[CI/Build] Replace `vllm.entrypoints.openai.api_server` entrypoint with `vllm...

[CI/Build] Replace `vllm.entrypoints.openai.api_server` entrypoint with `vllm serve` command (#25967)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

.buildkite/nightly-benchmarks/scripts/launch-server.sh

@@ -181,18 +181,14 @@ launch_vllm_server() {
   if echo "$common_params" | jq -e 'has("fp8")' >/dev/null; then
     echo "Key 'fp8' exists in common params. Use neuralmagic fp8 model for convenience."
     model=$(echo "$common_params" | jq -r '.neuralmagic_quantized_model')
-    server_command="python3 \
-        -m vllm.entrypoints.openai.api_server \
+    server_command="vllm serve $model \
         -tp $tp \
-        --model $model \
         --port $port \
         $server_args"
   else
     echo "Key 'fp8' does not exist in common params."
-    server_command="python3 \
-        -m vllm.entrypoints.openai.api_server \
+    server_command="vllm serve $model \
         -tp $tp \
-        --model $model \
         --port $port \
         $server_args"
   fi

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -365,8 +365,7 @@ run_serving_tests() {
       continue
     fi
 
-    server_command="$server_envs python3 \
-      -m vllm.entrypoints.openai.api_server \
+    server_command="$server_envs vllm serve \
       $server_args"
 
     # run the server

.buildkite/scripts/run-benchmarks.sh

@@ -18,7 +18,7 @@ vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
-python3 -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf &
+vllm serve meta-llama/Llama-2-7b-chat-hf &
 server_pid=$!
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 

benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh

@@ -55,9 +55,7 @@ benchmark() {
   output_len=$2
 
 
-  CUDA_VISIBLE_DEVICES=0 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
     --port 8100 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \
@@ -65,9 +63,7 @@ benchmark() {
     '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
 
 
-  CUDA_VISIBLE_DEVICES=1 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
     --port 8200 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \

benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh

@@ -38,16 +38,12 @@ wait_for_server() {
 launch_chunked_prefill() {
   model="meta-llama/Meta-Llama-3.1-8B-Instruct"
   # disagg prefill
-  CUDA_VISIBLE_DEVICES=0 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
     --port 8100 \
     --max-model-len 10000 \
     --enable-chunked-prefill \
     --gpu-memory-utilization 0.6 &
-  CUDA_VISIBLE_DEVICES=1 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
     --port 8200 \
     --max-model-len 10000 \
     --enable-chunked-prefill \
@@ -62,18 +58,14 @@ launch_chunked_prefill() {
 launch_disagg_prefill() {
   model="meta-llama/Meta-Llama-3.1-8B-Instruct"
   # disagg prefill
-  CUDA_VISIBLE_DEVICES=0 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=0 vllm serve $model \
     --port 8100 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \
     --kv-transfer-config \
     '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
 
-  CUDA_VISIBLE_DEVICES=1 python3 \
-    -m vllm.entrypoints.openai.api_server \
-    --model $model \
+  CUDA_VISIBLE_DEVICES=1 vllm serve $model \
     --port 8200 \
     --max-model-len 10000 \
     --gpu-memory-utilization 0.6 \

docker/Dockerfile

@@ -565,5 +565,5 @@ ENTRYPOINT ["./sagemaker-entrypoint.sh"]
 
 FROM vllm-openai-base AS vllm-openai
 
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
+ENTRYPOINT ["vllm", "serve"]
 #################### OPENAI API SERVER ####################

docker/Dockerfile.cpu

@@ -177,4 +177,4 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \
     uv pip install dist/*.whl
 
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
+ENTRYPOINT ["vllm", "serve"]

docker/Dockerfile.ppc64le

@@ -314,4 +314,4 @@ WORKDIR /workspace/
 
 RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
 
-ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"]
+ENTRYPOINT ["vllm", "serve"]

docker/Dockerfile.s390x

@@ -309,4 +309,4 @@ USER 2000
 WORKDIR /home/vllm
 
 # Set the default entrypoint
-ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"]
+ENTRYPOINT ["vllm", "serve"]

docker/Dockerfile.xpu

@@ -69,4 +69,4 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]
+ENTRYPOINT ["vllm", "serve"]

docs/contributing/benchmarks.md

@@ -661,8 +661,7 @@ Benchmark the performance of multi-modal requests in vLLM.
 Start vLLM:
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
-  --model Qwen/Qwen2.5-VL-7B-Instruct \
+vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
   --dtype bfloat16 \
   --limit-mm-per-prompt '{"image": 1}' \
   --allowed-local-media-path /path/to/sharegpt4v/images
@@ -688,8 +687,7 @@ vllm bench serve \
 Start vLLM:
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
-  --model Qwen/Qwen2.5-VL-7B-Instruct \
+vllm serve Qwen/Qwen2.5-VL-7B-Instruct \
   --dtype bfloat16 \
   --limit-mm-per-prompt '{"video": 1}' \
   --allowed-local-media-path /path/to/sharegpt4video/videos

docs/contributing/profiling.md

@@ -39,8 +39,7 @@ Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example
 
 ```bash
 VLLM_TORCH_PROFILER_DIR=./vllm_profile \
-    python -m vllm.entrypoints.openai.api_server \
-    --model meta-llama/Meta-Llama-3-70B
+    vllm serve meta-llama/Meta-Llama-3-70B
 ```
 
 vllm bench command:

docs/deployment/frameworks/autogen.md

@@ -19,8 +19,7 @@ pip install -U "autogen-agentchat" "autogen-ext[openai]"
 1. Start the vLLM server with the supported chat completion model, e.g.
 
