] [ 15: 32] Mappings:
VMAs:
00060000-00065000 r-xp 00000000 00:00 0 [usertrap]
00400000-00423000 r--p 00000000 00:19 23 /usr/bin/python3.8
00423000-006b5000 r-xp 00023000 00:19 23 /usr/bin/python3.8
006b5000-008ef000 r--p 002b5000 00:19 23 /usr/bin/python3.8
008ef000-008f0000 r--p 004ee000 00:19 23 /usr/bin/python3.8
008f0000-00937000 rw-p 004ef000 00:19 23 /usr/bin/python3.8
00937000-0095a000 rw-p 00000000 00:00 0
0095a000-14799000 rw-p 00000000 00:00 0 [heap]
200000000-200400000 ---p 00000000 00:00 0
200400000-200600000 rw-s 00000000 00:14 20 /dev/nvidia0
200600000-203e00000 rw-s 00000000 00:14 18 /dev/nvidiactl
203e00000-205c00000 ---p 00000000 00:00 0
205c00000-205e00000 rw-s 00000000 00:14 18 /dev/nvidiactl
205e00000-206000000 rw-s 00000000 00:14 18 /dev/nvidiactl
206000000-206200000 rw-s 00000000 00:14 18 /dev/nvidiactl
206200000-206400000 rw-s 206200000 00:14 19 /dev/nvidia-uvm
206400000-206600000 rw-s 00000000 00:14 18 /dev/nvidiactl
206600000-206800000 ---p 00000000 00:00 0
206800000-206a00000 rw-s 00000000 00:14 18 /dev/nvidiactl
206a00000-207000000 ---p 00000000 00:00 0
207000000-207200000 rw-s 00000000 00:14 18 /dev/nvidiactl
207200000-207400000 ---p 00000000 00:00 0
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042bf000-042e0000 r--p 09887000 *pgalloc.MemoryFile
042e0000-04352000 rw-p 24f43000 *pgalloc.MemoryFile
04352000-0437a000 rw-p 289ba000 *pgalloc.MemoryFile
0437a000-04400000 r--p 09ac2000 *pgalloc.MemoryFile
04400000-044e0000 rw-p 25ef6000 *pgalloc.MemoryFile
044e0000-04501000 rw-p 28122000 *pgalloc.MemoryFile
04501000-04522000 rw-p 28041000 *pgalloc.MemoryFile
04522000-04547000 r--p 09daa000 *pgalloc.MemoryFile
04547000-04568000 r--p 09e0f000 *pgalloc.MemoryFile
04568000-04589000 r--p 09e70000 *pgalloc.MemoryFile
04589000-045dc000 rw-p 24ef0000 *pgalloc.MemoryFile
045dc000-04600000 rw-p 262e2000 *pgalloc.MemoryFile
04600000-0461e000 rw-p 276df000 *pgalloc.MemoryFile
0461e000-04660000 r--p 09f64000 *pgalloc.MemoryFile
04660000-046a2000 rw-p 25e08000 *pgalloc.MemoryFile
046a2000-046c7000 r--p 0a08a000 *pgalloc.MemoryFile
046c7000-046ea000 r--p 0a12f000 *pgalloc.MemoryFile
046ea000-0470b000 r--p 0a192000 *pgalloc.MemoryFile
0470b000-0472c000 r--p 0a1f3000 *pgalloc.MemoryFile
0472c000-04755000 r--p 090d1000 *pgalloc.MemoryFile
04755000-04776000 r--p 0913a000 *pgalloc.MemoryFile
04776000-0479a000 rw-p 240d3000 *pgalloc.MemoryFile
0479a000-047e9000 r--p 0a4b9000 *pgalloc.MemoryFile
047e9000-04800000 r--p 09fa6000 *pgalloc.MemoryFile
04800000-0486e000 r--p 0a588000 *pgalloc.MemoryFile
0486e000-04891000 r--p 0a676000 *pgalloc.MemoryFile
04891000-048c2000 r--p 0a6d9000 *pgalloc.MemoryFile
048c2000-048e3000 r--p 0a74a000 *pgalloc.MemoryFile
048e3000-04905000 r--p 0a7ab000 *pgalloc.MemoryFile
04905000-04926000 rw-p 0a80d000 *pgalloc.MemoryFile
04926000-04947000 r--p 0a86e000 *pgalloc.MemoryFile
04947000-04978000 r--p 0a90f000 *pgalloc.MemoryFile
04978000-049d6000 r--p 0a9c0000 *pgalloc.MemoryFile
049d6000-049fa000 r--p 0ab1e000 *pgalloc.MemoryFile
049fa000-04a00000 r--p 0ab8f000 *pgalloc.MemoryFile
04a00000-04a42000 r--p 0abd5000 *pgalloc.MemoryFile
04a42000-04a66000 r--p 0acd7000 *pgalloc.MemoryFile
04a66000-04a8b000 rw-p 24d69000 *pgalloc.MemoryFile
04a8b000-04aae000 r--p 0ade2000 *pgalloc.MemoryFile
04aae000-04ad1000 rw-p 25d40000 *pgalloc.MemoryFile
04ad1000-04af5000 r--p 0aee8000 *pgalloc.MemoryFile
04af5000-04b17000 r--p 0af8c000 *pgalloc.MemoryFile
04b17000-04b47000 r--p 0afee000 *pgalloc.MemoryFile
04b47000-04c9a000 r--p 0b09e000 *pgalloc.MemoryFile
04c9a000-04cbb000 r--p 0b231000 *pgalloc.MemoryFile
04cbb000-04cde000 r--p 0b292000 *pgalloc.MemoryFile
04cde000-04d23000 r--p 0b335000 *pgalloc.MemoryFile
04d23000-04d46000 r--p 0b3ba000 *pgalloc.MemoryFile
04d46000-04d67000 r--p 0b45d000 *pgalloc.MemoryFile
04d67000-04d8b000 r--p 0b4fe000 *pgalloc.MemoryFile
04d8b000-04dcd000 r--p 0b562000 *pgalloc.MemoryFile
04dcd000-04e00000 r--p 3fef8000 *pgalloc.MemoryFile
04e00000-04e47000 r--p 0b5e4000 *pgalloc.MemoryFile
04e47000-04e68000 r--p 0b6ab000 *pgalloc.MemoryFile
04e68000-04e8b000 r--p 0b70c000 *pgalloc.MemoryFile
04e8b000-04ece000 rw-p 28668000 *pgalloc.MemoryFile
04ece000-04efb000 r--p 0b8b8000 *pgalloc.MemoryFile
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04f30000-04f5a000 r--p 0b9da000 *pgalloc.MemoryFile
04f5a000-04f7c000 r--p 0ba84000 *pgalloc.MemoryFile
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04fa9000-04fce000 r--p 0bc13000 *pgalloc.MemoryFile
