From 0d24f229feb5ff8e86b11e340eafc8b0e5a3d504 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 18 Jul 2025 04:01:28 +0200 Subject: [PATCH 01/27] feat: update requirements to include aiohttp Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/src/vllm_router/requirements.txt b/src/vllm_router/requirements.txt index 0f34dd981..c2c0ab8e5 100644 --- a/src/vllm_router/requirements.txt +++ b/src/vllm_router/requirements.txt @@ -10,3 +10,4 @@ sentry-sdk[fastapi,httpx]==2.27.0 uhashring==2.3 uvicorn==0.34.0 xxhash==3.5.0 +aiohttp>=3.8.0 From a3bc238f6888d40deb3e2446f3f53511d72b91d9 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 18 Jul 2025 04:05:59 +0200 Subject: [PATCH 02/27] feat: replace HTTPX with aiohttp for asynchronous client handling Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/httpx_client.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/src/vllm_router/httpx_client.py b/src/vllm_router/httpx_client.py index e5e32ae25..661a3ae61 100644 --- a/src/vllm_router/httpx_client.py +++ b/src/vllm_router/httpx_client.py @@ -11,14 +11,14 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -import httpx +import aiohttp from vllm_router.log import init_logger logger = init_logger(__name__) -class HTTPXClientWrapper: +class AiohttpClientWrapper: async_client = None @@ -26,24 +26,23 @@ def start(self): """Instantiate the client. Call from the FastAPI startup hook.""" # To fully leverage the router's concurrency capabilities, # we set the maximum number of connections to be unlimited. - limits = httpx.Limits(max_connections=None) - self.async_client = httpx.AsyncClient(limits=limits) - logger.info(f"httpx AsyncClient instantiated. Id {id(self.async_client)}") + self.async_client = aiohttp.ClientSession() + logger.info(f"aiohttp ClientSession instantiated. Id {id(self.async_client)}") async def stop(self): """Gracefully shutdown. Call from FastAPI shutdown hook.""" logger.info( - f"httpx async_client.is_closed(): {self.async_client.is_closed} - Now close it. Id (will be unchanged): {id(self.async_client)}" + f"aiohttp async_client.closed: {self.async_client.closed} - Now close it. Id (will be unchanged): {id(self.async_client)}" ) await self.async_client.aclose() logger.info( - f"httpx async_client.is_closed(): {self.async_client.is_closed}. Id (will be unchanged): {id(self.async_client)}" + f"aiohttp async_client.is_closed(): {self.async_client.is_closed}. Id (will be unchanged): {id(self.async_client)}" ) self.async_client = None - logger.info("httpx AsyncClient closed") + logger.info("aiohttp ClientSession closed") def __call__(self): - """Calling the instantiated HTTPXClientWrapper returns the wrapped singleton.""" + """Calling the instantiated AiohttpClientWrapper returns the wrapped singleton.""" # Ensure we don't use it if not started / running assert self.async_client is not None return self.async_client From 49ac0f74e76a110c39b04ff2016b3a7a88db7aac Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 18 Jul 2025 04:08:31 +0200 Subject: [PATCH 03/27] feat: replace HTTPXClientWrapper with AiohttpClientWrapper for improved async handling Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/app.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index dd1ae5a12..c3a7852be 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -25,7 +25,7 @@ initialize_dynamic_config_watcher, ) from vllm_router.experimental import get_feature_gates, initialize_feature_gates -from vllm_router.httpx_client import HTTPXClientWrapper +from vllm_router.httpx_client import AiohttpClientWrapper from vllm_router.parsers.parser import parse_args from vllm_router.routers.batches_router import batches_router from vllm_router.routers.files_router import files_router @@ -259,6 +259,7 @@ def initialize_all(app: FastAPI, args): app.include_router(batches_router) app.include_router(metrics_router) app.state.httpx_client_wrapper = HTTPXClientWrapper() +app.state.aiohttp_client_wrapper = AiohttpClientWrapper() app.state.semantic_cache_available = semantic_cache_available From c5fce3bcd4f380486196880c58362ff2498d33c3 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 18 Jul 2025 04:11:19 +0200 Subject: [PATCH 04/27] feat: replace HTTPXClientWrapper with AiohttpClientWrapper in lifespan management Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/app.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index c3a7852be..6b8c31359 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -82,11 +82,11 @@ @asynccontextmanager async def lifespan(app: FastAPI): - app.state.httpx_client_wrapper.start() + app.state.aiohttp_client_wrapper.start() if hasattr(app.state, "batch_processor"): await app.state.batch_processor.initialize() yield - await app.state.httpx_client_wrapper.stop() + await app.state.aiohttp_client_wrapper.stop() # Close the threaded-components logger.info("Closing engine stats scraper") @@ -258,7 +258,6 @@ def initialize_all(app: FastAPI, args): app.include_router(files_router) app.include_router(batches_router) app.include_router(metrics_router) -app.state.httpx_client_wrapper = HTTPXClientWrapper() app.state.aiohttp_client_wrapper = AiohttpClientWrapper() app.state.semantic_cache_available = semantic_cache_available From 95477f49d500f37be80ea87a62750748c28c3331 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 18 Jul 2025 04:48:49 +0200 Subject: [PATCH 05/27] feat: remove httpx from requirements for aiohttp integration Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/src/vllm_router/requirements.txt b/src/vllm_router/requirements.txt index c2c0ab8e5..77ae3e2b3 100644 --- a/src/vllm_router/requirements.txt +++ b/src/vllm_router/requirements.txt @@ -1,6 +1,5 @@ aiofiles==24.1.0 fastapi==0.115.8 -httpx==0.28.1 kubernetes==32.0.0 numpy==1.26.4 prometheus_client==0.21.1 From 1b45d8e7cc7ef8321104af0a8d64c4f21b4a36bf Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:29:44 +0200 Subject: [PATCH 06/27] refactor: switch from httpx to aiohttp for asynchronous client handling in service discovery Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/service_discovery.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/src/vllm_router/service_discovery.py b/src/vllm_router/service_discovery.py index 3c86a0ad9..affb0ce94 100644 --- a/src/vllm_router/service_discovery.py +++ b/src/vllm_router/service_discovery.py @@ -22,7 +22,7 @@ from dataclasses import dataclass from typing import Dict, List, Optional -import httpx +import aiohttp import requests from kubernetes import client, config, watch @@ -311,14 +311,14 @@ def get_endpoint_info(self) -> List[EndpointInfo]: ): for endpoint_info in endpoint_infos: if endpoint_info.model_label in self.prefill_model_labels: - self.app.state.prefill_client = httpx.AsyncClient( + self.app.state.prefill_client = aiohttp.ClientSession( base_url=endpoint_info.url, - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) elif endpoint_info.model_label in self.decode_model_labels: - self.app.state.decode_client = httpx.AsyncClient( + self.app.state.decode_client = aiohttp.ClientSession( base_url=endpoint_info.url, - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) return endpoint_infos @@ -607,14 +607,14 @@ def _add_engine( and self.decode_model_labels is not None ): if model_label in self.prefill_model_labels: - self.app.state.prefill_client = httpx.AsyncClient( + self.app.state.prefill_client = aiohttp.ClientSession( base_url=f"http://{engine_ip}:{self.port}", - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) elif model_label in self.decode_model_labels: - self.app.state.decode_client = httpx.AsyncClient( + self.app.state.decode_client = aiohttp.ClientSession( base_url=f"http://{engine_ip}:{self.port}", - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) # Store model information in the endpoint info self.available_engines[engine_name].model_info = model_info From 3855dfd3252ff09ec6255f78eef95ba892aa2fa5 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:32:09 +0200 Subject: [PATCH 07/27] feat: add AiohttpClientWrapper for asynchronous HTTP client handling Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/{httpx_client.py => aiohttp_client.py} | 0 src/vllm_router/app.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename src/vllm_router/{httpx_client.py => aiohttp_client.py} (100%) diff --git a/src/vllm_router/httpx_client.py b/src/vllm_router/aiohttp_client.py similarity index 100% rename from src/vllm_router/httpx_client.py rename to src/vllm_router/aiohttp_client.py diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index 6b8c31359..0be108b33 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -25,7 +25,7 @@ initialize_dynamic_config_watcher, ) from vllm_router.experimental import get_feature_gates, initialize_feature_gates -from vllm_router.httpx_client import AiohttpClientWrapper +from vllm_router.aiohttp_client import AiohttpClientWrapper from vllm_router.parsers.parser import parse_args from vllm_router.routers.batches_router import batches_router from vllm_router.routers.files_router import files_router From c3ec0e21af147bf96e38d2cf2c35e079af10efd6 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:32:58 +0200 Subject: [PATCH 08/27] chore: pin aiohttp version to 3.9.5 in requirements Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/vllm_router/requirements.txt b/src/vllm_router/requirements.txt index 77ae3e2b3..e1401d048 100644 --- a/src/vllm_router/requirements.txt +++ b/src/vllm_router/requirements.txt @@ -9,4 +9,4 @@ sentry-sdk[fastapi,httpx]==2.27.0 uhashring==2.3 uvicorn==0.34.0 xxhash==3.5.0 -aiohttp>=3.8.0 +aiohttp==3.9.5 \ No newline at end of file From b026de725c19cc962aedb17a70674545c1734e05 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:34:51 +0200 Subject: [PATCH 09/27] refactor: migrate from httpx to aiohttp for asynchronous HTTP requests in request service Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- .../services/request_service/request.py | 57 +++++++++---------- 1 file changed, 28 insertions(+), 29 deletions(-) diff --git a/src/vllm_router/services/request_service/request.py b/src/vllm_router/services/request_service/request.py index 5161f7747..09727e40a 100644 --- a/src/vllm_router/services/request_service/request.py +++ b/src/vllm_router/services/request_service/request.py @@ -18,7 +18,7 @@ import time import uuid -import httpx +import aiohttp from fastapi import BackgroundTasks, HTTPException, Request from fastapi.responses import JSONResponse, StreamingResponse from requests import JSONDecodeError @@ -97,12 +97,12 @@ async def process_request( # For non-streaming requests, collect the full response to cache it properly full_response = bytearray() - async with request.app.state.httpx_client_wrapper().stream( + async with request.app.state.aiohttp_client_wrapper().request( method=request.method, url=backend_url + endpoint, headers=dict(request.headers), - content=body, - timeout=None, + data=body, + timeout=aiohttp.ClientTimeout(total=None), ) as backend_response: # Yield headers and status code first. yield backend_response.headers, backend_response.status_code @@ -305,7 +305,7 @@ async def route_general_request( async def send_request_to_prefiller( - client: httpx.AsyncClient, endpoint: str, req_data: dict, request_id: str + client: aiohttp.ClientSession, endpoint: str, req_data: dict, request_id: str ): """ Send a request to a prefiller service. @@ -320,13 +320,13 @@ async def send_request_to_prefiller( "X-Request-Id": request_id, } - response = await client.post(endpoint, json=req_data, headers=headers) - response.raise_for_status() - return response + async with client.post(endpoint, json=req_data, headers=headers) as response: + response.raise_for_status() + return response async def send_request_to_decode( - client: httpx.AsyncClient, endpoint: str, req_data: dict, request_id: str + client: aiohttp.ClientSession, endpoint: str, req_data: dict, request_id: str ): """ Asynchronously stream the response from a service using a persistent client. @@ -336,11 +336,9 @@ async def send_request_to_decode( "X-Request-Id": request_id, } - async with client.stream( - "POST", endpoint, json=req_data, headers=headers - ) as response: + async with client.post(endpoint, json=req_data, headers=headers) as response: response.raise_for_status() - async for chunk in response.aiter_bytes(): + async for chunk in response.content.iter_chunked(8192): yield chunk @@ -367,15 +365,15 @@ async def route_disaggregated_prefill_request( f"Routing request {request_id} with session id None to {request.app.state.prefill_client.base_url} at {et}, process time = {et - in_router_time:.4f}" ) request_json["max_tokens"] = orig_max_tokens - except httpx.HTTPStatusError as e: + except aiohttp.ClientResponseError as e: logger.error(f"HTTP error in prefiller: {e}", exc_info=True) return JSONResponse( - status_code=e.response.status_code, + status_code=e.status, content={ "error": { - "message": f"Prefiller error: {e.response.text}", + "message": f"Prefiller error: {e.message}", "type": "prefiller_error", - "code": e.response.status_code, + "code": e.status, } }, headers={"X-Request-Id": request_id}, @@ -400,18 +398,18 @@ async def generate_stream(): request.app.state.decode_client, endpoint, request_json, request_id ): yield chunk - except httpx.HTTPStatusError as e: + except aiohttp.ClientResponseError as e: logger.error(f"HTTP error in decoder: {e}", exc_info=True) try: - error_text = e.response.text + error_text = e.message except Exception: - error_text = f"HTTP {e.response.status_code}" + error_text = f"HTTP {e.status}" # Yield error as JSON response error_response = { "error": { "message": f"Decoder error: {error_text}", "type": "decoder_error", - "code": e.response.status_code, + "code": e.status, } } yield json.dumps(error_response).encode("utf-8") @@ -487,19 +485,20 @@ async def route_sleep_wakeup_request( url = server_url + endpoint - async with httpx.AsyncClient() as client: + async with aiohttp.ClientSession() as client: if endpoint == "/is_sleeping": - response = await client.get(url, headers=headers) - response.raise_for_status() - return response.json() + async with client.get(url, headers=headers) as response: + response.raise_for_status() + return await response.json() else: request_body = await request.body() if request_body: req_data = json.loads(request_body) - response = await client.post(url, json=req_data, headers=headers) + async with client.post(url, json=req_data, headers=headers) as response: + response.raise_for_status() else: - response = await client.post(url, headers=headers) - response.raise_for_status() + async with client.post(url, headers=headers) as response: + response.raise_for_status() pod_name = endpoints[0].pod_name if endpoint == "/sleep": @@ -508,7 +507,7 @@ async def route_sleep_wakeup_request( service_discovery.remove_sleep_label(pod_name) return JSONResponse( - status_code=response.status_code, + status_code=response.status, content={"status": "success"}, headers={"X-Request-Id": request_id}, ) From 6db91932a88536a69cb736023df90ce790c2520f Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:37:55 +0200 Subject: [PATCH 10/27] refactor: update file upload implementation to use aiohttp for asynchronous requests Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- docs/source/conf.py | 2 +- src/examples/example_file_upload.py | 22 ++++++++++++---------- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 80b9ca620..61d092d49 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -95,7 +95,7 @@ def add_line(self, line: str, source: str, *lineno: int) -> None: "prometheus_client", "uhashring", "lmcache", - "httpx", + "aiohttp", "transformers", "os", ] diff --git a/src/examples/example_file_upload.py b/src/examples/example_file_upload.py index 2b2fb3245..fd4bc649d 100644 --- a/src/examples/example_file_upload.py +++ b/src/examples/example_file_upload.py @@ -1,22 +1,23 @@ import argparse -import httpx +import aiohttp -def upload_file(server_url: str, file_path: str): +async def upload_file(server_url: str, file_path: str): """Uploads a file to the production stack.""" try: with open(file_path, "rb") as file: files = {"file": (file_path, file, "application/octet-stream")} data = {"purpose": "unknown"} - with httpx.Client() as client: - response = client.post(server_url, files=files, data=data) - - if response.status_code == 200: - print("File uploaded successfully:", response.json()) - else: - print("Failed to upload file:", response.text) + async with aiohttp.ClientSession() as client: + async with client.post(server_url, data=data) as response: + if response.status == 200: + result = await response.json() + print("File uploaded successfully:", result) + else: + text = await response.text() + print("Failed to upload file:", text) except Exception as e: print(f"Error: {e}") @@ -31,7 +32,8 @@ def parse_args(): if __name__ == "__main__": + import asyncio args = parse_args() endpoint = args.url file_to_upload = args.path - upload_file(endpoint, file_to_upload) + asyncio.run(upload_file(endpoint, file_to_upload)) From d976bce14cc2b0192577e7fe1bf2c9e417d36932 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:38:12 +0200 Subject: [PATCH 11/27] chore: replace httpx with aiohttp in project dependencies and test requirements Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- pyproject.toml | 4 ++-- src/tests/requirements.txt | 2 +- src/vllm_router/requirements.txt | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6c344918e..aa890b3fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,12 +15,12 @@ dependencies = [ "aiofiles==24.1.0", "black>=25.1.0", "fastapi==0.115.8", - "httpx==0.28.1", + "aiohttp==3.9.1", "kubernetes==32.0.0", "numpy==1.26.4", "prometheus-client==0.21.1", "python-multipart==0.0.20", - "sentry-sdk[fastapi,httpx]==2.27.0", + "sentry-sdk[fastapi]==2.27.0", "uhashring==2.3", "uvicorn==0.34.0", "xxhash==3.5.0", diff --git a/src/tests/requirements.txt b/src/tests/requirements.txt index 743b58aab..4b48df1a5 100644 --- a/src/tests/requirements.txt +++ b/src/tests/requirements.txt @@ -1,5 +1,5 @@ fastapi -httpx +aiohttp openai uvicorn vllm diff --git a/src/vllm_router/requirements.txt b/src/vllm_router/requirements.txt index e1401d048..23a246f68 100644 --- a/src/vllm_router/requirements.txt +++ b/src/vllm_router/requirements.txt @@ -5,7 +5,7 @@ numpy==1.26.4 prometheus_client==0.21.1 psutil==7.0.0 python-multipart==0.0.20 -sentry-sdk[fastapi,httpx]==2.27.0 +sentry-sdk[fastapi]==2.27.0 uhashring==2.3 uvicorn==0.34.0 xxhash==3.5.0 From 321ebc5cce4d946e5a6c9b84e84159dbc7df3f34 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 13:41:54 +0200 Subject: [PATCH 12/27] chore: format dependencies in pyproject.toml for consistency Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- pyproject.toml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index aa890b3fd..9cf1e798e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,12 +15,12 @@ dependencies = [ "aiofiles==24.1.0", "black>=25.1.0", "fastapi==0.115.8", - "aiohttp==3.9.1", + "aiohttp==3.9.1", "kubernetes==32.0.0", "numpy==1.26.4", "prometheus-client==0.21.1", "python-multipart==0.0.20", - "sentry-sdk[fastapi]==2.27.0", + "sentry-sdk[fastapi]==2.27.0", "uhashring==2.3", "uvicorn==0.34.0", "xxhash==3.5.0", From b82026604beb344fa7ab54f936c0bc89457ca8db Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 15:45:20 +0200 Subject: [PATCH 13/27] chore: clean up imports and ensure consistent aiohttp version in requirements Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/examples/example_file_upload.py | 1 + src/tests/requirements.txt | 2 +- src/vllm_router/app.py | 2 +- src/vllm_router/requirements.txt | 2 +- 4 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/examples/example_file_upload.py b/src/examples/example_file_upload.py index fd4bc649d..d202a7c7e 100644 --- a/src/examples/example_file_upload.py +++ b/src/examples/example_file_upload.py @@ -33,6 +33,7 @@ def parse_args(): if __name__ == "__main__": import asyncio + args = parse_args() endpoint = args.url file_to_upload = args.path diff --git a/src/tests/requirements.txt b/src/tests/requirements.txt index 4b48df1a5..f6b887f0c 100644 --- a/src/tests/requirements.txt +++ b/src/tests/requirements.txt @@ -1,5 +1,5 @@ -fastapi aiohttp +fastapi openai uvicorn vllm diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index 0be108b33..a34c04623 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -19,13 +19,13 @@ import uvicorn from fastapi import FastAPI +from vllm_router.aiohttp_client import AiohttpClientWrapper from vllm_router.dynamic_config import ( DynamicRouterConfig, get_dynamic_config_watcher, initialize_dynamic_config_watcher, ) from vllm_router.experimental import get_feature_gates, initialize_feature_gates -from vllm_router.aiohttp_client import AiohttpClientWrapper from vllm_router.parsers.parser import parse_args from vllm_router.routers.batches_router import batches_router from vllm_router.routers.files_router import files_router diff --git a/src/vllm_router/requirements.txt b/src/vllm_router/requirements.txt index 23a246f68..4861ba4be 100644 --- a/src/vllm_router/requirements.txt +++ b/src/vllm_router/requirements.txt @@ -1,4 +1,5 @@ aiofiles==24.1.0 +aiohttp==3.9.5 fastapi==0.115.8 kubernetes==32.0.0 numpy==1.26.4 @@ -9,4 +10,3 @@ sentry-sdk[fastapi]==2.27.0 uhashring==2.3 uvicorn==0.34.0 xxhash==3.5.0 -aiohttp==3.9.5 \ No newline at end of file From ad48764ab1457f30995037611b5d3a39f7aef8f9 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Thu, 24 Jul 2025 18:15:58 +0200 Subject: [PATCH 14/27] fix: update aiohttp client closure method for consistency and accuracy in logging Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/aiohttp_client.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/vllm_router/aiohttp_client.py b/src/vllm_router/aiohttp_client.py index 661a3ae61..08b73e8e0 100644 --- a/src/vllm_router/aiohttp_client.py +++ b/src/vllm_router/aiohttp_client.py @@ -34,9 +34,9 @@ async def stop(self): logger.info( f"aiohttp async_client.closed: {self.async_client.closed} - Now close it. Id (will be unchanged): {id(self.async_client)}" ) - await self.async_client.aclose() + await self.async_client.close() logger.info( - f"aiohttp async_client.is_closed(): {self.async_client.is_closed}. Id (will be unchanged): {id(self.async_client)}" + f"aiohttp async_client.closed: {self.async_client.closed}. Id (will be unchanged): {id(self.async_client)}" ) self.async_client = None logger.info("aiohttp ClientSession closed") From ed6ad34a477814271fb7c0ebd0cc903e988e0c12 Mon Sep 17 00:00:00 2001 From: Ifta khairul Alam Adil Date: Fri, 25 Jul 2025 20:54:53 +0200 Subject: [PATCH 15/27] fix: correct status code handling in request processing for improved response accuracy Signed-off-by: Ifta khairul Alam Adil Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/services/request_service/request.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/src/vllm_router/services/request_service/request.py b/src/vllm_router/services/request_service/request.py index 09727e40a..f47a0b5ed 100644 --- a/src/vllm_router/services/request_service/request.py +++ b/src/vllm_router/services/request_service/request.py @@ -90,9 +90,7 @@ async def process_request( is_streaming = request_json.get("stream", False) except JSONDecodeError: # If we can't parse the body as JSON, assume it's not streaming - raise HTTPException( - status_code=400, detail="Request body is not JSON parsable." - ) + raise HTTPException(status=400, detail="Request body is not JSON parsable.") # For non-streaming requests, collect the full response to cache it properly full_response = bytearray() @@ -105,9 +103,9 @@ async def process_request( timeout=aiohttp.ClientTimeout(total=None), ) as backend_response: # Yield headers and status code first. - yield backend_response.headers, backend_response.status_code + yield backend_response.headers, backend_response.status # Stream response content. - async for chunk in backend_response.aiter_bytes(): + async for chunk in backend_response.content.iter_any(): total_len += len(chunk) if not first_token: first_token = True @@ -293,12 +291,12 @@ async def route_general_request( endpoint, background_tasks, ) - headers, status_code = await anext(stream_generator) + headers, status = await anext(stream_generator) headers_dict = {key: value for key, value in headers.items()} headers_dict["X-Request-Id"] = request_id return StreamingResponse( stream_generator, - status_code=status_code, + status_code=status, headers=headers_dict, media_type="text/event-stream", ) From da6fa9c8432d4c373107b68732b7913a88c529b8 Mon Sep 17 00:00:00 2001 From: Yuhan Liu <32589867+YuhanLiu11@users.noreply.github.com> Date: Mon, 21 Jul 2025 21:47:36 -0700 Subject: [PATCH 16/27] bumping version (#593) Signed-off-by: YuhanLiu11 Signed-off-by: Ifta Khairul Alam Adil --- helm/Chart.