add models endpoint

clearml_serving/serving/main.py

@@ -206,7 +206,7 @@ async def validate_json_request(raw_request: Request):
         )
 
 @router.post("/openai/v1/{endpoint_type:path}", dependencies=[Depends(validate_json_request)])
-@router.post("/openai/v1/{endpoint_type:path}/", dependencies=[Depends(validate_json_request)])
+@router.get("/openai/v1/{endpoint_type:path}", dependencies=[Depends(validate_json_request)])
 async def openai_serve_model(
     endpoint_type: str,
     request: Union[CompletionRequest, ChatCompletionRequest],

clearml_serving/serving/model_request_processor.py

@@ -1230,23 +1230,6 @@ class ModelRequestProcessor(object):
         processed = await processed_func(preprocessed, state, stats_collect_fn) \
             if processor.is_process_async \
             else processed_func(preprocessed, state, stats_collect_fn)
-        # if serve_type == "process":
-        #     # noinspection PyUnresolvedReferences
-        #     processed = await processor.process(preprocessed, state, stats_collect_fn) \
-        #         if processor.is_process_async \
-        #         else processor.process(preprocessed, state, stats_collect_fn)
-        # elif serve_type == "completions":
-        #     # noinspection PyUnresolvedReferences
-        #     processed = await processor.completions(preprocessed, state, stats_collect_fn) \
-        #         if processor.is_process_async \
-        #         else processor.completions(preprocessed, state, stats_collect_fn)
-        # elif serve_type == "chat/completions":
-        #     # noinspection PyUnresolvedReferences
-        #     processed = await processor.chat_completions(preprocessed, state, stats_collect_fn) \
-        #         if processor.is_process_async \
-        #         else processor.chat_completions(preprocessed, state, stats_collect_fn)
-        # else:
-        #     raise ValueError(f"wrong url_type: expected 'process', 'completions' or 'chat/completions', got {serve_type}")
         # noinspection PyUnresolvedReferences
         return_value = await processor.postprocess(processed, state, stats_collect_fn) \
             if processor.is_postprocess_async \

clearml_serving/serving/preprocess_service.py

@@ -763,6 +763,14 @@ class VllmEngine(Singleton):
         )
         self.logger.info("Model {} was added to vllm engine".format(name))
         return None
+    
+    def remove_model(self, name: str) -> None:
+        self.openai_serving_models.base_model_paths = [
+            model for model in self.openai_serving_models.base_model_paths
+            if model.name != name
+        ]
+        self.logger.info("Model {} was removed from vllm engine".format(name))
+        return None
 
     async def completions(
         self,

examples/vllm/readme.md

@@ -32,9 +32,17 @@
 5. Test new endpoint (do notice the first call will trigger the model pulling, so it might take longer, from here on, it's all in memory):
 
     ```bash
-    python examples/vllm/test_openai_app.py
+    python examples/vllm/test_openai_api.py
     ```
 
+    **Available routes**:
+
+    + /v1/completions
+    + /v1/chat/completions
+    + /v1/models
+
+    see [test_openai_api.py](test_openai_api.py) for more information.
+
 NOTE!
 
 If you want to use send_request method, keep in mind that you have to pass "completions" or "chat/completions" in entrypoint (and pass model as a part of "data" parameter) and use it for non-streaming models:

examples/vllm/test_openai_api.py

@@ -1,6 +1,6 @@
 from openai import OpenAI
 
-def main(model_name: str = "test_vllm"):
+def main(model_name: str):
     client = OpenAI(api_key="-")
     client.base_url = "http://127.0.0.1:8080/serve/openai/v1"
 
@@ -14,8 +14,7 @@ def main(model_name: str = "test_vllm"):
         max_tokens=1024,
         top_p=1.0
     )
-
+    print(f"ChatCompletion: \n\n {chat_response.choices[0].message.content}")
-    print(f"ChatCompletion: {chat_response.choices[0].message.content}")
 
     comp_response = client.completions.create(
         model=model_name,
@@ -23,10 +22,13 @@ def main(model_name: str = "test_vllm"):
         temperature=1.0,
         max_tokens=256
     )
+    print(f"\n\n Completion: \n\n {comp_response.choices[0].text}")
 
-    print(f"Completion: \n {comp_response.choices[0].text}")
+    fake_body = {"stream": False, "model": model_name, "prompt": "test"}
+    print(f"Models:\n")
+    print('\n\n'.join(map(str, client.models.list(extra_body=fake_body).data)))
 
     return None
 
 if __name__ == '__main__':
-    main()
+    main(model_name="test_vllm")