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04ff0000-05011000 r--p 0bd5a000 *pgalloc.MemoryFile
05011000-0503e000 rw-p 23ce5000 *pgalloc.MemoryFile
0503e000-05070000 r--p 0bf28000 *pgalloc.MemoryFile
05070000-0509a000 r--p 0bfda000 *pgalloc.MemoryFile
0509a000-050bb000 r--p 0c084000 *pgalloc.MemoryFile
050bb000-05108000 r--p 0c125000 *pgalloc.MemoryFile
05108000-0512a000 r--p 0c1f2000 *pgalloc.MemoryFile
0512a000-0516d000 r--p 0c254000 *pgalloc.MemoryFile
0516d000-0518f000 r--p 0c357000 *pgalloc.MemoryFile
0518f000-051b3000 r--p 0c3f9000 *pgalloc.MemoryFile
051b3000-051e1000 rw-p 23b93000 *pgalloc.MemoryFile
051e1000-05200000 r--p 0c50b000 *pgalloc.MemoryFile
05200000-05228000 r--p 0c56a000 *pgalloc.MemoryFile
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0524c000-05271000 r--p 0a254000 *pgalloc.MemoryFile
05271000-05297000 r--p 0a2f9000 *pgalloc.MemoryFile
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052b8000-052da000 r--p 0c777000 *pgalloc.MemoryFile
052da000-052fb000 r--p 0c7d9000 *pgalloc.MemoryFile
052fb000-0531d000 r--p 0ca40000 *pgalloc.MemoryFile
0531d000-05341000 r--p 0caa2000 *pgalloc.MemoryFile
05341000-05369000 r--p 0cb06000 *pgalloc.MemoryFile
05369000-05393000 r--p 0cb6e000 *pgalloc.MemoryFile
05393000-053dc000 r--p 0cc18000 *pgalloc.MemoryFile
053dc000-05400000 r--p 0cce1000 *pgalloc.MemoryFile
05400000-05421000 r--p 0cd45000 *pgalloc.MemoryFile
05421000-05448000 r--p 0cda6000 *pgalloc.MemoryFile
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0546c000-054b9000 rw-p 255b4000 *pgalloc.MemoryFile
054b9000-054da000 rw-p 286ab000 *pgalloc.MemoryFile
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054fc000-05535000 rw-p 237a3000 *pgalloc.MemoryFile
05535000-05556000 r--p 0d37a000 *pgalloc.MemoryFile
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055fb000-05600000 r--p 0a383000 *pgalloc.MemoryFile
05600000-056a6000 rw-p 276fd000 *pgalloc.MemoryFile
056a6000-056ca000 r--p 0d5a6000 *pgalloc.MemoryFile
056ca000-0570c000 r--p 0d60a000 *pgalloc.MemoryFile
0570c000-05758000 r--p 0d68c000 *pgalloc.MemoryFile
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05794000-057d6000 r--p 0d7d4000 *pgalloc.MemoryFile
057d6000-057f7000 rw-p 259b4000 *pgalloc.MemoryFile
057f7000-05800000 r--p 0a388000 *pgalloc.MemoryFile
05800000-05818000 r--p 0d877000 *pgalloc.MemoryFile
05818000-0587b000 r--p 0d8cf000 *pgalloc.MemoryFile
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058bd000-05920000 r--p 0d9f4000 *pgalloc.MemoryFile
05920000-05962000 r--p 0da97000 *pgalloc.MemoryFile
05962000-05986000 rw-p 2634a000 *pgalloc.MemoryFile
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059c9000-059ea000 r--p 0dc40000 *pgalloc.MemoryFile
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05a74000-05af8000 rw-p 28143000 *pgalloc.MemoryFile
05af8000-05bef000 rw-p 238dc000 *pgalloc.MemoryFile
05bef000-05c00000 rw-p 10acc000 *pgalloc.MemoryFile
05c00000-05c10000 r--p 0dff7000 *pgalloc.MemoryFile
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05c42000-05c71000 r--p 0e139000 *pgalloc.MemoryFile
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05c92000-05cb7000 rw-p 25169000 *pgalloc.MemoryFile
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05cd8000-05cfa000 rw-p 24ece000 *pgalloc.MemoryFile
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05e00000-05e3e000 rw-p 28718000 *pgalloc.MemoryFile
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05ef3000-05f19000 r--p 0ec2d000 *pgalloc.MemoryFile
05f19000-05f3d000 r--p 0ecd3000 *pgalloc.MemoryFile
05f3d000-05f6b000 rw-p 22ea2000 *pgalloc.MemoryFile
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06000000-06021000 rw-p 2636e000 *pgalloc.MemoryFile
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06400000-06411000 r--p 0f87b000 *pgalloc.MemoryFile
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06432000-06476000 rw-p 26306000 *pgalloc.MemoryFile
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064e0000-06505000 rw-p 24ff5000 *pgalloc.MemoryFile
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06599000-065c9000 rw-p 22667000 *pgalloc.MemoryFile
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06600000-06673000 r--p 10527000 *pgalloc.MemoryFile
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06800000-06873000 r--p 1059a000 *pgalloc.MemoryFile