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/helm/Chart.yaml b/helm/Chart.yaml index aeafc4006..265ec0343 100644 --- a/helm/Chart.yaml +++ b/helm/Chart.yaml @@ -15,7 +15,7 @@ type: application # This is the chart version. This version number should be incremented each time you make changes # to the chart and its templates, including the app version. # Versions are expected to follow Semantic Versioning (https://semver.org/) -version: 0.1.5 +version: 0.1.6 maintainers: - name: apostac From e24bdf647ac1cf4eb21990baca05c7b4a6e465a5 Mon Sep 17 00:00:00 2001 From: Fabhiahn <79161847+Fabhiahn@users.noreply.github.com> Date: Tue, 22 Jul 2025 07:13:17 +0200 Subject: [PATCH 17/27] Added option to specify priority class (#557) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Fabijan Marič Vild Co-authored-by: Yuhan Liu <32589867+YuhanLiu11@users.noreply.github.com> Signed-off-by: Ifta Khairul Alam Adil --- helm/templates/deployment-cache-server.yaml | 3 +++ helm/templates/deployment-router.yaml | 3 +++ helm/templates/deployment-vllm-multi.yaml | 3 +++ helm/values.schema.json | 3 +++ helm/values.yaml | 4 ++++ 5 files changed, 16 insertions(+) diff --git a/helm/templates/deployment-cache-server.yaml b/helm/templates/deployment-cache-server.yaml index cac0f49fb..6ed2ecf27 100644 --- a/helm/templates/deployment-cache-server.yaml +++ b/helm/templates/deployment-cache-server.yaml @@ -16,6 +16,9 @@ spec: labels: {{- include "chart.cacheserverLabels" . | nindent 8 }} spec: + {{- if .Values.cacheserverSpec.priorityClassName}} + priorityClassName: {{ .Values.cacheserverSpec.priorityClassName | quote }} + {{- end }} {{- if .Values.cacheserverSpec.nodeSelectorTerms}} affinity: nodeAffinity: diff --git a/helm/templates/deployment-router.yaml b/helm/templates/deployment-router.yaml index 64711b37e..4e27e5c0c 100644 --- a/helm/templates/deployment-router.yaml +++ b/helm/templates/deployment-router.yaml @@ -18,6 +18,9 @@ spec: {{- include "chart.routerLabels" . | nindent 8 }} spec: serviceAccountName: {{ .Release.Name }}-router-service-account + {{- if .Values.routerSpec.priorityClassName }} + priorityClassName: {{ .Values.routerSpec.priorityClassName | quote }} + {{- end }} {{- if .Values.routerSpec.nodeSelectorTerms }} affinity: nodeAffinity: diff --git a/helm/templates/deployment-vllm-multi.yaml b/helm/templates/deployment-vllm-multi.yaml index 1a1119ed3..ccbfa943b 100644 --- a/helm/templates/deployment-vllm-multi.yaml +++ b/helm/templates/deployment-vllm-multi.yaml @@ -43,6 +43,9 @@ spec: helm-release-name: {{ .Release.Name }} {{- include "chart.engineLabels" . | nindent 8 }} spec: + {{- if $modelSpec.priorityClassName }} + priorityClassName: {{ $modelSpec.priorityClassName | quote }} + {{- end }} {{- if hasKey $modelSpec "initContainer" }} {{- $container := $modelSpec.initContainer }} initContainers: diff --git a/helm/values.schema.json b/helm/values.schema.json index 14393dd77..a8ff97454 100644 --- a/helm/values.schema.json +++ b/helm/values.schema.json @@ -88,6 +88,9 @@ "requestGPUType": { "type": "string" }, + "priorityClassName": { + "type": "string" + }, "pvcStorage": { "type": "string" }, diff --git a/helm/values.yaml b/helm/values.yaml index b4862742b..4578a8093 100644 --- a/helm/values.yaml +++ b/helm/values.yaml @@ -18,6 +18,7 @@ servingEngineSpec: # Each entry in the modelSpec array should contain the following fields: # - annotations: (Optional, map) The annotations to add to the deployment, e.g., {model: "opt125m"} # - serviceAccountName: (Optional, string) The name of the service account to use for the deployment, e.g., "vllm-service-account" + # - priorityClassName: (Optional, string) The name of the priority class name for the deployment, e.g., "high-priority" # - podAnnotations: (Optional, map) The annotations to add to the pod, e.g., {model: "opt125m"} # - name: (string) The name of the model, e.g., "example-model" # - repository: (string) The repository of the model, e.g., "vllm/vllm-openai" @@ -267,6 +268,9 @@ routerSpec: # -- Number of replicas replicaCount: 1 + # -- Priority Class + priorityClassName: "" + # -- Container port containerPort: 8000 From d43d061514bcabe19f936cd06684e8b1b6380fb8 Mon Sep 17 00:00:00 2001 From: Shaoting Date: Tue, 22 Jul 2025 18:39:46 -0700 Subject: [PATCH 18/27] [CI/Build] Change CI runner to L4 (#595) * Add env clean up before run Signed-off-by: Shaoting Feng * Delete all pods Signed-off-by: Shaoting Feng * Delete all release Signed-off-by: Shaoting Feng * test ci runner Signed-off-by: Shaoting * test ci runner Signed-off-by: Shaoting * Test again Signed-off-by: Shaoting Feng * Test again Signed-off-by: Shaoting Feng * Change python version from 3.12 to 3.10 Signed-off-by: Shaoting Feng * delete get helm Signed-off-by: Shaoting Feng * change back python version Signed-off-by: Shaoting Feng * trigger Signed-off-by: Shaoting Feng * Add cache: pip Signed-off-by: Shaoting Feng * Try check-latest: true Signed-off-by: Shaoting Feng * Changing to 3.10 Signed-off-by: Shaoting Feng * change to 3.9 Signed-off-by: Shaoting Feng * Specify 3.9.2 Signed-off-by: Shaoting Feng * Use conda python Signed-off-by: Shaoting Feng * Fix vllm without conda Signed-off-by: Shaoting Feng --------- Signed-off-by: Shaoting Feng Signed-off-by: Shaoting Signed-off-by: Ifta Khairul Alam Adil --- .../workflows/functionality-helm-chart.yml | 6 ++--- .github/workflows/router-e2e-test.yml | 24 +++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/.github/workflows/functionality-helm-chart.yml b/.github/workflows/functionality-helm-chart.yml index 356e88d3e..42bb9541a 100644 --- a/.github/workflows/functionality-helm-chart.yml +++ b/.github/workflows/functionality-helm-chart.yml @@ -66,7 +66,7 @@ jobs: - name: Validate the installation and send query to the stack run: | bash .github/port-forward.sh curl-05-secure-vllm - timeout-minutes: 2 + timeout-minutes: 3 - name: Archive functionality results uses: actions/upload-artifact@v4 if: always() @@ -112,7 +112,7 @@ jobs: - name: Validate the installation and send query to the stack run: | bash .github/port-forward.sh curl-02-two-pods - timeout-minutes: 2 + timeout-minutes: 3 - name: Archive functionality results uses: actions/upload-artifact@v4 if: always() @@ -156,7 +156,7 @@ jobs: - name: Validate the installation and send query to the stack run: | bash .github/port-forward.sh curl-04-multiple-models - timeout-minutes: 3 + timeout-minutes: 5 - name: Archive functionality results uses: actions/upload-artifact@v4 if: always() diff --git a/.github/workflows/router-e2e-test.yml b/.github/workflows/router-e2e-test.yml index b689223ad..85f7c4c47 100644 --- a/.github/workflows/router-e2e-test.yml +++ b/.github/workflows/router-e2e-test.yml @@ -109,13 +109,10 @@ jobs: - name: Check out repository code uses: actions/checkout@v4 - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: Install Python dependencies run: | + source /usr/local/bin/conda-init + conda activate llmstack python -m pip install --upgrade pip pip install -r benchmarks/multi-round-qa/requirements.txt pip install -e . @@ -142,6 +139,8 @@ jobs: - name: Run all k8s discovery routing tests run: | + source /usr/local/bin/conda-init + conda activate llmstack echo "🧪 Running all k8s discovery routing tests" ./tests/e2e/run-k8s-routing-test.sh all \ --model "facebook/opt-125m" \ @@ -173,23 +172,24 @@ jobs: - name: Check out repository code uses: actions/checkout@v4 - - name: Setup Python - uses: actions/setup-python@v5 - with: - python-version: "3.12" - - name: Install Python dependencies run: | + source /usr/local/bin/conda-init + conda activate llmstack python -m pip install --upgrade pip pip install -e . - name: Install vLLM and lmcache run: | + source /usr/local/bin/conda-init + conda activate llmstack pip install vllm pip install lmcache - name: Start 2 vLLM serve backends run: | + source /usr/local/bin/conda-init + conda activate llmstack echo "🚀 Starting vLLM serve backend" mkdir -p "$LOG_DIR" CUDA_VISIBLE_DEVICES=0 vllm serve facebook/opt-125m --port 8001 --gpu-memory-utilization 0.7 --chat-template .github/template-chatml.jinja > "$LOG_DIR/backend1.log" 2>&1 & @@ -202,9 +202,9 @@ jobs: ./tests/e2e/wait-for-backends.sh 180 "http://localhost:8001" "http://localhost:8002" - name: Run All Static Discovery Routing Tests - env: - PYTHONPATH: ${{ github.workspace }}/src run: | + source /usr/local/bin/conda-init + conda activate llmstack echo "🧪 Running all static discovery routing tests sequentially" chmod +x tests/e2e/run-static-discovery-routing-test.sh ./tests/e2e/run-static-discovery-routing-test.sh all \ From 981315ac2b92bf9cbc5ac89a1131ed29a422740d Mon Sep 17 00:00:00 2001 From: Rui Zhang <51696593+zerofishnoodles@users.noreply.github.com> Date: Thu, 24 Jul 2025 11:40:31 -0700 Subject: [PATCH 19/27] [Bugfix] fix dynamic config (#598) * [hotfix] fix dynamic config Signed-off-by: Rui Zhang * [Bugfix] Add missing field for reconfiguration Signed-off-by: Rui Zhang --------- Signed-off-by: Rui Zhang Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/dynamic_config.py | 23 +++++++++++++++++++++++ src/vllm_router/service_discovery.py | 12 ++++++------ src/vllm_router/utils.py | 5 ++++- 3 files changed, 33 insertions(+), 7 deletions(-) diff --git a/src/vllm_router/dynamic_config.py b/src/vllm_router/dynamic_config.py index bf693cc50..a009ba945 100644 --- a/src/vllm_router/dynamic_config.py +++ b/src/vllm_router/dynamic_config.py @@ -50,6 +50,11 @@ class DynamicRouterConfig: static_backends: Optional[str] = None static_models: Optional[str] = None static_aliases: Optional[str] = None + static_model_labels: Optional[str] = None + static_model_types: Optional[str] = None + static_backend_health_checks: Optional[bool] = False + prefill_model_labels: Optional[str] = None + decode_model_labels: Optional[str] = None k8s_port: Optional[int] = None k8s_namespace: Optional[str] = None k8s_label_selector: Optional[str] = None @@ -137,15 +142,33 @@ def reconfigure_service_discovery(self, config: DynamicRouterConfig): if config.service_discovery == "static": reconfigure_service_discovery( ServiceDiscoveryType.STATIC, + app=self.app, urls=parse_static_urls(config.static_backends), models=parse_comma_separated_args(config.static_models), + aliases=parse_comma_separated_args(config.static_aliases), + model_labels=parse_comma_separated_args(config.static_model_labels), + model_types=parse_comma_separated_args(config.static_model_types), + static_backend_health_checks=config.static_backend_health_checks, + prefill_model_labels=parse_comma_separated_args( + config.prefill_model_labels + ), + decode_model_labels=parse_comma_separated_args( + config.decode_model_labels + ), ) elif config.service_discovery == "k8s": reconfigure_service_discovery( ServiceDiscoveryType.K8S, + app=self.app, namespace=config.k8s_namespace, port=config.k8s_port, label_selector=config.k8s_label_selector, + prefill_model_labels=parse_comma_separated_args( + config.prefill_model_labels + ), + decode_model_labels=parse_comma_separated_args( + config.decode_model_labels + ), ) else: raise ValueError( diff --git a/src/vllm_router/service_discovery.py b/src/vllm_router/service_discovery.py index affb0ce94..e993e1ac1 100644 --- a/src/vllm_router/service_discovery.py +++ b/src/vllm_router/service_discovery.py @@ -206,12 +206,12 @@ def __init__( app, urls: List[str], models: List[str], - aliases: List[str] | None, - model_labels: List[str] | None, - model_types: List[str] | None, - static_backend_health_checks: bool, - prefill_model_labels: List[str] | None, - decode_model_labels: List[str] | None, + aliases: List[str] | None = None, + model_labels: List[str] | None = None, + model_types: List[str] | None = None, + static_backend_health_checks: bool = False, + prefill_model_labels: List[str] | None = None, + decode_model_labels: List[str] | None = None, ): self.app = app assert len(urls) == len(models), "URLs and models should have the same length" diff --git a/src/vllm_router/utils.py b/src/vllm_router/utils.py index 762d34afe..512356e8a 100644 --- a/src/vllm_router/utils.py +++ b/src/vllm_router/utils.py @@ -3,6 +3,7 @@ import json import re import resource +from typing import Optional import requests from fastapi.requests import Request @@ -132,7 +133,9 @@ def parse_static_urls(static_backends: str): return backend_urls -def parse_comma_separated_args(comma_separated_string: str): +def parse_comma_separated_args(comma_separated_string: Optional[str]): + if comma_separated_string is None: + return None return comma_separated_string.split(",") From 04de252cf7f41f91d5b6902b164b41e68f6c23ff Mon Sep 17 00:00:00 2001 From: Kobe Chen Date: Thu, 24 Jul 2025 17:21:27 -0700 Subject: [PATCH 20/27] [refactor] redesign RST documentation (#592) * initial reorg Signed-off-by: Kobe Chen * pass CICD Signed-off-by: Kobe Chen * fix Signed-off-by: Kobe Chen * fix Signed-off-by: Kobe Chen * fix: update helm.rst Signed-off-by: Kobe Chen * fix Signed-off-by: Kobe Chen * fix Signed-off-by: Kobe Chen * Sharing KV Cache Across Instances Signed-off-by: Kobe Chen * docker Signed-off-by: Kobe Chen * community meeting Signed-off-by: Kobe Chen * add deployment 3 options Signed-off-by: Kobe Chen * remove A: Signed-off-by: Kobe Chen * docker Signed-off-by: Kobe Chen --------- Signed-off-by: Kobe Chen Co-authored-by: Yuhan Liu <32589867+YuhanLiu11@users.noreply.github.com> Signed-off-by: Ifta Khairul Alam Adil --- docs/source/benchmarks/multiround-qa.rst | 133 -- docs/source/community/meetings.rst | 11 + .../deployment/cloud-deployment/aws.rst | 138 -- .../deployment/cloud-deployment/azure.rst | 137 -- .../deployment/cloud-deployment/gcp.rst | 132 -- .../deployment/cloud-deployment/index.rst | 15 - .../gateway-inference-extension.rst | 286 +++ docs/source/deployment/helm.rst | 223 ++- docs/source/deployment/index.rst | 25 + docs/source/deployment/ray/ray.rst | 6 - .../source/dev_guide/dev_api/engine-stats.rst | 10 - docs/source/dev_guide/dev_api/index.rst | 13 - .../dev_guide/dev_api/request-stats.rst | 12 - .../source/dev_guide/dev_api/router-logic.rst | 16 - .../dev_guide/dev_api/service-discovery.rst | 37 - docs/source/dev_guide/peripheral/index.rst | 11 - .../dev_guide/peripheral/interfaces.rst | 89 - docs/source/dev_guide/peripheral/models.rst | 128 -- docs/source/developer_guide/contributing.rst | 98 + docs/source/developer_guide/docker.rst | 13 + docs/source/getting_started/examples.rst | 170 -- docs/source/getting_started/faq.rst | 109 ++ docs/source/getting_started/installation.rst | 59 - docs/source/getting_started/prerequisite.rst | 251 +++ docs/source/getting_started/quickstart.rst | 207 ++ .../getting_started/troubleshooting.rst | 6 - docs/source/index.rst | 57 +- docs/source/tutorials/disagg.rst | 195 -- docs/source/tutorials/kv_cache.rst | 119 -- docs/source/use_cases/autoscaling-keda.rst | 184 ++ docs/source/use_cases/benchmarking.rst | 6 + .../use_cases/disaggregated-prefill.rst | 178 ++ docs/source/use_cases/distributed-tracing.rst | 121 ++ .../kv-cache-aware-routing.rst} | 59 +- .../pipeline-parallelism-kuberay.rst | 327 ++++ .../prefix-aware-routing.rst} | 56 +- docs/source/use_cases/sharing-kv-cache.rst | 151 ++ docs/source/use_cases/sleep-wakeup-mode.rst | 234 +++ .../use_cases/tool-enabled-installation.rst | 194 ++ docs/source/user_manual/kv_cache/index.rst | 47 - docs/source/user_manual/lora/index.rst | 12 - docs/source/user_manual/lora/manual.rst | 105 - docs/source/user_manual/router/cmd.rst | 124 -- docs/source/user_manual/router/index.rst | 13 - docs/source/user_manual/router/json.rst | 54 - uv.lock | 1688 +++++++++-------- 46 files changed, 3514 insertions(+), 2745 deletions(-) delete mode 100644 docs/source/benchmarks/multiround-qa.rst create mode 100644 docs/source/community/meetings.rst delete mode 100644 docs/source/deployment/cloud-deployment/aws.rst delete mode 100644 docs/source/deployment/cloud-deployment/azure.rst delete mode 100644 docs/source/deployment/cloud-deployment/gcp.rst delete mode 100644 docs/source/deployment/cloud-deployment/index.rst create mode 100644 docs/source/deployment/gateway-inference-extension.rst create mode 100644 docs/source/deployment/index.rst delete mode 100644 docs/source/deployment/ray/ray.rst delete mode 100644 docs/source/dev_guide/dev_api/engine-stats.rst delete mode 100644 docs/source/dev_guide/dev_api/index.rst delete mode 100644 docs/source/dev_guide/dev_api/request-stats.rst delete mode 100644 docs/source/dev_guide/dev_api/router-logic.rst delete mode 100644 docs/source/dev_guide/dev_api/service-discovery.rst delete mode 100644 docs/source/dev_guide/peripheral/index.rst delete mode 100644 docs/source/dev_guide/peripheral/interfaces.rst delete mode 100644 docs/source/dev_guide/peripheral/models.rst create mode 100644 docs/source/developer_guide/contributing.rst create mode 100644 docs/source/developer_guide/docker.rst delete mode 100644 docs/source/getting_started/examples.rst create mode 100644 docs/source/getting_started/faq.rst delete mode 100644 docs/source/getting_started/installation.rst create mode 100644 docs/source/getting_started/prerequisite.rst create mode 100644 docs/source/getting_started/quickstart.rst delete mode 100644 docs/source/getting_started/troubleshooting.rst delete mode 100644 docs/source/tutorials/disagg.rst delete mode 100644 docs/source/tutorials/kv_cache.rst create mode 100644 docs/source/use_cases/autoscaling-keda.rst create mode 100644 docs/source/use_cases/benchmarking.rst create mode 100644 docs/source/use_cases/disaggregated-prefill.rst create mode 100644 docs/source/use_cases/distributed-tracing.rst rename docs/source/{tutorials/kvaware.rst => use_cases/kv-cache-aware-routing.rst} (63%) create mode 100644 docs/source/use_cases/pipeline-parallelism-kuberay.rst rename docs/source/{tutorials/prefixaware.rst => use_cases/prefix-aware-routing.rst} (70%) create mode 100644 docs/source/use_cases/sharing-kv-cache.rst create mode 100644 docs/source/use_cases/sleep-wakeup-mode.rst create mode 100644 docs/source/use_cases/tool-enabled-installation.rst delete mode 100644 docs/source/user_manual/kv_cache/index.rst delete mode 100644 docs/source/user_manual/lora/index.rst delete mode 100644 docs/source/user_manual/lora/manual.rst delete mode 100644 docs/source/user_manual/router/cmd.rst delete mode 100644 docs/source/user_manual/router/index.rst delete mode 100644 docs/source/user_manual/router/json.rst diff --git a/docs/source/benchmarks/multiround-qa.rst b/docs/source/benchmarks/multiround-qa.rst deleted file mode 100644 index 72dbdd58f..000000000 --- a/docs/source/benchmarks/multiround-qa.rst +++ /dev/null @@ -1,133 +0,0 @@ -.. _multiround-qa: - -Multi-round QA -============== - -Benchmarking vLLM Production Stack Performance with multi-round QA - -Overview --------- - -This repository contains benchmarking tools for evaluating vLLM Production Stack's performance (e.g., latency, throughput). The initial focus of this benchmark is on the multi-round QA (Question Answering) use case. The script ``multi-round-qa.py`` simulates multiple users interacting with a language model concurrently for multiple rounds, allowing you to analyze the serving engine's throughput and latency. - -The overall workflow of this script is shown below: - -.. figure:: ../assets/multi-round.png - :width: 75% - :align: center - :alt: multi-round-qa - :class: no-scaled-link - -Setup ------ - -Install the required packages needed to run the benchmark: - -.. code-block:: bash - - pip install -r requirements.txt - -Running benchmarks ------------------- - -To run the multi-round QA benchmark, use the following example command: - -.. code-block:: python - - python3 multi-round-qa.py \ - --num-users 10 \ - --num-rounds 5 \ - --qps 0.5 \ - --shared-system-prompt 1000 \ - --user-history-prompt 2000 \ - --answer-len 100 \ - --model meta-llama/Llama-3.1-8B-Instruct \ - --base-url http://localhost:30080/v1 - -Use ``Ctrl-C`` to terminate the benchmark at any time, and the script will write each request's detailed stats to ``summary.csv``. - -Note: The above command requires a serving engine with the ``meta-llama/Llama-3.1-8B-Instruct`` model served locally at ``http://localhost:30080/v1``. Here's an example command to launch the serving engine with vLLM Production Stack: - -.. code-block:: bash - - helm repo add vllm https://vllm-project.github.io/production-stack - helm install vllm vllm/vllm-stack -f model.yaml - -Then, perform port-forwarding with the following command: - -.. code-block:: bash - - kubectl port-forward svc/vllm-router-service 30080:80 - -Explanation of the arguments ----------------------------- - -Configuring the workload -~~~~~~~~~~~~~~~~~~~~~~~~~ - -- ``--num-users ``: The maximum number of concurrent users in the system (N in the above figure). -- ``--num-rounds ``: The number of rounds per user (M in the above figure). -- ``--qps ``: The overall queries per second (QPS) rate for the system. -- ``--shared-system-prompt ``: Length of the system prompt shared across all users (in tokens). -- ``--user-history-prompt ``: Length of the user-specific context (simulating existing chat history) (in tokens). -- ``--answer-len ``: Length of the answer expected (in tokens). -- ``--init-user-id ``: The initial user ID to start the benchmark (default = 0). This is useful when you want to resume the benchmark from a specific user ID or avoid serving engine caching the request from previous runs. -- ``--request-with-user-id``: If this option is present, the script will include the user ID in the request header. -- ``--sharegpt``: If this option is present, the script will use ShareGPT workload instead of dummy context. - -.. note:: - If you use the ShareGPT dataset, the length of the expected answer (in tokens) will be determined by the minimum value between the dataset response and ``--answer-len``. You also need to follow the instructions in **ShareGPT Datasets** first. - -Configuring the serving engine connection -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- ``--model ``: The model name (e.g., ``mistralai/Mistral-7B-Instruct-v0.2``). -- ``--base-url ``: The URL endpoint for the language model server. - -Configuring the experiment (Optional) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- ``--output ``: The CSV file to dump the detailed stats for each query (default = ``summary.csv``). -- ``--log-interval ``: Time between each performance summary log in seconds (default = 30). -- ``--time ``: Total time to run the experiment (default = forever). - -Processing previous outputs only (Optional) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -- ``--process-summary ``: If this option is present, the script will only process the existing output CSV and print out the summary without running any experiment. - -Benchmark Metrics ------------------ - -- **Queries Per Second (QPS)**: The average number of queries processed by the model per second. -- **Average Prompt Throughput**: Tokens generated in the prompt per second. -- **Average Generation Throughput**: Tokens generated as part of the response per second. -- **Average TTFT (Time to First Token)**: Average time taken for the model to generate the first token of a response. - -ShareGPT Datasets ------------------ - -1. Download and prepare the ShareGPT dataset. - You can specify the proportion of data to process by providing a number between 0 and 1 as an argument to the script. - - .. code-block:: bash - - bash prepare_sharegpt_data.sh 1 - - In this example, ``1`` indicates processing 100% of the dataset. You can adjust this value as needed. - -2. Run the benchmark. - Example: - - .. code-block:: python - - python3 multi-round-qa.py \ - --num-users 10 \ - --num-rounds 5 \ - --qps 0.3 \ - --shared-system-prompt 1000 \ - --user-history-prompt 2000 \ - --answer-len 100 \ - --model mistralai/Mistral-7B-Instruct-v0.2 \ - --base-url http://localhost:8000/v1 \ - --sharegpt diff --git a/docs/source/community/meetings.rst b/docs/source/community/meetings.rst new file mode 100644 index 000000000..ffebe1509 --- /dev/null +++ b/docs/source/community/meetings.rst @@ -0,0 +1,11 @@ +Community Meetings +================== + +Community Events +---------------- + +We host bi-weekly community meetings at the following timeslot: + +**Every other Tuesday at 5:30 PM PT starting July 29th** – `Add to Calendar `_ + +All are welcome to join! diff --git a/docs/source/deployment/cloud-deployment/aws.rst b/docs/source/deployment/cloud-deployment/aws.rst deleted file mode 100644 index 4f3f5a631..000000000 --- a/docs/source/deployment/cloud-deployment/aws.rst +++ /dev/null @@ -1,138 +0,0 @@ -.. _aws: - -Amazon Web Services -==================== - -Introduction ------------- -This script automatically configures an EKS LLM inference cluster. -Make sure your ``AWS CLI (v2)`` is installed, logged in, and the region is properly configured. -You must have the following dependencies installed: - -- ``awscli`` (Amazon Web Services CLI) -- ``eksctl`` (for managing Kubernetes clusters on AWS EKS) -- ``kubectl`` (Kubernetes command-line tool) -- ``helm`` (Kubernetes package manager) - -Additionally, ensure that the following ``AWS`` services are set up: - -- ``IAM`` roles and policies for ``EKS`` and ``EFS`` -- ``VPC`` with properly configured subnets -- ``EKS`` cluster networking and security groups - - -Steps to Follow ---------------- - -1. Deploy EKS vLLM Stack -~~~~~~~~~~~~~~~~~~~~~~~~ - -1.1 Modify the Configuration -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Before running the deployment, ensure that the configuration file ``production_stack_specification.yaml`` is properly set up. -You need to configure: - -- ``servingEngineSpec``: Define the model repository, resource requests, and storage settings. -- ``routerSpec``: Set up routing resource limits and requests. -- ``Persistent Storage``: If using ``AWS EFS``, ensure that the persistent volume configuration matches your storage needs. - -Modify these fields as needed to match your cluster requirements. - -1.2 Execute the Deployment Script -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Run the deployment script by replacing ``YOUR_AWSREGION`` and ``YAML_FILE_PATH`` with the actual values: - -.. code-block:: bash - - bash entry_point.sh YOUR_AWSREGION YAML_FILE_PATH - -After executing the script, Kubernetes will start deploying the ``vLLM`` inference stack. -You can monitor the status of the deployment. - - -2. Validate Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -2.1 Monitor Deployment Status -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To check whether the pods for ``vLLM`` deployment are up and running, use: - -.. code-block:: bash - - kubectl get pods - -Expected output: - -.. code-block:: bash - - NAME READY STATUS RESTARTS AGE - vllm-deployment-router-69b7f9748d-xrkvn 1/1 Running 0 75s - vllm-opt125m-deployment-vllm-696c998c6f-mvhg4 1/1 Running 0 75s - -.. note:: - - It may take some time for the pods to reach the ``Running`` state, depending on cluster setup and image download speed. - -2.2 Inspect Pod Logs -^^^^^^^^^^^^^^^^^^^^ -If a pod is not transitioning to ``Running``, use the following command to inspect logs: - -.. code-block:: bash - - kubectl logs -f - -To get more detailed information about the pod, run: - -.. code-block:: bash - - kubectl describe pod - - -3. Persistent Storage Considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If using ``AWS EFS`` for storage, keep in mind: - -- ``EFS`` must be created within the same ``VPC`` as the ``EKS`` cluster. -- The security group for ``EFS`` must allow ``NFS`` traffic (``port 2049``) from the ``EKS`` node group. -- The ``EFS`` storage should be properly mounted as a PersistentVolume for long-term model storage. -- Ensure that the ``IAM`` policies are correctly set up to allow ``EFS CSI`` driver operation. - -If you need to manually delete ``EFS`` resources, you can do so via the ``AWS Console`` or using ``AWS CLI`` commands. - - -4. Uninstall -~~~~~~~~~~~~ - -To remove the deployed ``vLLM`` stack and clean up resources, run: - -.. code-block:: bash - - bash clean_up.sh production-stack YOUR_AWSREGION - -You may also need to manually delete the VPC and clean up the CloudFormation stack in the AWS Console if they were created as part of the deployment. - - -5. Troubleshooting -~~~~~~~~~~~~~~~~~~~ - -If you encounter issues, refer to the following solutions: - -- **Pods stuck in** ``Pending`` **state:** Check available resources and ensure that the cluster has enough nodes: - - .. code-block:: bash - - kubectl describe nodes - -- **Pods in** ``CrashLoopBackOff`` **state:** Inspect logs to find the issue: - - .. code-block:: bash - - kubectl logs - -- **Cannot connect to** ``EKS`` **cluster:** Ensure that your ``AWS CLI`` is properly configured: - - .. code-block:: bash - - aws eks update-kubeconfig --name production-stack --region - -Following these steps should help ensure a successful deployment. diff --git a/docs/source/deployment/cloud-deployment/azure.rst b/docs/source/deployment/cloud-deployment/azure.rst deleted file mode 100644 index 03b32bd65..000000000 --- a/docs/source/deployment/cloud-deployment/azure.rst +++ /dev/null @@ -1,137 +0,0 @@ -.. _azure: - -Azure Kubernetes Service -======================== - -Introduction ------------- -This script automatically configures an AKS LLM inference cluster. -Make sure your ``Azure CLI`` is installed, logged in, and the region is properly configured. -You must have the following dependencies installed: - -- ``az`` (Azure Command-Line Interface) -- ``kubectl`` (Kubernetes command-line tool) -- ``helm`` (Kubernetes package manager) - -Additionally, ensure that the following ``Azure`` services are set up: - -- ``Resource Groups`` for managing resources -- ``AKS`` cluster with proper networking configuration -- ``Azure Files`` or ``Azure Managed Disks`` for persistent storage - - -Steps to Follow ---------------- - -1. Deploy AKS vLLM Stack -~~~~~~~~~~~~~~~~~~~~~~~~ - -1.1 Modify the Configuration -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Before running the deployment, ensure that the configuration file ``production_stack_specification.yaml`` is properly set up. -You need to configure: - -- ``servingEngineSpec``: Define the model repository, resource requests, and storage settings. -- ``routerSpec``: Set up routing resource limits and requests. -- ``Persistent Storage``: If using ``Azure Files``, ensure that the persistent volume configuration matches your storage needs. - -Modify these fields as needed to match your cluster requirements. - -1.2 Execute the Deployment Script -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Run the deployment script by replacing ``RESOURCE_GROUP`` and ``YAML_FILE_PATH`` with the actual values: - -.. code-block:: bash - - bash entry_point.sh setup RESOURCE_GROUP YAML_FILE_PATH - -After executing the script, Kubernetes will start deploying the ``vLLM`` inference stack. -You can monitor the status of the deployment. - - -2. Validate Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -2.1 Monitor Deployment Status -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To check whether the pods for ``vLLM`` deployment are up and running, use: - -.. code-block:: bash - - kubectl get pods - -Expected output: - -.. code-block:: bash - - NAME READY STATUS RESTARTS AGE - vllm-deployment-router-69b7f9748d-xrkvn 1/1 Running 0 75s - vllm-opt125m-deployment-vllm-696c998c6f-mvhg4 1/1 Running 0 75s - -.. note:: - - It may take some time for the pods to reach the ``Running`` state, depending on cluster setup and image download speed. - -2.2 Inspect Pod Logs -^^^^^^^^^^^^^^^^^^^^ -If a pod is not transitioning to ``Running``, use the following command to inspect logs: - -.. code-block:: bash - - kubectl logs -f - -To get more detailed information about the pod, run: - -.. code-block:: bash - - kubectl describe pod - - -3. Persistent Storage Considerations -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -If using ``Azure Files`` or ``Azure Managed Disks`` for storage, keep in mind: - -- ``Azure Files`` must be mounted to the ``AKS`` cluster as a persistent volume. -- The storage account must be in the same region as the ``AKS`` cluster. -- The ``AKS`` node pool should have the appropriate permissions to access ``Azure Files``. -- Ensure that the ``RBAC`` policies are correctly set up for ``Azure CSI`` driver operation. - -If you need to manually delete storage resources, you can do so via the ``Azure Portal`` or using ``Azure CLI`` commands. - - -4. Uninstall -~~~~~~~~~~~~ - -To remove the deployed ``vLLM`` stack and clean up resources, run: - -.. code-block:: bash - - bash entry_point.sh cleanup RESOURCE_GROUP - -You may also need to manually delete the resource group and clean up any remaining resources via the Azure Portal. - - -5. Troubleshooting -~~~~~~~~~~~~~~~~~~~ - -If you encounter issues, refer to the following solutions: - -- **Pods stuck in** ``Pending`` **state:** Check available resources and ensure that the cluster has enough nodes: - - .. code-block:: bash - - kubectl describe nodes - -- **Pods in** ``CrashLoopBackOff`` **state:** Inspect logs to find the issue: - - .. code-block:: bash - - kubectl logs - -- **Cannot connect to** ``AKS`` **cluster:** Ensure that your ``Azure CLI`` is properly configured: - - .. code-block:: bash - - az aks get-credentials --resource-group --name - -Following these steps should help ensure a successful deployment. diff --git a/docs/source/deployment/cloud-deployment/gcp.rst b/docs/source/deployment/cloud-deployment/gcp.rst deleted file mode 100644 index c9325a73e..000000000 --- a/docs/source/deployment/cloud-deployment/gcp.rst +++ /dev/null @@ -1,132 +0,0 @@ -.. _gcp: - -Google Cloud Platform -===================== - -Introduction ------------- -This script automatically configures a ``GKE LLM`` inference cluster. -Make sure your ``GCP CLI`` is set up, logged in, and the region is properly configured. -You must have the following dependencies installed: - -- ``gcloud`` (Google Cloud CLI) -- ``kubectl`` (Kubernetes command-line tool) -- ``helm`` (Kubernetes package manager) - -Ensure that all the required tools are installed before proceeding. -Additionally, ensure that the following ``GCP`` ``APIs`` are enabled: - -- ``Kubernetes Engine API`` -- ``Cloud Resource Manager API`` -- ``IAM API`` -- ``Compute Engine API`` - -To enable these ``APIs``, run: - -.. code-block:: bash - - gcloud services enable container.googleapis.com cloudresourcemanager.googleapis.com iam.googleapis.com compute.googleapis.com - - -Steps to Follow ---------------- - -1. Deploy GKE vLLM Stack -~~~~~~~~~~~~~~~~~~~~~~~~ - -1.1 Modify the Configuration -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Before running the deployment, ensure that the configuration file ``production_stack_specification.yaml`` is properly set up. -You need to configure: - -- ``servingEngineSpec``: Define the model repository, resource requests, and storage settings. -- ``routerSpec``: Set up routing resource limits and requests. - -Modify these fields as needed to match your cluster requirements. - -1.2 Execute the Deployment Script -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Run the deployment script by replacing ``YAML_FILE_PATH`` with the actual configuration file path: - -.. code-block:: bash - - bash entry_point_basic.sh YAML_FILE_PATH - -After executing the script, ``Kubernetes`` will start deploying the ``vLLM`` inference stack. -You can monitor the status of the deployment. - - -2. Validate Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -2.1 Monitor Deployment Status -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -To check whether the pods for ``vLLM`` deployment are up and running, use: - -.. code-block:: bash - - kubectl get pods - -Expected output: - -.. code-block:: bash - - NAME READY STATUS RESTARTS AGE - vllm-deployment-router-6786bdcc5b-flj2x 1/1 Running 0 54s - vllm-llama3-deployment-vllm-7dd564bc8f-7mf5x 1/1 Running 0 54s - -.. note:: - - It may take some time for the pods to reach the ``Running`` state, depending on cluster setup and image download speed. - -2.2 Inspect Pod Logs -^^^^^^^^^^^^^^^^^^^^ -If a pod is not transitioning to ``Running``, use the following command to inspect logs: - -.. code-block:: bash - - kubectl logs -f - -To get more detailed information about the pod, run: - -.. code-block:: bash - - kubectl describe pod - - -3. Uninstall -~~~~~~~~~~~~ - -To remove the deployed ``vLLM`` stack and clean up resources, run: - -.. code-block:: bash - - bash clean_up_basic.sh production-stack - -This command will remove all ``Kubernetes`` resources associated with the ``vLLM`` deployment. - - -4. Troubleshooting -~~~~~~~~~~~~~~~~~~~ - -If you encounter issues, refer to the following solutions: - -- **Pods stuck in** ``Pending`` **state:** Check available resources and ensure that the cluster has enough nodes: - - .. code-block:: bash - - kubectl describe nodes - -- **Pods in** ``CrashLoopBackOff`` **state:** Inspect logs to find the issue: - - .. code-block:: bash - - kubectl logs - -- **Cannot connect to** ``GKE`` **cluster: Ensure that your** ``gcloud`` **CLI is properly configured:** - - .. code-block:: bash - - gcloud container clusters get-credentials vllm-gke-cluster --region - -Following these steps should help ensure a successful deployment. diff --git a/docs/source/deployment/cloud-deployment/index.rst b/docs/source/deployment/cloud-deployment/index.rst deleted file mode 100644 index d462a39c6..000000000 --- a/docs/source/deployment/cloud-deployment/index.rst +++ /dev/null @@ -1,15 +0,0 @@ -.. _index: - - -Cloud Environments -============================================== - -📈 Easily deploy the stack on AWS, GCP, Azure or any other cloud provider - -.. toctree:: - :maxdepth: 1 - :caption: Deployment - - aws.rst - gcp.rst - azure.rst diff --git a/docs/source/deployment/gateway-inference-extension.rst b/docs/source/deployment/gateway-inference-extension.rst new file mode 100644 index 000000000..f281c8d95 --- /dev/null +++ b/docs/source/deployment/gateway-inference-extension.rst @@ -0,0 +1,286 @@ +Gateway Inference Extension +=========================== + +This tutorial guides you through setting up and using the Gateway Inference Extension in a production environment. The extension enables inference capabilities through the gateway, supporting both individual inference models and inference pools. + +Prerequisites +------------- + +Before starting this tutorial, ensure you have: + +- A Kubernetes cluster with GPU nodes available +- ``kubectl`` configured to access your cluster +- ``helm`` installed +- A Hugging Face account with API token +- Basic understanding of Kubernetes concepts + +Overview +-------- + +The Gateway Inference Extension provides: + +- **Individual Model Inference**: Direct access to specific models +- **Inference Pools**: Load-balanced access to multiple model instances +- **Gateway API Integration**: Standard Kubernetes Gateway API for routing +- **vLLM Integration**: High-performance inference engine support + +Step 1: Environment Setup +------------------------- + +1.1 Create Hugging Face Token Secret +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First, create a Kubernetes secret with your Hugging Face token: + +.. code-block:: bash + + # Replace with your actual Hugging Face token + kubectl create secret generic hf-token --from-literal=token= + +1.2 Install Gateway API CRDs +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Install the required Custom Resource Definitions (CRDs): + +.. code-block:: bash + + # Install KGateway CRDs + KGTW_VERSION=v2.0.2 + helm upgrade -i --create-namespace --namespace kgateway-system --version $KGTW_VERSION kgateway-crds oci://cr.kgateway.dev/kgateway-dev/charts/kgateway-crds + + # Install Gateway API CRDs + kubectl apply -f https://github.com/kubernetes-sigs/gateway-api/releases/download/v1.3.0/standard-install.yaml + + # Install Gateway API inference extension CRDs + VERSION=v0.3.0 + kubectl apply -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/$VERSION/manifests.yaml + +1.3 Install KGateway with Inference Extension +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Install KGateway with inference extension enabled + helm upgrade -i --namespace kgateway-system --version $KGTW_VERSION kgateway oci://cr.kgateway.dev/kgateway-dev/charts/kgateway --set inferenceExtension.enabled=true + +Step 2: Deploy vLLM Models +-------------------------- + +2.1 Understanding vLLM Runtime +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The vLLM Runtime is a custom resource that manages model deployments. Please check ``configs/vllm/gpu-deployment.yaml`` for an example config. + +2.2 Apply vLLM Deployment +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Apply the vLLM deployment configuration + kubectl apply -f configs/vllm/gpu-deployment.yaml + +**Production Considerations**: + +- Adjust resource requests/limits based on your model size and GPU capacity +- Consider using multiple replicas for high availability +- Monitor GPU utilization and adjust accordingly + +Step 3: Configure Inference Resources +------------------------------------- + +3.1 Individual Model Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Create an InferenceModel resource for direct model access: + +.. code-block:: yaml + + apiVersion: inference.networking.x-k8s.io/v1alpha2 + kind: InferenceModel + metadata: + name: legogpt + spec: + modelName: legogpt + criticality: Standard + poolRef: + name: vllm-llama3-1b-instruct + targetModels: + - name: legogpt + weight: 100 + +3.2 Inference Pool Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +For routing to multiple model instances, check ``configs/inferencepool-resources.yaml`` for example. + +3.3 Apply Inference Resources +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Apply individual model configuration + kubectl apply -f configs/inferencemodel.yaml + + # Apply inference pool configuration + kubectl apply -f configs/inferencepool-resources.yaml + +Step 4: Configure Gateway Routing +--------------------------------- + +4.1 Gateway Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~ + +The gateway acts as the entry point for inference requests: + +.. code-block:: yaml + + apiVersion: gateway.networking.k8s.io/v1 + kind: Gateway + metadata: + name: inference-gateway + spec: + gatewayClassName: kgateway + listeners: + - name: http + port: 80 + protocol: HTTP + +4.2 HTTPRoute Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +HTTPRoute defines how requests are routed to inference resources: + +.. code-block:: yaml + + apiVersion: gateway.networking.k8s.io/v1 + kind: HTTPRoute + metadata: + name: llm-route + spec: + parentRefs: + - group: gateway.networking.k8s.io + kind: Gateway + name: inference-gateway + rules: + - backendRefs: + - group: inference.networking.x-k8s.io + kind: InferencePool + name: vllm-llama3-1b-instruct + matches: + - path: + type: PathPrefix + value: / + +4.3 Apply Gateway Resources +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Apply gateway configuration + kubectl apply -f configs/gateway/kgateway/gateway.yaml + + # Apply HTTP route configuration + kubectl apply -f configs/httproute.yaml + +Step 5: Testing the Setup +------------------------- + +5.1 Get Gateway IP Address +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Get the external IP of the gateway + IP=$(kubectl get gateway/inference-gateway -o jsonpath='{.status.addresses[0].value}') + PORT=80 + + echo "Gateway IP: $IP" + echo "Gateway Port: $PORT" + +5.2 Send Test Inference Request +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Test with a simple completion request + curl -i http://${IP}:${PORT}/v1/completions \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "legogpt", + "prompt": "Write as if you were a critic: San Francisco", + "max_tokens": 100, + "temperature": 0.5 + }' + +5.3 Test Chat Completion +~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Test chat completion endpoint + curl -i http://${IP}:${PORT}/v1/chat/completions \ + -H 'Content-Type: application/json' \ + -d '{ + "model": "legogpt", + "messages": [ + {"role": "user", "content": "Hello, how are you?"} + ], + "max_tokens": 50, + "temperature": 0.7 + }' + +Step 6: Monitoring and Troubleshooting +-------------------------------------- + +6.1 Check Resource Status +~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Check vLLM runtime status + kubectl get vllmruntime + + # Check inference model status + kubectl get inferencemodel + + # Check inference pool status + kubectl get inferencepool + + # Check gateway status + kubectl get gateway + +6.2 View Logs +~~~~~~~~~~~~~ + +.. code-block:: bash + + # Get vLLM runtime logs + kubectl logs -l app=vllm-runtime + + # Get gateway logs + kubectl logs -n kgateway-system -l app=kgateway + +Step 7: Uninstall +----------------- + +To uninstall all the resources installed on the cluster, run the following: + +.. code-block:: bash + + # Delete the inference extension + kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/raw/main/config/manifests/gateway/kgateway/gateway.yaml --ignore-not-found=true + + # Delete the inference model and pool resources + kubectl delete -f configs/inferencemodel.yaml --ignore-not-found=true + kubectl delete -f configs/inferencepool-resources.yaml --ignore-not-found=true + + # Delete the VLLM deployment + kubectl delete -f configs/vllm/gpu-deployment.yaml --ignore-not-found=true + kubectl delete -f https://github.com/kubernetes-sigs/gateway-api-inference-extension/releases/download/v0.3.0/manifests.yaml --ignore-not-found=true + + # Delete helm releases + helm uninstall kgateway -n kgateway-system + helm uninstall kgateway-crds -n kgateway-system + + # Delete the namespace last to ensure all resources are removed + kubectl delete ns kgateway-system --ignore-not-found=true diff --git a/docs/source/deployment/helm.rst b/docs/source/deployment/helm.rst index 7184bfa20..10aa02376 100644 --- a/docs/source/deployment/helm.rst +++ b/docs/source/deployment/helm.rst @@ -1,55 +1,194 @@ -.. _helm_charts: +Helm Chart Deployment +===================== -Helm Charts -======================================= +This tutorial guides you through the basic configurations required to deploy a vLLM serving engine in a Kubernetes environment with GPU support. You will learn how to specify the model details, set up necessary environment variables (like ``HF_TOKEN``), and launch the vLLM serving engine. -Production Stack uses Helm charts for deployment. It lets users deploy multiple serving engines and a router into the Kubernetes cluster. +Table of Contents +----------------- -Key features ------------- - -- Support running multiple serving engines with multiple different models -- Load the model weights directly from the existing PersistentVolumes +1. Prerequisites_ +2. `Step 1: Preparing the Configuration File`_ +3. `Step 2: Applying the Configuration`_ +4. `Step 3: Verifying the Deployment`_ +5. `Step 4 (Optional): Multi-GPU Deployment`_ Prerequisites ------------- -1. A running Kubernetes cluster with GPU. (You can set it up through `minikube `_) -2. `Helm `_ installed. +- A Kubernetes environment with GPU support, as set up in the :doc:`../getting_started/prerequisite` tutorial. +- Helm installed on your system. +- Access to a HuggingFace token (``HF_TOKEN``). + +Step 1: Preparing the Configuration File +---------------------------------------- + +1. Locate the example configuration file `tutorials/assets/values-02-basic-config.yaml `_. +2. Open the file and update the following fields: + - Write your actual huggingface token in ``hf_token: `` in the yaml file. + +Explanation of Key Items in ``values-02-basic-config.yaml`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **name**: The unique identifier for your model deployment. +- **repository**: The Docker repository containing the model's serving engine image. +- **tag**: Specifies the version of the model image to use. +- **modelURL**: The URL pointing to the model on Hugging Face or another hosting service. +- **replicaCount**: The number of replicas for the deployment, allowing scaling for load. +- **requestCPU**: The amount of CPU resources requested per replica. +- **requestMemory**: Memory allocation for the deployment; sufficient memory is required to load the model. +- **requestGPU**: Specifies the number of GPUs to allocate for the deployment. +- **pvcStorage**: Defines the Persistent Volume Claim size for model storage. +- **vllmConfig**: Contains model-specific configurations: + + - ``enableChunkedPrefill``: Optimizes performance by prefetching model chunks. + - ``enablePrefixCaching``: Speeds up response times for common prefixes in queries. + - ``maxModelLen``: The maximum sequence length the model can handle. + - ``dtype``: Data type for computations, e.g., ``bfloat16`` for faster performance on modern GPUs. + - ``extraArgs``: Additional arguments passed to the vLLM engine for fine-tuning behavior. + +- **hf_token**: The Hugging Face token for authenticating with the Hugging Face model hub. +- **env**: Extra environment variables to pass to the model-serving engine. + +Example Snippet +~~~~~~~~~~~~~~~ + +.. code-block:: yaml + + servingEngineSpec: + modelSpec: + - name: "llama3" + repository: "vllm/vllm-openai" + tag: "latest" + modelURL: "meta-llama/Llama-3.1-8B-Instruct" + replicaCount: 1 + + requestCPU: 10 + requestMemory: "16Gi" + requestGPU: 1 + + pvcStorage: "50Gi" + + vllmConfig: + enableChunkedPrefill: false + enablePrefixCaching: false + maxModelLen: 16384 + dtype: "bfloat16" + extraArgs: ["--disable-log-requests", "--gpu-memory-utilization", "0.8"] + + hf_token: + +Step 2: Applying the Configuration +---------------------------------- + +Deploy the configuration using Helm: + +.. code-block:: bash + + helm repo add vllm https://vllm-project.github.io/production-stack + helm install vllm vllm/vllm-stack -f tutorials/assets/values-02-basic-config.yaml + +Expected output: + +You should see output indicating the successful deployment of the Helm chart: + +.. code-block:: text + + Release "vllm" has been deployed. Happy Helming! + NAME: vllm + LAST DEPLOYED: + NAMESPACE: default + STATUS: deployed + REVISION: 1 + +Step 3: Verifying the Deployment +--------------------------------- + +1. Check the status of the pods: + + .. code-block:: bash + + kubectl get pods + + Expected output: + + You should see the following pods: + + .. code-block:: text + + NAME READY STATUS RESTARTS AGE + pod/vllm-deployment-router-xxxx-xxxx 1/1 Running 0 3m23s + vllm-llama3-deployment-vllm-xxxx-xxxx 1/1 Running 0 3m23s + + - The ``vllm-deployment-router`` pod acts as the router, managing requests and routing them to the appropriate model-serving pod. + - The ``vllm-llama3-deployment-vllm`` pod serves the actual model for inference. + +2. Verify the service is exposed correctly: + + .. code-block:: bash + + kubectl get services + + Expected output: + + Ensure there are services for both the serving engine and the router: + + .. code-block:: text + + NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE + vllm-engine-service ClusterIP 10.103.98.170 80/TCP 4m + vllm-router-service ClusterIP 10.103.110.107 80/TCP 4m + + - The ``vllm-engine-service`` exposes the serving engine. + - The ``vllm-router-service`` handles routing and load balancing across model-serving pods. + +3. Test the health endpoint: + + .. code-block:: bash + + curl http:///health + + Replace ```` with the external IP of the service. If everything is configured correctly, you will get: + + .. code-block:: text + + {"status":"healthy"} -Values for Helm charts are found in a `values.yaml `_ file. +Please refer to Step 3 in the :doc:`../getting_started/quickstart` tutorial for querying the deployed vLLM service. -To configure the file automatically, you can use the a json file as a schema called `values.schema.json `_. +Step 4 (Optional): Multi-GPU Deployment +--------------------------------------- -Example ``values.yaml`` file -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +So far, you have configured and deployed a vLLM serving engine with a single GPU. You may also deploy a serving engine on multiple GPUs with the following example configuration snippet: .. code-block:: yaml - servingEngineSpec: - modelSpec: - - name: "opt125m" - repository: "lmcache/vllm-openai" - tag: "latest" - modelURL: "facebook/opt-125m" - - replicaCount: 1 - - requestCPU: 6 - requestMemory: "16Gi" - requestGPU: 1 - - pvcStorage: "10Gi" - -Explanation of the fields -------------------------- - -- ``name``: The name of the model. -- ``repository``: The repository of the model to download the weights. -- ``tag``: The tag of the model to download the weights. -- ``modelURL``: The model URL to download the weights. -- ``replicaCount``: The number of replicas to run. -- ``requestCPU``: The CPU request for the serving engine. -- ``requestMemory``: The memory request for the serving engine. -- ``requestGPU``: The GPU request for the serving engine. -- ``pvcStorage``: The storage request for the serving engine. + servingEngineSpec: + runtimeClassName: "" + modelSpec: + - name: "llama3" + repository: "vllm/vllm-openai" + tag: "latest" + modelURL: "meta-llama/Llama-3.1-8B-Instruct" + replicaCount: 1 + requestCPU: 10 + requestMemory: "16Gi" + requestGPU: 2 + pvcStorage: "50Gi" + pvcAccessMode: + - ReadWriteOnce + vllmConfig: + enableChunkedPrefill: false + enablePrefixCaching: false + maxModelLen: 4096 + tensorParallelSize: 2 + dtype: "bfloat16" + extraArgs: ["--disable-log-requests", "--gpu-memory-utilization", "0.8"] + hf_token: + shmSize: "20Gi" + +Note that only tensor parallelism is supported for now. The field ``shmSize`` has to be configured if you are requesting ``requestGPU`` to be more than one, to enable appropriate shared memory across multiple processes used to run tensor parallelism. + +Conclusion +---------- + +In this tutorial, you configured and deployed a vLLM serving engine with GPU support (both on a single GPU or multiple GPUs) in a Kubernetes environment. You also learned how to verify its deployment and ensure it is running as expected. For further customization, refer to the ``values.yaml`` file and Helm chart documentation. diff --git a/docs/source/deployment/index.rst b/docs/source/deployment/index.rst new file mode 100644 index 000000000..c55fda9d5 --- /dev/null +++ b/docs/source/deployment/index.rst @@ -0,0 +1,25 @@ +Deployment Overview +=================== + +The vLLM Production Stack provides three primary deployment options to suit different use cases and infrastructure requirements. Each option offers unique capabilities and advantages: + +Deployment Options +------------------ + +.. toctree:: + :maxdepth: 1 + + helm + crd + gateway-inference-extension + +**Helm Chart Deployment** + The standard deployment method using Helm charts for Kubernetes. This provides a streamlined way to deploy vLLM with configurable parameters for models, resources, and routing logic. + +**Custom Resource Definitions (CRD)** + Deploy using Kubernetes CRDs for more advanced configurations and operator-based management. This option provides greater flexibility and integration with Kubernetes-native workflows. + +**Gateway Inference Extension** + Advanced deployment option that enables inference capabilities through gateway infrastructure, supporting both individual inference models and inference pools with sophisticated routing capabilities. + +Choose the deployment option that best fits your infrastructure requirements and use case complexity. diff --git a/docs/source/deployment/ray/ray.rst b/docs/source/deployment/ray/ray.rst deleted file mode 100644 index 78a09d381..000000000 --- a/docs/source/deployment/ray/ray.rst +++ /dev/null @@ -1,6 +0,0 @@ -.. _ray_deploy: - -Ray Deployment -======================================= - -Coming soon! diff --git a/docs/source/dev_guide/dev_api/engine-stats.rst b/docs/source/dev_guide/dev_api/engine-stats.rst deleted file mode 100644 index e61b8f13d..000000000 --- a/docs/source/dev_guide/dev_api/engine-stats.rst +++ /dev/null @@ -1,10 +0,0 @@ -.. _engine-stats: - -Engine Stats -============ - -.. autoclass:: vllm_router.stats.engine_stats.EngineStats - -.. autoclass:: vllm_router.stats.engine_stats.EngineStatsScraper - :members: - :show-inheritance: diff --git a/docs/source/dev_guide/dev_api/index.rst b/docs/source/dev_guide/dev_api/index.rst deleted file mode 100644 index 53e29e078..000000000 --- a/docs/source/dev_guide/dev_api/index.