GPU Container Crash Loop For GPT-4o After Driver Upgrade

Q: Investigation and Debugging 1. Verify driver and toolkit versions on the host

nvidia-smi --query-gpu=driver_version --format=csv,noheader # Expected output: 550.90 dpkg -l | grep nvidia-container-toolkit # Expected output: nvidia-container-toolkit 2.12.0 2. Inspect container runtime libraries docker run --rm --gpus all nvidia/cuda:12.2-base-ubuntu22.04 \ ldconfig -p | grep libcuda # Expected output: # libcuda.so.1 (libc6,x86-64) => /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcuda.so.1 If the host’s /usr/lib/x86_64-linux-gnu/libcuda.so.1 points to a different version than the container’s, a mismatch exists.

Q: 3. Check pod resource limits and OOM events

kubectl describe pod gpt4o-infer-abc123 # Look for: # OOMKilled: true # Container ID: docker://... 4. Capture NVML and NCCL errors kubectl logs gpt4o-infer-abc123 -c inference # Sample snippet: # [2024-09-03 12:01:07] ERROR: Failed to initialize NVML: Driver/library version mismatch # [2024-09-03 12:01:09] ERROR: NCCL error: unhandled system error 5. Confirm CUDA compatibility matrix Reference the NVIDIA CUDA Toolkit Compatibility Guide: driver 550.xx supports CUDA 12.2 and later. The container’s CUDA version is 12.2, so the driver is sufficient, but the toolkit on the host must expose the matching libraries.

In this article

Table of Contents

Problem Description

The GPT‑4o inference service runs in a Kubernetes pod that launches a Docker container with GPU acceleration (A100/H100). After upgrading the NVIDIA driver from 525.xx to 550.xx, the pod entered a crash loop:

Container exit code 1 with log line: Failed to initialize NVML: Driver/library version mismatch
Subsequent restarts showed Segmentation fault (core dumped) during model loading.
On some nodes the container exited with code 137 (OOMKilled) despite identical resource requests.
When the pod finally started, NCCL reported: NCCL error: unhandled system error.

Impact:

Inference latency spiked to minutes; requests timed out.
Kubernetes CrashLoopBackOff prevented autoscaling.
Service-level agreement (SLA) breach for downstream applications.

Root Cause Analysis

The crash loop resulted from a combination of driver‑runtime mismatches and changed cgroup memory accounting introduced by the driver upgrade:

Driver / CUDA runtime version mismatch – The container image bundles CUDA 12.2 (per the OpenAI Cookbook deployment guide). After the driver upgrade, the NVIDIA Container Toolkit (nvidia-docker2) remained at version 2.12, which still referenced the older libcuda.so.1 from driver 525. This produced the error CUDA driver version is insufficient for CUDA runtime version and the NVML initialization failure, matching the production incident where logs showed “Failed to initialize NVML: Driver/library version mismatch”.
Memory accounting change – NVIDIA driver 550.xx introduced stricter enforcement of cgroup v2 memory limits. The pod spec requested memory: 32Gi, but the container’s CUDA allocator now accounted for GPU memory as part of the pod’s RAM cgroup, causing the process to exceed its limit and be OOM‑killed (exit code 137). This aligns with the Stack Overflow case where containers were killed after a driver upgrade.
NCCL communication breakdown – NCCL relies on the NVML library to discover GPU topology. The NVML failure prevented NCCL from initializing, leading to the “NCCL error: unhandled system error” observed in the H100 cluster incident.

Investigation and Debugging

1. Verify driver and toolkit versions on the host

nvidia-smi --query-gpu=driver_version --format=csv,noheader
# Expected output: 550.90

dpkg -l | grep nvidia-container-toolkit
# Expected output: nvidia-container-toolkit 2.12.0

2. Inspect container runtime libraries

docker run --rm --gpus all nvidia/cuda:12.2-base-ubuntu22.04 \
    ldconfig -p | grep libcuda
# Expected output:
# libcuda.so.1 (libc6,x86-64) => /usr/local/cuda-12.2/targets/x86_64-linux/lib/libcuda.so.1

If the host’s /usr/lib/x86_64-linux-gnu/libcuda.so.1 points to a different version than the container’s, a mismatch exists.

3. Check pod resource limits and OOM events

kubectl describe pod gpt4o-infer-abc123
# Look for:
#   OOMKilled: true
#   Container ID: docker://...

4. Capture NVML and NCCL errors

kubectl logs gpt4o-infer-abc123 -c inference
# Sample snippet:
# [2024-09-03 12:01:07] ERROR: Failed to initialize NVML: Driver/library version mismatch
# [2024-09-03 12:01:09] ERROR: NCCL error: unhandled system error

5. Confirm CUDA compatibility matrix

Reference the NVIDIA CUDA Toolkit Compatibility Guide: driver 550.xx supports CUDA 12.2 and later. The container’s CUDA version is 12.2, so the driver is sufficient, but the toolkit on the host must expose the matching libraries.