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06c00000-07400000 r--p 1bc00000 *pgalloc.MemoryFile
07400000-07600000 rw-p 27200000 *pgalloc.MemoryFile
07600000-07800000 rw-p 27000000 *pgalloc.MemoryFile
07800000-07a00000 r--p 1c800000 *pgalloc.MemoryFile
07a00000-07c00000 rw-p 26c00000 *pgalloc.MemoryFile
07c00000-08200000 r--p 1cc00000 *pgalloc.MemoryFile
08200000-08400000 rw-p 26400000 *pgalloc.MemoryFile
08400000-0842d000 rw-p 00694000 *pgalloc.MemoryFile
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0a000000-0a179000 r--p 1967d000 *pgalloc.MemoryFile
0a179000-0a200000 r--p 3bdec000 *pgalloc.MemoryFile
0a200000-0a279000 r--p 1067a000 *pgalloc.MemoryFile
0a279000-0a3b9000 r--p 197f6000 *pgalloc.MemoryFile
0a3b9000-0a400000 r--p 3fe11000 *pgalloc.MemoryFile
0a400000-0a4b9000 r--p 10b27000 *pgalloc.MemoryFile
0a4b9000-0a600000 r--p 3bca5000 *pgalloc.MemoryFile
0a600000-0ac00000 r--p 19a00000 *pgalloc.MemoryFile
0ac00000-0acb9000 r--p 19936000 *pgalloc.MemoryFile
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0aeb9000-0b000000 r--p 3ba17000 *pgalloc.MemoryFile
0b000000-0b600000 r--p 1ac00000 *pgalloc.MemoryFile
0b600000-0b7b9000 r--p 1aa00000 *pgalloc.MemoryFile
0b7b9000-0b800000 r--p 3b9d0000 *pgalloc.MemoryFile
0b800000-0ba00000 r--p 1b400000 *pgalloc.MemoryFile
0ba00000-0bbb9000 rw-p 26e00000 *pgalloc.MemoryFile
0bbb9000-0bc00000 r--p 3b989000 *pgalloc.MemoryFile
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0be00000-0bfb9000 r--p 1b600000 *pgalloc.MemoryFile
0bfb9000-0c000000 r--p 3b942000 *pgalloc.MemoryFile
0c000000-0c200000 r--p 1ba00000 *pgalloc.MemoryFile
0c200000-0c800000 r--p 1d400000 *pgalloc.MemoryFile
0c800000-0c8b9000 r--p 1a0b9000 *pgalloc.MemoryFile
0c8b9000-0ca00000 r--p 3b7fb000 *pgalloc.MemoryFile
0ca00000-0cc00000 r--p 1dc00000 *pgalloc.MemoryFile
0cc00000-0ccb9000 r--p 1da00000 *pgalloc.MemoryFile
0ccb9000-0ce00000 r--p 3b6b4000 *pgalloc.MemoryFile
0ce00000-0d000000 r--p 1de00000 *pgalloc.MemoryFile
0d000000-0d0b9000 rw-p 2271c000 *pgalloc.MemoryFile
0d0b9000-0d200000 r--p 3b56d000 *pgalloc.MemoryFile
0d200000-0d800000 r--p 1e200000 *pgalloc.MemoryFile
0d800000-0d9b9000 r--p 1e000000 *pgalloc.MemoryFile
0d9b9000-0da00000 r--p 3b526000 *pgalloc.MemoryFile
0da00000-0dc00000 r--p 1ea00000 *pgalloc.MemoryFile
0dc00000-0ddba000 rw-p 26600000 *pgalloc.MemoryFile
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0de00000-0e000000 rw-p 29800000 *pgalloc.MemoryFile
0e000000-0e400000 rw-p 2a000000 *pgalloc.MemoryFile
0e400000-0e594000 rw-p 223d3000 *pgalloc.MemoryFile
0e594000-0e600000 rw-p 10789000 *pgalloc.MemoryFile
0e600000-0e65e000 rw-p 2695b000 *pgalloc.MemoryFile
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0e683000-0e6b9000 rw-p 26a2b000 *pgalloc.MemoryFile
0e6b9000-0e749000 rw-p 1f400000 *pgalloc.MemoryFile
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0e851000-0e924000 rw-p 28adc000 *pgalloc.MemoryFile
0e924000-0e967000 rw-p 28a99000 *pgalloc.MemoryFile
0e967000-0e9f8000 r--p 1f522000 *pgalloc.MemoryFile
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0ea00000-0ea3a000 r--p 1b7b9000 *pgalloc.MemoryFile
0ea3a000-0ea7d000 r--p 1e1b9000 *pgalloc.MemoryFile
0ea7d000-0eb0e000 r--p 1f5b3000 *pgalloc.MemoryFile
0eb0e000-0eb52000 r--p 1e9ba000 *pgalloc.MemoryFile
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0ef18000-0f000000 r--p 3b3f8000 *pgalloc.MemoryFile
0f000000-0f11d000 r--p 1fa00000 *pgalloc.MemoryFile
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0f200000-0f223000 r--p 1fb1d000 *pgalloc.MemoryFile
0f223000-0f400000 r--p 3b138000 *pgalloc.MemoryFile
0f400000-0f567000 r--p 1fb40000 *pgalloc.MemoryFile
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0f800000-0f9b0000 r--p 1fd13000 *pgalloc.MemoryFile
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0fe00000-0fefe000 r--p 20171000 *pgalloc.MemoryFile
0fefe000-10000000 r--p 3ac6e000 *pgalloc.MemoryFile
10000000-10140000 r--p 2026f000 *pgalloc.MemoryFile
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11000000-11193000 r--p 2079a000 *pgalloc.MemoryFile
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12000000-121ac000 rw-p 22815000 *pgalloc.MemoryFile
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12a00000-12aaf000 r--p 20e18000 *pgalloc.MemoryFile
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13200000-132ba000 r--p 20f78000 *pgalloc.MemoryFile
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13a00000-13bbd000 r--p 21032000 *pgalloc.MemoryFile
