feat: rename dynamo decorator (#1133)

deploy/sdk/README.md

@@ -37,7 +37,7 @@ The code for the pipeline looks like this:
 from fastapi import FastAPI
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
-from dynamo.sdk import DYNAMO_IMAGE, depends, dynamo_endpoint, service, dynamo_api
+from dynamo.sdk import DYNAMO_IMAGE, depends, endpoint, service, api
 
 
 class RequestType(BaseModel):
@@ -52,7 +52,7 @@ class ResponseType(BaseModel):
     dynamo={"namespace": "inference"},
 )
 class Backend:
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         text = f"{req.text}-back"
         for token in text.split():
@@ -65,7 +65,7 @@ class Backend:
 class Middle:
     backend = depends(Backend)
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         text = f"{req.text}-mid"
         next_request = RequestType(text=text).model_dump_json()
@@ -83,7 +83,7 @@ app = FastAPI(title="Hello World!")
 class Frontend:
     middle = depends(Middle)
 
-    @dynamo_api()
+    @api()
     async def generate(self, request: RequestType):
         async def content_generator():
             async for response in self.middle.generate(request.model_dump_json()):

deploy/sdk/docs/sdk/README.md

@@ -74,7 +74,7 @@ class ServiceA:
             await self.engine.shutdown()
             print("ServiceA engine shut down")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: ChatCompletionRequest):
         # Call dependent service
         processed_request = await self.service_b.preprocess(request)
@@ -89,8 +89,8 @@ Dynamo follows a class-based architecture similar to BentoML making it intuitive
 1. Class attributes for dependencies using `depends()`
 2. An `__init__` method for standard initialization
 3. Optional lifecycle hooks like `@async_on_start` and `@async_on_shutdown`
-4. Endpoints defined with `@dynamo_endpoint()`. Optionally, an endpoint can be given a name
-   via `@dynamo_endpoint("my_endpoint_name")`, but otherwise will default to the name of the
+4. Endpoints defined with `@endpoint()`. Optionally, an endpoint can be given a name
+   via `@endpoint("my_endpoint_name")`, but otherwise will default to the name of the
    function being decorated if omitted.
 
 This approach provides a clean separation of concerns and makes the service structure easy to understand.

deploy/sdk/src/dynamo/sdk/__init__.py

@@ -17,7 +17,7 @@ from typing import Any
 
 from bentoml import on_shutdown as async_on_shutdown
 
-from dynamo.sdk.core.decorators.endpoint import dynamo_api, dynamo_endpoint
+from dynamo.sdk.core.decorators.endpoint import api, endpoint
 from dynamo.sdk.core.lib import DYNAMO_IMAGE, depends, service
 from dynamo.sdk.lib.decorators import async_on_start
 
@@ -29,7 +29,7 @@ __all__ = [
     "async_on_start",
     "depends",
     "dynamo_context",
-    "dynamo_endpoint",
-    "dynamo_api",
+    "endpoint",
+    "api",
     "service",
 ]

deploy/sdk/src/dynamo/sdk/cli/serve_dynamo.py

@@ -214,7 +214,7 @@ def main(
             for name, endpoint in dynamo_endpoints.items():
                 bound_method = endpoint.func.__get__(class_instance)
                 # Only pass request type for now, use Any for response
-                # TODO: Handle a dynamo_endpoint not having types
+                # TODO: Handle an endpoint not having types
                 # TODO: Handle multiple endpoints in a single component
                 dynamo_wrapped_method = dynamo_endpoint(endpoint.request_type, Any)(
                     bound_method

deploy/sdk/src/dynamo/sdk/core/decorators/endpoint.py

@@ -31,7 +31,7 @@ T = TypeVar("T")
 class DynamoEndpoint(DynamoEndpointInterface):
     """
     Base class for dynamo endpoints
-    Dynamo endpoints are methods decorated with @dynamo_endpoint.
+    Dynamo endpoints are methods decorated with @endpoint.
     """
 
     def __init__(
@@ -72,7 +72,7 @@ class DynamoEndpoint(DynamoEndpointInterface):
         return self._transports
 
 
-def dynamo_endpoint(
+def endpoint(
     name: Optional[str] = None,
     transports: Optional[List[DynamoTransport]] = None,
     **kwargs,
@@ -85,7 +85,7 @@ def dynamo_endpoint(
     return decorator
 
 
-def dynamo_api(
+def api(
     name: Optional[str] = None,
     **kwargs,
 ) -> Callable[[Callable], DynamoEndpoint]:

deploy/sdk/src/dynamo/sdk/lib/decorators.py

@@ -63,7 +63,7 @@ class DynamoEndpoint:
         return await self.func(*args, **kwargs)
 
 
-def dynamo_endpoint(
+def endpoint(
     name: str | None = None,
     is_api: bool = False,
 ) -> t.Callable[[t.Callable], DynamoEndpoint]:
@@ -74,11 +74,11 @@ def dynamo_endpoint(
         is_api: Whether to expose the endpoint as an API. Defaults to False.
 