Resolution

Step 1 – Upgrade NVIDIA Container Toolkit to match the driver

Before:

# /etc/apt/sources.list.d/nvidia-docker.list
deb https://nvidia.github.io/libnvidia-container/ubuntu22.04/$(ARCH) /
# Installed version 2.12.0

After adding the latest repository and upgrading:

# Add the new repository for driver 550
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/libnvidia-container/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo systemctl restart docker

Result: nvidia-container-toolkit version 2.14.0 which bundles libcuda.so.1 compatible with driver 550.

Step 2 – Align container CUDA runtime with host driver

If the image uses CUDA 12.2, ensure the host driver is ≥ 525 (which it is). No change needed, but verify by running:

docker run --rm --gpus all nvidia/cuda:12.2-base-ubuntu22.04 \
    nvcc --version
# Should report CUDA 12.2

Step 3 – Adjust pod memory limits to account for GPU memory accounting

Before (causing OOM):

resources:
  limits:
    memory: "32Gi"
    nvidia.com/gpu: "4"

After adding a buffer for GPU memory (e.g., 8 Gi per A100):

resources:
  limits:
    memory: "64Gi"   # 32Gi RAM + 4×8Gi GPU memory accounting
    nvidia.com/gpu: "4"
  requests:
    memory: "64Gi"
    nvidia.com/gpu: "4"

Step 4 – Enable explicit NCCL version matching

Set environment variables to force NCCL to use the driver‑provided libraries:

env:
  - name: NCCL_DEBUG
    value: "INFO"
  - name: NCCL_SOCKET_IFNAME
    value: "eth0"
  - name: NCCL_IB_DISABLE
    value: "1"

Step 5 – Redeploy the service

kubectl rollout restart deployment gpt4o-inference
# Verify pods reach Ready state
kubectl get pods -l app=gpt4o-inference

Validation

Pod status: kubectl get pod … -o jsonpath='{.status.containerStatuses[0].state}' should show running without restartCount increasing.

Log sanity check:

kubectl logs gpt4o-infer-xyz -c inference | grep -i "error"
# No NVML or NCCL errors should appear.

GPU health:

kubectl exec -it gpt4o-infer-xyz -- nvidia-smi
# Should list all GPUs with no errors.

Inference test (using OpenAI Python client):

import openai
openai.api_key = "sk-..."
resp = openai.ChatCompletion.create(
    model="gpt-4o",
    messages=[{"role":"user","content":"Hello"}]
)
print(resp.choices[0].message.content)
# Expected: quick response, no timeout.

Metrics: Verify that container_cpu_usage_seconds_total and container_memory_working_set_bytes stay below the defined limits for at least 30 minutes of sustained load.

Prevention and Best Practices

Area	Recommendation
Driver / Toolkit Sync	Pin `nvidia-container-toolkit` version in your host provisioning scripts; run `nvidia-container-cli info` after any driver upgrade to confirm library compatibility.
Memory Accounting	When using CUDA ≥12.2, add `GPU_MEMORY_BUFFER=8Gi` per GPU to pod memory limits, or enable `cgroup2` memory swap accounting to isolate GPU memory.
Monitoring	Alert on `container_restart_count` > 3 within 5 min and on logs containing “NVML” or “NCCL”. Use Prometheus rule: `ALERT GPUContainerCrashLoop IF increase(kube_pod_container_status_restarts_total{container="inference"}[5m]) > 3 FOR 2m LABELS {severity="critical"} ANNOTATIONS { summary = "GPT‑4o inference container crash loop", description = "Container restarted >3 times in 5 min; check driver/toolkit versions." }`
Version Pinning	Reference the CUDA Compatibility Guide in CI pipelines; enforce that the container’s `CUDA_VERSION` ≤ host driver’s supported max.
Testing	Run a smoke test after any driver or toolkit upgrade in a staging namespace that loads the model and performs a single inference call.

Related Topic Hub: LLM Systems Troubleshooting Hub

FAQ

Why does the container exit with code 1 after a driver upgrade?
The host driver (550.xx) no longer matches the libcuda.so.1 version exposed by the older NVIDIA Container Toolkit, causing NVML initialization to fail and the process to abort.
What does exit code 137 mean in this context?
Code 137 indicates the container was OOM‑killed. The newer driver counts GPU memory against the pod’s cgroup, so the previously sufficient RAM limit now appears exceeded.
Do I need to rebuild the GPT‑4o inference image after a driver upgrade?
Rebuilding is not required if the image already bundles a CUDA runtime compatible with the new driver (e.g., CUDA 12.2). The critical step is updating the host nvidia-container-toolkit so the container can access matching libraries.
How can I verify which CUDA runtime version the container is using?
Run docker run --rm --gpus all nvidia/cuda:12.2-base-ubuntu22.04 nvcc --version inside the container; the output shows the bundled CUDA toolkit version.
Is there a way to avoid GPU memory being counted toward pod RAM?
Enable the experimental --gpus=all,capabilities=compute,utility flag with the nvidia-container-runtime and set cgroup2 memory.swap=true, or increase the pod memory limit to include an estimated GPU memory buffer.