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14000000-14200000 rw-p 28c00000 *pgalloc.MemoryFile
14200000-14600000 rw-p 29400000 *pgalloc.MemoryFile
14600000-14799000 rw-p 24197000 *pgalloc.MemoryFile
200400000-200600000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
200600000-203e00000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
205c00000-205e00000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
205e00000-206000000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
206000000-206200000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
206200000-206400000 rw-s 206200000 *nvproxy.uvmFDMemmapFile
206400000-206600000 rw-s 00000000 *nvproxy.frontendFDMemmapFile
206800000-206a00000 r--s 00000000 *nvproxy.frontendFDMemmapFile
7eb0b8000000-7eb0b810f000 rw-p 35eec000 *pgalloc.MemoryFile
7eb0b810f000-7eb0b811c000 rw-p 37040000 *pgalloc.MemoryFile
7eb0b811c000-7eb0b811f000 rw-p 35ffb000 *pgalloc.MemoryFile
7eb0b811f000-7eb0b8133000 rw-p 3704d000 *pgalloc.MemoryFile
7eb0b8133000-7eb0b8134000 rw-p 35ffe000 *pgalloc.MemoryFile
7eb0b8134000-7eb0b814b000 rw-p 37061000 *pgalloc.MemoryFile
7eb0b814b000-7eb0b814c000 rw-p 35fff000 *pgalloc.MemoryFile
7eb0b814c000-7eb0b81df000 rw-p 37078000 *pgalloc.MemoryFile
7eb0b81df000-7eb0b8200000 rw-p 3e523000 *pgalloc.MemoryFile
7eb0b8200000-7eb0b82d2000 rw-p 3710b000 *pgalloc.MemoryFile
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7eb0b82fa000-7eb0b8311000 rw-p 371dd000 *pgalloc.MemoryFile
7eb0b8311000-7eb0b832d000 rw-p 3746b000 *pgalloc.MemoryFile
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7eb0b8330000-7eb0b8381000 rw-p 37487000 *pgalloc.MemoryFile
7eb0b8381000-7eb0b8384000 rw-p 371f7000 *pgalloc.MemoryFile
7eb0b8384000-7eb0b8398000 rw-p 374d8000 *pgalloc.MemoryFile
7eb0b8398000-7eb0b839b000 rw-p 371fa000 *pgalloc.MemoryFile
7eb0b839b000-7eb0b83c5000 rw-p 374ec000 *pgalloc.MemoryFile
7eb0b83c5000-7eb0b83c7000 rw-p 371fd000 *pgalloc.MemoryFile
7eb0b83c7000-7eb0b83cb000 rw-p 37516000 *pgalloc.MemoryFile
7eb0b83cb000-7eb0b83cc000 rw-p 371ff000 *pgalloc.MemoryFile
7eb0b83cc000-7eb0b83ea000 rw-p 3751a000 *pgalloc.MemoryFile
7eb0b83ea000-7eb0b8400000 rw-p 3e50d000 *pgalloc.MemoryFile
7eb0b8400000-7eb0b84be000 rw-p 37538000 *pgalloc.MemoryFile
7eb0b84be000-7eb0b84cb000 rw-p 37d46000 *pgalloc.MemoryFile
7eb0b84cb000-7eb0b84d5000 rw-p 375f6000 *pgalloc.MemoryFile
7eb0b84d5000-7eb0b8581000 rw-p 37d53000 *pgalloc.MemoryFile
7eb0b8581000-7eb0b8596000 rw-p 3814f000 *pgalloc.MemoryFile
7eb0b8596000-7eb0b8597000 rw-p 37dff000 *pgalloc.MemoryFile
7eb0b8597000-7eb0b85fb000 rw-p 38164000 *pgalloc.MemoryFile
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7eb1f7e7d000-7eb1f7e80000 r-xs 00115000 *gofer.dentryPlatformFile
7eb1f7e80000-7eb1f7e81000 r-xp 00297000 *pgalloc.MemoryFile
7eb1f7e81000-7eb1f7e87000 r-xs 00119000 *gofer.dentryPlatformFile
7eb1f7e87000-7eb1f7e88000 r-xp 006c1000 *pgalloc.MemoryFile
7eb1f7e88000-7eb1f7e89000 r-xp 10add000 *pgalloc.MemoryFile
7eb1f7e89000-7eb1f7f02000 r-xs 00121000 *gofer.dentryPlatformFile
7eb1f7f02000-7eb1f7f50000 r--s 0019a000 *gofer.dentryPlatformFile
7eb1f7f50000-7eb1f7f5a000 rw-p 106f9000 *pgalloc.MemoryFile
7eb1f7f5e000-7eb1f7f60000 r--p 00757000 *pgalloc.MemoryFile
7eb1f7f60000-7eb1f7f62000 rw-p 1a1fe000 *pgalloc.MemoryFile
7eb1f7f62000-7eb1f7f63000 r--s 00002000 *pgalloc.MemoryFile
7eb1f7f63000-7eb1f7f64000 r-xs 00000000 *pgalloc.MemoryFile
7eb1f7f64000-7eb1f7f65000 r-xp 1b7f3000 *pgalloc.MemoryFile
7eb1f7f65000-7eb1f7f66000 r--p 0015e000 *pgalloc.MemoryFile
7eb1f7f66000-7eb1f7f81000 r-xs 00001000 *gofer.dentryPlatformFile
7eb1f7f81000-7eb1f7f82000 r-xp 00229000 *pgalloc.MemoryFile
7eb1f7f82000-7eb1f7f83000 r-xp 00228000 *pgalloc.MemoryFile
7eb1f7f83000-7eb1f7f84000 r-xp 0022d000 *pgalloc.MemoryFile
7eb1f7f84000-7eb1f7f85000 r-xp 0022a000 *pgalloc.MemoryFile
7eb1f7f85000-7eb1f7f88000 r-xs 00020000 *gofer.dentryPlatformFile
7eb1f7f88000-7eb1f7f89000 r-xp 0015f000 *pgalloc.MemoryFile
7eb1f7f89000-7eb1f7f91000 r--p 00160000 *pgalloc.MemoryFile
7eb1f7f92000-7eb1f7f94000 rw-p 00232000 *pgalloc.MemoryFile
7eb1f7f94000-7eb1f7f95000 rw-p 10ae6000 *pgalloc.MemoryFile
7ed1a4000000-7ed1a40ad000 r--p 0016a000 *pgalloc.MemoryFile
7ed1a40ad000-7ed1a40af000 rw-p 1abfc000 *pgalloc.MemoryFile
7ed1a40af000-7ed1a40b1000 rw-p 1abfa000 *pgalloc.MemoryFile
7ed1a40b1000-7ed1a40b3000 rw-p 10a61000 *pgalloc.MemoryFile
7ed1a40b3000-7ed1a40b4000 rw-p 10a60000 *pgalloc.MemoryFile
