1、现象描述
a)当在GPUStack(Docker方式部署)中使用QWen3-8B模型执行推理时,如下图所示,输出了几个字之后,此时SSH会卡顿,有时系统会死机、有时会自动重启、有时GPU会看不到了,很奇怪。

当GPU掉了之后,在执行npu-smi info时会报如下错误:

在GPUStack中的现象则是看不到GPU卡了。
b)刚开始怀疑是GPUStack镜像的问题,后面又更换了昇腾给出的官方镜像。
使用mindie:2.1.RC1-300I-Duo-py311-openeuler24.03-lts官方镜像,加载DeepSeek-R1-Distill-Qwen-7B模型,调用如下命令测试推理:
curl 127.0.0.1:1040/generate -d '{
"prompt": "What is deep learning?",
"max_tokens": 32,
"stream": false,
"do_sample":true,
"repetition_penalty": 1.00,
"temperature": 0.01,
"top_p": 0.001,
"top_k": 1,
"model": "qwen"
}'
可以推理成功,但是当我把max_tokens值变大后3200,系统直接就自动重启了,显存溢出还是啥情况?
2、环境说明
服务器:华为泰山服务器 鲲鹏920*2 + Atlas 300I Pro*2
操作系统:银河麒麟高级服务器版本Arm
推理环境:使用Docker镜像 gpustack:latest-npu-310p-linuxarm64 进行推理
物理机驱动版本:
MCU:24.6.3
NPU:Ascend-hdk-310p-npu-driver_25.2.0_linux-aarch64.run
Ascend-hdk-310p-npu-firmware_7.7.0.6.236.run
CANN:Ascend-cann-toolkit_8.2.RC1_linux-aarch64.run
Ascend-cann-kernels-310p_8.2.RC1_linux-aarch64.run
Ascend-cann-nnae_8.2.RC1_linux-aarch64.run
Ascend-cann-nnal_8.2.RC1_linux-aarch64.run
Ascend-cann-nnrt_8.2.RC1_linux-aarch64.run
GPUStack镜像 驱动版本:
Ascend-cann-nnal_8.2.RC1
Ascend-toolkit版本:
runtime_running_version=[8.2.0.0.201:8.2.RC1]
compiler_running_version=[8.2.0.0.201:8.2.RC1]
hccl_running_version=[8.2.0.0.201:8.2.RC1]
opp_running_version=[8.2.0.0.201:8.2.RC1]
toolkit_running_version=[8.2.0.0.201:8.2.RC1]
aoe_running_version=[8.2.0.0.201:8.2.RC1]
ncs_running_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_running_version=[8.2.0.0.201:8.2.RC1]
runtime_upgrade_version=[8.2.0.0.201:8.2.RC1]
compiler_upgrade_version=[8.2.0.0.201:8.2.RC1]
hccl_upgrade_version=[8.2.0.0.201:8.2.RC1]
opp_upgrade_version=[8.2.0.0.201:8.2.RC1]
toolkit_upgrade_version=[8.2.0.0.201:8.2.RC1]
aoe_upgrade_version=[8.2.0.0.201:8.2.RC1]
ncs_upgrade_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_upgrade_version=[8.2.0.0.201:8.2.RC1]
runtime_installed_version=[8.2.0.0.201:8.2.RC1]
compiler_installed_version=[8.2.0.0.201:8.2.RC1]
hccl_installed_version=[8.2.0.0.201:8.2.RC1]
opp_installed_version=[8.2.0.0.201:8.2.RC1]
toolkit_installed_version=[8.2.0.0.201:8.2.RC1]
aoe_installed_version=[8.2.0.0.201:8.2.RC1]
ncs_installed_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_installed_version=[8.2.0.0.201:8.2.RC1]
1、现象描述
a)当在GPUStack(Docker方式部署)中使用QWen3-8B模型执行推理时,如下图所示,输出了几个字之后,此时SSH会卡顿,有时系统会死机、有时会自动重启、有时GPU会看不到了,很奇怪。
当GPU掉了之后,在执行npu-smi info时会报如下错误:
在GPUStack中的现象则是看不到GPU卡了。
b)刚开始怀疑是GPUStack镜像的问题,后面又更换了昇腾给出的官方镜像。
使用mindie:2.1.RC1-300I-Duo-py311-openeuler24.03-lts官方镜像,加载DeepSeek-R1-Distill-Qwen-7B模型,调用如下命令测试推理:
curl 127.0.0.1:1040/generate -d '{
"prompt": "What is deep learning?",
"max_tokens": 32,
"stream": false,
"do_sample":true,
"repetition_penalty": 1.00,
"temperature": 0.01,
"top_p": 0.001,
"top_k": 1,
"model": "qwen"
}'
可以推理成功,但是当我把max_tokens值变大后3200,系统直接就自动重启了,显存溢出还是啥情况?
2、环境说明
服务器:华为泰山服务器 鲲鹏920*2 + Atlas 300I Pro*2
操作系统:银河麒麟高级服务器版本Arm
推理环境:使用Docker镜像 gpustack:latest-npu-310p-linuxarm64 进行推理
物理机驱动版本:
MCU:24.6.3
NPU:Ascend-hdk-310p-npu-driver_25.2.0_linux-aarch64.run
Ascend-hdk-310p-npu-firmware_7.7.0.6.236.run
CANN:Ascend-cann-toolkit_8.2.RC1_linux-aarch64.run
Ascend-cann-kernels-310p_8.2.RC1_linux-aarch64.run
Ascend-cann-nnae_8.2.RC1_linux-aarch64.run
Ascend-cann-nnal_8.2.RC1_linux-aarch64.run
Ascend-cann-nnrt_8.2.RC1_linux-aarch64.run
GPUStack镜像 驱动版本:
Ascend-cann-nnal_8.2.RC1
Ascend-toolkit版本:
runtime_running_version=[8.2.0.0.201:8.2.RC1]
compiler_running_version=[8.2.0.0.201:8.2.RC1]
hccl_running_version=[8.2.0.0.201:8.2.RC1]
opp_running_version=[8.2.0.0.201:8.2.RC1]
toolkit_running_version=[8.2.0.0.201:8.2.RC1]
aoe_running_version=[8.2.0.0.201:8.2.RC1]
ncs_running_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_running_version=[8.2.0.0.201:8.2.RC1]
runtime_upgrade_version=[8.2.0.0.201:8.2.RC1]
compiler_upgrade_version=[8.2.0.0.201:8.2.RC1]
hccl_upgrade_version=[8.2.0.0.201:8.2.RC1]
opp_upgrade_version=[8.2.0.0.201:8.2.RC1]
toolkit_upgrade_version=[8.2.0.0.201:8.2.RC1]
aoe_upgrade_version=[8.2.0.0.201:8.2.RC1]
ncs_upgrade_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_upgrade_version=[8.2.0.0.201:8.2.RC1]
runtime_installed_version=[8.2.0.0.201:8.2.RC1]
compiler_installed_version=[8.2.0.0.201:8.2.RC1]
hccl_installed_version=[8.2.0.0.201:8.2.RC1]
opp_installed_version=[8.2.0.0.201:8.2.RC1]
toolkit_installed_version=[8.2.0.0.201:8.2.RC1]
aoe_installed_version=[8.2.0.0.201:8.2.RC1]
ncs_installed_version=[8.2.0.0.201:8.2.RC1]
opp_kernel_installed_version=[8.2.0.0.201:8.2.RC1]