The current timeout seems to be 900 seconds. We should have a startup probe instead that is up to 30 minutes. Since large models can take a long time. As a follow up we may want to consider making a startup timeout configurable with a default of 30 minutes. current pod config for ...
VLLM_ENGINE_ITERATION_TIMEOUT_S此处为vllm配置 该参数控制引擎每次迭代的超时时间,主要用于处理长时间运行的请求。默认为60,单位s,若需要修改,直接使用环境变量修改为180,vllm源码如下: 请求端配置,需要 把request请求时间延长 3. Engine iteration timed out. This should never happen! 报错: Engine iteration t...
CompletionRequest, ErrorResponse)fromvllm.entrypoints.openai.serving_chatimportOpenAIServingChatfromvllm.entrypoints.openai.serving_completionimportOpenAIServingCompletionfromvllm.loggerimportinit_loggerfromvllm.usage.usage_libimportUsageContext TIMEOUT_KEEP_ALIVE= 5#secondsopenai_serving_chat: OpenAIServingChat ...
CMake version: version 3.30.3 Libc version: glibc-2.35 Python version: 3.10.12 (main, Sep 11 2024, 15:47:36) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.8.0-1016-aws-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set ...
碎片化问题:频繁分配/释放导致显存碎片化,剩余总显存足够但无法找到连续空间。 解决方案 调整block_size,为长序列场景预分配更大块。 启用gpu_memory_utilization参数限制显存占用比例(如 0.9)。 2.2 请求超时或卡死 故障现象 客户端收到TimeoutError或服务无响应。
TIMEOUT_KEEP_ALIVE = 5 # seconds openai_serving_chat: OpenAIServingChat openai_serving_completion: OpenAIServingCompletion logger = init_logger(__name__) @asynccontextmanager async def lifespan(app: fastapi.FastAPI): async def _force_log(): ...
--connect-timeout 600连接超时600秒 --read-timeout 600响应超时600秒 --api openai接口协议类型 --prompt '写一个科幻小说,不少于2000字'测试用提示词-n 2048总请求数 2.4 vLLM&SGLang压测关键性能数据 三、压测性能分析结论 3.1 吞吐量与并发效率优势 ...
TimeoutStartSec=0 RestartSec=2 Restart=always StartLimitBurst=3 StartLimitInterval=60s LimitNOFILE=infinity LimitNPROC=infinity LimitCORE=infinity TasksMax=infinity Delegate=yes KillMode=process OOMScoreAdjust=-500 [Install] WantedBy=multi-user.target ...
startupTimeout: 1200 # 启动超时时间(秒) deviceType: "gpu" # 使用 GPU asyncCommunication: true # 开启异步通信 parallelType: "custom" # 自定义并行,适用于多 GPU 环境 parallelLevel: 4 # 使用 4 个 GPU # Handler 参数 handler: model_path: "meta-llama/Meta-Llama-3.1-70B-Instruct" # Hugging...
vllm [Bug]: 在多次调用后使用线程,KeyError: request_id回溯(最近的调用):文件 "./swift/demo_...