7ed1a40b4000-7ed1a40b6000 rw-p 1039d000 *pgalloc.MemoryFile
7ed1a40b6000-7ed1a40b8000 r--p 00220000 *pgalloc.MemoryFile
D1220 18:48:20.291737 31376 task_log.go:73] [ 15: 32] FDTable:
fd:46 => name pipe:[8]
fd:48 => name socket:[28]
fd:2 => name host:[3]
fd:17 => name pipe:[7]
fd:20 => name /dev/nvidiactl
fd:23 => name /dev/nvidia0
fd:41 => name /dev/nvidia0
fd:29 => name /dev/nvidia0
fd:35 => name /dev/nvidia0
fd:43 => name /dev/nvidia0
fd:1 => name host:[2]
fd:3 => name /dev/nvidiactl
fd:7 => name /dev/nvidia0
fd:9 => name pipe:[3]
fd:16 => name pipe:[7]
fd:49 => name socket:[35]
fd:30 => name /dev/nvidia0
fd:32 => name /dev/nvidia0
fd:47 => name socket:[34]
fd:10 => name pipe:[4]
fd:13 => name pipe:[5]
fd:22 => name /dev/nvidia0
fd:24 => name /dev/nvidia0
fd:28 => name /dev/nvidia0
fd:4 => name /dev/nvidia0
fd:8 => name pipe:[3]
fd:19 => name socket:[20]
fd:39 => name /dev/nvidia0
fd:6 => name /dev/nvidia0
fd:15 => name socket:[18]
fd:42 => name /dev/nvidia0
fd:50 => name socket:[36]
fd:40 => name /dev/nvidia0
fd:44 => name /dev/nvidia0
fd:45 => name pipe:[8]
fd:0 => name host:[1]
fd:21 => name /dev/nvidia-uvm
fd:27 => name anon_inode:[eventfd]
fd:31 => name /dev/nvidia0
fd:36 => name anon_inode:[eventfd]
fd:38 => name /dev/nvidia0
fd:11 => name pipe:[4]
fd:12 => name /dev/null
fd:14 => name pipe:[6]
fd:18 => name socket:[19]
fd:33 => name /dev/nvidia0
fd:5 => name /dev/nvidia-uvm
fd:25 => name anon_inode:[eventfd]
fd:26 => name socket:[22]
fd:34 => name /dev/nvidia0
fd:37 => name /dev/nvidia0
RIP = 0x7eb1f7de9ddb
VMAs:
...
7eb1f7d68000-7eb1f7d8a000 r--p 00000000 00:19 34 /usr/lib/x86_64-linux-gnu/libc-2.31.so
7eb1f7d8a000-7eb1f7f02000 r-xp 00022000 00:19 34 /usr/lib/x86_64-linux-gnu/libc-2.31.so
So we need to look at offset 0x00022000 + (0x7eb1f7de9ddb - 0x7eb1f7d8a000) = 0x81ddb in /usr/lib/x86_64-linux-gnu/libc-2.31.so.
Using objdump -d:
81dbd: 48 89 44 24 28 mov %rax,0x28(%rsp)
81dc2: 8b 44 24 24 mov 0x24(%rsp),%eax
81dc6: 48 8d 35 1d 42 11 00 lea 0x11421d(%rip),%rsi # 195fea <_libc_intl_domainname@@GLIBC_2.2.5+0x1012>
81dcd: 4c 89 ef mov %r13,%rdi
81dd0: 89 c2 mov %eax,%edx
81dd2: 83 c0 01 add $0x1,%eax
81dd5: 89 44 24 24 mov %eax,0x24(%rsp)
81dd9: 31 c0 xor %eax,%eax
81ddb: e8 50 1e fd ff callq 53c30 <fprintf@@GLIBC_2.2.5> <----- HERE
81de0: 64 8b 04 25 18 00 00 mov %fs:0x18,%eax
81de7: 00
@nixprime pointed out that I was looking at the objdump of the wrong file. He figured out the actual fault instruction:
# objdump -d /usr/lib/x86_64-linux-gnu/libc-2.31.so | less
...
0000000000081dd0 <_IO_fclose@@GLIBC_2.2.5>:
81dd0: f3 0f 1e fa endbr64
81dd4: 41 54 push %r12
81dd6: 55 push %rbp
81dd7: 48 89 fd mov %rdi,%rbp
81dda: 53 push %rbx
81ddb: 8b 07 mov (%rdi),%eax <--- here
Per logs %rdi = 0, so the segfault lines up. He found out that if libnccl.so.2:getHostHash() fails to open /proc/sys/kernel/random/boot_id, then it calls fclose(NULL) and takes SIGSEGV.
#9833 fixes this issue, but now we get a different exception:
Epoch 0: 0%| | 0/391 [00:00<?, ?it/s] ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
ERROR: Unexpected bus error encountered in worker. This might be caused by insufficient shared memory (shm).
Traceback (most recent call last):
File "repro.py", line 65, in <module>
trainer.fit(model, train_dl, val_dl)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 141, in launch
while not process_context.join():
File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 163, in join
raise ProcessRaisedException(msg, error_index, failed_process.pid)
torch.multiprocessing.spawn.ProcessRaisedException:
-- Process 0 terminated with the following error:
Traceback (most recent call last):
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py", line 1132, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/usr/lib/python3.8/multiprocessing/queues.py", line 107, in get
if not self._poll(timeout):
File "/usr/lib/python3.8/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/usr/lib/python3.8/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/usr/lib/python3.8/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/usr/lib/python3.8/selectors.py", line 415, in select
fd_event_list = self._selector.poll(timeout)
File "/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 36) is killed by signal: Bus error. It is possible that dataloader's workers are out of shared memory. Please try to raise your shared memory limit.