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _dev_api_index: - -Developer API -================ - -.. toctree:: - :maxdepth: 1 - :caption: Developer Guide - - router-logic.rst - engine-stats.rst - request-stats.rst - service-discovery.rst diff --git a/docs/source/dev_guide/dev_api/request-stats.rst b/docs/source/dev_guide/dev_api/request-stats.rst deleted file mode 100644 index 0f5632e5e..000000000 --- a/docs/source/dev_guide/dev_api/request-stats.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _request_stats: - -Request Stats -============= - -.. autoclass:: vllm_router.stats.request_stats.RequestStats - :members: - :show-inheritance: - -.. autoclass:: vllm_router.stats.request_stats.RequestStatsMonitor - :members: - :show-inheritance: diff --git a/docs/source/dev_guide/dev_api/router-logic.rst b/docs/source/dev_guide/dev_api/router-logic.rst deleted file mode 100644 index 62feffcbc..000000000 --- a/docs/source/dev_guide/dev_api/router-logic.rst +++ /dev/null @@ -1,16 +0,0 @@ -.. _router-logic: - -Router Logic -============ - -.. autoclass:: vllm_router.routers.routing_logic.SessionRouter - :members: - :show-inheritance: - -.. autoclass:: vllm_router.routers.routing_logic.RoundRobinRouter - :members: - :show-inheritance: - -.. autoclass:: vllm_router.routers.routing_logic.KvawareRouter - :members: - :show-inheritance: diff --git a/docs/source/dev_guide/dev_api/service-discovery.rst b/docs/source/dev_guide/dev_api/service-discovery.rst deleted file mode 100644 index 2866f1b36..000000000 --- a/docs/source/dev_guide/dev_api/service-discovery.rst +++ /dev/null @@ -1,37 +0,0 @@ -.. _service-discovery: - -Service Discovery -================= - -Batch Service -++++++++++++++ - -.. autoclass:: vllm_router.services.batch_service.batch.BatchStatus - :members: - :show-inheritance: - -.. autoclass:: vllm_router.services.batch_service.batch.BatchEndpoint - :members: - :show-inheritance: - -.. autoclass:: vllm_router.services.batch_service.batch.BatchRequest - :members: - :show-inheritance: - -.. autoclass:: vllm_router.services.batch_service.batch.BatchInfo - :members: - :show-inheritance: - -File Service -++++++++++++ - -.. autoclass:: vllm_router.services.files_service.file_storage.FileStorage - :members: - :show-inheritance: - -Request Service -+++++++++++++++ - -.. autofunction:: vllm_router.services.request_service.request.process_request - -.. autofunction:: vllm_router.services.request_service.request.route_general_request diff --git a/docs/source/dev_guide/peripheral/index.rst b/docs/source/dev_guide/peripheral/index.rst deleted file mode 100644 index ea4aed753..000000000 --- a/docs/source/dev_guide/peripheral/index.rst +++ /dev/null @@ -1,11 +0,0 @@ -.. _peripheral_index: - -Peripheral -================ - -.. toctree:: - :maxdepth: 1 - :caption: Developer Guide - - models.rst - interfaces.rst diff --git a/docs/source/dev_guide/peripheral/interfaces.rst b/docs/source/dev_guide/peripheral/interfaces.rst deleted file mode 100644 index 19b9e974c..000000000 --- a/docs/source/dev_guide/peripheral/interfaces.rst +++ /dev/null @@ -1,89 +0,0 @@ -.. _dev_interfaces: - -Interfaces -================ - -Adding a new router using ``RoutingInterface`` ----------------------------------------------- - -The following class provides the structure of the ``RoutingInterface``. This strategy cycles through the available endpoints in order. - -.. code-block:: python - - class RoutingInterface(metaclass=SingletonABCMeta): - @abc.abstractmethod - def route_request( - self, - endpoints: List[EndpointInfo], - engine_stats: Dict[str, EngineStats], - request_stats: Dict[str, RequestStats], - request: Request, - ) -> str: - """ - Route the request to the appropriate engine URL - - Args: - endpoints (List[EndpointInfo]): The list of engine URLs - engine_stats (Dict[str, EngineStats]): The engine stats indicating - the 'physical' load of each engine - request_stats (Dict[str, RequestStats]): The request stats - indicating the request-level performance of each engine - request (Request): The incoming request - """ - raise NotImplementedError() - -Implementing a Custom Router ----------------------------- - -To add a new routing strategy, follow these steps: - -1. Create a new class that inherits from ``RoutingInterface``. -2. Implement the ``route_request`` method to define custom routing logic. -3. Ensure the new class maintains the expected method signature. - -Example: Least Connections Router -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Below is an example implementation of a ``LeastConnectionsRouter`` that routes requests to the endpoint with the fewest active connections. - -.. code-block:: python - - class LeastConnectionsRouter(RoutingInterface): - def __init__(self): - if hasattr(self, "_initialized"): - return - self._initialized = True - - def route_request( - self, - endpoints: List[EndpointInfo], - engine_stats: Dict[str, EngineStats], - request_stats: Dict[str, RequestStats], - request: Request, - ) -> str: - """ - Route the request to the engine with the least active connections. - - Args: - endpoints (List[EndpointInfo]): The list of engine URLs. - engine_stats (Dict[str, EngineStats]): The engine stats indicating - the 'physical' load of each engine. - request_stats (Dict[str, RequestStats]): The request stats - indicating the request-level performance of each engine. - request (Request): The incoming request. - """ - chosen = min(endpoints, key=lambda e: engine_stats[e.url].active_connections) - return chosen.url - -Usage -~~~~~ - -To use a custom router, instantiate it and call ``route_request`` with the required parameters: - -.. code-block:: python - - router = LeastConnectionsRouter() - selected_url = router.route_request(endpoints, engine_stats, request_stats, request) - print(f"Routing to: {selected_url}") - -By following this approach, you can extend the routing system with custom logic tailored to different workload distribution strategies. diff --git a/docs/source/dev_guide/peripheral/models.rst b/docs/source/dev_guide/peripheral/models.rst deleted file mode 100644 index ddae402ac..000000000 --- a/docs/source/dev_guide/peripheral/models.rst +++ /dev/null @@ -1,128 +0,0 @@ -.. _observability: - -Observability Models -==================== - -The current ``production-stack`` uses a combination of Prometheus and Grafana to provide observability for the stack. - -.. figure:: ../../assets/graf.png - :width: 110% - :height: 300pt - :align: center - :alt: grafana - :class: no-scaled-link - -Deploy the observability stack ------------------------------- - -The stack is based on `kube-prom-stack `_. - -To launch the observability stack you need - - * A running Kubernetes (K8s) environment with GPUs - * Please follow the steps in the :ref:`examples` section. - -After that you can run: - -.. code-block:: bash - - cd production-stack/observability - sudo bash install.sh - - -After installing, the dashboard can be accessed through the service ``service/kube-prom-stack-grafana`` in the ``monitoring`` namespace. - -Access the Grafana & Prometheus dashboard ------------------------------------------ - -To access the Grafana dashboard, you need to port-forward the Grafana service to your local machine. - -Forward the Grafana dashboard port to the local node-port - -.. code-block:: bash - - sudo kubectl --namespace monitoring port-forward svc/kube-prom-stack-grafana 3000:80 --address 0.0.0.0 - - -Forward the Prometheus dashboard - -.. code-block:: bash - - sudo kubectl --namespace monitoring port-forward prometheus-kube-prom-stack-kube-prome-prometheus-0 9090:9090 - - -Open the webpage at ``http://:3000`` to access the Grafana web page. The default user name is admin and the password can be configured in ``values.yaml`` (default is ``prom-operator``). - -Import the dashboard using the ``vllm-dashboard.json`` in this folder. - - -Use Prometheus Adapter to export vLLM metrics ---------------------------------------------- - -The vLLM router can export metrics to Prometheus using the `Prometheus Adapter `_. When running the ``install.sh script``, the Prometheus Adapter will be installed and configured to export the vLLM metrics. - -We provide a minimal example of how to use the Prometheus Adapter to export vLLM metrics. See `prom-adapter.yaml `_ for more details. - -The exported metrics can be used for different purposes, such as horizontal scaling of the vLLM deployments. - -To verify the metrics are being exported, you can use the following command: - -.. code-block:: bash - - kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1/namespaces/default/metrics | jq | grep vllm_num_requests_waiting -C 10 - - -You should see the following output: - -.. code-block:: bash - - { - "name": "namespaces/vllm_num_requests_waiting", - "singularName": "", - "namespaced": false, - "kind": "MetricValueList", - "verbs": [ - "get" - ] - } - - -The following command will show the current value of the metric: - -.. code-block:: bash - - kubectl get --raw /apis/custom.metrics.k8s.io/v1beta1/namespaces/default/metrics/vllm_num_requests_waiting | jq - -You should see the following output: - -.. code-block:: bash - - { - "kind": "MetricValueList", - "apiVersion": "custom.metrics.k8s.io/v1beta1", - "metadata": {}, - "items": [ - { - "describedObject": { - "kind": "Namespace", - "name": "default", - "apiVersion": "/v1" - }, - "metricName": "vllm_num_requests_waiting", - "timestamp": "2025-03-02T01:56:01Z", - "value": "0", - "selector": null - } - ] - } - - -Uninstall the observability stack ---------------------------------- - -To uninstall the observability stack, you can run: - -.. code-block:: bash - - cd production-stack/observability - sudo bash uninstall.sh diff --git a/docs/source/developer_guide/contributing.rst b/docs/source/developer_guide/contributing.rst new file mode 100644 index 000000000..38844a585 --- /dev/null +++ b/docs/source/developer_guide/contributing.rst @@ -0,0 +1,98 @@ +Contributing +============ + +Thank you for your contribution to production-stack! As a potential contributor, your changes and ideas are welcome at any hour of the day or night, weekdays, weekends, and holidays. Please do not ever hesitate to ask a question or send a pull request. + +Submitting a Proposal +--------------------- + +For **major changes, new features, or significant architectural modifications**, please **submit a proposal** under `proposals/ `_ folder using the `designated template `_ before contributing code. This ensures alignment with the project's goals, allows maintainers and contributors to provide feedback early, and helps prevent unnecessary rework. + +Once submitted, your proposal will be reviewed by the maintainers, and discussions may take place before approval. We encourage open collaboration, so feel free to participate in the discussion and refine your proposal based on feedback. + +*For small changes like bug fixes, documentation updates, minor optimizations, and simple features, feel free to directly create an issue or PR without the proposal.* + +Opening a Pull Request +---------------------- + +Before submitting your pull request, please ensure it meets the following criteria. This helps maintain code quality and streamline the review process. + +Follow the standard GitHub workflow: + +1. Fork the repository. +2. Create a feature branch. +3. Submit a pull request with a detailed description. + +PR Title and Classification +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Please try to classify PRs for easy understanding of the type of changes. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following: + +- ``[Bugfix]`` for bug fixes. +- ``[CI/Build]`` for build or continuous integration improvements. +- ``[Doc]`` for documentation fixes and improvements. +- ``[Feat]`` for new features in the cluster (e.g., autoscaling, disaggregated prefill, etc.). +- ``[Router]`` for changes to the ``vllm_router`` (e.g., routing algorithm, router observability, etc.). +- ``[Misc]`` for PRs that do not fit the above categories. Please use this sparingly. + +**Note:** If the PR spans more than one category, please include all relevant prefixes. + +Code Quality and Validation +--------------------------- + +Linter Checks +~~~~~~~~~~~~~ + +Linter checks are parts of our github workflows. To pass all linter checks, please use ``pre-commit`` to format your code. It is installed as follows: + +.. code-block:: bash + + uv sync --all-extras --all-groups + uv run pre-commit install + +It will run automatically before every commit. You can also run it manually on all files with: + +.. code-block:: bash + + uv run pre-commit run --all-files + +There are a subset of hooks which require additional dependencies that you may not have installed in your development environment (i.e. Docker and non-Python packages). These are configured to only run in the ``manual`` ``pre-commit`` stage. In CI they are run in the ``pre-commit-manual`` job, and locally they can be run with: + +.. code-block:: bash + + # Runs all hooks including manual stage hooks + uv run pre-commit run --all-files --hook-stage manual + # Runs only the manual stage hook shellcheck + uv run pre-commit run --all-files --hook-stage manual shellcheck + +If any of these hooks are failing in CI but you cannot run them locally, you can identify what needs changing by examining the GitHub Actions logs in your pull request. + +.. note:: + You can read more about ``pre-commit`` at https://pre-commit.com. + +Github Workflows +~~~~~~~~~~~~~~~~ + +The PR must pass all GitHub workflows, which include: + +- Router E2E tests +- Functionality tests of the helm chart + +If any test fails, please check GitHub Actions for details on the failure. If you believe the error is unrelated to your PR, please explain your reasoning in the PR comments. + +Adding Examples and Tests +------------------------- + +Please include sufficient examples in your PR. Unit tests and integrations are also welcome, and you're encouraged to contribute them in future PRs. + +DCO and Signed-off-by +--------------------- + +When contributing changes to this project, you must agree to the `DCO `_. Commits must include a ``Signed-off-by:`` header which certifies agreement with the terms of the DCO. + +Using ``-s`` with ``git commit`` will automatically add this header. + +What to Expect for the Reviews +------------------------------ + +We aim to address all PRs in a timely manner. If no one reviews your PR within 5 days, please @-mention one of YuhanLiu11, Shaoting-Feng or ApostaC. diff --git a/docs/source/developer_guide/docker.rst b/docs/source/developer_guide/docker.rst new file mode 100644 index 000000000..b035397fc --- /dev/null +++ b/docs/source/developer_guide/docker.rst @@ -0,0 +1,13 @@ +Docker Guide +============ + +This section provides information about Docker containerization and custom image development for the vLLM Production Stack. + +Build Docker Image +------------------ + +Run this command from the root folder path of the project: + +.. code-block:: bash + + docker build -t : -f docker/Dockerfile . diff --git a/docs/source/getting_started/examples.rst b/docs/source/getting_started/examples.rst deleted file mode 100644 index 6803d3d36..000000000 --- a/docs/source/getting_started/examples.rst +++ /dev/null @@ -1,170 +0,0 @@ -.. _examples: - -Minimal Example -=============== - -Introduction ------------- - -This is a minimal working example of the vLLM Production Stack using one vLLM instance with the ``facebook/opt-125m`` model. -The goal is to have a working deployment of vLLM on a Kubernetes environment with GPU. - -Prerequisites -------------- - -- A Kubernetes environment with GPU support. If not set up, follow the `install-kubernetes-env `_ guide. -- Helm installed. Refer to the `install-helm.sh `_ script for instructions. -- kubectl should be installed. Refer to the `install-kubectl.sh `_ script for instructions. -- The project repository cloned: `vLLM Production Stack repository `_. -- Basic familiarity with Kubernetes and Helm. - -Steps to follow ---------------- - -1. Deploy vLLM Instance -~~~~~~~~~~~~~~~~~~~~~~~~ - -1.1 Use existing configuration -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The vLLM Production Stack repository provides a predefined configuration file, `values-01-minimal-example.yaml`, located `here `_. -This file contains the following content: - -.. code-block:: yaml - - servingEngineSpec: - runtimeClassName: "" - modelSpec: - - name: "opt125m" - repository: "vllm/vllm-openai" - tag: "latest" - modelURL: "facebook/opt-125m" - - replicaCount: 1 - - requestCPU: 6 - requestMemory: "16Gi" - requestGPU: 1 - - -1.2 Deploy the stack -^^^^^^^^^^^^^^^^^^^^ - -Deploy the Helm chart using the predefined configuration file: - -.. code-block:: bash - - helm repo add vllm https://vllm-project.github.io/production-stack - helm install vllm vllm/vllm-stack -f tutorials/assets/values-01-minimal-example.yaml - - -2. Validate Installation -~~~~~~~~~~~~~~~~~~~~~~~~ - -2.1 Monitor Deployment Status -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Monitor the deployment status using: - -.. code-block:: bash - - kubectl get pods - - -Expected output: - -.. code-block:: console - - NAME READY STATUS RESTARTS AGE - vllm-deployment-router-859d8fb668-2x2b7 1/1 Running 0 2m38s - vllm-opt125m-deployment-vllm-84dfc9bd7-vb9bs 1/1 Running 0 2m38s - -.. note:: - - It may take some time for the containers to download the Docker images and LLM weights. - -3. Send a Query to the Stack -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -3.1 Forward the Service Port -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Expose the `vllm-router-service` port to the host machine: - -.. code-block:: bash - - kubectl port-forward svc/vllm-router-service 30080:80 - - -3.2 Query the OpenAI-Compatible API to list the available models -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Test the stack's OpenAI-compatible API by querying the available models: - -.. code-block:: bash - - curl -o- http://localhost:30080/v1/models - - -Expected output: - -.. code-block:: json - - { - "object": "list", - "data": [ - { - "id": "facebook/opt-125m", - "object": "model", - "created": 1737428424, - "owned_by": "vllm", - "root": null - } - ] - } - - - -3.3 Query the OpenAI Completion Endpoint -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Send a query to the OpenAI `/completion` endpoint to generate a completion for a prompt: - -.. code-block:: bash - - curl -X POST http://localhost:30080/v1/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "facebook/opt-125m", - "prompt": "Once upon a time,", - "max_tokens": 10 - }' - - -Expected output: - -.. code-block:: json - - { - "id": "completion-id", - "object": "text_completion", - "created": 1737428424, - "model": "facebook/opt-125m", - "choices": [ - { - "text": " there was a brave knight who...", - "index": 0, - "finish_reason": "length" - } - ] - } - - -4. Uninstall -~~~~~~~~~~~~ - -To remove the deployment, run: - -.. code-block:: bash - - helm uninstall vllm diff --git a/docs/source/getting_started/faq.rst b/docs/source/getting_started/faq.rst new file mode 100644 index 000000000..832fa5761 --- /dev/null +++ b/docs/source/getting_started/faq.rst @@ -0,0 +1,109 @@ +FAQ +=== + +Frequently Asked Questions about vLLM Production Stack. + +Installation & Setup +--------------------- + +to be updated + +Deployment & Configuration +--------------------------- + +Q: How do I update to a new version of vLLM Production Stack? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Update your ``values.yaml`` file with the new version and upgrade: + +.. code-block:: bash + + helm upgrade my-vllm-stack vllm/vllm-stack -f values.yaml + + +Q: How do I scale my deployment? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +You can scale in several ways: + +* **Horizontal scaling**: Increase ``replicaCount`` in your values +* **Vertical scaling**: Allocate more GPUs per replica +* **Auto-scaling**: Use :doc:`../use_cases/autoscaling-keda` for automatic scaling + +Q: What's the difference between router and vLLM instances? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +A: + +* **Router**: Handles request routing, load balancing, and advanced features like KV cache management +* **vLLM instances**: Run the actual model inference +* The router distributes requests across multiple vLLM instances for better performance and availability + +Performance & Optimization +--------------------------- + +Q: How can I improve inference performance? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Several optimization strategies are available: + +* **KV Cache optimization**: See :doc:`../use_cases/kv-cache-aware-routing` +* **Prefix caching**: See :doc:`../use_cases/prefix-aware-routing` +* **Disaggregated prefill**: See :doc:`../use_cases/disaggregated-prefill` +* **Multiple GPU utilization**: Distribute load across multiple GPUs + +Q: What is KV cache and why does it matter? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +KV (Key-Value) cache stores computed attention keys and values from previous tokens, enabling faster generation of subsequent tokens. Proper KV cache management significantly improves performance for: + +* Long conversations +* Similar prompts +* Batch processing + +Q: How do I monitor performance? +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Use the built-in monitoring features: + +* **Prometheus metrics**: Built-in metrics collection +* **Distributed tracing**: See :doc:`../use_cases/distributed-tracing` +* **Benchmarking tools**: See :doc:`../use_cases/benchmarking` + +Troubleshooting +--------------- + +Q: Pods are stuck in Pending state +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Check: + +.. code-block:: bash + + kubectl describe pod -n vllm-system + +Common causes: +* Insufficient GPU resources +* Node selector/affinity issues +* Resource quotas exceeded +* Image pull failures + +Q: Where can I get help? +~~~~~~~~~~~~~~~~~~~~~~~~~ + +A: + +* **GitHub Issues**: Report bugs and feature requests +* **Community meetings**: See :doc:`../community/meetings` +* **Documentation**: Check other sections of this documentation +* **vLLM Community**: Join the broader vLLM community discussions + +Q: How can I contribute? +~~~~~~~~~~~~~~~~~~~~~~~~ + +See :doc:`../developer_guide/contributing` for contribution guidelines. + +Q: Is there a roadmap? +~~~~~~~~~~~~~~~~~~~~~~ + +Check the GitHub repository for the latest roadmap and feature plans. diff --git a/docs/source/getting_started/installation.rst b/docs/source/getting_started/installation.rst deleted file mode 100644 index 5eb5fe0f0..000000000 --- a/docs/source/getting_started/installation.rst +++ /dev/null @@ -1,59 +0,0 @@ -.. _installation: - -.. role:: raw-html(raw) - :format: html - -Installation -============ - -Architecture ------------- - -.. figure:: ../assets/prodarch.png - :width: 60% - :align: center - :alt: production-arch - :class: no-scaled-link - - -The stack is set up using Helm, and contains the following key parts: - - - * **Serving engine**: The vLLM engines that run different LLMs - * **Request router**: Directs requests to appropriate backends based on routing keys or session IDs to maximize KV cache reuse. - * **Observability stack**: monitors the metrics of the backends through `Prometheus `_ and `Grafana `_. - - -Prerequisites -------------- - -- A running Kubernetes (K8s) environment with GPUs -- Run ``cd utils`` && ``bash install-minikube-cluster.sh`` -- Or follow our `tutorial `_ - - -Deployment ----------- - -vLLM Production Stack can be deployed via helm charts. Clone the repo to local and execute the following commands for a minimal deployment: - -.. code:: bash - - git clone https://github.com/vllm-project/production-stack.git - cd production-stack/ - helm repo add vllm https://vllm-project.github.io/production-stack - helm install vllm vllm/vllm-stack -f tutorials/assets/values-01-minimal-example.yaml - - -The deployed stack provides the same `OpenAI API interface `_ as vLLM, and can be accessed through kubernetes service. - -To validate the installation and and send query to the stack, refer to this `example `_. - -Uninstallation --------------- - -To uninstall the stack, run: - -.. code:: bash - - helm uninstall vllm diff --git a/docs/source/getting_started/prerequisite.rst b/docs/source/getting_started/prerequisite.rst new file mode 100644 index 000000000..90405d60a --- /dev/null +++ b/docs/source/getting_started/prerequisite.rst @@ -0,0 +1,251 @@ +Prerequisite +============ + +This tutorial guides you through the process of setting up a Kubernetes environment on a GPU-enabled server. We will install and configure ``kubectl``, ``helm``, and ``minikube``, ensuring GPU compatibility for workloads requiring accelerated computing. By the end of this tutorial, you will have a fully functional Kubernetes environment ready for deploy the vLLM Production Stack. + +Table of Contents +----------------- + +- `Table of Contents`_ +- Prerequisites_ +- Steps_ + + - `Step 1: Installing kubectl`_ + - `Step 2: Installing Helm`_ + - `Step 3: Installing Minikube with GPU Support`_ + - `Step 4: Verifying GPU Configuration`_ + +Prerequisites +------------- + +Before you begin, ensure the following: + +1. **GPU Server Requirements:** + + - A server with a GPU and drivers properly installed (e.g., NVIDIA drivers). + - `NVIDIA Container Toolkit `_ installed for GPU workloads. + +2. **Access and Permissions:** + + - Root or administrative access to the server. + - Internet connectivity to download required packages and tools. + +3. **Environment Setup:** + + - A Linux-based operating system (e.g., Ubuntu 20.04 or later). + - Basic understanding of Linux shell commands. + +Steps +----- + +Step 1: Installing kubectl +~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. Clone the repository and navigate to the `utils/ `_ folder: + + .. code-block:: bash + + git clone https://github.com/vllm-project/production-stack.git + cd production-stack/utils + +2. Execute the script `install-kubectl.sh `_: + + .. code-block:: bash + + bash install-kubectl.sh + +3. **Explanation:** + This script downloads the latest version of `kubectl `_, the Kubernetes command-line tool, and places it in your PATH for easy execution. + +4. **Expected Output:** + + - Confirmation that ``kubectl`` was downloaded and installed. + - Verification message using: + + .. code-block:: bash + + kubectl version --client + + Example output: + + .. code-block:: text + + Client Version: v1.32.1 + +Step 2: Installing Helm +~~~~~~~~~~~~~~~~~~~~~~~~ + +1. Execute the script `install-helm.sh `_: + + .. code-block:: bash + + bash install-helm.sh + +2. **Explanation:** + + - Downloads and installs Helm, a package manager for Kubernetes. + - Places the Helm binary in your PATH. + +3. **Expected Output:** + + - Successful installation of Helm. + - Verification message using: + + .. code-block:: bash + + helm version + + Example output: + + .. code-block:: text + + version.BuildInfo{Version:"v3.17.0", GitCommit:"301108edc7ac2a8ba79e4ebf5701b0b6ce6a31e4", GitTreeState:"clean", GoVersion:"go1.23.4"} + +Step 3: Installing Minikube with GPU Support +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Before proceeding, ensure Docker runs without requiring sudo. To add your user to the docker group, run: + +.. code-block:: bash + + sudo usermod -aG docker $USER && newgrp docker + +If Minikube is already installed on your system, we recommend uninstalling the existing version before proceeding. You may use one of the following commands based on your operating system and package manager: + +.. code-block:: bash + + # Ubuntu / Debian + sudo apt remove minikube + + # RHEL / CentOS / Fedora + sudo yum remove minikube + # or + sudo dnf remove minikube + + # macOS (installed via Homebrew) + brew uninstall minikube + + # Arch Linux + sudo pacman -Rs minikube + + # Windows (via Chocolatey) + choco uninstall minikube + + # Windows (via Scoop) + scoop uninstall minikube + +After removing the previous installation, please execute the script provided below to install the latest version. + +1. Execute the script ``install-minikube-cluster.sh``: + + .. code-block:: bash + + bash install-minikube-cluster.sh + +2. **Explanation:** + + - Installs Minikube if not already installed. + - Configures the system to support GPU workloads by enabling the NVIDIA Container Toolkit and starting Minikube with GPU support. + - Installs the NVIDIA ``gpu-operator`` chart to manage GPU resources within the cluster. + +3. **Expected Output:** + If everything goes smoothly, you should see the example output like following: + + .. code-block:: text + + 😄 minikube v1.35.0 on Ubuntu 22.04 (kvm/amd64) + ❗ minikube skips various validations when --force is supplied; this may lead to unexpected behavior + ✨ Using the docker driver based on user configuration + ...... + ...... + 🏄 Done! kubectl is now configured to use "minikube" cluster and "default" namespace by default + "nvidia" has been added to your repositories + Hang tight while we grab the latest from your chart repositories... + ...... + ...... + NAME: gpu-operator-1737507918 + LAST DEPLOYED: Wed Jan 22 01:05:21 2025 + NAMESPACE: gpu-operator + STATUS: deployed + REVISION: 1 + TEST SUITE: None + +4. Some troubleshooting tips for installing gpu-operator: + + If gpu-operator fails to start because of the commonly seen "too many open files" issue for minikube (and `kind `_), then a quick fix below may be helpful. + + The issue can be observed by one or more gpu-operator pods in ``CrashLoopBackOff`` status, and be confirmed by checking their logs. For example, + + .. code-block:: console + + $ kubectl -n gpu-operator logs daemonset/nvidia-device-plugin-daemonset -c nvidia-device-plugin + IS_HOST_DRIVER=true + NVIDIA_DRIVER_ROOT=/ + DRIVER_ROOT_CTR_PATH=/host + NVIDIA_DEV_ROOT=/ + DEV_ROOT_CTR_PATH=/host + Starting nvidia-device-plugin + I0131 19:35:42.895845 1 main.go:235] "Starting NVIDIA Device Plugin" version=< + d475b2cf + commit: d475b2cfcf12b983a4975d4fc59d91af432cf28e + > + I0131 19:35:42.895917 1 main.go:238] Starting FS watcher for /var/lib/kubelet/device-plugins + E0131 19:35:42.895933 1 main.go:173] failed to create FS watcher for /var/lib/kubelet/device-plugins/: too many open files + + The fix is `well documented `_ by kind, it also works for minikube. + +Step 4: Verifying GPU Configuration +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +1. Ensure Minikube is running: + + .. code-block:: bash + + minikube status + + Expected output: + + .. code-block:: text + + minikube + type: Control Plane + host: Running + kubelet: Running + apiserver: Running + kubeconfig: Configured + +2. Verify GPU access within Kubernetes: + + .. code-block:: bash + + kubectl describe nodes | grep -i gpu + + Expected output: + + .. code-block:: text + + nvidia.com/gpu: 1 + ... (plus many lines related to gpu information) + +3. Deploy a test GPU workload: + + .. code-block:: bash + + kubectl run gpu-test --image=nvidia/cuda:12.2.0-runtime-ubuntu22.04 --restart=Never -- nvidia-smi + + Wait for kubernetes to download and create the pod and then check logs to confirm GPU usage: + + .. code-block:: bash + + kubectl logs gpu-test + + You should see the nvidia-smi output from the terminal + +Conclusion +---------- + +By following this tutorial, you have successfully set up a Kubernetes environment with GPU support on your server. You are now ready to deploy and test vLLM Production Stack on Kubernetes. For further configuration and workload-specific setups, consult the official documentation for ``kubectl``, ``helm``, and ``minikube``. + +What's next: + +- :doc:`quickstart` diff --git a/docs/source/getting_started/quickstart.rst b/docs/source/getting_started/quickstart.rst new file mode 100644 index 000000000..386c7bf54 --- /dev/null +++ b/docs/source/getting_started/quickstart.rst @@ -0,0 +1,207 @@ +Quick Start +=========== + +This tutorial guides you through a minimal setup of the vLLM Production Stack using one vLLM instance with the `facebook/opt-125m `_ model. By the end of this tutorial, you will have a working deployment of vLLM on a Kubernetes environment with GPU. + +Table of Contents +----------------- + +- `Quick Start`_ + + - `Table of Contents`_ + - Prerequisites_ + - Steps_ + + - `1. Deploy vLLM Instance`_ + + - `1.1: Use Predefined Configuration`_ + - `1.2: Deploy the Helm Chart`_ + + - `2. Validate Installation`_ + + - `2.1: Monitor Deployment Status`_ + + - `3. Send a Query to the Stack`_ + + - `3.1: Forward the Service Port`_ + - `3.2: Query the OpenAI-Compatible API to list the available models`_ + - `3.3: Query the OpenAI Completion Endpoint`_ + + - `4. Uninstall`_ + +Prerequisites +------------- + +1. A Kubernetes environment with GPU support. If not set up, follow the :doc:`prerequisite` guide. +2. Helm installed. Refer to the `install-helm.sh `_ script for instructions. +3. kubectl installed. Refer to the `install-kubectl.sh `_ script for instructions. +4. The project repository cloned: `vLLM Production Stack repository `_. +5. Basic familiarity with Kubernetes and Helm. + +Steps +----- + +1. Deploy vLLM Instance +~~~~~~~~~~~~~~~~~~~~~~~ + +1.1: Use Predefined Configuration +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The vLLM Production Stack repository provides a predefined configuration file, ``values-01-minimal-example.yaml``, located at `tutorials/assets/values-01-minimal-example.yaml `_. This file contains the following content: + +.. code-block:: yaml + + servingEngineSpec: + runtimeClassName: "" + modelSpec: + - name: "opt125m" + repository: "vllm/vllm-openai" + tag: "latest" + modelURL: "facebook/opt-125m" + + replicaCount: 1 + + requestCPU: 6 + requestMemory: "16Gi" + requestGPU: 1 + # Optional resource limits - if not specified, only GPU will have a limit + # limitCPU: "8" + # limitMemory: "32Gi" + +Explanation of the key fields: + +- **modelSpec**: Defines the model configuration, including: + + - ``name``: A name for the model deployment. + - ``repository``: Docker repository hosting the model image. + - ``tag``: Docker image tag. + - ``modelURL``: Specifies the LLM model to use. + +- **replicaCount**: Sets the number of replicas to deploy. +- **requestCPU and requestMemory**: Specifies the CPU and memory resource requests for the pod. +- **limitCPU and limitMemory**: Specifies the maximum limit of CPU and memory resource for the pod +- **requestGPU**: Specifies the number of GPUs required. + +**Note:** If you intend to set up TWO vllm pods, please refer to `tutorials/assets/values-01-2pods-minimal-example.yaml `_. + +1.2: Deploy the Helm Chart +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Deploy the Helm chart using the predefined configuration file: + +.. code-block:: bash + + helm repo add vllm https://vllm-project.github.io/production-stack + helm install vllm vllm/vllm-stack -f tutorials/assets/values-01-minimal-example.yaml + +Explanation of the command: + +- ``vllm`` in the first command: The Helm repository. +- ``vllm`` in the second command: The name of the Helm release. +- ``-f tutorials/assets/values-01-minimal-example.yaml``: Specifies the predefined configuration file. You can check out the `default config values `_ for the chart `vllm-stack `_. + +2. Validate Installation +~~~~~~~~~~~~~~~~~~~~~~~~ + +2.1: Monitor Deployment Status +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Monitor the deployment status using: + +.. code-block:: bash + + kubectl get pods + +Expected output: + +- Pods for the ``vllm`` deployment should transition to ``Ready`` and the ``Running`` state. + +.. code-block:: text + + NAME READY STATUS RESTARTS AGE + vllm-deployment-router-859d8fb668-2x2b7 1/1 Running 0 2m38s + vllm-opt125m-deployment-vllm-84dfc9bd7-vb9bs 1/1 Running 0 2m38s + +*Note*: It may take some time for the containers to download the Docker images and LLM weights. + +3. Send a Query to the Stack +~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +3.1: Forward the Service Port +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Expose the ``vllm-router-service`` port to the host machine: + +.. code-block:: bash + + kubectl port-forward svc/vllm-router-service 30080:80 + +3.2: Query the OpenAI-Compatible API to list the available models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Test the stack's OpenAI-compatible API by querying the available models: + +.. code-block:: bash + + curl -o- http://localhost:30080/v1/models + +Expected output: + +.. code-block:: json + + { + "object": "list", + "data": [ + { + "id": "facebook/opt-125m", + "object": "model", + "created": 1737428424, + "owned_by": "vllm", + "root": null + } + ] + } + +3.3: Query the OpenAI Completion Endpoint +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Send a query to the OpenAI ``/completion`` endpoint to generate a completion for a prompt: + +.. code-block:: bash + + curl -X POST http://localhost:30080/v1/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "facebook/opt-125m", + "prompt": "Once upon a time,", + "max_tokens": 10 + }' + +Example output of the generated completions: + +.. code-block:: json + + { + "id": "completion-id", + "object": "text_completion", + "created": 1737428424, + "model": "facebook/opt-125m", + "choices": [ + { + "text": " there was a brave knight who...", + "index": 0, + "finish_reason": "length" + } + ] + } + +This demonstrates the model generating a continuation for the provided prompt. + +4. Uninstall +~~~~~~~~~~~~ + +To remove the deployment, run: + +.. code-block:: bash + + helm uninstall vllm diff --git a/docs/source/getting_started/troubleshooting.rst b/docs/source/getting_started/troubleshooting.rst deleted file mode 100644 index 177b4a0af..000000000 --- a/docs/source/getting_started/troubleshooting.rst +++ /dev/null @@ -1,6 +0,0 @@ -.. _troubleshooting: - -Troubleshooting -=========================== - -Coming soon! diff --git a/docs/source/index.rst b/docs/source/index.rst index 10f563482..62b352108 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -48,50 +48,43 @@ Documentation .. toctree:: - :maxdepth: 1 + :maxdepth: 2 :caption: Getting Started - getting_started/installation - getting_started/examples - getting_started/troubleshooting + getting_started/prerequisite + getting_started/quickstart + getting_started/faq .. toctree:: - :maxdepth: 1 - :caption: Tutorials - - tutorials/disagg.rst - tutorials/kv_cache.rst - tutorials/kvaware.rst - tutorials/prefixaware.rst - -.. toctree:: - :maxdepth: 1 + :maxdepth: 2 :caption: Deployment - deployment/helm - deployment/crd - deployment/cloud-deployment/index.rst - deployment/ray/ray.rst + deployment/index .. toctree:: - :maxdepth: 1 - :caption: User Manual - - user_manual/router/index.rst - user_manual/lora/index.rst - user_manual/kv_cache/index.rst + :maxdepth: 2 + :caption: Use Cases + + use_cases/kv-cache-aware-routing + use_cases/prefix-aware-routing + use_cases/disaggregated-prefill + use_cases/sharing-kv-cache + use_cases/benchmarking + use_cases/distributed-tracing + use_cases/tool-enabled-installation + use_cases/pipeline-parallelism-kuberay + use_cases/sleep-wakeup-mode + use_cases/autoscaling-keda .. toctree:: - :maxdepth: 1 + :maxdepth: 2 :caption: Developer Guide - dev_guide/peripheral/index.rst - dev_guide/dev_api/index.rst - - + developer_guide/contributing + developer_guide/docker .. toctree:: - :maxdepth: 1 - :caption: Benchmarks + :maxdepth: 2 + :caption: Community - benchmarks/multiround-qa + community/meetings diff --git a/docs/source/tutorials/disagg.rst b/docs/source/tutorials/disagg.rst deleted file mode 100644 index 9763d98bd..000000000 --- a/docs/source/tutorials/disagg.rst +++ /dev/null @@ -1,195 +0,0 @@ -.. _tutorial_disagg: - -Disaggregated Prefill -===================== - -Introduction ------------------------- - -This tutorial explains how to run the disaggregated prefill system, which splits the model execution into prefill and decode phases across different servers. This approach can improve throughput and resource utilization by separating the initial processing (prefill) from the token generation (decode) phases. - -Prerequisites -------------------------- - -* A Kubernetes cluster with GPU support and NVLink enabled -* NVIDIA GPUs available (at least 2 GPUs recommended) -* ``kubectl`` configured to talk to your cluster -* Helm installed and initialized locally -* Completion of the following setup tutorials: - - * `00-install-kubernetes-env.md `__ - * `01-minimal-helm-installation.md `__ - -Kubernetes Deployment -------------------------------- - -For production environments, you can deploy the disaggregated prefill system using Kubernetes and Helm. This approach provides better scalability, resource management, and high availability. - -Step 1: Create Configuration File -++++++++++++++++++++++++++++++++++ - -Create a configuration file ``values-16-disagg-prefill.yaml`` with the following content: - -.. code-block:: yaml - - servingEngineSpec: - enableEngine: true - runtimeClassName: "" - containerPort: 8000 - modelSpec: - # Prefill node configuration - - name: "llama-prefill" - repository: "lmcache/vllm-openai" - tag: "2025-05-27-v1" - modelURL: "meta-llama/Llama-3.1-8B-Instruct" - replicaCount: 1 - requestCPU: 8 - requestMemory: "30Gi" - # requestGPU: 1 - pvcStorage: "50Gi" - vllmConfig: - enablePrefixCaching: true - maxModelLen: 32000 - v1: 1 - lmcacheConfig: - cudaVisibleDevices: "0" - enabled: true - kvRole: "kv_producer" - enableNixl: true - nixlRole: "sender" - nixlPeerHost: "vllm-llama-decode-engine-service" - nixlPeerPort: "55555" - nixlBufferSize: "1073741824" # 1GB - nixlBufferDevice: "cuda" - nixlEnableGc: true - enablePD: true - cpuOffloadingBufferSize: 0 - hf_token: - labels: - model: "llama-prefill" - # Decode node configuration - - name: "llama-decode" - repository: "lmcache/vllm-openai" - tag: "2025-05-27-v1" - modelURL: "meta-llama/Llama-3.1-8B-Instruct" - replicaCount: 1 - requestCPU: 8 - requestMemory: "30Gi" - # requestGPU: 1 - pvcStorage: "50Gi" - vllmConfig: - enablePrefixCaching: true - maxModelLen: 32000 - v1: 1 - lmcacheConfig: - cudaVisibleDevices: "1" - enabled: true - kvRole: "kv_consumer" # Set decode node as consumer - enableNixl: true - nixlRole: "receiver" - nixlPeerHost: "0.0.0.0" - nixlPeerPort: "55555" - nixlBufferSize: "1073741824" # 1GB - nixlBufferDevice: "cuda" - nixlEnableGc: true - enablePD: true - hf_token: - labels: - model: "llama-decode" - containerSecurityContext: - capabilities: - add: - - SYS_PTRACE - routerSpec: - enableRouter: true - repository: "lmcache/lmstack-router" - tag: "pd" - replicaCount: 1 - containerPort: 8000 - servicePort: 80 - routingLogic: "disaggregated_prefill" - engineScrapeInterval: 15 - requestStatsWindow: 60 - enablePD: true - resources: - requests: - cpu: "4" - memory: "16G" - limits: - cpu: "4" - memory: "32G" - labels: - environment: "router" - release: "router" - extraArgs: - - "--prefill-model-labels" - - "llama-prefill" - - "--decode-model-labels" - - "llama-decode" - - -Step 2: Deploy Using Helm -++++++++++++++++++++++++++++++++++ - -Install the deployment using Helm with the configuration file: - -.. code-block:: bash - - helm install vllm helm/ -f tutorials/assets/values-16-disagg-prefill.yaml - -This will deploy: - -* A prefill server with the specified configuration -* A decode server with the specified configuration -* A router to coordinate between them - -The configuration includes: - -* Resource requests and limits for each component -* NIXL communication settings for LMCache -* Model configurations -* Router settings for disaggregated prefill - -Step 3: Verify Deployment -++++++++++++++++++++++++++++++++++ - -Check the status of your deployment: - -.. code-block:: bash - - kubectl get pods - kubectl get services - -You should see pods for: - -* The prefill server -* The decode server -* The router - -Step 4: Access the Service -++++++++++++++++++++++++++++++++++ - -First do port forwarding to access the service: - -.. code-block:: bash - - kubectl port-forward svc/vllm-router-service 30080:80 - -And then send a request to the router by: - -.. code-block:: bash - - curl http://localhost:30080/v1/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "meta-llama/Llama-3.1-8B-Instruct", - "prompt": "Your prompt here", - "max_tokens": 100 - }' - -You should see logs from LMCache like the following on the decoder instance's side: - -.. code-block:: console - - [2025-05-26 20:12:21,913] LMCache DEBUG: Scheduled to load 6 tokens for request cmpl-058cf35e022a479f849a60daefbade9e-0 (vllm_v1_adapter.py:299:lmcache.integration.vllm.vllm_v1_adapter) - [2025-05-26 20:12:21,915] LMCache DEBUG: Retrieved 6 out of 6 out of total 6 tokens (cache_engine.py:330:lmcache.experimental.cache_engine) diff --git a/docs/source/tutorials/kv_cache.rst b/docs/source/tutorials/kv_cache.rst deleted file mode 100644 index 0a2b3d3d3..000000000 --- a/docs/source/tutorials/kv_cache.rst +++ /dev/null @@ -1,119 +0,0 @@ -.. tutorial_kv_cache: - -KV Cache Offloading -=================== - -Introduction ------------- - -This tutorial demonstrates how to enable KV cache offloading using LMCache in a vLLM deployment. KV cache offloading moves large KV caches from GPU memory to CPU or disk, enabling more potential KV cache hits. vLLM Production Stack uses LMCache for KV cache offloading. - -Prerequisites -------------- - -* Installation of minimal example shown in :ref:`examples` -* Kubernetes environment with GPU support - -Steps to follow ---------------- - -1. Configuring KV Cache Offloading -++++++++++++++++++++++++++++++++++ - -Locate the file ``tutorials/assets/values-05-cpu-offloading.yaml`` with the following content: - -.. code-block:: yaml - - servingEngineSpec: - modelSpec: - - name: "mistral" - repository: "lmcache/vllm-openai" - tag: "latest" - modelURL: "mistralai/Mistral-7B-Instruct-v0.2" - replicaCount: 1 - requestCPU: 10 - requestMemory: "40Gi" - requestGPU: 1 - pvcStorage: "50Gi" - vllmConfig: - enablePrefixCaching: true - maxModelLen: 16384 - - lmcacheConfig: - enabled: true - cpuOffloadingBufferSize: "20" - - hf_token: - -.. note:: - - Note: Replace with your actual Hugging Face token. - -.. note:: - - The ``lmcacheConfig`` field enables LMCache and sets the CPU offloading buffer size to ``20GB``. You can adjust this value based on your workload. - -Step 2: Deploy the Stack -++++++++++++++++++++++++ - -Deploy the Helm chart using the predefined configuration file: - -.. code-block:: bash - - helm install vllm vllm/vllm-stack -f tutorials/assets/values-05-cpu-offloading.yaml - - -Step 3: Validate Installation -++++++++++++++++++++++++++++++ - -3.1 Check the pod logs to verify LMCache is active: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - sudo kubectl get pods - - -Identify the pod name for the vLLM deployment (e.g., ``vllm-mistral-deployment-vllm-xxxx-xxxx``). Then run: - -.. code-block:: bash - - sudo kubectl logs -f - - -Look for the following log message to confirm LMCache is active: - -.. code-block:: console - - INFO 01-21 20:16:58 lmcache_connector.py:41] Initializing LMCacheConfig under kv_transfer_config kv_connector='LMCacheConnector' kv_buffer_device='cuda' kv_buffer_size=1000000000.0 kv_role='kv_both' kv_rank=None kv_parallel_size=1 kv_ip='127.0.0.1' kv_port=14579 - INFO LMCache: Creating LMCacheEngine instance vllm-instance [2025-01-21 20:16:58,732] -- /usr/local/lib/python3.12/dist-packages/lmcache/experimental/cache_engine.py:237 - - -2. Forward the router service port to access the stack locally: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - sudo kubectl port-forward svc/vllm-router-service 30080:80 - - -3. Send a request to the stack and observe the logs: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - curl -X POST http://localhost:30080/v1/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "mistralai/Mistral-7B-Instruct-v0.2", - "prompt": "Explain the significance of KV cache in language models.", - "max_tokens": 10 - }' - -Expected output: - -The response from the stack should contain the completion result, and the logs should show LMCache activity, for example: - -.. code-block:: console - - DEBUG LMCache: Store skips 0 tokens and then stores 13 tokens [2025-01-21 20:23:45,113] -- /usr/local/lib/python3.12/dist-packages/lmcache/integration/vllm/vllm_adapter.py:490 diff --git a/docs/source/use_cases/autoscaling-keda.rst b/docs/source/use_cases/autoscaling-keda.rst new file mode 100644 index 000000000..1515acf17 --- /dev/null +++ b/docs/source/use_cases/autoscaling-keda.rst @@ -0,0 +1,184 @@ +Autoscaling with KEDA +===================== + +This tutorial shows you how to automatically scale a vLLM deployment using `KEDA `_ and Prometheus-based metrics. You'll configure KEDA to monitor queue length and dynamically adjust the number of replicas based on load. + +Table of Contents +----------------- + +- Prerequisites_ +- Steps_ + + - `1. Install the vLLM Production Stack`_ + - `2. Deploy the Observability Stack`_ + - `3. Install KEDA`_ + - `4. Verify Metric Export`_ + - `5. Configure the ScaledObject`_ + - `6. Test Autoscaling`_ + - `7. Cleanup`_ + +- `Additional Resources`_ + +Prerequisites +------------- + +- A working vLLM deployment on Kubernetes (see :doc:`../getting_started/quickstart`) +- Access to a Kubernetes cluster with at least 2 GPUs +- ``kubectl`` and ``helm`` installed +- Basic understanding of Kubernetes and Prometheus metrics + +Steps +----- + +1. Install the vLLM Production Stack +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Install the production stack using a single pod by following the instructions in :doc:`../deployment/helm`. + +2. Deploy the Observability Stack +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This stack includes Prometheus, Grafana, and necessary exporters. + +.. code-block:: bash + + cd observability + bash install.sh + +3. Install KEDA +~~~~~~~~~~~~~~~ + +.. code-block:: bash + + kubectl