chore: vendor sglang v0.5.10 snapshot

third_party/sglang/examples/sagemaker/deploy_and_serve_endpoint.py

@@ -0,0 +1,69 @@
+import json
+
+import boto3
+from sagemaker import serializers
+from sagemaker.model import Model
+from sagemaker.predictor import Predictor
+
+boto_session = boto3.session.Session()
+sm_client = boto_session.client("sagemaker")
+sm_role = boto_session.resource("iam").Role("SageMakerRole").arn
+
+endpoint_name = "<YOUR_ENDPOINT_NAME>"
+image_uri = "<YOUR_DOCKER_IMAGE_URI>"
+model_id = (
+    "<YOUR_MODEL_ID>"  # eg: Qwen/Qwen3-0.6B from https://huggingface.co/Qwen/Qwen3-0.6B
+)
+hf_token = "<YOUR_HUGGINGFACE_TOKEN>"
+prompt = "<YOUR_ENDPOINT_PROMPT>"
+
+model = Model(
+    name=endpoint_name,
+    image_uri=image_uri,
+    role=sm_role,
+    env={
+        "SM_SGLANG_MODEL_PATH": model_id,
+        "HF_TOKEN": hf_token,
+    },
+)
+print("Model created successfully")
+print("Starting endpoint deployment (this may take 10-15 minutes)...")
+
+endpoint_config = model.deploy(
+    instance_type="ml.g5.12xlarge",
+    initial_instance_count=1,
+    endpoint_name=endpoint_name,
+    inference_ami_version="al2-ami-sagemaker-inference-gpu-3-1",
+    wait=True,
+)
+print("Endpoint deployment completed successfully")
+
+
+print(f"Creating predictor for endpoint: {endpoint_name}")
+predictor = Predictor(
+    endpoint_name=endpoint_name,
+    serializer=serializers.JSONSerializer(),
+)
+
+payload = {
+    "model": model_id,
+    "messages": [{"role": "user", "content": prompt}],
+    "max_tokens": 2400,
+    "temperature": 0.01,
+    "top_p": 0.9,
+    "top_k": 50,
+}
+print(f"Sending inference request with prompt: '{prompt[:50]}...'")
+response = predictor.predict(payload)
+print("Inference request completed successfully")
+
+if isinstance(response, bytes):
+    response = response.decode("utf-8")
+
+if isinstance(response, str):
+    try:
+        response = json.loads(response)
+    except json.JSONDecodeError:
+        print("Warning: Response is not valid JSON. Returning as string.")
+
+print(f"Received model response: '{response}'")