The logs also show an unimplemented control command (NV2080_CTRL_CMD_NVLINK_GET_NVLINK_CAPS). I added support for it in #9835. But it did not fix the issue.
The logs also shows 4 user faults happening at the same instruction:
D1221 15:38:39.022730 228800 task_run.go:312] [ 60: 60] Unhandled user fault: addr=7ea232582000 ip=7ea3c2f95963 access=r-- sig=11 err=BusError: no space left on device
...
VMAs:
...
7ea3c2e0a000-7ea3c2e2c000 r--p 00000000 00:19 34 /usr/lib/x86_64-linux-gnu/libc-2.31.so
7ea3c2e2c000-7ea3c2fa4000 r-xp 00022000 00:19 34 /usr/lib/x86_64-linux-gnu/libc-2.31.so
So need to look at (0x7ea3c2f95963 - 0x7ea3c2e2c000) + 0x22000 = 0x18b963 offset in /usr/lib/x86_64-linux-gnu/libc-2.31.so.
root@2b2350fada82:/# objdump -d /usr/lib/x86_64-linux-gnu/libc-2.31.so | less
...
18b927: c5 fe 6f 06 vmovdqu (%rsi),%ymm0
18b92b: c5 fe 6f 4c 16 e0 vmovdqu -0x20(%rsi,%rdx,1),%ymm1
18b931: c5 fe 7f 07 vmovdqu %ymm0,(%rdi)
18b935: c5 fe 7f 4c 17 e0 vmovdqu %ymm1,-0x20(%rdi,%rdx,1)
18b93b: c5 f8 77 vzeroupper
18b93e: c3 retq
18b93f: 48 3b 15 72 5b 06 00 cmp 0x65b72(%rip),%rdx # 1f14b8 <mallwatch@@GLIBC_2.2.5+0x8>
18b946: 0f 83 25 01 00 00 jae 18ba71 <__nss_database_lookup@GLIBC_2.2.5+0x283f1>
18b94c: 48 39 f7 cmp %rsi,%rdi
18b94f: 72 0f jb 18b960 <__nss_database_lookup@GLIBC_2.2.5+0x282e0>
18b951: 74 12 je 18b965 <__nss_database_lookup@GLIBC_2.2.5+0x282e5>
18b953: 4c 8d 0c 16 lea (%rsi,%rdx,1),%r9
18b957: 4c 39 cf cmp %r9,%rdi
18b95a: 0f 82 c5 01 00 00 jb 18bb25 <__nss_database_lookup@GLIBC_2.2.5+0x284a5>
18b960: 48 89 d1 mov %rdx,%rcx
18b963: f3 a4 rep movsb %ds:(%rsi),%es:(%rdi) <--- here
Our driver version is still 525.60.13.
Sorry I couldn't get a multi-GPU A100 VM to test the repro myself. Looks like it's thrown up a lot of things! Internally we populate boot_id so makes sense that I didn't see that running the Modal script.
Do you know if the A100 GPU has 40GB memory or 80GB?
So the BusError: no space left on device error on page fault in gVisor happens because we are hitting tmpfs size limit for /dev/shm. The OCI spec shows that /dev/shm has a limit of 67108864 bytes:
...
{
"destination": "/dev/shm",
"type": "tmpfs",
"source": "/run/containerd/io.containerd.runtime.v2.task/moby/bfb43b3231cbb1f7e60e78f81eab2f25d80f48e802cb5d29185979785746b814/shm",
"options": [
"nosuid",
"noexec",
"nodev",
"mode=1777",
"size=67108864"
]
},
...
Some investigation showed that we were over allocating in tmpfs on page faults. See:
gvisor/pkg/sentry/fsimpl/tmpfs/regular_file.go
Lines 300 to 310 in 149350e
We were trying to allocate optional range if possible. Many times, optional was much bigger than required range. MemoryManager.getPMAsInternalLocked() passes a larger optional range if the PMA gap is larger than the requested range length. Allocating the entire PMA gap can prevent future page faults. But in this case, it is causing us to hit tmpfs size limits really quickly.
So I updated tmpfs to only allocate required range on page faults in #9839. Maybe it helped, but we are still hitting the same error. Need to investigate if tmpfs is overallocating in some other place, or not releasing pages, or there is some page accounting bug.