     Example:
-        @dynamo_endpoint()
+        @endpoint()
         def my_endpoint(self, input: str) -> str:
             return input
 
-        @dynamo_endpoint(name="custom_name")
+        @endpoint(name="custom_name")
         def another_endpoint(self, input: str) -> str:
             return input
     """

deploy/sdk/src/dynamo/sdk/tests/pipeline.py

@@ -20,7 +20,7 @@
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
-from dynamo.sdk import depends, dynamo_endpoint, service
+from dynamo.sdk import depends, endpoint, service
 from dynamo.sdk.core.protocol.interface import DynamoTransport
 
 """
@@ -68,7 +68,7 @@ class Backend:
     def __init__(self) -> None:
         print("Starting backend")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         """Generate tokens."""
         req_text = req.text
@@ -77,7 +77,7 @@ class Backend:
         for token in text.split():
             yield f"Backend: {token}"
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate_v2(self, req: RequestType):
         """Generate tokens."""
         req_text = req.text
@@ -98,7 +98,7 @@ class Backend2:
     def __init__(self) -> None:
         print("Starting backend2")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         """Forward requests to backend."""
 
@@ -121,7 +121,7 @@ class Middle:
     def __init__(self) -> None:
         print("Starting middle")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         """Forward requests to backend."""
         req_text = req.text
@@ -155,7 +155,7 @@ class Frontend:
     def __init__(self) -> None:
         print("Starting frontend")
 
-    @dynamo_endpoint(transports=[DynamoTransport.HTTP])
+    @endpoint(transports=[DynamoTransport.HTTP])
     async def generate(self, request: RequestType):
         """Stream results from the pipeline."""
         print(f"Frontend received: {request.text}")

docs/backend.md

@@ -16,7 +16,7 @@ see the [Dynamo Serve Guide](../docs/guides/dynamo_serve.md).
 When deploying a python-based worker with `dynamo serve` or `dynamo deploy`, it is
 a Python class based definition that requires a few key decorators to get going:
 - `@service`: used to define a worker class
-- `@dynamo_endpoint`: marks methods that can be called by other workers or clients
+- `@endpoint`: marks methods that can be called by other workers or clients
 
 For more detailed information on these concepts, see the
 [Dynamo SDK Docs](../deploy/sdk/docs/sdk/README.md).
@@ -26,7 +26,7 @@ For more detailed information on these concepts, see the
 Here is the rough outline of what a worker may look like in its simplest form:
 
 ```python
-from dynamo.sdk import dynamo_endpoint, service
+from dynamo.sdk import endpoint, service
 
 @service(
     dynamo={
@@ -37,7 +37,7 @@ class YourWorker:
     # Worker implementation
     # ...
 
-    @dynamo_endpoint()
+    @endpoint()
     async def your_endpoint(self, request: RequestType) -> AsyncIterator[ResponseType]:
         # Endpoint Implementation
         pass
@@ -48,7 +48,7 @@ When addressing this worker's endpoint with the `namespace/component/endpoint` s
 based on the definitions above, it would be: `your_namespace/YourWorker/your_endpoint`:
 - `namespace="your_namespace"`: Defined in the `@service` decorator
 - `component="YourWorker"`: Defined by the Python Class name
-- `endpoint="your_endpoint"`: Defined by the `@dynamo_endpoint` decorator, or by default the name of the function being decorated.
+- `endpoint="your_endpoint"`: Defined by the `@endpoint` decorator, or by default the name of the function being decorated.
 
 For more details about service configuration, resource management, and dynamo endpoints,
 see the [Dynamo SDK Docs](../deploy/sdk/docs/README.md).
@@ -79,7 +79,7 @@ Chat Completions objects, such as the ones specified in the OpenAI API. For exam
 from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 
 class YourLLMWorker:
-    @dynamo_endpoint(name="my_chat_completions_endpoint")
+    @endpoint(name="my_chat_completions_endpoint")
     async def generate(self, request: ChatCompletionRequest):
         # Endpoint Implementation
         pass
@@ -95,7 +95,7 @@ via custom RequestType/ResponseType definitions:
 # This can be run standalone with `dynamo serve basic_worker:YourWorker`
 
 from pydantic import BaseModel
-from dynamo.sdk import dynamo_endpoint, service
+from dynamo.sdk import endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -114,7 +114,7 @@ class YourWorker:
     def __init__(self) -> None:
         logger.info("Starting worker...")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: RequestType):
         """Generate tokens and stream them back"""
         logger.info(f"Worker endpoint received: {request.text}")
@@ -204,7 +204,7 @@ import random
 
 from pydantic import BaseModel
 from dynamo.llm import KvMetricsPublisher
-from dynamo.sdk import dynamo_endpoint, service, dynamo_context
+from dynamo.sdk import endpoint, service, dynamo_context
 
 logger = logging.getLogger(__name__)
 
@@ -269,7 +269,7 @@ class YourWorker:
             self.gpu_prefix_cache_hit_rate,
         )
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: RequestType):
         """Generate tokens, update KV Cache metrics, and stream the tokens back"""
         # Increment the number of active requests on receiving one
@@ -384,7 +384,7 @@ class Router:
         return best_worker_id
 
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: Tokens) -> AsyncIterator[WorkerId]:
         try:
             # lora_id is a placeholder for lora support, but not used in this example
@@ -563,7 +563,7 @@ class DecodeWorker:
                 .endpoint("generate")
                 .client()
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request):
         if self.do_remote_prefill:
             # Forward the request to the prefill worker
@@ -580,7 +580,7 @@ class PrefillWorker:
     def __init__(self):
         # ...
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request):
         # ... framework-specific prefill logic ...
 ```
@@ -612,7 +612,7 @@ For more information on Disaggregated Serving, see the
 