create namespace keda + helm repo add kedacore https://kedacore.github.io/charts + helm repo update + helm install keda kedacore/keda --namespace keda + +4. Verify Metric Export +~~~~~~~~~~~~~~~~~~~~~~~ + +Check that Prometheus is scraping the queue length metric ``vllm:num_requests_waiting``. + +.. code-block:: bash + + kubectl port-forward svc/prometheus-operated -n monitoring 9090:9090 + +In a separate terminal: + +.. code-block:: bash + + curl -G 'http://localhost:9090/api/v1/query' --data-urlencode 'query=vllm:num_requests_waiting' + +Example output: + +.. code-block:: json + + { + "status": "success", + "data": { + "result": [ + { + "metric": { + "__name__": "vllm:num_requests_waiting", + "pod": "vllm-llama3-deployment-vllm-xxxxx" + }, + "value": [ 1749077215.034, "0" ] + } + ] + } + } + +This means that at the given timestamp, there were 0 pending requests in the queue. + +5. Configure the ScaledObject +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The following ``ScaledObject`` configuration is provided in ``tutorials/assets/values-19-keda.yaml``. Review its contents: + +.. code-block:: yaml + + apiVersion: keda.sh/v1alpha1 + kind: ScaledObject + metadata: + name: vllm-scaledobject + namespace: default + spec: + scaleTargetRef: + name: vllm-llama3-deployment-vllm + minReplicaCount: 1 + maxReplicaCount: 2 + pollingInterval: 15 + cooldownPeriod: 30 + triggers: + - type: prometheus + metadata: + serverAddress: http://prometheus-operated.monitoring.svc:9090 + metricName: vllm:num_requests_waiting + query: vllm:num_requests_waiting + threshold: '5' + +Apply the ScaledObject: + +.. code-block:: bash + + cd ../tutorials + kubectl apply -f assets/values-19-keda.yaml + +This tells KEDA to: + +- Monitor ``vllm:num_requests_waiting`` +- Scale between 1 and 2 replicas +- Scale up when the queue exceeds 5 requests + +6. Test Autoscaling +~~~~~~~~~~~~~~~~~~~ + +Watch the deployment: + +.. code-block:: bash + + kubectl get hpa -n default -w + +You should initially see: + +.. code-block:: text + + NAME REFERENCE TARGETS MINPODS MAXPODS REPLICAS + keda-hpa-vllm-scaledobject Deployment/vllm-llama3-deployment-vllm 0/5 (avg) 1 2 1 + +``TARGETS`` shows the current metric value vs. the target threshold. +``0/5 (avg)`` means the current value of ``vllm:num_requests_waiting`` is 0, and the threshold is 5. + +Generate load: + +.. code-block:: bash + + kubectl port-forward svc/vllm-router-service 30080:80 + +In a separate terminal: + +.. code-block:: bash + + python3 assets/example-10-load-generator.py --num-requests 100 --prompt-len 3000 + +Within a few minutes, the ``REPLICAS`` value should increase to 2. + +7. Cleanup +~~~~~~~~~~ + +To remove KEDA configuration and observability components: + +.. code-block:: bash + + kubectl delete -f assets/values-19-keda.yaml + helm uninstall keda -n keda + kubectl delete namespace keda + + cd ../observability + bash uninstall.sh + +Additional Resources +-------------------- + +- `KEDA Documentation `_ diff --git a/docs/source/use_cases/benchmarking.rst b/docs/source/use_cases/benchmarking.rst new file mode 100644 index 000000000..863cefa6d --- /dev/null +++ b/docs/source/use_cases/benchmarking.rst @@ -0,0 +1,6 @@ +Benchmarking +============ + +A new benchmarking platform is coming soon. Stay tuned! + +Follow our x account for updates & latest results: https://x.com/lmcache diff --git a/docs/source/use_cases/disaggregated-prefill.rst b/docs/source/use_cases/disaggregated-prefill.rst new file mode 100644 index 000000000..312e7826c --- /dev/null +++ b/docs/source/use_cases/disaggregated-prefill.rst @@ -0,0 +1,178 @@ +Disaggregated Prefill +===================== + +This tutorial explains how to run the disaggregated prefill system, which splits the model execution into prefill and decode phases across different servers. This approach can improve throughput and resource utilization by separating the initial processing (prefill) from the token generation (decode) phases. + +Prerequisites +------------- + +- A Kubernetes cluster with GPU support and NVLink enabled +- NVIDIA GPUs available (at least 2 GPUs recommended) +- ``kubectl`` configured to talk to your cluster +- Helm installed and initialized locally +- Completion of the following setup tutorials: + + - :doc:`../getting_started/prerequisite` + - :doc:`../getting_started/quickstart` + +Kubernetes Deployment +--------------------- + +For production environments, you can deploy the disaggregated prefill system using Kubernetes and Helm. This approach provides better scalability, resource management, and high availability. + +Step 1: Create Configuration File +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Create a configuration file ``values-16-disagg-prefill.yaml`` with the following content: + +.. code-block:: yaml + + # Unified configuration for disaggregated prefill setup + servingEngineSpec: + enableEngine: true + runtimeClassName: "" + containerPort: 8000 + modelSpec: + # Prefill node configuration + - name: "llama-prefill" + repository: "lmcache/vllm-openai" + tag: "2025-05-17-v1" + modelURL: "meta-llama/Llama-3.1-8B-Instruct" + replicaCount: 1 + requestCPU: 8 + requestMemory: "30Gi" + requestGPU: 1 + pvcStorage: "50Gi" + vllmConfig: + enableChunkedPrefill: false + enablePrefixCaching: false + maxModelLen: 32000 + v1: 1 + lmcacheConfig: + enabled: true + kvRole: "kv_producer" + enableNixl: true + nixlRole: "sender" + nixlPeerHost: "pd-llama-decode-engine-service" + nixlPeerPort: "55555" + nixlBufferSize: "1073741824" # 1GB + nixlBufferDevice: "cuda" + nixlEnableGc: true + enablePD: true + hf_token: + labels: + model: "llama-prefill" + # Decode node configuration + - name: "llama-decode" + repository: "lmcache/vllm-openai" + tag: "2025-05-17-v1" + modelURL: "meta-llama/Llama-3.1-8B-Instruct" + replicaCount: 1 + requestCPU: 8 + requestMemory: "30Gi" + requestGPU: 1 + pvcStorage: "50Gi" + vllmConfig: + enableChunkedPrefill: false + enablePrefixCaching: false + maxModelLen: 32000 + v1: 1 + lmcacheConfig: + enabled: true + kvRole: "kv_consumer" + enableNixl: true + nixlRole: "receiver" + nixlPeerHost: "0.0.0.0" + nixlPeerPort: "55555" + nixlBufferSize: "1073741824" # 1GB + nixlBufferDevice: "cuda" + nixlEnableGc: true + enablePD: true + hf_token: + labels: + model: "llama-decode" + routerSpec: + enableRouter: true + repository: "lmcache/lmstack-router" + tag: "pd-05-26" + replicaCount: 1 + containerPort: 8000 + servicePort: 80 + routingLogic: "disaggregated_prefill" + engineScrapeInterval: 15 + requestStatsWindow: 60 + enablePD: true + resources: + requests: + cpu: "4" + memory: "16G" + limits: + cpu: "4" + memory: "32G" + labels: + environment: "router" + release: "router" + extraArgs: + - "--prefill-model-labels" + - "llama-prefill" + - "--decode-model-labels" + - "llama-decode" + +Step 2: Deploy Using Helm +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Install the deployment using Helm with the configuration file: + +.. code-block:: bash + + helm install pd helm/ -f tutorials/assets/values-16-disagg-prefill.yaml + +This will deploy: + +- A prefill server with the specified configuration +- A decode server with the specified configuration +- A router to coordinate between them + +The configuration includes: + +- Resource requests and limits for each component +- NIXL communication settings +- Model configurations +- Router settings for disaggregated prefill + +Step 3: Verify Deployment +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Check the status of your deployment: + +.. code-block:: bash + + kubectl get pods + kubectl get services + +You should see pods for: + +- The prefill server +- The decode server +- The router + +Step 4: Access the Service +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First do port forwarding to access the service: + +.. code-block:: bash + + kubectl port-forward svc/pd-router-service 30080:80 + +And then send a request to the router by: + +.. code-block:: bash + + curl http://localhost:30080/v1/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "meta-llama/Llama-3.1-8B-Instruct", + "prompt": "Your prompt here", + "max_tokens": 100 + }' diff --git a/docs/source/use_cases/distributed-tracing.rst b/docs/source/use_cases/distributed-tracing.rst new file mode 100644 index 000000000..7ac434732 --- /dev/null +++ b/docs/source/use_cases/distributed-tracing.rst @@ -0,0 +1,121 @@ +Distributed Tracing +=================== + +This tutorial guides you through the basic configurations required to collect traces, metrics, and logs from a vLLM serving engine in a Kubernetes environment with GPU support. You will learn how to use OpenTelemetry tooling along with the Jaeger distributed tracing observability platform to monitor running vLLM instances. You will learn how to specify the tracing configuration with necessary environment variables (like ``OTEL_SERVICE_NAME``, ``OTEL_EXPORTER_OTLP_ENDPOINT``, and ``OTEL_RESOURCE_ATTRIBUTES``). + +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Preparing the Jaeger Configuration File`_ +3. `Step 2: Preparing the OpenTelemetry Collector File`_ +4. `Step 3: Configuring Model and Monitoring`_ + +Prerequisites +------------- + +- A Kubernetes environment with GPU support, as set up in the :doc:`../getting_started/prerequisite` tutorial. +- Helm installed on your system. +- Access to a HuggingFace token (``HF_TOKEN``). +- A self-defined api key or an existing secret (``VLLM_API_KEY``). + +Step 1: Preparing the Jaeger Configuration File +----------------------------------------------- + +1. Locate the example configuration files `tutorials/assets/otel-example/{jaeger, jaeger-query, jaeger-collector}.yaml `_. +2. Open the files and examine the following fields: + + - Specify your desired ports for ``jaeger-collector`` and ``jaeger-query`` services for trace storage and retrieval, respectively. + - Verify that ``COLLECTOR_OTLP_ENABLED`` is set to ``true``; this enables Jaeger's native support for OpenTelemetry + +Explanation of Key Items in Jaeger Files +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **ports**: The ports through which Jaeger performs its operations (collection and querying in this case). +- **name**: The unique identifier for your Jaeger deployment. +- **image**: The single Docker image that runs all Jaeger's backend parts as well as its UI in a container. +- **type**: The ClusterIP type ensures the query service is only reachable from within the cluster. + +Step 2: Preparing the OpenTelemetry Collector File +-------------------------------------------------- + +1. Locate the example collector files `tutorials/assets/otel-example/{otel-collector, otel-collector-config}.yaml `_. +2. Open the file and examine the following fields: + + - Specify the ``receivers``, ``processors``, and ``exporters`` of the collector + - The OpenTelemetry collection is an intermediary step of collecting traces from the vLLM service and then providing these to Jaeger for its UI. + - The ``resources`` specify the compute resources available to the container running the OpenTelemetry collector. + +Step 3: Configuring Model and Monitoring +---------------------------------------- + +1. Feel free to inspect the ``values-12-otel-vllm.yaml`` file. Note that the ``OTEL_EXPORTER_OTLP_ENDPOINT`` specification enables metrics, traces, and logs to be collected. Further configurations can be explored in the `OpenTelemetry OTLP Exporter documentation `_. + + Run the following from the ``tutorials/assets`` directory: + + .. code-block:: bash + + sudo kubectl apply -f otel-example/jaeger.yaml + sudo kubectl apply -f otel-example/jaeger-collector.yaml + sudo kubectl apply -f otel-example/jaeger-query.yaml + sudo kubectl apply -f otel-example/otel-collector-config.yaml + sudo kubectl apply -f otel-example/otel-collector.yaml + sudo helm install vllm ../../helm/ -f values-12-otel-vllm.yaml + + .. code-block:: bash + + sudo kubectl get pods + + Expected output: + + You should see pods such as the following: + + .. code-block:: text + + NAME READY STATUS RESTARTS AGE + jaeger-744484b5bc-rdrgn 1/1 Running 0 13m + otel-collector-859db69dd4-kj8x6 1/1 Running 0 12m + vllm-deployment-router-6888598c6-m6gl9 1/1 Running 0 12m + vllm-opt125m-deployment-vllm-b489dfd8b-95gb5 1/1 Running 0 12m + + - The ``vllm-deployment-router`` pod acts as the router, managing requests and routing them to the appropriate model-serving pod. + - The ``vllm-opt125m-deployment-vllm`` pod serves the actual model for inference. + +2. Check service usage: + + .. code-block:: bash + + sudo kubectl get services + + Expected output: + + Ensure there are services for the serving engine, router, jaeger-collector, and jaeger-query. Note that the OpenTelemetry deployment does not require its own service: + + .. code-block:: text + + NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE + jaeger-collector ClusterIP 10.99.125.245 4317/TCP,4318/TCP 86m + jaeger-query ClusterIP 10.97.59.30 16686/TCP 86m + vllm-engine-service ClusterIP 10.102.6.58 80/TCP 86m + vllm-router-service ClusterIP 10.103.127.48 80/TCP 86m + + - The ``vllm-engine-service`` exposes the serving engine. + - The ``vllm-router-service`` handles routing and load balancing across model-serving pods. + - The ``jaeger-collector`` service handles collection of trace data from OpenTelemetry. + - The ``jaeger-query`` service pulls data from the jaeger collector to use in the UI. + +3. Expose the model and the Jaeger UI: + + .. code-block:: bash + + sudo kubectl port-forward svc/vllm-router-service 30080:80 + sudo kubectl port-forward svc/jaeger-query 16686:16686 + + Note that 30080:80 can be replaced with any TCP/UDP port and that port 16686 is not used if a different ``jaeger-query`` port is chosen instead. + +Please refer to Step 3 in the :doc:`../getting_started/quickstart` tutorial for querying the deployed vLLM service. You can monitor all queries by navigating to localhost:16686 or wherever your jaeger-query port is specified, select ``jaeger-all-in-one`` from the Service dropdown menu on the Jaeger UI and click "Find Traces" to yield the traces. + +Conclusion +---------- + +In this tutorial, you configured and deployed a vLLM serving engine in a Kubernetes environment, processed and exported resulting traces to Jaeger using an OpenTelemetry collector, and viewed the traces in the Jaeger UI. For further customization, please look at the various data sources available for monitoring `here `_. diff --git a/docs/source/tutorials/kvaware.rst b/docs/source/use_cases/kv-cache-aware-routing.rst similarity index 63% rename from docs/source/tutorials/kvaware.rst rename to docs/source/use_cases/kv-cache-aware-routing.rst index 574d2cc52..a0676b944 100644 --- a/docs/source/tutorials/kvaware.rst +++ b/docs/source/use_cases/kv-cache-aware-routing.rst @@ -1,38 +1,49 @@ KV Cache Aware Routing -====================================== - -Introduction ------------------------------------------------- +====================== This tutorial demonstrates how to use KV cache aware routing in the vLLM Production Stack. KV cache aware routing ensures that subsequent requests with the same prompt prefix are routed to the same instance, maximizing KV cache utilization and improving performance. +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Deploy with KV Cache Aware Routing`_ +3. `Step 2: Port Forwarding`_ +4. `Step 3: Testing KV Cache Aware Routing`_ +5. `Step 4: Clean Up`_ + Prerequisites ------------------------------------------------- +------------- + +- Completion of the following tutorials: -* Installation of minimal example shown in :ref:`examples` -* Kubernetes environment with GPU support + - :doc:`../getting_started/prerequisite` + - :doc:`../getting_started/quickstart` -Deploy with KV Cache Aware Routing ------------------------------------------------- +- A Kubernetes environment with GPU support +- Basic familiarity with Kubernetes and Helm + +Step 1: Deploy with KV Cache Aware Routing +------------------------------------------ We'll use the predefined configuration file ``values-17-kv-aware.yaml`` which sets up two vLLM instances with KV cache aware routing enabled. 1. Deploy the Helm chart with the configuration: -.. code-block:: bash + .. code-block:: bash - helm install vllm helm/ -f tutorials/assets/values-17-kv-aware.yaml + helm install vllm helm/ -f tutorials/assets/values-17-kv-aware.yaml -Note that to add more instances, you need to specify **different** ``instanceId`` in ``lmcacheConfig``. + Note that to add more instances, you need to specify different ``instanceId`` in ``lmcacheConfig``. -Wait for the deployment to complete: + Wait for the deployment to complete: -.. code-block:: bash + .. code-block:: bash - kubectl get pods -w + kubectl get pods -w -Port Forwarding ------------------------------------------------- +Step 2: Port Forwarding +----------------------- Forward the router service port to your local machine: @@ -40,8 +51,8 @@ Forward the router service port to your local machine: kubectl port-forward svc/vllm-router-service 30080:80 -Testing KV Cache Aware Routing ------------------------------------------------- +Step 3: Testing KV Cache Aware Routing +-------------------------------------- First, send a request to the router: @@ -50,7 +61,7 @@ First, send a request to the router: curl http://localhost:30080/v1/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "meta-llama/Llama-3.2-1B-Instruct", + "model": "meta-llama/Llama-3.1-8B-Instruct", "prompt": "What is the capital of France?", "max_tokens": 100 }' @@ -62,15 +73,15 @@ Then, send another request with the same prompt prefix: curl http://localhost:30080/v1/completions \ -H "Content-Type: application/json" \ -d '{ - "model": "meta-llama/Llama-3.2-1B-Instruct", + "model": "meta-llama/Llama-3.1-8B-Instruct", "prompt": "What is the capital of France? And what is its population?", "max_tokens": 100 }' You should observe that the second request is routed to the same instance as the first request. This is because the KV cache aware router detects that the second request shares a prefix with the first request and routes it to the same instance to maximize KV cache utilization. -Clean Up ------------------------------------------------- +Step 4: Clean Up +----------------- To clean up the deployment: @@ -79,7 +90,7 @@ To clean up the deployment: helm uninstall vllm Conclusion ------------------------------------------------- +---------- In this tutorial, we've demonstrated how to: diff --git a/docs/source/use_cases/pipeline-parallelism-kuberay.rst b/docs/source/use_cases/pipeline-parallelism-kuberay.rst new file mode 100644 index 000000000..1bf1ce310 --- /dev/null +++ b/docs/source/use_cases/pipeline-parallelism-kuberay.rst @@ -0,0 +1,327 @@ +Pipeline Parallelism with KubeRay +================================= + +This tutorial provides a step-by-step guide for configuring and deploying the vLLM serving engine on a multi-node Kubernetes cluster with support for distributed inference using KubeRay. It also explains how to launch the vLLM serving engine with pipeline parallelism enabled. + +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Basic explanation of Ray and Kuberay`_ +3. `Step 2: Preparing the Configuration File`_ +4. `Step 3: Applying the Configuration`_ +5. `Step 4: Verifying the Deployment`_ + +Prerequisites +------------- + +- A Kubernetes cluster with multiple nodes with GPU support, as set up in the `00-a-install-multinode-kubernetes-env tutorial `_. +- Install kuberay operator on the Kubernetes environment with `00-b-install-kuberay-operator tutorial `_. +- Helm installed on your system. +- Access to a HuggingFace token (``HF_TOKEN``). +- A basic understanding of Ray is recommended. For more information, refer to the `official ray documentation `_. + +Step 1: Basic explanation of Ray and Kuberay +-------------------------------------------- + +1. Ray is a framework designed for distributed workloads, such as distributed training and inference. It operates by running multiple processes—typically containers or pods—to distribute and synchronize tasks efficiently. + +2. Ray organizes these processes into a Ray cluster, which consists of a single head node and multiple worker nodes. The term "node" here refers to a logical process, which can be deployed as a container or pod. + +3. KubeRay is a Kubernetes operator that simplifies the creation and management of Ray clusters within a Kubernetes environment. Without KubeRay, setting up Ray nodes requires manual configuration. + +4. Using KubeRay, you can easily deploy Ray clusters on Kubernetes. These clusters enable distributed inference with vLLM, supporting both tensor parallelism and pipeline parallelism. + +Step 2: Preparing the Configuration File +---------------------------------------- + +1. Locate the example configuration file `tutorials/assets/values-15-a-minimal-pipeline-parallel-example-raycluster.yaml `_. + +2. Open the file and update the following fields: + + - Write your actual huggingface token in ``hf_token: `` in the yaml file. + +Explanation of Key Items in ``values-15-a-minimal-pipeline-parallel-example-raycluster.yaml`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +- **raySpec**: Required when using KubeRay to enable pipeline parallelism. +- **headNode**: Specifies the resource requirements for the Kuberay head node and must be defined accordingly: + + - **requestCPU**: The amount of CPU resources requested for Kuberay head pod. + - **requestMemory**: Memory allocation for Kuberay head pod. Sufficient memory is required to load the model. + - **requestGPU**: Defines the number of GPUs to allocate for the KubeRay head pod. Currently, the Ray head node must also participate in both tensor parallelism and pipeline parallelism. This requirement exists because the ``vllm serve ...`` command is executed on the Ray head node, and vLLM mandates that the pod where this command is run must have at least one visible GPU. + +- **name**: The unique identifier for your model deployment. +- **repository**: The Docker repository containing the model's serving engine image. +- **tag**: Specifies the version of the model image to use. +- **modelURL**: The URL pointing to the model on Hugging Face or another hosting service. +- **replicaCount**: The number of total Kuberay worker pods. +- **requestCPU**: The amount of CPU resources requested per Kuberay worker pod. +- **requestMemory**: Memory allocation for each Kuberay worker pod. Sufficient memory is required to load the model. +- **requestGPU**: Specifies the number of GPUs to allocate for each Kuberay worker pod. +- **vllmConfig**: Contains model-specific configurations: + + - ``tensorParallelSize``: Specifies the number of GPUs assigned to each worker pod. This value must be identical to both ``requestGPU`` and ``raySpec.headNode.requestGPU``. + - ``pipelineParallelSize``: Indicates the level of pipeline parallelism. This value must be equal to ``replicaCount + 1``, representing the total number of Ray cluster nodes, including both head and worker nodes. + - **Important Note:** + + - The total number of GPUs required is computed as ``pipelineParallelSize × tensorParallelSize``. + - This total must exactly match the sum of: + + - ``replicaCount × requestGPU`` (the total number of GPUs allocated to Ray worker nodes), and + - ``raySpec.headNode.requestGPU`` (the number of GPUs allocated to the Ray head node). + + - The ``requestGPU`` value for the Ray head node must be identical to that of each worker node. + - ``tensorParallelSize`` defines the number of GPUs allocated per Ray node (including both head and worker nodes), and must be consistent across all nodes. + - ``pipelineParallelSize`` represents the total number of Ray nodes, and must therefore be set to replicaCount + 1 (i.e., the number of worker nodes plus the head node). + +- **shmSize**: Configures the shared memory size to ensure adequate memory is available for inter-process communication during tensor and pipeline parallelism execution. +- **hf_token**: The Hugging Face token for authenticating with the Hugging Face model hub. + +Example Snippet +~~~~~~~~~~~~~~~ + +In the following example, we configure a total of two Ray nodes each equipped with two GPUs (one head node and one worker node) to serve a distilgpt2 model. We set the tensor parallelism size to 2, as each node contains two GPUs, and the pipeline parallelism size to 2, corresponding to the two Ray nodes being utilized. + +.. code-block:: yaml + + servingEngineSpec: + runtimeClassName: "" + modelSpec: + - name: "distilgpt2" + repository: "vllm/vllm-openai" + tag: "latest" + modelURL: "distilbert/distilgpt2" + + replicaCount: 1 + + requestCPU: 2 + requestMemory: "20Gi" + requestGPU: 2 + + vllmConfig: + tensorParallelSize: 2 + pipelineParallelSize: 2 + + shmSize: "20Gi" + + raySpec: + headNode: + requestCPU: 2 + requestMemory: "20Gi" + requestGPU: 2 + + hf_token: + +Step 3: Applying the Configuration +---------------------------------- + +Deploy the configuration using Helm: + +.. code-block:: bash + + helm repo add vllm https://vllm-project.github.io/production-stack + helm install vllm vllm/vllm-stack -f tutorials/assets/values-15-a-minimal-pipeline-parallel-example-raycluster.yaml + +Expected output: + +You should see output indicating the successful deployment of the Helm chart: + +.. code-block:: text + + NAME: vllm + LAST DEPLOYED: Sun May 11 15:10:34 2025 + NAMESPACE: default + STATUS: deployed + REVISION: 1 + TEST SUITE: None + +Step 4: Verifying the Deployment +--------------------------------- + +1. Check the status of the pods: + + .. code-block:: bash + + kubectl wait --for=condition=ready pod -l environment=router,release=router --namespace=default --timeout=60s && \ + kubectl get pods + + Expected output: + + You should see the following pods: + + .. code-block:: text + + pod/vllm-deployment-router-8666bf6464-v97v8 condition met + NAME READY STATUS RESTARTS AGE IP NODE NOMINATED NODE READINESS GATES + kuberay-operator-f89ddb644-858bw 1/1 Running 0 12h 192.168.165.203 insudevmachine + vllm-deployment-router-8666bf6464-v97v8 1/1 Running 0 12h 192.168.165.206 insudevmachine + vllm-distilgpt2-raycluster-head-wvqj5 1/1 Running 0 12h 192.168.190.20 instance-20250503-060921 + vllm-distilgpt2-raycluster-ray-worker-fdvnh 1/1 Running 0 12h 192.168.165.207 insudevmachine + + - In this example, the production stack is deployed in a Kubernetes environment consisting of two nodes, each equipped with two GPUs. + + - The Ray head and worker nodes are scheduled on separate nodes. A total of four GPUs are utilized, with each node contributing two GPUs. + + - The vllm-deployment-router pod functions as the request router, directing incoming traffic to the appropriate model-serving pod. + + - The vllm-distilgpt2-raycluster-head pod is responsible for running the primary vLLM command. + + - The vllm-distilgpt2-raycluster-ray-worker-* pods serve the model and handle inference requests. + +2. Verify the service is exposed correctly: + + .. code-block:: bash + + kubectl get services + + Expected output: + + Ensure there are services for both the serving engine and the router: + + .. code-block:: text + + NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE + kuberay-operator ClusterIP 10.97.0.153 8080/TCP 13h + kubernetes ClusterIP 10.96.0.1 443/TCP 13h + vllm-distilgpt2-engine-service ClusterIP 10.106.237.111 80/TCP 12h + vllm-distilgpt2-raycluster-head-svc ClusterIP None 8000/TCP,8080/TCP 12h + vllm-router-service ClusterIP 10.97.229.184 80/TCP 12h + + - The ``vllm-*-engine-service`` exposes the head node of the ray cluster. + - The ``vllm-*-router-service`` handles routing and load balancing across model-serving pods. + +3. Test the health endpoint: + + To verify that the service is operational, execute the following commands: + + .. code-block:: bash + + kubectl port-forward svc/vllm-router-service 30080:80 + curl http://localhost:30080/v1/models + + **Note:** Port forwarding must be performed from a separate shell session. If the deployment is configured correctly, you should receive a response similar to the following: + + .. code-block:: text + + { + "object": "list", + "data": [ + { + "id": "distilbert/distilgpt2", + "object": "model", + "created": 1747465656, + "owned_by": "vllm", + "root": null + } + ] + } + + You may also perform a basic inference test to validate that pipeline parallelism is functioning as expected. Use the following curl command: + + .. code-block:: bash + + curl -X POST http://localhost:30080/v1/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "distilbert/distilgpt2", + "prompt": "Once upon a time,", + "max_tokens": 10 + }' + + A successful response should resemble the following output: + + .. code-block:: text + + { + "id": "cmpl-92c4ceef0f1c42c9bba10da8306bf86c", + "object": "text_completion", + "created": 1747465724, + "model": "distilbert/distilgpt2", + "choices": [ + { + "index": 0, + "text": "? Huh, are you all red?