--log-level:设定日志的详细程度,如DEBUG、INFO、WARNING等,用于控制输出的日志信息量。 --max-concurrent-requests:最大并发请求数。限制了同时接收的最大请求数,用于避免资源过载。 --sharding:启用分片存储,将模型权重在多个 GPU 间分片存储。这在处理大模型或内存限制较严苛的情况下有效。 --low-cpu-memory:是...
Add a new flag to benchmark_serving.py that allows you to specify the maximum number of concurrent requests. If not specified, it defaults to the current behavior of unbounded concurrency. Closes #...
apiVersion: ray.io/v1 kind: RayService metadata: name: text2sql spec: serveConfigV2: | applications: - name: text2sql route_prefix: / import_path: serve:model deployments: - name: VLLMDeployment max_ongoing_requests: 100 autoscaling_config: target_ongoing_requests: 1 min_replicas: 1 max...
requests==2.31.0 gradio==4.14.0 vLLM初步使用 线下批量推理 线下批量推理:为输入的prompts列表,使用vLLM生成答案 importosos.environ["CUDA_VISIBLE_DEVICES"]="6,7"fromvllmimportLLM,SamplingParamsllm=LLM('/data-ai/model/llama2/llama2_hf/Llama-2-13b-chat-hf')INFO01-1808:13:26llm_engine.py:70...
然后看看 _request_tracker.add_request 是怎么做的。它首先创建了一个 AsyncStream 实例,然后将这个 AsyncStream 实例和请求一起放到 self._new_requests 这个异步队列里面,然后设置了 self.new_requests_event 这个事件,最后返回了这个 AsyncStream 实例。
"max_tokens": 7, "temperature": 0 }'| jq . 输出: {"id":"cmpl-d1ba6b9f1551443e87d80258a3bedad1","object":"text_completion","created":19687093,"model":"llama-2-13b-chat-hf","choices": [ {"index":0,"text":" city that is known for its v","logprobs":null,"finish_reason...
{"model":"/vllm_workspace/weights/backbone/llama-7b-hf","disable_log_requests":"true","gpu_memory_utilization":0.8,"tensor_parallel_size":2,"block_size":16,"enforce_eager":"true","enable_lora":"true","max_lora_rank":16} model: The path to your model repository ...
self._run_workers('update_environment_variables',all_args=all_args_to_update_environment_variables)self._run_workers('init_worker', all_kwargs=init_worker_all_kwargs)self._run_workers('init_device')self._run_workers('load_model',max_concurrent_workers=self.parallel_config.max_parallel_loading...
同样的错误也发生在我身上。这个bug还在持续吗？
self._run_workers('update_environment_variables',all_args=all_args_to_update_environment_variables)self._run_workers('init_worker', all_kwargs=init_worker_all_kwargs)self._run_workers('init_device')self._run_workers('load_model',max_concurrent_workers=self.parallel_config.max_parallel_loading...