Strace logs show an interesting pattern:
I1223 19:14:31.881425 937434 strace.go:564] [ 15: 15] python3 E lstat(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c50a0)
I1223 19:14:31.881442 937434 strace.go:602] [ 15: 15] python3 X lstat(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c50a0) = 0 (0x0) errno=2 (no such file or directory) (4.447µs)
I1223 19:14:31.881462 937434 strace.go:570] [ 15: 15] python3 E openat(AT_FDCWD /, 0x7ef4b30c5190 /dev/shm/ZVh4Mj, O_RDWR|O_CREAT|O_EXCL, 0o600)
I1223 19:14:31.881486 937434 strace.go:608] [ 15: 15] python3 X openat(AT_FDCWD /, 0x7ef4b30c5190 /dev/shm/ZVh4Mj, O_RDWR|O_CREAT|O_EXCL, 0o600) = 73 (0x49) (11.537µs)
I1223 19:14:31.881529 937434 strace.go:567] [ 15: 15] python3 E write(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c5240 "\xff\xff\xff\x7f\x00\x00\x00\x00\x80\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00", 0x20)
I1223 19:14:31.881556 937434 strace.go:605] [ 15: 15] python3 X write(0x49 /dev/shm/ZVh4Mj, ..., 0x20) = 32 (0x20) (14.519µs)
I1223 19:14:31.881587 937434 strace.go:576] [ 15: 15] python3 E mmap(0x0, 0x20, PROT_READ|PROT_WRITE, MAP_SHARED, 0x49 /dev/shm/ZVh4Mj, 0x0)
I1223 19:14:31.881609 937434 strace.go:614] [ 15: 15] python3 X mmap(0x0, 0x20, PROT_READ|PROT_WRITE, MAP_SHARED, 0x49 /dev/shm/ZVh4Mj, 0x0) = 139497752580096 (0x7edf59fd5000) (9.119µs)
I1223 19:14:31.881649 937434 strace.go:564] [ 15: 15] python3 E link(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c51b0 /dev/shm/sem.mp-uq5k_1mc)
I1223 19:14:31.881670 937434 strace.go:602] [ 15: 15] python3 X link(0x7ef4b30c5190 /dev/shm/ZVh4Mj, 0x7ef4b30c51b0 /dev/shm/sem.mp-uq5k_1mc) = 0 (0x0) (9.127µs)
I1223 19:14:31.881685 937434 strace.go:564] [ 15: 15] python3 E fstat(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c50b0)
I1223 19:14:31.881708 937434 strace.go:602] [ 15: 15] python3 X fstat(0x49 /dev/shm/ZVh4Mj, 0x7ef4b30c50b0 {dev=24, ino=27, mode=S_IFREG|0o600, nlink=2, uid=0, gid=0, rdev=0, size=32, blksize=4096, blocks=1, atime=2023-12-23 19:14:31.88147723 +0000 UTC, mtime=2023-12-23 19:14:31.881553145 +0000 UTC, ctime=2023-12-23 19:14:31.881553145 +0000 UTC}) = 0 (0x0) (2.177µs)
I1223 19:14:31.882240 937434 strace.go:561] [ 15: 15] python3 E unlink(0x7ef4b30c5190 /dev/shm/ZVh4Mj)
I1223 19:14:31.882362 937434 strace.go:599] [ 15: 15] python3 X unlink(0x7ef4b30c5190 /dev/shm/ZVh4Mj) = 0 (0x0) (12.97µs)
I1223 19:14:31.882448 937434 strace.go:561] [ 15: 15] python3 E close(0x49 /dev/shm/ZVh4Mj (deleted))
I1223 19:14:31.882459 937434 strace.go:599] [ 15: 15] python3 X close(0x49 /dev/shm/ZVh4Mj (deleted)) = 0 (0x0) (2.197µs)
I1223 19:14:31.882599 937434 strace.go:561] [ 15: 15] python3 E unlink(0x7ef4b30c5270 /dev/shm/sem.mp-uq5k_1mc)
I1223 19:14:31.882633 937434 strace.go:599] [ 15: 15] python3 X unlink(0x7ef4b30c5270 /dev/shm/sem.mp-uq5k_1mc) = 0 (0x0) (13.497µs)
These files are created in /dev/shm, written to, then mmap(2)-ed, then hard linked, then deleted (both the original path and the link path). The pages are referenced from the mmap(2)-ed VMA. In fact, there are 94 such deleted /dev/shm files that still have an active VMA due to mmap(2). How does the application plan on munmap(2)-ing these VMAs?
Hey, just got back from PTO. Thanks again for your investigation. Looks like I should incorporate the related fixes into our runtime and see what I get with the repro program.
I added a modified version of the Dockerfile above to https://github.com/google/gvisor/blob/e6a42ae59450fe561cb37eb23ca14e5202067637/images/gpu/pytorch/, but currently disabled since the bug isn't fixed yet, and because this Python script runs forever when it does work (with runc).
Can you assist with making it run for just a small amount of time under runc? I tried reducing the model size and using fast_dev_run=True but it doesn't seem to have any effect. If we can limit it to a few seconds of test time under runc, then we can turn it into a permanent regression test for gVisor.
I think /dev/shm is just undersized; each /dev/shm/torch_* file is ~77MB. Oddly, with /dev/shm set to a larger size (and with #9828 applied), the test passes under runsc but still hangs under runc:
$ sudo docker run --gpus all --runtime=runsc --shm-size=128g sha256:9ecb6090c8fae027985f0b7bf5be27ec4438a0ac6d6ba966bafea55f0fd3d772
Hello from inside container.
Processes: current_process=psutil.Process(pid=1, name='python3', status='running', started='19:13:03') parent_process=None
Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to data/cifar-100-python.tar.gz
100%|██████████| 169001437/169001437 [00:02<00:00, 72318989.98it/s]Extracting data/cifar-100-python.tar.gz to data
Files already downloaded and verified
Downloading: "https://download.pytorch.org/models/resnet50-11ad3fa6.pth" to /root/.cache/torch/hub/checkpoints/resnet50-11ad3fa6.pth
100%|██████████| 97.8M/97.8M [00:00<00:00, 112MB/s]
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:67: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/1
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 1 processes
----------------------------------------------------------------------------------------------------
Missing logger folder: /lightning_logs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
| Name | Type | Params
----------------------------------
0 | module | ResNet | 23.7 M
----------------------------------
23.7 M Trainable params
0 Non-trainable params
23.7 M Total params
94.852 Total estimated model params size (MB)
Epoch 0: 100%|██████████| 391/391 [07:46<00:00, 0.84it/s, v_num=0]`Trainer.fit` stopped: `max_epochs=1` reached.
Epoch 0: 100%|██████████| 391/391 [07:47<00:00, 0.84it/s, v_num=0]
Training duration (seconds): 491.89744663238525
Per Jamie's findings, the reproducer should now work with gVisor (after increasing /dev/shm size limit).
There are TODOs still referencing this issue:
- test/gpu/pytorch_test.go:57: Don't skip this once the
- images/gpu/pytorch/issue_9827.py:87: Make this not take forever.
Search TODO
Thanks for all your great help here! Still meaning to look into https://github.com/google/gvisor/blob/HEAD/images/gpu/pytorch/issue_9827.py#L87
We tested this in A100s with 40 and 80GB and the merged fix works.
Revisting the currently skipped test added during this issue's investigation. Could use your guidance @ayushr2 on how to get debug logs enabled for test executions. I've tried the typical way (daemon.json and reloading docker) but that has not done anything. I read through the test code a bit and didn't see anything.