\n\n", + "logprobs": null, + "finish_reason": "length", + "stop_reason": null, + "prompt_logprobs": null + } + ], + "usage": { + "prompt_tokens": 5, + "total_tokens": 15, + "completion_tokens": 10, + "prompt_tokens_details": null + } + } + + You can also monitor GPU usage for each Ray head and worker pod: + + .. code-block:: text + + kubectl exec -it vllm-distilgpt2-raycluster-head-wvqj5 -- /bin/bash + root@vllm-distilgpt2-raycluster-head-wvqj5:/vllm-workspace# nvidia-smi + Sat May 17 00:10:48 2025 + +-----------------------------------------------------------------------------------------+ + | NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 | + |-----------------------------------------+------------------------+----------------------+ + | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | + | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | + | | | MIG M. | + |=========================================+========================+======================| + | 0 NVIDIA L4 Off | 00000000:00:03.0 Off | 0 | + | N/A 76C P0 35W / 72W | 20313MiB / 23034MiB | 0% Default | + | | | N/A | + +-----------------------------------------+------------------------+----------------------+ + | 1 NVIDIA L4 Off | 00000000:00:04.0 Off | 0 | + | N/A 70C P0 33W / 72W | 20305MiB / 23034MiB | 0% Default | + | | | N/A | + +-----------------------------------------+------------------------+----------------------+ + + +-----------------------------------------------------------------------------------------+ + | Processes: | + | GPU GI CI PID Type Process name GPU Memory | + | ID ID Usage | + |=========================================================================================| + | 0 N/A N/A 8 C /usr/bin/python3 0MiB | + | 1 N/A N/A 1082 C ray::RayWorkerWrapper 0MiB | + +-----------------------------------------------------------------------------------------+ + + ########################################################################################### + + kubectl exec -it vllm-distilgpt2-raycluster-ray-worker-fdvnh -- /bin/bash + Defaulted container "vllm-ray-worker" out of: vllm-ray-worker, wait-gcs-ready (init) + root@vllm-distilgpt2-raycluster-ray-worker-fdvnh:/vllm-workspace# nvidia-smi + Sat May 17 00:12:06 2025 + +-----------------------------------------------------------------------------------------+ + | NVIDIA-SMI 550.90.07 Driver Version: 550.90.07 CUDA Version: 12.4 | + |-----------------------------------------+------------------------+----------------------+ + | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | + | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | + | | | MIG M. | + |=========================================+========================+======================| + | 0 NVIDIA L4 Off | 00000000:00:03.0 Off | 0 | + | N/A 76C P0 40W / 72W | 20065MiB / 23034MiB | 0% Default | + | | | N/A | + +-----------------------------------------+------------------------+----------------------+ + | 1 NVIDIA L4 Off | 00000000:00:04.0 Off | 0 | + | N/A 72C P0 38W / 72W | 20063MiB / 23034MiB | 0% Default | + | | | N/A | + +-----------------------------------------+------------------------+----------------------+ + + +-----------------------------------------------------------------------------------------+ + | Processes: | + | GPU GI CI PID Type Process name GPU Memory | + | ID ID Usage | + |=========================================================================================| + | 0 N/A N/A 243 C ray::RayWorkerWrapper 0MiB | + | 1 N/A N/A 244 C ray::RayWorkerWrapper 0MiB | + +-----------------------------------------------------------------------------------------+ + +Conclusion +---------- + +In this tutorial, you configured and deployed the vLLM serving engine with support for pipeline parallelism across multiple GPUs within a multi-node Kubernetes environment using KubeRay. Additionally, you learned how to verify the deployment and monitor the associated pods to ensure proper operation. For further customization and configuration options, please consult the ``values.yaml`` file and the Helm chart documentation. + +To deploy both a Ray cluster and standard Kubernetes deployments using a single Helm release, please refer to the example configuration file available at `tutorials/assets/values-15-b-minimal-pipeline-parallel-example-multiple-modelspec.yaml `_. diff --git a/docs/source/tutorials/prefixaware.rst b/docs/source/use_cases/prefix-aware-routing.rst similarity index 70% rename from docs/source/tutorials/prefixaware.rst rename to docs/source/use_cases/prefix-aware-routing.rst index fa25ff170..b1324f90b 100644 --- a/docs/source/tutorials/prefixaware.rst +++ b/docs/source/use_cases/prefix-aware-routing.rst @@ -1,37 +1,47 @@ Prefix Aware Routing -====================================== - -Introduction --------------------------------- +==================== This tutorial demonstrates how to use prefix aware routing in the vLLM Production Stack. Prefix aware routing ensures that subsequent requests with the same prompt prefix are routed to the same instance, maximizing KV cache utilization and improving performance. +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Deploy with Prefix Aware Routing`_ +3. `Step 2: Port Forwarding`_ +4. `Step 3: Testing Prefix Aware Routing`_ +5. `Step 4: Clean Up`_ + Prerequisites --------------------------------- +------------- -* Installation of minimal example shown in :ref:`examples` -* Kubernetes environment with GPU support +- Completion of the following tutorials: + - :doc:`../getting_started/prerequisite` + - :doc:`../getting_started/quickstart` -Deploy with Prefix Aware Routing ---------------------------------------------- +- A Kubernetes environment with GPU support +- Basic familiarity with Kubernetes and Helm + +Step 1: Deploy with Prefix Aware Routing +---------------------------------------- We'll use the predefined configuration file ``values-18-prefix-aware.yaml`` which sets up two vLLM instances with prefix aware routing enabled. 1. Deploy the Helm chart with the configuration: -.. code-block:: bash + .. code-block:: bash - helm install vllm helm/ -f tutorials/assets/values-18-prefix-aware.yaml + helm install vllm helm/ -f tutorials/assets/values-18-prefix-aware.yaml -2. Wait for the deployment to complete: + Wait for the deployment to complete: -.. code-block:: bash + .. code-block:: bash - kubectl get pods -w + kubectl get pods -w -Port Forwarding ---------------------------------------------- +Step 2: Port Forwarding +----------------------- Forward the router service port to your local machine: @@ -39,10 +49,10 @@ Forward the router service port to your local machine: kubectl port-forward svc/vllm-router-service 30080:80 -Testing Prefix Aware Routing ---------------------------------------------- +Step 3: Testing Prefix Aware Routing +------------------------------------ -1. First, send a request to the router: +First, send a request to the router: .. code-block:: bash @@ -54,7 +64,7 @@ Testing Prefix Aware Routing "max_tokens": 100 }' -2. Then, send another request with the same prompt prefix: +Then, send another request with the same prompt prefix: .. code-block:: bash @@ -75,8 +85,8 @@ Specifically, you should see some log like the following: [2025-06-03 06:16:28,963] LMCache DEBUG: Scheduled to load 5 tokens for request cmpl-306538839e87480ca5604ecc5f75c847-0 (vllm_v1_adapter.py:299:lmcache.integration.vllm.vllm_v1_adapter) [2025-06-03 06:16:28,966] LMCache DEBUG: Retrieved 6 out of 6 out of total 6 tokens (cache_engine.py:330:lmcache.experimental.cache_engine) -Clean Up ---------------------------------------------- +Step 4: Clean Up +----------------- To clean up the deployment: @@ -85,7 +95,7 @@ To clean up the deployment: helm uninstall vllm Conclusion ---------------------------------------------- +---------- In this tutorial, we've demonstrated how to: diff --git a/docs/source/use_cases/sharing-kv-cache.rst b/docs/source/use_cases/sharing-kv-cache.rst new file mode 100644 index 000000000..8c68e1d3c --- /dev/null +++ b/docs/source/use_cases/sharing-kv-cache.rst @@ -0,0 +1,151 @@ +Sharing KV Cache Across Instances +================================== + +This tutorial demonstrates how to enable remote KV cache storage using LMCache in a vLLM deployment. Remote KV cache sharing moves large KV caches from GPU memory to a remote shared storage, enabling more KV cache hits and potentially making the deployment more fault tolerant. + +vLLM Production Stack uses LMCache for remote KV cache sharing. For more details, see the `LMCache GitHub repository `_. + +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Configuring Remote KV Cache Storage`_ +3. `Step 2: Deploying the Helm Chart`_ +4. `Step 3: Verifying the Installation`_ +5. `Benchmark the Performance Gain of Remote Shared Storage (Work in Progress)`_ + +Prerequisites +------------- + +- Completion of the following tutorials: + + - :doc:`../getting_started/prerequisite` + - :doc:`../getting_started/quickstart` + - :doc:`../deployment/helm` + +- A Kubernetes environment with GPU support. + +Step 1: Configuring Remote KV Cache Storage +-------------------------------------------- + +Locate the file `tutorials/assets/values-06-remote-shared-storage.yaml `_ with the following content: + +.. code-block:: yaml + + servingEngineSpec: + runtimeClassName: "" + modelSpec: + - name: "mistral" + repository: "lmcache/vllm-openai" + tag: "latest" + modelURL: "mistralai/Mistral-7B-Instruct-v0.2" + replicaCount: 2 + requestCPU: 10 + requestMemory: "40Gi" + requestGPU: 1 + pvcStorage: "50Gi" + vllmConfig: + enableChunkedPrefill: false + enablePrefixCaching: false + maxModelLen: 16384 + v1: 1 + + lmcacheConfig: + enabled: true + cpuOffloadingBufferSize: "20" + + hf_token: + + cacheserverSpec: + replicaCount: 1 + containerPort: 8080 + servicePort: 81 + serde: "naive" + + repository: "lmcache/vllm-openai" + tag: "latest" + resources: + requests: + cpu: "4" + memory: "8G" + limits: + cpu: "4" + memory: "10G" + + labels: + environment: "cacheserver" + release: "cacheserver" + +.. note:: + Replace ```` with your actual Hugging Face token. + +Also, right now ``v1`` has to be set to ``1`` to use LMCache docker image with ``latest`` tag. + +The ``CacheserverSpec`` starts a remote shared KV cache storage. + +Step 2: Deploying the Helm Chart +--------------------------------- + +Deploy the Helm chart using the customized values file: + +.. code-block:: bash + + helm install vllm vllm/vllm-stack -f tutorials/assets/values-06-shared-storage.yaml + +Step 3: Verifying the Installation +----------------------------------- + +1. Check the pod logs to verify LMCache is active: + + .. code-block:: bash + + kubectl get pods + + Identify the pod name for the vLLM deployment (e.g., ``vllm-mistral-deployment-vllm-xxxx-xxxx``). Then run: + + .. code-block:: bash + + kubectl logs -f + + Look for entries in the log indicating LMCache is enabled and operational. An example output (indicating KV cache is stored) is: + + .. code-block:: text + + INFO 01-21 20:16:58 lmcache_connector.py:41] Initializing LMCacheConfig under kv_transfer_config kv_connector='LMCacheConnector' kv_buffer_device='cuda' kv_buffer_size=1000000000.0 kv_role='kv_both' kv_rank=None kv_parallel_size=1 kv_ip='127.0.0.1' kv_port=14579 + INFO LMCache: Creating LMCacheEngine instance vllm-instance [2025-01-21 20:16:58,732] -- /usr/local/lib/python3.12/dist-packages/lmcache/experimental/cache_engine.py:237 + +2. Forward the router service port to access the stack locally: + + .. code-block:: bash + + kubectl port-forward svc/vllm-router-service 30080:80 + +3. Send a request to the stack and observe the logs: + + .. code-block:: bash + + curl -X POST http://localhost:30080/v1/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "mistralai/Mistral-7B-Instruct-v0.2", + "prompt": "Explain the significance of KV cache in language models.", + "max_tokens": 10 + }' + + Expected output: + + The response from the stack should contain the completion result, and the logs should show LMCache activity, for example: + + .. code-block:: text + + DEBUG LMCache: Store skips 0 tokens and then stores 13 tokens [2025-01-21 20:23:45,113] -- /usr/local/lib/python3.12/dist-packages/lmcache/integration/vllm/vllm_adapter.py:490 + +Benchmark the Performance Gain of Remote Shared Storage (Work in Progress) +--------------------------------------------------------------------------- + +In this section, we will benchmark the performance improvement when using LMCache for remote KV cache shared storage. Stay tuned for updates. + +Conclusion +---------- + +This tutorial demonstrated how to enable a shared KV cache storage across multiple vllm nodes in a vLLM deployment using LMCache. By storing KV cache to a remote shared storage, you can improve KV cache hit rate and potentially make the deployment more fault tolerant. Explore further configurations to tailor LMCache to your workloads. diff --git a/docs/source/use_cases/sleep-wakeup-mode.rst b/docs/source/use_cases/sleep-wakeup-mode.rst new file mode 100644 index 000000000..40a5e2285 --- /dev/null +++ b/docs/source/use_cases/sleep-wakeup-mode.rst @@ -0,0 +1,234 @@ +Sleep and Wakeup Mode +===================== + +This tutorial demonstrates how to use vLLM v1 ``sleep`` and ``wake_up mode`` feature with vLLM Production Stack. A sleeping engine does not process any requests and free up resources (e.g., GPU memory). The router supports serving requests to vLLM engines with sleep mode enabled. + +Table of Contents +----------------- + +1. Prerequisites_ +2. `Step 1: Deploy with Sleep Mode Enable`_ +3. `Step 2: Port Forwarding`_ +4. `Step 3: Testing sleep Mode Feature for the Engine`_ +5. `Step 4: Clean Up`_ + +Prerequisites +------------- + +- Completion of the following tutorials: + + - :doc:`../getting_started/prerequisite` + - :doc:`../getting_started/quickstart` + +- A Kubernetes environment with GPU support +- Basic familiarity with Kubernetes and Helm + +Step 1: Deploy with Sleep Mode Enable +------------------------------------- + +We'll use the predefined configuration file ``values-19-sleep-mode-aware.yaml`` which sets up a vLLM instance with sleep mode enabled. + +1. Deploy the Helm chart with the configuration: + + .. code-block:: bash + + helm install vllm helm/ -f tutorials/assets/values-19-sleep-mode-aware.yaml + + Wait for the deployment to complete: + + .. code-block:: bash + + kubectl get pods -w + +Step 2: Port Forwarding +----------------------- + +Forward the router service port to your local machine: + +.. code-block:: bash + + kubectl port-forward svc/vllm-router-service 30080:80 + +Step 3: Testing sleep Mode Feature for the Engine +------------------------------------------------- + +First, get the list of available engines: + +.. code-block:: bash + + curl -o- http://localhost:30080/engines | jq + +Expected similar output: + +.. code-block:: json + + [ + { + "engine_id": "b36921ab-6611-58c0-a941-16c51296446b", + "serving_models": [ + "ibm-granite/granite-3.0-3b-a800m-instruct" + ], + "created": 1750035988 + } + ] + +Using the id of the target engine, check the sleeping state of the vLLM engine: + +.. code-block:: bash + + curl -o- http://localhost:30080/is_sleeping?id=b36921ab-6611-58c0-a941-16c51296446b | jq + +Expected output: + +.. code-block:: json + + { + "is_sleeping": false + } + +Put the engine to sleep and check its sleeping state: + +.. code-block:: bash + + curl -X POST http://localhost:30080/sleep?id=b36921ab-6611-58c0-a941-16c51296446b | jq + +Expected output: + +.. code-block:: json + + { + "status": "success" + } + +.. code-block:: bash + + curl -o- http://localhost:30080/is_sleeping?id=b36921ab-6611-58c0-a941-16c51296446b | jq + +Expected output: + +.. code-block:: json + + { + "is_sleeping": true + } + +The logs of the vLLM pod shows that the engine was put to sleep: + +.. code-block:: text + + INFO 06-15 18:08:18 [gpu_worker.py:81] Sleep mode freed 39.26 GiB memory, 1.20 GiB memory is still in use. + INFO 06-15 18:08:18 [executor_base.py:210] It took 5.749613 seconds to fall asleep. + INFO: 10.130.2.172:47082 - "POST /sleep HTTP/1.1" 200 OK + +Then, send a request to the router: + +.. code-block:: bash + + curl http://localhost:30080/v1/completions?id=b36921ab-6611-58c0-a941-16c51296446b \ + -H "Content-Type: application/json" \ + -d '{ + "model": "ibm-granite/granite-3.0-3b-a800m-instruct", + "prompt": "What is the capital of France?", + "max_tokens": 100 + }' | jq + +Expected output: + +.. code-block:: json + + { + "error": "Model ibm-granite/granite-3.0-3b-a800m-instruct not found or vLLM engine is sleeping." + } + +Now, wake up the vLLM engine and check its sleeping state: + +.. code-block:: bash + + curl -X POST http://localhost:30080/wake_up?id=b36921ab-6611-58c0-a941-16c51296446b | jq + +Expected output: + +.. code-block:: json + + { + "status": "success" + } + +.. code-block:: bash + + curl -o- http://localhost:30080/is_sleeping?id=b36921ab-6611-58c0-a941-16c51296446b | jq + +Expected output: + +.. code-block:: json + + { + "is_sleeping": false + } + +The logs of the vLLM pod shows that the engine was waked up: + +.. code-block:: text + + INFO 06-15 18:11:37 [api_server.py:719] wake up the engine with tags: None + INFO 06-15 18:11:37 [executor_base.py:226] It took 0.284914 seconds to wake up tags {'kv_cache', 'weights'}. + INFO: 10.130.2.172:46672 - "POST /wake_up HTTP/1.1" 200 OK + +Lastly, re-send a request to the router: + +.. code-block:: bash + + curl http://localhost:30080/v1/completions?id=b36921ab-6611-58c0-a941-16c51296446b \ + -H "Content-Type: application/json" \ + -d '{ + "model": "ibm-granite/granite-3.0-3b-a800m-instruct", + "prompt": "What is the capital of France?", + "max_tokens": 100 + }' | jq + +Expected similar output: + +.. code-block:: json + + { + "id": "cmpl-125b905e89a34384af754a24bc8ea686", + "object": "text_completion", + "created": 1750036455, + "model": "ibm-granite/granite-3.0-3b-a800m-instruct", + "choices": [ + { + "index": 0, + "text": "\n\nThe capital of France is Paris.\n\n[Answer] The capital of France is Paris.", + "logprobs": null, + "finish_reason": "stop", + "stop_reason": null, + "prompt_logprobs": null + } + ], + "usage": { + "prompt_tokens": 7, + "total_tokens": 31, + "completion_tokens": 24, + "prompt_tokens_details": null + } + } + +Step 4: Clean Up +----------------- + +To clean up the deployment: + +.. code-block:: bash + + helm uninstall vllm + +Conclusion +---------- + +In this tutorial, we've demonstrated how to: + +1. Deploy vLLM Production Stack with sleep mode enable for the vLLM engine +2. Set up port forwarding to access the router +3. Test the sleep mode feature for the vLLM engine and the sleep mode aware routing functionalities + +The sleep aware routing feature helps to ensure that router does not forward requests to a sleeping engine. Hence, improving performance of the Production Stack. diff --git a/docs/source/use_cases/tool-enabled-installation.rst b/docs/source/use_cases/tool-enabled-installation.rst new file mode 100644 index 000000000..0a538b881 --- /dev/null +++ b/docs/source/use_cases/tool-enabled-installation.rst @@ -0,0 +1,194 @@ +Tool Enabled Installation +========================= + +This tutorial guides you through setting up the vLLM Production Stack with tool calling support using the Llama-3.1-8B-Instruct model. This setup enables your model to interact with external tools and functions through a structured interface. + +Prerequisites +------------- + +1. All prerequisites from the :doc:`../getting_started/quickstart` tutorial +2. A Hugging Face account with access to Llama-3.1-8B-Instruct +3. Accepted terms for meta-llama/Llama-3.1-8B-Instruct on Hugging Face +4. A valid Hugging Face token +5. Python 3.7+ installed on your local machine +6. The ``openai`` Python package installed (``pip install openai``) +7. Access to a Kubernetes cluster with storage provisioner support + +Steps +----- + +1. Set up vLLM Templates and Storage +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +First, run the setup script to download templates and create the necessary Kubernetes resources: + +.. code-block:: bash + + # Make the script executable + chmod +x scripts/setup_vllm_templates.sh + + # Run the setup script + ./scripts/setup_vllm_templates.sh + +This script will: + +1. Download the required templates from the vLLM repository +2. Create a PersistentVolume for storing the templates +3. Create a PersistentVolumeClaim for accessing the templates +4. Verify the setup is complete + +The script uses consistent naming that matches the deployment configuration: + +- PersistentVolume: ``vllm-templates-pv`` +- PersistentVolumeClaim: ``vllm-templates-pvc`` + +2. Set up Hugging Face Credentials +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Create a Kubernetes secret with your Hugging Face token: + +.. code-block:: bash + + kubectl create secret generic huggingface-credentials \ + --from-literal=HUGGING_FACE_HUB_TOKEN=your_token_here + +3. Deploy vLLM Instance with Tool Calling Support +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +3.1: Use the Example Configuration +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We'll use the example configuration file located at ``tutorials/assets/values-08-tool-enabled.yaml``. This file contains all the necessary settings for enabling tool calling: + +.. code-block:: yaml + + servingEngineSpec: + runtimeClassName: "" + modelSpec: + - name: "llama3-8b" + repository: "vllm/vllm-openai" + tag: "latest" + modelURL: "meta-llama/Llama-3.1-8B-Instruct" + + # Tool calling configuration + enableTool: true + toolCallParser: "llama3_json" # Parser to use for tool calls (e.g., "llama3_json" for Llama models) + chatTemplate: "tool_chat_template_llama3.1_json.jinja" # Template file name (will be mounted at /vllm/templates) + + # Mount Hugging Face credentials + env: + - name: HUGGING_FACE_HUB_TOKEN + valueFrom: + secretKeyRef: + name: huggingface-credentials + key: HUGGING_FACE_HUB_TOKEN + + replicaCount: 1 + + # Resource requirements for Llama-3.1-8B-Instruct + requestCPU: 8 + requestMemory: "32Gi" + requestGPU: 1 + +.. note:: + The tool calling configuration is now simplified: + + - ``enableTool: true`` enables the feature + - ``toolCallParser``: specifies how the model's tool calls are parsed (using "llama3_json" for Llama-3 models) + - ``chatTemplate``: specifies the template file name (will be mounted at ``/vllm/templates/``) + + The chat templates are managed through a PersistentVolume that we created in step 1, which provides several benefits: + + - Templates are downloaded once and stored persistently + - Templates can be shared across multiple deployments + - Templates can be updated by updating the files in the PersistentVolume + - Templates are version controlled with the vLLM repository + +3.2: Deploy the Helm Chart +^^^^^^^^^^^^^^^^^^^^^^^^^^ + +.. code-block:: bash + + # Add the vLLM Helm repository if you haven't already + helm repo add vllm https://vllm-project.github.io/production-stack + + # Deploy the vLLM stack with tool calling support using the example configuration + helm install vllm-tool vllm/vllm-stack -f tutorials/assets/values-08-tool-enabled.yaml + +The deployment will: + +1. Use the PersistentVolume we created in step 1 to access the templates +2. Mount the templates at ``/vllm/templates`` in the container +3. Configure the model to use the specified template for tool calling + +You can verify the deployment with: + +.. code-block:: bash + + # Check the deployment status + kubectl get deployments + + # Check the