 2. **Async Operations**: Use async/await for I/O operations:
    ```python
-   @dynamo_endpoint()
+   @endpoint()
    async def generate(self, request):
        # Use async operations for better performance
        result = await self.some_async_operation()

docs/distributed_runtime.md

@@ -17,7 +17,7 @@ For example, the deployment configuration `examples/llm/configs/disagg.yaml` hav
 - `Processor`: When a new request arrives, `Processor` applies the chat template and perform the tokenization. Then, it route the request to the `VllmWorker`.
 - `VllmWorker` and `PrefillWorker`: Perform the actual decode and prefill computation.
 
-Since the four workers are deployed in different processes, each of them have their own `DistributedRuntime`. Within their own `DistributedRuntime`, they all have their own `Namespace`s named `dynamo`. Then, under their own `dynamo` namespace, they have their own `Component`s named `Frontend/Processor/VllmWorker/PrefillWorker`. Lastly, for the `Endpoint`, `Frontend` has no `Endpoints`, `Processor` and `VllmWorker` each has a `generate` endpoint, and `PrefillWorker` has a placeholder `mock` endpoint. Their `DistributedRuntime`s and `Namespace`s are set in the `@service` decorators in `examples/llm/components/<frontend/processor/worker/prefill_worker>.py`. Their `Component`s are set by their name in `/deploy/dynamo/sdk/src/dynamo/sdk/cli/serve_dynamo.py`. Their `Endpoint`s are set by the `@dynamo_endpoint` decorators in `examples/llm/components/<frontend/processor/worker/prefill_worker>.py`.
+Since the four workers are deployed in different processes, each of them have their own `DistributedRuntime`. Within their own `DistributedRuntime`, they all have their own `Namespace`s named `dynamo`. Then, under their own `dynamo` namespace, they have their own `Component`s named `Frontend/Processor/VllmWorker/PrefillWorker`. Lastly, for the `Endpoint`, `Frontend` has no `Endpoints`, `Processor` and `VllmWorker` each has a `generate` endpoint, and `PrefillWorker` has a placeholder `mock` endpoint. Their `DistributedRuntime`s and `Namespace`s are set in the `@service` decorators in `examples/llm/components/<frontend/processor/worker/prefill_worker>.py`. Their `Component`s are set by their name in `/deploy/dynamo/sdk/src/dynamo/sdk/cli/serve_dynamo.py`. Their `Endpoint`s are set by the `@endpoint` decorators in `examples/llm/components/<frontend/processor/worker/prefill_worker>.py`.
 