I'm running the test on an A100 80GB SXM4 server and getting SIGSEV:
ubuntu@207-211-185-116:~/gvisor$ make test TARGETS="//test/gpu:pytorch_test"
--- TAG default
--- DOCKER BUILD
gvisor-builder-7a84ed41-x86_64
sha256:0c0586de8611f38f0fd8041a6c3aaebc65685cc4ac32b8742bec947a12224c89
--- DOCKER RUN
gvisor-bazel-7a84ed41-x86_64
1a4bf151325dd1c159f5338141e21e4de689b5e8000641adcca48d86398d35da
--- TEST //test/gpu:pytorch_test
Another command holds the client lock:
pid=139701
owner=client
cwd=/home/ubuntu/gvisor
Waiting for it to complete...
Another command (pid=139701) is running. Waiting for it to complete on the server (server_pid=139703)...
Loading:
Loading:
Loading: 1 packages loaded
Analyzing: target //test/gpu:pytorch_test (2 packages loaded, 0 targets configured)
Analyzing: target //test/gpu:pytorch_test (63 packages loaded, 767 targets configured)
Analyzing: target //test/gpu:pytorch_test (84 packages loaded, 11204 targets configured)
Analyzing: target //test/gpu:pytorch_test (435 packages loaded, 15706 targets configured)
INFO: Analyzed target //test/gpu:pytorch_test (440 packages loaded, 17542 targets configured).
INFO: Found 1 test target...
[2 / 25] [Prepa] BazelWorkspaceStatusAction stable-status.txt
[1,605 / 1,606] Testing //test/gpu:pytorch_test; 0s linux-sandbox
[1,605 / 1,606] Testing //test/gpu:pytorch_test; 11s linux-sandbox
FAIL: //test/gpu:pytorch_test (see /home/ubuntu/.cache/bazel/_bazel_ubuntu/9cf5d2f37e613aac6d73fb8ae06b7c50/execroot/__main__/bazel-out/k8-fastbuild/testlogs/test/gpu/pytorch_test/test.log)
[1,605 / 1,606] 1 / 1 tests, 1 failed; Testing //test/gpu:pytorch_test; 38s linux-sandbox
INFO: From Testing //test/gpu:pytorch_test:
==================== Test output for //test/gpu:pytorch_test:
--- FAIL: TestIssue9827 (21.07s)
pytorch_test.go:60: Failed: container returned non-zero status: 1, msg: ""
Container output:
==========
== CUDA ==
==========
CUDA Version 12.2.0
Container image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This container image and its contents are governed by the NVIDIA Deep Learning Container License.
By pulling and using the container, you accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
A copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.
GPU available: True (cuda), used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/connectors/logger_connector/logger_connector.py:75: Starting from v1.9.0, `tensorboardX` has been removed as a dependency of the `lightning.pytorch` package, due to potential conflicts with other packages in the ML ecosystem. For this reason, `logger=True` will use `CSVLogger` as the default logger, unless the `tensorboard` or `tensorboardX` packages are found. Please `pip install lightning[extra]` or one of them to enable TensorBoard support by default
Processes: current_process=psutil.Process(pid=1, name='python3', status='running', started='02:53:39') parent_process=None
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 0, MEMBER: 1/8
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 2, MEMBER: 3/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 7, MEMBER: 8/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 6, MEMBER: 7/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 4, MEMBER: 5/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 3, MEMBER: 4/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 1, MEMBER: 2/8
Missing logger folder: /lightning_logs
/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.
Initializing distributed: GLOBAL_RANK: 5, MEMBER: 6/8
Missing logger folder: /lightning_logs
----------------------------------------------------------------------------------------------------
distributed_backend=nccl
All distributed processes registered. Starting with 8 processes
----------------------------------------------------------------------------------------------------
Missing logger folder: /lightning_logs
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 4 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 3 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 6 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 2 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 7 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 5 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
LOCAL_RANK: 1 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]
| Name | Type | Params
----------------------------------
0 | module | ResNet | 23.7 M
----------------------------------
23.7 M Trainable params
0 Non-trainable params
23.7 M Total params
94.852 Total estimated model params size (MB)
Traceback (most recent call last):
File "/issue_9827.py", line 88, in <module>
trainer.fit(model, train_dl, val_dl)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit
call._call_and_handle_interrupt(
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/trainer/call.py", line 43, in _call_and_handle_interrupt
return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
File "/usr/local/lib/python3.8/dist-packages/lightning/pytorch/strategies/launchers/multiprocessing.py", line 144, in launch
while not process_context.join():
File "/usr/local/lib/python3.8/dist-packages/torch/multiprocessing/spawn.py", line 140, in join
raise ProcessExitedException(
torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGSEGV
FAIL
================================================================================
Target //test/gpu:pytorch_test up-to-date:
bazel-bin/test/gpu/pytorch_test_/pytorch_test
INFO: Elapsed time: 46.666s, Critical Path: 39.11s
INFO: 2 processes: 1 internal, 1 linux-sandbox.
INFO: Build completed, 1 test FAILED, 2 total actions
//test/gpu:pytorch_test FAILED in 38.6s
/home/ubuntu/.cache/bazel/_bazel_ubuntu/9cf5d2f37e613aac6d73fb8ae06b7c50/execroot/__main__/bazel-out/k8-fastbuild/testlogs/test/gpu/pytorch_test/test.log
Executed 1 out of 1 test: 1 fails locally.
INFO: Build completed, 1 test FAILED, 2 total actions
INFO: Build Event Protocol files produced successfully.
make: *** [Makefile:64: test] Error 3
how to get debug logs enabled for test executions
I usually look at the Makefile and Buildkite configuration to see how the make command is being used. In this case, we have a Makefile target named gpu-all-tests:
Lines 304 to 310 in a5b10b7
And see how this is invoked in Buildkite:
gvisor/.buildkite/pipeline.yaml
Lines 180 to 185 in a5b10b7
So for your usecase, maybe comment out Line 307-309 in the Makefile and invoke make gpu-all-tests RUNTIME_ARGS="--debug". Note that the flag --debug-log is automatically added in
Line 141 in a5b10b7
To find the debug logs, see what --debug-log flag was used in /etc/docker/daemon.json and you will find all logs there. Hope that's helpful.
Thanks a lot 🙏