pods + kubectl get pods + + # Check the logs + kubectl logs -f deployment/vllm-tool-llama3-8b-deployment-vllm + +4. Test Tool Calling Setup +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Now that the deployment is running, let's test the tool calling functionality using the example script. + +4.1: Port Forward the Router Service +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +First, we need to set up port forwarding to access the router service: + +.. code-block:: bash + + # Get the service name + kubectl get svc + + # Set up port forwarding to the router service + kubectl port-forward svc/vllm-tool-router-service 8000:80 + +4.2: Run the Example Script +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In a new terminal, run the example script to test tool calling: + +.. code-block:: bash + + # Navigate to the examples directory + cd src/examples + + # Run the example script + python tool_calling_example.py + +The script will: + +1. Connect to the vLLM service through the port-forwarded endpoint +2. Send a test query asking about the weather +3. Demonstrate the model's ability to: + + - Understand the available tools + - Make appropriate tool calls + - Process the tool responses + +Expected output should look something like: + +.. code-block:: text + + Function called: get_weather + Arguments: {"location": "San Francisco, CA", "unit": "celsius"} + Result: Getting the weather for San Francisco, CA in celsius... + +This confirms that: + +1. The vLLM service is running correctly +2. Tool calling is properly enabled +3. The model can understand and use the defined tools +4. The template system is working as expected + +.. note:: + The example uses a mock weather function for demonstration. In a real application, you would replace this with actual API calls to weather services. diff --git a/docs/source/user_manual/kv_cache/index.rst b/docs/source/user_manual/kv_cache/index.rst deleted file mode 100644 index 1e0ba5595..000000000 --- a/docs/source/user_manual/kv_cache/index.rst +++ /dev/null @@ -1,47 +0,0 @@ -.. _kv_cache_index: - -KV Cache Offloading -=================== - -Enable KV cache offloading using LMCache in a vLLM deployment. KV cache offloading moves large KV caches from GPU memory to CPU or disk, enabling more potential KV cache hits. vLLM Production Stack uses LMCache for KV cache offloading. For more details, see the LMCache GitHub `repository `_. - -KV Cache Offloading Configuration -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Use the following yaml - -.. code-block:: yaml - - servingEngineSpec: - modelSpec: - - name: "mistral" - repository: "lmcache/vllm-openai" - tag: "latest" - modelURL: "mistralai/Mistral-7B-Instruct-v0.2" - replicaCount: 1 - requestCPU: 10 - requestMemory: "40Gi" - requestGPU: 1 - pvcStorage: "50Gi" - vllmConfig: - enableChunkedPrefill: false - enablePrefixCaching: false - maxModelLen: 16384 - - lmcacheConfig: - enabled: true - cpuOffloadingBufferSize: "20" - - hf_token: - - -.. note:: - - Note: Replace with your actual Hugging Face token. - -.. note:: - - The ``lmcacheConfig`` field enables LMCache and sets the CPU offloading buffer size to ``20GB``. You can adjust this value based on your workload. - - -Deploy the Stack using Helm as shown in the :ref:`examples` section. diff --git a/docs/source/user_manual/lora/index.rst b/docs/source/user_manual/lora/index.rst deleted file mode 100644 index fa89a14ab..000000000 --- a/docs/source/user_manual/lora/index.rst +++ /dev/null @@ -1,12 +0,0 @@ -.. _lora_index: - -LORA Configuration -================== - -Test text - -.. toctree:: - :maxdepth: 1 - :caption: User Manual - - manual.rst diff --git a/docs/source/user_manual/lora/manual.rst b/docs/source/user_manual/lora/manual.rst deleted file mode 100644 index ca3e7cd9c..000000000 --- a/docs/source/user_manual/lora/manual.rst +++ /dev/null @@ -1,105 +0,0 @@ -.. _lora_manual: - -Manually Load LORA -=================== - -Download LoRA adapters ----------------------- - -Download a LoRA adapter from HuggingFace to your persistent volume: -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code-block:: bash - - # Get into the vLLM pod - kubectl exec -it $(kubectl get pods | grep vllm-lora-llama2-7b-deployment-vllm | awk '{print $1}') -- bash - - # Inside the pod, download the adapter using Python - mkdir -p /data/lora-adapters - cd /data/lora-adapters - python3 -c " - from huggingface_hub import snapshot_download - adapter_id = 'yard1/llama-2-7b-sql-lora-test' # Example SQL adapter - sql_lora_path = snapshot_download( - repo_id=adapter_id, - local_dir='./sql-lora', - token=__import__('os').environ['HUGGING_FACE_HUB_TOKEN'] - ) - " - - # Verify the adapter files are downloaded - ls -l /data/lora-adapters/sql-lora - - -Access the vLLM API -~~~~~~~~~~~~~~~~~~~~~~~~ - -Set up port forwarding to access the vLLM API: - -.. code-block:: bash - - kubectl port-forward svc/vllm-lora-router-service 8000:80 - - -Verify the connection in a new terminal: - -.. code-block:: bash - - curl http://localhost:8000/v1/models - - -Load and list the models -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Forward the port to the vLLM service: - -.. code-block:: bash - - kubectl port-forward svc/vllm-lora-engine-service 8001:80 - - -List available models: - -.. code-block:: bash - - curl http://localhost:8001/v1/models - - -Load the SQL LoRA adapter: - -.. code-block:: bash - - curl -X POST http://localhost:8001/v1/load_lora_adapter \ - -H "Content-Type: application/json" \ - -d '{ - "lora_name": "sql_adapter", - "lora_path": "/data/lora-adapters/sql-lora" - }' - -Generate Text with LoRA -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Send a query to the vLLM API to generate text using the loaded LoRA adapter: - -.. code-block:: bash - - curl -X POST http://localhost:8000/v1/completions \ - -H "Content-Type: application/json" \ - -d '{ - "model": "meta-llama/Llama-2-7b-hf", - "prompt": "Write a SQL query to select all users who have made a purchase in the last 30 days", - "max_tokens": 100, - "temperature": 0.7, - "lora_adapter": "sql_adapter" - }' - - -Unload the adapter: - -.. code-block:: bash - - curl -X POST http://localhost:8001/v1/unload_lora_adapter \ - -H "Content-Type: application/json" \ - -d '{ - "lora_name": "sql_adapter" - }' diff --git a/docs/source/user_manual/router/cmd.rst b/docs/source/user_manual/router/cmd.rst deleted file mode 100644 index fe2999bb9..000000000 --- a/docs/source/user_manual/router/cmd.rst +++ /dev/null @@ -1,124 +0,0 @@ -.. _cmd: - -Command Line based configuration -================================ - -Configuring and running the router ------------------------------------ - -The router can be configured using command-line arguments. Below are the available options: - -Basic Options -+++++++++++++ - -- ``--host``: The host to run the server on. Default is ``0.0.0.0``. -- ``--port``: The port to run the server on. Default is ``8001``. - - -Service Discovery Options -+++++++++++++++++++++++++ - -- ``--service-discovery``: The service discovery type. Options are ``static`` or ``k8s``. This option is required. -- ``--static-backends``: The URLs of static serving engines, separated by commas (e.g., ``http://localhost:8000,http://localhost:8001``). -- ``--static-models``: The models running in the static serving engines, separated by commas (e.g., ``model1,model2``). -- ``--k8s-port``: The port of vLLM processes when using K8s service discovery. Default is ``8000``. -- ``--k8s-namespace``: The namespace of vLLM pods when using K8s service discovery. Default is ``default``. -- ``--k8s-label-selector``: The label selector to filter vLLM pods when using K8s service discovery. - - -Routing Logic Options -+++++++++++++++++++++ - -- ``--routing-logic``: The routing logic to use. Options are ``roundrobin`` or ``session``. This option is required. -- ``--session-key``: The key (in the header) to identify a session. - - -Monitoring Options -++++++++++++++++++ - -- ``--engine-stats-interval``: The interval in seconds to scrape engine statistics. Default is ``30``. -- ``--request-stats-window``: The sliding window seconds to compute request statistics. Default is ``60``. - - -Logging Options -+++++++++++++++ - -- ``--log-stats``: Log statistics every 30 seconds. -- ``--callbacks``: The path to the callback instance extending CustomCallbackHandler (e.g. ``my_callbacks.my_callback_handler_instance``). - - -Build docker image ------------------- - -.. code-block:: bash - - docker build -t : -f docker/Dockerfile . - - -Example commands to run the router ----------------------------------- - -You can install the router using the following command: - -.. code-block:: bash - - pip install -e . - - -.. raw:: html - - Example : running the router locally at port 8000 in front of multiple serving engines - - -.. code-block:: bash - - vllm-router --port 8000 \ - --service-discovery static \ - --static-backends "http://localhost:9001,http://localhost:9002,http://localhost:9003" \ - --static-models "facebook/opt-125m,meta-llama/Llama-3.1-8B-Instruct,facebook/opt-125m" \ - --engine-stats-interval 10 \ - --log-stats \ - --routing-logic roundrobin - - -Hooking into custom callbacks ------------------------------ - -The router can be extended to add custom callbacks at various points in the request lifecycle. - -For this you will need to create a custom callback handler instance, implementing at least one of the available callback methods. You can find all available callbacks along with detailed descriptions in the abstract `CustomCallbackHandler `_ class. - -.. code-block:: python - - # my_callbacks.py - - from fastapi import Request, Response - - from vllm_router.services.callbacks_service.custom_callbacks import CustomCallbackHandler - - - class MyCustomCallbackHandler(CustomCallbackHandler): - def pre_request(self, request: Request, request_body: bytes, request_json: any) -> Response | None: - """ - Receives the request object before it gets proxied. - """ - if b"coffee" in request_body: - return Response("I'm a teapot", 418) - - def post_request(self, request: Request, response_content: bytes) -> None: - """ - Is executed as a background task, receives the request object - and the complete response_content. - """ - with open("/tmp/response.txt", "ab") as f: - f.write(response_content) - - - my_callback_handler_instance = MyCustomCallbackHandler() - - -You can pass the instance to the router with the filename first (without the file ending), followed by the instance, separated by a dot like this: - -.. code-block:: bash - - vllm-router ... --callbacks my_callbacks.my_callback_handler_instance diff --git a/docs/source/user_manual/router/index.rst b/docs/source/user_manual/router/index.rst deleted file mode 100644 index 6ff76ac11..000000000 --- a/docs/source/user_manual/router/index.rst +++ /dev/null @@ -1,13 +0,0 @@ -.. _router_index: - -Router Configuration -==================== - -Test text - -.. toctree:: - :maxdepth: 1 - :caption: User Manual - - json.rst - cmd.rst diff --git a/docs/source/user_manual/router/json.rst b/docs/source/user_manual/router/json.rst deleted file mode 100644 index 4a1225a4e..000000000 --- a/docs/source/user_manual/router/json.rst +++ /dev/null @@ -1,54 +0,0 @@ -.. _json: - -JSON based configuration -===================================== - -The router can be configured dynamically using a json file when passing the ``--dynamic-config-json`` option. The router will watch the json file for changes and update the configuration accordingly (every 10 seconds). - -Currently, the dynamic config supports the following fields: - -Required fields: - -* ``service_discovery``: The service discovery type. Options are ``static`` or ``k8s``. -* ``routing_logic``: The routing logic to use. Options are ``roundrobin`` or ``session``. - - -Optional fields: - -* (When using static service discovery) ``static_backends``: The URLs of static serving engines, separated by commas (e.g., ``http://localhost:9001,http://localhost:9002,http://localhost:9003``). -* (When using static service discovery) ``static_models``: The models running in the static serving engines, separated by commas (e.g., ``model1,model2``). -* (When using k8s service discovery) ``k8s_port``: The port of vLLM processes when using ``K8s`` service discovery. Default is ``8000``. -* (When using k8s service discovery) ``k8s_namespace``: The namespace of vLLM pods when using K8s service discovery. Default is ``default``. -* (When using k8s service discovery) ``k8s_label_selector``: The label selector to filter vLLM pods when using ``K8s`` service discovery. -* session_key: The key (in the header) to identify a session when using session-based routing. - - -Example dynamic config file: - -.. code-block:: json - - { - "service_discovery": "static", - "routing_logic": "roundrobin", - "static_backends": "http://localhost:9001,http://localhost:9002,http://localhost:9003", - "static_models": "facebook/opt-125m,meta-llama/Llama-3.1-8B-Instruct,facebook/opt-125m" - } - -Get current dynamic config --------------------------- - -If the dynamic config is enabled, the router will reflect the current dynamic config in the ``/health`` endpoint. - -.. code-block:: bash - - curl http://:/health - - -The response will be a JSON object with the current dynamic config. - -.. code-block:: json - - { - "status": "healthy", - "dynamic_config": "" - } diff --git a/uv.lock b/uv.lock index 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19:20:22 +0200 Subject: [PATCH 22/27] fix: update response content iteration method for improved performance Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/services/request_service/request.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/vllm_router/services/request_service/request.py b/src/vllm_router/services/request_service/request.py index f47a0b5ed..6bf172be8 100644 --- a/src/vllm_router/services/request_service/request.py +++ b/src/vllm_router/services/request_service/request.py @@ -336,7 +336,7 @@ async def send_request_to_decode( async with client.post(endpoint, json=req_data, headers=headers) as response: response.raise_for_status() - async for chunk in response.content.iter_chunked(8192): + async for chunk in response.content.iter_any(): yield chunk From 086fa9d287f1af0329d4959641540f25cf2042b7 Mon Sep 17 00:00:00 2001 From: Ifta Khairul Alam Adil Date: Wed, 30 Jul 2025 19:32:36 +0200 Subject: [PATCH 23/27] Replace httpx with aiohttp for prefill and decode clients in K8sServiceNameServiceDiscovery, updating timeout handling accordingly. Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/service_discovery.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/vllm_router/service_discovery.py b/src/vllm_router/service_discovery.py index a79904900..886330403 100644 --- a/src/vllm_router/service_discovery.py +++ b/src/vllm_router/service_discovery.py @@ -1005,14 +1005,14 @@ def _add_engine(self, engine_name: str, model_names: List[str], model_label: str and self.decode_model_labels is not None ): if model_label in self.prefill_model_labels: - self.app.state.prefill_client = httpx.AsyncClient( + self.app.state.prefill_client = aiohttp.ClientSession( base_url=f"http://{engine_name}:{self.port}", - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) elif model_label in self.decode_model_labels: - self.app.state.decode_client = httpx.AsyncClient( + self.app.state.decode_client = aiohttp.ClientSession( base_url=f"http://{engine_name}:{self.port}", - timeout=None, + timeout=aiohttp.ClientTimeout(total=None), ) # Store model information in the endpoint info self.available_engines[engine_name].model_info = model_info From 4b86abb55587ab62b25137df58a5ce5cee61c0b6 Mon Sep 17 00:00:00 2001 From: Ifta Khairul Alam Adil Date: Wed, 30 Jul 2025 19:40:40 +0200 Subject: [PATCH 24/27] Update file upload function to include file parameter in aiohttp post request Signed-off-by: Ifta Khairul Alam Adil --- src/examples/example_file_upload.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/examples/example_file_upload.py b/src/examples/example_file_upload.py index d202a7c7e..27cf228f2 100644 --- a/src/examples/example_file_upload.py +++ b/src/examples/example_file_upload.py @@ -11,7 +11,7 @@ async def upload_file(server_url: str, file_path: str): data = {"purpose": "unknown"} async with aiohttp.ClientSession() as client: - async with client.post(server_url, data=data) as response: + async with client.post(server_url, files=files, data=data) as response: if response.status == 200: result = await response.json() print("File uploaded successfully:", result) From 5db9e10c5bc4aec698bf46ff6b781b92beabdf55 Mon Sep 17 00:00:00 2001 From: Ifta Khairul Alam Adil Date: Wed, 30 Jul 2025 21:00:22 +0200 Subject: [PATCH 25/27] Refactor response handling in request service to return JSON data and correctly set response status code in sleep/wakeup routing. Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/services/request_service/request.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/vllm_router/services/request_service/request.py b/src/vllm_router/services/request_service/request.py index 6bf172be8..3e8285f49 100644 --- a/src/vllm_router/services/request_service/request.py +++ b/src/vllm_router/services/request_service/request.py @@ -320,7 +320,7 @@ async def send_request_to_prefiller( async with client.post(endpoint, json=req_data, headers=headers) as response: response.raise_for_status() - return response + return await response.json() async def send_request_to_decode( @@ -490,13 +490,16 @@ async def route_sleep_wakeup_request( return await response.json() else: request_body = await request.body() + response_status = None if request_body: req_data = json.loads(request_body) async with client.post(url, json=req_data, headers=headers) as response: response.raise_for_status() + response_status = response.status else: async with client.post(url, headers=headers) as response: response.raise_for_status() + response_status = response.status pod_name = endpoints[0].pod_name if endpoint == "/sleep": @@ -505,7 +508,7 @@ async def route_sleep_wakeup_request( service_discovery.remove_sleep_label(pod_name) return JSONResponse( - status_code=response.status, + status_code=response_status, content={"status": "success"}, headers={"X-Request-Id": request_id}, ) From 6aebf9a80dd9e11950a1139070ffc6e41a91e550 Mon Sep 17 00:00:00 2001 From: Ifta Khairul Alam Adil Date: Thu, 31 Jul 2025 00:25:20 +0000 Subject: [PATCH 26/27] Add initialization of aiohttp ClientSession in service discovery during app startup - Implemented method in , , and classes to set up aiohttp ClientSession for prefill and decode endpoints. - Updated function in to call if available, ensuring proper session management during application startup. - Adjusted logging statements in to reference the attribute of the client sessions for accurate logging of routing requests. Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/app.py | 5 ++ src/vllm_router/service_discovery.py | 83 ++++++++++++------- .../services/request_service/request.py | 4 +- 3 files changed, 61 insertions(+), 31 deletions(-) diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index ca63d8796..9a977ffb7 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -85,6 +85,11 @@ async def lifespan(app: FastAPI): app.state.aiohttp_client_wrapper.start() if hasattr(app.state, "batch_processor"): await app.state.batch_processor.initialize() + + service_discovery = get_service_discovery() + if hasattr(service_discovery, 'initialize_client_sessions'): + await service_discovery.initialize_client_sessions() + yield await app.state.aiohttp_client_wrapper.stop() diff --git a/src/vllm_router/service_discovery.py b/src/vllm_router/service_discovery.py index 886330403..f94840562 100644 --- a/src/vllm_router/service_discovery.py +++ b/src/vllm_router/service_discovery.py @@ -308,10 +308,18 @@ def get_endpoint_info(self) -> List[EndpointInfo]: model_info=self._get_model_info(model), ) endpoint_infos.append(endpoint_info) + return endpoint_infos + + async def initialize_client_sessions(self) -> None: + """ + Initialize aiohttp ClientSession objects for prefill and decode endpoints. + This must be called from an async context during app startup. + """ if ( self.prefill_model_labels is not None and self.decode_model_labels is not None ): + endpoint_infos = self.get_endpoint_info() for endpoint_info in endpoint_infos: if endpoint_info.model_label in self.prefill_model_labels: self.app.state.prefill_client = aiohttp.ClientSession( @@ -323,7 +331,6 @@ def get_endpoint_info(self) -> List[EndpointInfo]: base_url=endpoint_info.url, timeout=aiohttp.ClientTimeout(total=None), ) - return endpoint_infos class K8sPodIPServiceDiscovery(ServiceDiscovery): @@ -605,20 +612,7 @@ def _add_engine( namespace=self.namespace, model_info=model_info, ) - if ( - self.prefill_model_labels is not None - and self.decode_model_labels is not None - ): - if model_label in self.prefill_model_labels: - self.app.state.prefill_client = aiohttp.ClientSession( - base_url=f"http://{engine_ip}:{self.port}", - timeout=aiohttp.ClientTimeout(total=None), - ) - elif model_label in self.decode_model_labels: - self.app.state.decode_client = aiohttp.ClientSession( - base_url=f"http://{engine_ip}:{self.port}", - timeout=aiohttp.ClientTimeout(total=None), - ) + # Store model information in the endpoint info self.available_engines[engine_name].model_info = model_info @@ -696,6 +690,28 @@ def close(self): self.k8s_watcher.stop() self.watcher_thread.join() + async def initialize_client_sessions(self) -> None: + """ + Initialize aiohttp ClientSession objects for prefill and decode endpoints. + This must be called from an async context during app startup. + """ + if ( + self.prefill_model_labels is not None + and self.decode_model_labels is not None + ): + endpoint_infos = self.get_endpoint_info() + for endpoint_info in endpoint_infos: + if endpoint_info.model_label in self.prefill_model_labels: + self.app.state.prefill_client = aiohttp.ClientSession( + base_url=endpoint_info.url, + timeout=aiohttp.ClientTimeout(total=None), + ) + elif endpoint_info.model_label in self.decode_model_labels: + self.app.state.decode_client = aiohttp.ClientSession( + base_url=endpoint_info.url, + timeout=aiohttp.ClientTimeout(total=None), + ) + class K8sServiceNameServiceDiscovery(ServiceDiscovery): def __init__( @@ -1000,20 +1016,7 @@ def _add_engine(self, engine_name: str, model_names: List[str], model_label: str namespace=self.namespace, model_info=model_info, ) - if ( - self.prefill_model_labels is not None - and self.decode_model_labels is not None - ): - if model_label in self.prefill_model_labels: - self.app.state.prefill_client = aiohttp.ClientSession( - base_url=f"http://{engine_name}:{self.port}", - timeout=aiohttp.ClientTimeout(total=None), - ) - elif model_label in self.decode_model_labels: - self.app.state.decode_client = aiohttp.ClientSession( - base_url=f"http://{engine_name}:{self.port}", - timeout=aiohttp.ClientTimeout(total=None), - ) + # Store model information in the endpoint info self.available_engines[engine_name].model_info = model_info @@ -1090,6 +1093,28 @@ def close(self): self.k8s_watcher.stop() self.watcher_thread.join() + async def initialize_client_sessions(self) -> None: + """ + Initialize aiohttp ClientSession objects for prefill and decode endpoints. + This must be called from an async context during app startup. + """ + if ( + self.prefill_model_labels is not None + and self.decode_model_labels is not None + ): + endpoint_infos = self.get_endpoint_info() + for endpoint_info in endpoint_infos: + if endpoint_info.model_label in self.prefill_model_labels: + self.app.state.prefill_client = aiohttp.ClientSession( + base_url=endpoint_info.url, + timeout=aiohttp.ClientTimeout(total=None), + ) + elif endpoint_info.model_label in self.decode_model_labels: + self.app.state.decode_client = aiohttp.ClientSession( + base_url=endpoint_info.url, + timeout=aiohttp.ClientTimeout(total=None), + ) + def _create_service_discovery( service_discovery_type: ServiceDiscoveryType, *args, **kwargs diff --git a/src/vllm_router/services/request_service/request.py b/src/vllm_router/services/request_service/request.py index 3e8285f49..54e2ac667 100644 --- a/src/vllm_router/services/request_service/request.py +++ b/src/vllm_router/services/request_service/request.py @@ -360,7 +360,7 @@ async def route_disaggregated_prefill_request( et = time.time() logger.info(f"{request_id} prefill time (TTFT): {et - st:.4f}") logger.info( - f"Routing request {request_id} with session id None to {request.app.state.prefill_client.base_url} at {et}, process time = {et - in_router_time:.4f}" + f"Routing request {request_id} with session id None to {request.app.state.prefill_client._base_url} at {et}, process time = {et - in_router_time:.4f}" ) request_json["max_tokens"] = orig_max_tokens except aiohttp.ClientResponseError as e: @@ -425,7 +425,7 @@ async def generate_stream(): curr_time = time.time() logger.info( - f"Routing request {request_id} with session id None to {request.app.state.decode_client.base_url} at {curr_time}, process time = {curr_time - et:.4f}" + f"Routing request {request_id} with session id None to {request.app.state.decode_client._base_url} at {curr_time}, process time = {curr_time - et:.4f}" ) return StreamingResponse( From 5703efac996a0280f4aa58a8f0a225cb4412d414 Mon Sep 17 00:00:00 2001 From: Ifta Khairul Alam Adil Date: Thu, 31 Jul 2025 23:18:31 +0200 Subject: [PATCH 27/27] Refactor whitespace and formatting in app.py for improved readability Signed-off-by: Ifta Khairul Alam Adil --- src/vllm_router/app.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/vllm_router/app.py b/src/vllm_router/app.py index 9a977ffb7..93ad94222 100644 --- a/src/vllm_router/app.py +++ b/src/vllm_router/app.py @@ -85,11 +85,11 @@ async def lifespan(app: FastAPI): app.state.aiohttp_client_wrapper.start() if hasattr(app.state, "batch_processor"): await app.state.batch_processor.initialize() - + service_discovery = get_service_discovery() - if hasattr(service_discovery, 'initialize_client_sessions'): + if hasattr(service_discovery, "initialize_client_sessions"): await service_discovery.initialize_client_sessions() - + yield await app.state.aiohttp_client_wrapper.stop()