 ## Initialization
 

examples/hello_world/disagg_skeleton/components/frontend.py

@@ -22,7 +22,7 @@ from components.utils import GeneralRequest
 from fastapi import FastAPI
 from fastapi.responses import StreamingResponse
 
-from dynamo.sdk import DYNAMO_IMAGE, depends, dynamo_api, service
+from dynamo.sdk import DYNAMO_IMAGE, api, depends, service
 
 logger = logging.getLogger(__name__)
 
@@ -45,7 +45,7 @@ class Frontend:
         logger.debug(f"Received signal {signum}, shutting down...")
         sys.exit(0)
 
-    @dynamo_api()
+    @api()
     async def generate(self, prompt, request_id):  # from request body keys
         """Stream results from the pipeline."""
         logger.info(f"Received: {prompt=},{request_id=}")

examples/hello_world/disagg_skeleton/components/kv_router.py

@@ -21,7 +21,7 @@ from typing import AsyncIterator
 from components.utils import check_required_workers
 from components.worker import DummyWorker
 
-from dynamo.sdk import async_on_start, depends, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
 
 WorkerId = str
 
@@ -92,7 +92,7 @@ class Router:
     # A dummy hit rate checking endpoint
     # The actual worker selection is based on custom cost function
     # See details at examples/llm/components/kv_router.py
-    @dynamo_endpoint()
+    @endpoint()
     async def check_hit_rate(self, request_prompt: str) -> AsyncIterator[WorkerId]:
         max_id, max_hit_rate = self._cost_function(request_prompt)
         yield f"{max_id}_{max_hit_rate}"

examples/hello_world/disagg_skeleton/components/prefill_worker.py

@@ -23,7 +23,7 @@ import sys
 from components.utils import NixlMetadataStore, PrefillQueue, RemotePrefillRequest
 from vllm.distributed.device_communicators.nixl import NixlMetadata
 
-from dynamo.sdk import async_on_start, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, dynamo_context, endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -102,6 +102,6 @@ class PrefillWorker:
         print("Prefill invoked and will read KV cache from worker and write it back")
         yield "prefill invoked"
 
-    @dynamo_endpoint()
+    @endpoint()
     async def mock(self, req: RemotePrefillRequest):
         yield f"mock_response: {req}"

examples/hello_world/disagg_skeleton/components/processor.py

@@ -21,7 +21,7 @@ from components.utils import GeneralRequest, GeneralResponse, check_required_wor
 from components.worker import DummyWorker
 
 from dynamo._core import Client
-from dynamo.sdk import async_on_start, depends, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
 from dynamo.sdk.lib.dependency import DynamoClient
 
 logger = logging.getLogger(__name__)
@@ -96,7 +96,7 @@ class Processor(Protocol):
         async for resp in engine_generator:
             yield GeneralResponse.model_validate_json(resp.data())
 
-    @dynamo_endpoint()
+    @endpoint()
     async def processor_generate(self, raw_request: GeneralRequest):
         async for response in self._generate(raw_request):
             yield response.model_dump_json()

examples/hello_world/disagg_skeleton/components/worker.py

@@ -26,7 +26,7 @@ from components.utils import (
 )
 from vllm.distributed.device_communicators.nixl import NixlMetadata
 
-from dynamo.sdk import async_on_start, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, dynamo_context, endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -86,7 +86,7 @@ class DummyWorker:
 
         return callback
 
-    @dynamo_endpoint()
+    @endpoint()
     async def worker_generate(self, request: GeneralRequest):
         # TODO: consider prefix hit when deciding prefill locally or remotely
 

examples/hello_world/hello_world.py

@@ -19,7 +19,7 @@ from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
 from dynamo.runtime.logging import configure_dynamo_logging
-from dynamo.sdk import DYNAMO_IMAGE, depends, dynamo_api, dynamo_endpoint, service
+from dynamo.sdk import DYNAMO_IMAGE, api, depends, endpoint, service
 from dynamo.sdk.lib.config import ServiceConfig
 
 logger = logging.getLogger(__name__)
@@ -70,7 +70,7 @@ class Backend:
         self.message = config.get("Backend", {}).get("message", "back")
         logger.info(f"Backend config message: {self.message}")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         """Generate tokens."""
         req_text = req.text
@@ -93,7 +93,7 @@ class Middle:
         self.message = config.get("Middle", {}).get("message", "mid")
         logger.info(f"Middle config message: {self.message}")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, req: RequestType):
         """Forward requests to backend."""
         req_text = req.text
@@ -125,8 +125,8 @@ class Frontend:
         logger.info(f"Frontend config message: {self.message}")
         logger.info(f"Frontend config port: {self.port}")
 
-    # alternative syntax: @dynamo_endpoint(transports=[DynamoTransport.HTTP])
-    @dynamo_api()
+    # alternative syntax: @endpoint(transports=[DynamoTransport.HTTP])
+    @api()
     async def generate(self, request: RequestType):
         """Stream results from the pipeline."""
         logger.info(f"Frontend received: {request.text}")

examples/llm/components/kv_router.py

@@ -26,7 +26,7 @@ from utils.protocol import Tokens
 from utils.vllm import RouterType
 
 from dynamo.llm import AggregatedMetrics, KvIndexer, KvMetricsAggregator, OverlapScores
-from dynamo.sdk import async_on_start, depends, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
 from dynamo.sdk.lib.config import ServiceConfig
 
