remove ray for launching api server

tests/async_engine/test_openapi_server_ray.py

@@ -1,24 +1,14 @@
 import openai  # use the official client for correctness check
 import pytest
-# using Ray for overall ease of process management, parallel requests,
-# and debugging.
-import ray
 
-from ..utils import VLLM_PATH, RemoteOpenAIServer
+from ..utils import RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "facebook/opt-125m"
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def server(ray_ctx):
+def server():
     return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,

tests/distributed/test_pipeline_parallel.py

@@ -2,11 +2,8 @@
 
 import openai  # use the official client for correctness check
 import pytest
-# using Ray for overall ease of process management, parallel requests,
-# and debugging.
-import ray
 
-from ..utils import VLLM_PATH, RemoteOpenAIServer
+from ..utils import RemoteOpenAIServer
 
 # downloading lora to test lora requests
 
@@ -19,6 +16,7 @@
 
 pytestmark = pytest.mark.asyncio
 
+
 @pytest.fixture(scope="module")
 def server():
     args = [

tests/entrypoints/openai/test_chat.py

@@ -6,15 +6,12 @@
 import jsonschema
 import openai  # use the official client for correctness check
 import pytest
-# using Ray for overall ease of process management, parallel requests,
-# and debugging.
-import ray
 import torch
 # downloading lora to test lora requests
 from huggingface_hub import snapshot_download
 from openai import BadRequestError
 
-from ...utils import VLLM_PATH, RemoteOpenAIServer
+from ...utils import RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
@@ -76,14 +73,7 @@ def zephyr_lora_files():
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def server(zephyr_lora_files, ray_ctx):
+def server(zephyr_lora_files):
     return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,

tests/entrypoints/openai/test_completion.py

@@ -6,17 +6,14 @@
 import jsonschema
 import openai  # use the official client for correctness check
 import pytest
-# using Ray for overall ease of process management, parallel requests,
-# and debugging.
-import ray
 import requests
 # downloading lora to test lora requests
 from huggingface_hub import snapshot_download
 from openai import BadRequestError
 
 from vllm.transformers_utils.tokenizer import get_tokenizer
 
-from ...utils import VLLM_PATH, RemoteOpenAIServer
+from ...utils import RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
@@ -78,14 +75,7 @@ def zephyr_lora_files():
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def server(zephyr_lora_files, ray_ctx):
+def server(zephyr_lora_files):
     return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,

tests/entrypoints/openai/test_embedding.py

@@ -3,22 +3,14 @@
 import numpy as np
 import openai
 import pytest
-import ray
 
-from ...utils import VLLM_PATH, RemoteOpenAIServer
+from ...utils import RemoteOpenAIServer
 
 EMBEDDING_MODEL_NAME = "intfloat/e5-mistral-7b-instruct"
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def embedding_server(ray_ctx):
+def embedding_server():
     return RemoteOpenAIServer([
         "--model",
         EMBEDDING_MODEL_NAME,

tests/entrypoints/openai/test_models.py

@@ -1,12 +1,9 @@
 import openai  # use the official client for correctness check
 import pytest
-# using Ray for overall ease of process management, parallel requests,
-# and debugging.
-import ray
 # downloading lora to test lora requests
 from huggingface_hub import snapshot_download
 
-from ...utils import VLLM_PATH, RemoteOpenAIServer
+from ...utils import RemoteOpenAIServer
 
 # any model with a chat template should work here
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
@@ -21,14 +18,7 @@ def zephyr_lora_files():
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def server(zephyr_lora_files, ray_ctx):
+def server(zephyr_lora_files):
     return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,

tests/entrypoints/openai/test_vision.py

@@ -3,7 +3,6 @@
 import openai
 import pytest
 import pytest_asyncio
-import ray
 
 from vllm.multimodal.utils import ImageFetchAiohttp, encode_image_base64
 
@@ -23,14 +22,7 @@
 
 
 @pytest.fixture(scope="module")
-def ray_ctx():
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-    yield
-    ray.shutdown()
-
-
-@pytest.fixture(scope="module")
-def server(ray_ctx):
+def server():
     return RemoteOpenAIServer([
         "--model",
         MODEL_NAME,

tests/tensorizer_loader/test_tensorizer.py

@@ -6,7 +6,6 @@
 
 import openai
 import pytest
-import ray
 import torch
 from tensorizer import EncryptionParams
 
@@ -22,7 +21,7 @@
                                                          tensorize_vllm_model)
 
 from ..conftest import VllmRunner, cleanup
-from ..utils import VLLM_PATH, RemoteOpenAIServer
+from ..utils import RemoteOpenAIServer
 
 # yapf conflicts with isort for this docstring
 
@@ -220,8 +219,6 @@ def test_openai_apiserver_with_tensorizer(vllm_runner, tmp_path):
         json.dumps(model_loader_extra_config),
     ]
 
-    ray.init(runtime_env={"working_dir": VLLM_PATH})
-
     server = RemoteOpenAIServer(openai_args)
     print("Server ready.")
 
@@ -282,7 +279,6 @@ def test_deserialized_encrypted_vllm_model_with_tp_has_same_outputs(vllm_runner,
     base_model.model.llm_engine.model_executor.shutdown()
     del base_model
     cleanup()
-    ray.shutdown()
 
     # load model with two shards and serialize with encryption
     model_path = str(tmp_path / (model_ref + "-%02d.tensors"))
@@ -305,7 +301,6 @@ def test_deserialized_encrypted_vllm_model_with_tp_has_same_outputs(vllm_runner,
     assert os.path.isfile(model_path % 0), "Serialization subprocess failed"
     assert os.path.isfile(model_path % 1), "Serialization subprocess failed"
     cleanup()
-    ray.shutdown()
 
     loaded_vllm_model = vllm_runner(
         model_ref,