Docs.vllm.ai
Production Metrics — vLLM
WebProduction Metrics #. Production Metrics. #. vLLM exposes a number of metrics that can be used to monitor the health of the system. These metrics are exposed via the /metrics endpoint on the vLLM OpenAI compatible API server. The following metrics are exposed: class Metrics: def __init__(self, labelnames: List[str]): # Unregister any existing
Actived: 1 days ago
Welcome to vLLM! — vLLM
WebWelcome to vLLM! Easy, fast, and cheap LLM serving for everyone. Star Watch Fork. vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput. Efficient management of attention key and value memory with PagedAttention. Continuous batching of incoming requests.
Welcome to vLLM! — vLLM
WebStar Watch Fork. vLLM is a fast and easy-to-use library for LLM inference and serving. vLLM is fast with: State-of-the-art serving throughput. Efficient management of attention key and value memory with PagedAttention. Continuous batching of incoming requests. Fast model execution with CUDA/HIP graph. Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV
OpenAI Compatible Server — vLLM
WebAPI Reference#. Please see the OpenAI API Reference for more information on the API. We support all parameters except: Chat: tools, and tool_choice. Completions: suffix. Extra Parameters#. vLLM supports a set of parameters that are not part of the OpenAI API.
Production Metrics — vLLM
WebvLLM exposes a number of metrics that can be used to monitor the health of the system. These metrics are exposed via the /metrics endpoint on the vLLM OpenAI compatible API server. The following metrics are exposed: gauge_avg_prompt_throughput = Gauge ("vllm:
Production Metrics — vLLM
WebvLLM exposes a number of metrics that can be used to monitor the health of the system. These metrics are exposed via the /metrics endpoint on the vLLM OpenAI compatible API server. The following metrics are exposed: classMetrics:labelname_finish_reason="finished_reason"def__init__(self,labelnames:List[str],max_model_len:int):# …
Deploying with Docker — vLLM
WebvLLM offers official docker image for deployment. The image can be used to run OpenAI compatible server. The image is available on Docker Hub as vllm/vllm-openai. You can either use the ipc=host flag or --shm-size flag to allow the container to access the host’s shared memory. vLLM uses PyTorch, which uses shared memory to share data between
Quickstart — vLLM
WebThe code example can also be found in examples/offline_inference.py.. OpenAI-Compatible Server#. vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API.
Distributed Inference and Serving — vLLM
WebvLLM supports distributed tensor-parallel inference and serving. Currently, we support Megatron-LM’s tensor parallel algorithm. We manage the distributed runtime with Ray. To run distributed inference, install Ray with: To run multi-GPU inference with the LLM class, set the tensor_parallel_size argument to the number of GPUs you want to use.
Engine Arguments — vLLM
WebBelow, you can find an explanation of every engine argument for vLLM: --model<model_name_or_path> #. Name or path of the huggingface model to use. --tokenizer<tokenizer_name_or_path> #. Name or path of the huggingface tokenizer to use. --revision<revision> #. The specific model version to use. It can be a branch name, a tag …
AsyncLLMEngine — vLLM
WebAsyncLLMEngine #. An asynchronous wrapper for LLMEngine. This class is used to wrap the LLMEngine class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The LLMEngine is kicked by the generate method when there are requests in the waiting queue.
vllm.engine.llm_engine — vLLM
WebThis is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory
vllm.engine.async_llm_engine — vLLM
WebThis class is used to wrap the LLMEngine class to make it asynchronous. It uses asyncio to create a background loop that keeps processing incoming requests. The LLMEngine is kicked by the generate method when there are requests in the waiting queue. The generate method yields the outputs from the LLMEngine to the caller.
vllm.engine.llm_engine — vLLM
WebThis is the main class for the vLLM engine. It receives requests from clients and generates texts from the LLM. It includes a tokenizer, a language model (possibly distributed across multiple GPUs), and GPU memory space allocated for intermediate states (aka KV cache). This class utilizes iteration-level scheduling and efficient memory
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