 WorkerId = str
@@ -247,7 +247,7 @@ class Router:
         )
         return best_worker_id, kv_load[best_worker_id]
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: Tokens) -> AsyncIterator[Tuple[WorkerId, float]]:
         metrics = await self.metrics_aggregator.get_metrics()
 

examples/llm/components/planner_service.py

@@ -21,7 +21,7 @@ from pydantic import BaseModel
 from components.planner import start_planner  # type: ignore[attr-defined]
 from dynamo.planner.defaults import PlannerDefaults
 from dynamo.runtime.logging import configure_dynamo_logging
-from dynamo.sdk import async_on_start, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, dynamo_context, endpoint, service
 from dynamo.sdk.core.protocol.interface import ComponentType
 from dynamo.sdk.lib.config import ServiceConfig
 from dynamo.sdk.lib.image import DYNAMO_IMAGE
@@ -109,7 +109,7 @@ class Planner:
         await start_planner(self.runtime, self.args)
         logger.info("Planner started")
 
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: RequestType):
         """Dummy endpoint to satisfy that each component has an endpoint"""
         yield "mock endpoint"

examples/llm/components/prefill_worker.py

@@ -30,7 +30,7 @@ from vllm.entrypoints.openai.api_server import (
 from vllm.inputs.data import TokensPrompt
 from vllm.remote_prefill import RemotePrefillParams, RemotePrefillRequest
 
-from dynamo.sdk import async_on_start, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, dynamo_context, endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -206,6 +206,6 @@ class PrefillWorker:
         ):
             yield
 
-    @dynamo_endpoint()
+    @endpoint()
     async def mock(self, req: RequestType):
         yield f"mock_response: {req}"

examples/llm/components/processor.py

@@ -33,7 +33,7 @@ from vllm.transformers_utils.tokenizer import AnyTokenizer
 
 from dynamo.llm import KvMetricsAggregator
 from dynamo.runtime import EtcdKvCache
-from dynamo.sdk import async_on_start, depends, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -173,16 +173,16 @@ class Processor(ProcessMixIn):
     async def _get_kv_load(self):
         metrics = await self.metrics_aggregator.get_metrics()
         kv_load = {}
-        for endpoint in metrics.endpoints:
-            worker_id = endpoint.worker_id
-            kv_load[worker_id] = getattr(endpoint, "gpu_cache_usage_perc", 0.0)
+        for end_point in metrics.endpoints:
+            worker_id = end_point.worker_id
+            kv_load[worker_id] = getattr(end_point, "gpu_cache_usage_perc", 0.0)
         return kv_load
 
     async def _get_pending_requests(self):
         metrics = await self.metrics_aggregator.get_metrics()
         pending_requests = {}
-        for endpoint in metrics.endpoints:
-            worker_id = endpoint.worker_id
+        for end_point in metrics.endpoints:
+            worker_id = end_point.worker_id
             pending_requests[worker_id] = getattr(endpoint, "num_requests_waiting", 0)
         return pending_requests
 
@@ -327,12 +327,12 @@ class Processor(ProcessMixIn):
                     f"Request type {request_type} not implemented"
                 )
 
-    @dynamo_endpoint(name="chat/completions")
+    @endpoint(name="chat/completions")
     async def chat_completions(self, raw_request: ChatCompletionRequest):
         async for response in self._generate(raw_request, RequestType.CHAT):
             yield response
 
-    # @dynamo_endpoint()
+    # @endpoint()
     # async def completions(self, raw_request: CompletionRequest):
     #     async for response in self._generate(raw_request, RequestType.COMPLETION):
     #         yield response

examples/llm/components/worker.py

@@ -32,7 +32,7 @@ from vllm.remote_prefill import RemotePrefillParams, RemotePrefillRequest
 from vllm.sampling_params import RequestOutputKind
 
 from dynamo.llm import KvMetricsPublisher
-from dynamo.sdk import async_on_start, depends, dynamo_context, dynamo_endpoint, service
+from dynamo.sdk import async_on_start, depends, dynamo_context, endpoint, service
 
 logger = logging.getLogger(__name__)
 
@@ -183,7 +183,7 @@ class VllmWorker:
         return callback
 
     # TODO: use the same child lease for metrics publisher endpoint and generate endpoint
-    @dynamo_endpoint()
+    @endpoint()
     async def generate(self, request: vLLMGenerateRequest):
         # TODO: consider prefix hit when deciding prefill locally or remotely