[Bug]: Subprocess health check / automatic restart for V1 EngineCore

[movchan74]

Your current environment

The output of python collect_env.py

INFO 06-19 09:05:59 [__init__.py:244] Automatically detected platform cuda.
Collecting environment information...
uv is set
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.3 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.7.0+cu126
Is debug build               : False
CUDA used to build PyTorch   : 12.6
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.10.12 (main, May 27 2025, 17:12:29) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-6.5.0-45-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.3.107
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : GPU 0: NVIDIA A40
Nvidia driver version        : 550.127.05
cuDNN version                : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6342 CPU @ 2.80GHz
CPU family:                         6
Model:                              106
Thread(s) per core:                 2
Core(s) per socket:                 24
Socket(s):                          2
Stepping:                           6
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5600.00
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 pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          2.3 MiB (48 instances)
L1i cache:                          1.5 MiB (48 instances)
L2 cache:                           60 MiB (48 instances)
L3 cache:                           72 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-23,48-71
NUMA node1 CPU(s):                  24-47,72-95
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: 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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

==============================
Versions of relevant libraries
==============================
[pip3] mypy==1.15.0
[pip3] mypy-extensions==1.1.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] onnxruntime==1.22.0
[pip3] onnxruntime-gpu==1.22.0
[pip3] pynvml==12.0.0
[pip3] pytorch-lightning==2.5.1.post0
[pip3] pytorch-metric-learning==2.8.1
[pip3] pyzmq==26.4.0
[pip3] sentence-transformers==4.1.0
[pip3] torch==2.7.0
[pip3] torch-audiomentations==0.12.0
[pip3] torch-pitch-shift==1.2.5
[pip3] torchaudio==2.7.0
[pip3] torchmetrics==1.7.1
[pip3] torchvision==0.22.0
[pip3] transformers==4.52.4
[pip3] triton==3.3.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
Neuron SDK Version           : N/A
vLLM Version                 : 0.9.1
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
        GPU0    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-23,48-71      0               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
CUDA_VERSION=12.3.2
LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
NCCL_VERSION=2.20.3-1
NVIDIA_DRIVER_CAPABILITIES=compute,display,graphics,utility,video
NVIDIA_PRODUCT_NAME=CUDA
NVIDIA_REQUIRE_CUDA=cuda>=12.3 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536
NVIDIA_VISIBLE_DEVICES=GPU-ca922923-391e-240f-9d6e-3e3894286c97
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_root
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

I’ve been experimenting with the new V1 engine in vLLM because of its impressive speed and much lower memory usage compared to V0. I understand that V1 is still experimental, but these performance gains are exactly what I need. Unfortunately, there’s a blocker that’s making it effectively unusable for me right now.

---

What’s happening

When I spin up an AsyncLLMEngine, vLLM correctly launches a separate EngineCore subprocess. However, if that subprocess crashes (OOM, segfault, manual kill, etc.), the parent process never notices. The built-in health check is literally:

async def check_health(self) -> None:
    logger.debug("Called check_health.")

So it just returns immediately and keeps sending RPCs to a dead socket. No errors, no restart—just a silent hang.

Additionally, if the parent process itself restarts (for example, due to an orchestrator reboot or code reload), the EngineCore process sometimes remains alive, continues to hold GPU memory, and blocks any subsequent vLLM restart because the memory cannot be reclaimed.

---

Expected behavior

1. A crash or unexpected exit of the EngineCore subprocess should be detected by the parent, surface an error, and trigger a restart of the engine.
2. If the parent process restarts, any orphaned EngineCore subprocesses should be cleaned up, freeing GPU memory, or at least the parent should detect and reap stale processes before launching new ones.

---

Questions / Request for guidance

1. Is there an officially recommended way to health-check or automatically restart a dead V1 EngineCore?
2. Would it make sense for vLLM to expose a built-in watchdog or supervisor API for V1 engines—e.g. a wrapper that monitors the child process and offers auto-restart?
3. Has anyone run V1 in production and found a more robust pattern for detecting and recovering from subprocess death, or for garbage-collecting orphaned GPU processes when the parent dies?

---

Any pointers, examples, or links to ongoing discussion would be hugely appreciated. Thanks in advance!

Before submitting a new issue...

• Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

[movchan74]

I’ve put together two minimal examples:

1. Detecting a dead EngineCore subprocess (because the built-in check_health is a no-op)
2. Showing how EngineCore can become orphaned (and continue holding GPU memory)

Feel free to adapt these snippets for your own testing or to propose upstream patches.

---

Example 1: Detecting a crashed EngineCore subprocess

This snippet demonstrates that after killing the EngineCore process, calling await engine.check_health() still silently succeeds. Instead, you can directly inspect proc.is_alive() to detect the failure and trigger a restart.

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["VLLM_USE_V1"] = "1"

from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine

# 1) Launch a V1 engine on GPU 0
args = AsyncEngineArgs(
    model="Qwen/Qwen2-1.5B-Instruct",
    gpu_memory_utilization=0.9,
    max_model_len=8192,
)
engine = AsyncLLMEngine.from_engine_args(args)

# 2) Simulate the EngineCore crashing (e.g. OOM or SIGKILL)
engine_core_proc = engine.engine_core.resources.engine_manager.processes[0]
os.kill(engine_core_proc.pid, 9)

# 3) The default health check does nothing:
await engine.check_health()
# so it won’t notice the subprocess is gone.

# 4) Workaround: directly check the OS process (a candidate for check_health?)
alive = engine_core_proc.is_alive()
print(f"EngineCore still alive? {alive}")

---

Example 2: Orphaned EngineCore after parent dies

This snippet shows that if your main Python process is killed, the EngineCore child remains alive and continues to occupy GPU memory.

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
os.environ["VLLM_USE_V1"] = "1"

from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine

# 1) Spin up the V1 engine as usual
args = AsyncEngineArgs(
    model="Qwen/Qwen2-1.5B-Instruct",
    gpu_memory_utilization=0.9,
    max_model_len=8192,
)
engine = AsyncLLMEngine.from_engine_args(args)

# 2) Kill the parent process (this script):
os.kill(os.getpid(), 9)

# → After this, the EngineCore subprocess remains and retains GPU RAM.
#    You can observe it via `nvidia-smi`.

---

Suggested next steps

• Implement a real health check, possibly using proc.is_alive().
• Figure out the solution for orphaned EngineCore issue.

Thanks for considering these workarounds — happy to iterate further!

[robertgshaw2-redhat]

Thanks for the detailed rport

[njhill]

Thanks @movchan74 I'm planning to address this soon.