     ```bash
-    python -m vllm.entrypoints.openai.api_server \
-        --model mistralai/Mistral-7B-Instruct-v0.2
+    vllm serve mistralai/Mistral-7B-Instruct-v0.2
     ```
 
 1. Call it with AutoGen:

docs/deployment/frameworks/open-webui.md

@@ -20,7 +20,7 @@ To get started with Open WebUI using vLLM, follow these steps:
         For example:
 
         ```console
-        python -m vllm.entrypoints.openai.api_server --host 0.0.0.0 --port 8000
+        vllm serve <model> --host 0.0.0.0 --port 8000
         ```
 
 3. Start the Open WebUI Docker container:

docs/deployment/frameworks/skypilot.md

@@ -32,6 +32,7 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil
       ports: 8081  # Expose to internet traffic.
 
     envs:
+      PYTHONUNBUFFERED: 1
       MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
       HF_TOKEN: <your-huggingface-token>  # Change to your own huggingface token, or use --env to pass.
 
@@ -47,9 +48,8 @@ See the vLLM SkyPilot YAML for serving, [serving.yaml](https://github.com/skypil
     run: |
       conda activate vllm
       echo 'Starting vllm api server...'
-      python -u -m vllm.entrypoints.openai.api_server \
+      vllm serve $MODEL_NAME \
         --port 8081 \
-        --model $MODEL_NAME \
         --trust-remote-code \
         --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
         2>&1 | tee api_server.log &
@@ -131,6 +131,7 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
       ports: 8081  # Expose to internet traffic.
 
     envs:
+      PYTHONUNBUFFERED: 1
       MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
       HF_TOKEN: <your-huggingface-token>  # Change to your own huggingface token, or use --env to pass.
 
@@ -146,9 +147,8 @@ SkyPilot can scale up the service to multiple service replicas with built-in aut
     run: |
       conda activate vllm
       echo 'Starting vllm api server...'
-      python -u -m vllm.entrypoints.openai.api_server \
+      vllm serve $MODEL_NAME \
         --port 8081 \
-        --model $MODEL_NAME \
         --trust-remote-code \
         --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
         2>&1 | tee api_server.log
@@ -243,6 +243,7 @@ This will scale the service up to when the QPS exceeds 2 for each replica.
       ports: 8081  # Expose to internet traffic.
 
     envs:
+      PYTHONUNBUFFERED: 1
       MODEL_NAME: meta-llama/Meta-Llama-3-8B-Instruct
       HF_TOKEN: <your-huggingface-token>  # Change to your own huggingface token, or use --env to pass.
 
@@ -258,9 +259,8 @@ This will scale the service up to when the QPS exceeds 2 for each replica.
     run: |
       conda activate vllm
       echo 'Starting vllm api server...'
-      python -u -m vllm.entrypoints.openai.api_server \
+      vllm serve $MODEL_NAME \
         --port 8081 \
-        --model $MODEL_NAME \
         --trust-remote-code \
         --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
         2>&1 | tee api_server.log

docs/design/arch_overview.md

@@ -69,6 +69,11 @@ Sometimes you may see the API server entrypoint used directly instead of via the
 python -m vllm.entrypoints.openai.api_server --model <model>
 ```
 
+!!! warning
+
+    `python -m vllm.entrypoints.openai.api_server` is deprecated
+    and may become unsupported in a future release.
+
 That code can be found in <gh-file:vllm/entrypoints/openai/api_server.py>.
 
 More details on the API server can be found in the [OpenAI-Compatible Server](../serving/openai_compatible_server.md) document.

docs/features/sleep_mode.md

@@ -64,8 +64,7 @@ To enable sleep mode in a vLLM server you need to initialize it with the flag `V
 When using the flag `VLLM_SERVER_DEV_MODE=1` you enable development endpoints, and these endpoints should not be exposed to users.
 
 ```bash
-VLLM_SERVER_DEV_MODE=1 python -m vllm.entrypoints.openai.api_server \
-  --model Qwen/Qwen3-0.6B \
+VLLM_SERVER_DEV_MODE=1 vllm serve Qwen/Qwen3-0.6B \
   --enable-sleep-mode \
   --port 8000
 ```

docs/features/spec_decode.md

@@ -48,10 +48,9 @@ The following code configures vLLM in an offline mode to use speculative decodin
 To perform the same with an online mode launch the server:
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
+vllm serve facebook/opt-6.7b \
     --host 0.0.0.0 \
     --port 8000 \
-    --model facebook/opt-6.7b \
     --seed 42 \
     -tp 1 \
     --gpu_memory_utilization 0.8 \

docs/getting_started/installation/gpu/xpu.inc.md

@@ -67,8 +67,7 @@ docker run -it \
 XPU platform supports **tensor parallel** inference/serving and also supports **pipeline parallel** as a beta feature for online serving. For **pipeline parallel**, we support it on single node with mp as the backend. For example, a reference execution like following:
 
 ```bash
-python -m vllm.entrypoints.openai.api_server \
-     --model=facebook/opt-13b \
+vllm serve facebook/opt-13b \
      --dtype=bfloat16 \
      --max_model_len=1024 \
      --distributed-executor-backend=mp \

examples/online_serving/sagemaker-entrypoint.sh

@@ -21,4 +21,4 @@ while IFS='=' read -r key value; do
 done < <(env | grep "^${PREFIX}")
 
 # Pass the collected arguments to the main entrypoint
-exec python3 -m vllm.entrypoints.openai.api_server "${ARGS[@]}"
+exec vllm serve "${ARGS[@]}"
\ No newline at end of file