Add instructions for running vLLM backend (#8)

Co-authored-by: Neelay Shah <[email protected]> Co-authored-by: Olga Andreeva <[email protected]> Co-authored-by: Ryan McCormick <[email protected]> Co-authored-by: Tanmay Verma <[email protected]>
triton-inference-server · Oct 18, 2023 · 912896b · 912896b
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+
+[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)
+
+# vLLM Backend
+
+The Triton backend for [vLLM](https://github.com/vllm-project/vllm)
+is designed to run
+[supported models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)
+on a
+[vLLM engine](https://github.com/vllm-project/vllm/blob/main/vllm/engine/async_llm_engine.py).
+You can learn more about Triton backends in the [backend
+repo](https://github.com/triton-inference-server/backend).
+
+
+This is a Python-based backend. When using this backend, all requests are placed on the
+vLLM AsyncEngine as soon as they are received. Inflight batching and paged attention is handled
+by the vLLM engine.
+
+Where can I ask general questions about Triton and Triton backends?
+Be sure to read all the information below as well as the [general
+Triton documentation](https://github.com/triton-inference-server/server#triton-inference-server)
+available in the main [server](https://github.com/triton-inference-server/server)
+repo. If you don't find your answer there you can ask questions on the
+main Triton [issues page](https://github.com/triton-inference-server/server/issues).
+
+## Building the vLLM Backend
+
+There are several ways to install and deploy the vLLM backend.
+
+### Option 1. Use the Pre-Built Docker Container.
+
+Pull the container with vLLM backend from [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver) registry. This container has everything you need to run your vLLM model.
+
+### Option 2. Build a Custom Container From Source
+You can follow steps described in the
+[Building With Docker](https://github.com/triton-inference-server/server/blob/main/docs/customization_guide/build.md#building-with-docker)
+guide and use the
+[build.py](https://github.com/triton-inference-server/server/blob/main/build.py)
+script.
+
+A sample command to build a Triton Server container with all options enabled is shown below. Feel free to customize flags according to your needs.
+
+```
+./build.py -v  --enable-logging
+                --enable-stats
+                --enable-tracing
+                --enable-metrics
+                --enable-gpu-metrics
+                --enable-cpu-metrics
+                --enable-gpu
+                --filesystem=gcs
+                --filesystem=s3
+                --filesystem=azure_storage
+                --endpoint=http
+                --endpoint=grpc
+                --endpoint=sagemaker
+                --endpoint=vertex-ai
+                --upstream-container-version=23.10
+                --backend=python:r23.10
+                --backend=vllm:r23.10
+```
+
+### Option 3. Add the vLLM Backend to the Default Triton Container
+
+You can install the vLLM backend directly into the NGC Triton container.
+In this case, please install vLLM first. You can do so by running
+`pip install vllm==<vLLM_version>`. Then, set up the vLLM backend in the
+container with the following commands:
+
+```
+mkdir -p /opt/tritonserver/backends/vllm
+wget -P /opt/tritonserver/backends/vllm https://raw.githubusercontent.com/triton-inference-server/vllm_backend/main/src/model.py
+```
+
+## Using the vLLM Backend
+
+You can see an example
+[model_repository](samples/model_repository)
+in the [samples](samples) folder.
+You can use this as is and change the model by changing the `model` value in `model.json`.
+`model.json` represents a key-value dictionary that is fed to vLLM's AsyncLLMEngine when initializing the model.
+You can see supported arguments in vLLM's
+[arg_utils.py](https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py).
+Specifically,
+[here](https://github.com/vllm-project/vllm/blob/ee8217e5bee5860469204ee57077a91138c9af02/vllm/engine/arg_utils.py#L11)
+and
+[here](https://github.com/vllm-project/vllm/blob/ee8217e5bee5860469204ee57077a91138c9af02/vllm/engine/arg_utils.py#L201).
+
+For multi-GPU support, EngineArgs like tensor_parallel_size can be specified in
+[model.json](samples/model_repository/vllm_model/1/model.json).
+
+Note: vLLM greedily consume up to 90% of the GPU's memory under default settings.
+The sample model updates this behavior by setting gpu_memory_utilization to 50%.
+You can tweak this behavior using fields like gpu_memory_utilization and other settings in
+[model.json](samples/model_repository/vllm_model/1/model.json).
+
+In the [samples](samples) folder, you can also find a sample client,
+[client.py](samples/client.py).
+
+## Running the Latest vLLM Version
+
+To see the version of vLLM in the container, see the
+[version_map](https://github.com/triton-inference-server/server/blob/85487a1e15438ccb9592b58e308a3f78724fa483/build.py#L83)
+in [build.py](https://github.com/triton-inference-server/server/blob/main/build.py)
+for the Triton version you are using.
+
+If you would like to use a specific vLLM commit or the latest version of vLLM, you
+will need to use a
+[custom execution environment](https://github.com/triton-inference-server/python_backend#creating-custom-execution-environments).
+
+
+## Sending Your First Inference
+
+After you
+[start Triton](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/getting_started/quickstart.html)
+with the
+[sample model_repository](samples/model_repository),
+you can quickly run your first inference request with the
+[generate endpoint](https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_generate.md).
+
+Try out the command below.
+
+```
+$ curl -X POST localhost:8000/v2/models/vllm_model/generate -d '{"text_input": "What is Triton Inference Server?", "parameters": {"stream": false, "temperature": 0}}'
+```
+
+## Running Multiple Instances of Triton Server
+
+If you are running multiple instances of Triton server with a Python-based backend,
+you need to specify a different `shm-region-prefix-name` for each server. See
+[here](https://github.com/triton-inference-server/python_backend#running-multiple-instances-of-triton-server)
+for more information.
+
+## Referencing the Tutorial
+
+You can read further in the
+[vLLM Quick Deploy guide](https://github.com/triton-inference-server/tutorials/tree/main/Quick_Deploy/vLLM)
+in the
+[tutorials](https://github.com/triton-inference-server/tutorials/) repository.