故障现象 日志报错:RuntimeError: CUDA out of memory. 服务崩溃或拒绝新请求。 根因分析 块分配策略不当:默认块大小(如 16MB)无法适配长序列请求。 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...
Is debug build: False CUDA used to build PyTorch: None ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.5 LTS (x86_64) GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0 Clang version: Could not collect CMake version: version 3.30.3 Libc version: glibc-2.35 Python version: 3.10....
[Bug]: vllm.engine.async_llm_engine.AsyncEngineDeadError: Background loop has errored already. · Issue #5060 · vllm-project/vllm (github.com) 以及添加参数 ENGINE_ITERATION_TIMEOUT_S ## 设置为 180 timeout=configuration.request_timeout or 180.0...
(秒)@taskdefgenerate_text(self): data = self.send_post_request()try: response = self.client.post("/v1/workflows/run", headers=headers, json=data, timeout=120) logger.info(f"状态码:{response.status_code},输入文本:{data},响应结果:{response.text}")exceptExceptionase: logger.error(trace...
CompletionRequest, ErrorResponse)fromvllm.entrypoints.openai.serving_chatimportOpenAIServingChatfromvllm.entrypoints.openai.serving_completionimportOpenAIServingCompletionfromvllm.loggerimportinit_loggerfromvllm.usage.usage_libimportUsageContext TIMEOUT_KEEP_ALIVE= 5#secondsopenai_serving_chat: OpenAIServingChat ...
1. 高性能批量推理 AI检测代码解析 from vllm import LLM, SamplingParams # 初始化多GPU并行模型(假设可用4张A100) llm = LLM(model="meta-llama/Llama-3-70b-instruct", tensor_parallel_size=4) # 批量处理提示(支持高并发) prompts = [ "解释量子计算的量子比特原理。", ...
target [Service] Type=notify ExecStart=/usr/local/bin/dockerd ExecReload=/bin/kill -s HUP $MAINPID TimeoutStartSec=0 RestartSec=2 Restart=always StartLimitBurst=3 StartLimitInterval=60s LimitNOFILE=infinity LimitNPROC=infinity LimitCORE=infinity TasksMax=infinity Delegate=yes KillMode=process OOM...
设置环境变量VLLM_ENGINE_ITERATION_TIMEOUT_S为更大的值(如180秒),以延长引擎每次迭代的超时时间。 在请求端配置中延长请求时间。 禁用自定义AllReduce:在启动参数中添加--disable-custom-all-reduce,可能有助于解决某些并发请求导致的错误。 4. 应用解决方案 根据具体情况选择上述解决方案中的一种或多种进行尝试...
Hello everyone, I always got this error for Baichuan and LLaMA models. And I found it's caused by the single_query_cached_kv_attention method in vllm\model_executor\layers\attention.py. After calling of this method, the hidden output has...
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(): ...