(misc) Deprecate some hf-inference specific features (wait-for-model header, can't override model's task, get_model_status, list_deployed_models)

docs/source/de/guides/inference.md

@@ -107,7 +107,7 @@ Das Ziel von [`InferenceClient`] ist es, die einfachste Schnittstelle zum Ausfü
 | | [Feature Extraction](https://huggingface.co/tasks/feature-extraction)             | ✅ | [`~InferenceClient.feature_extraction`] |
 | | [Fill Mask](https://huggingface.co/tasks/fill-mask)                      | ✅ | [`~InferenceClient.fill_mask`] |
 | | [Question Answering](https://huggingface.co/tasks/question-answering)             | ✅ | [`~InferenceClient.question_answering`] |
-| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity)            | ✅ | [`~InferenceClient.sentence_similarity`] |
+| | [Sentence Similarity](https://huggingface.co/tasks/sentence-similarity) | ✅ | [`~InferenceClient.sentence_similarity`] |
 | | [Summarization](https://huggingface.co/tasks/summarization)                  | ✅ | [`~InferenceClient.summarization`] |
 | | [Table Question Answering](https://huggingface.co/tasks/table-question-answering)       | ✅ | [`~InferenceClient.table_question_answering`] |
 | | [Text Classification](https://huggingface.co/tasks/text-classification)            | ✅ | [`~InferenceClient.text_classification`] |

src/huggingface_hub/inference/_client.py

@@ -35,7 +35,6 @@
 import base64
 import logging
 import re
-import time
 import warnings
 from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Literal, Optional, Union, overload
 
@@ -301,8 +300,6 @@ def _inner_post(
         if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers:
             request_parameters.headers["Accept"] = "image/png"
 
-        t0 = time.time()
-        timeout = self.timeout
         while True:
             with _open_as_binary(request_parameters.data) as data_as_binary:
                 try:
@@ -326,30 +323,9 @@ def _inner_post(
             except HTTPError as error:
                 if error.response.status_code == 422 and request_parameters.task != "unknown":
                     msg = str(error.args[0])
-                    print(error.response.text)
                     if len(error.response.text) > 0:
                         msg += f"\n{error.response.text}\n"
-                    msg += f"\nMake sure '{request_parameters.task}' task is supported by the model."
                     error.args = (msg,) + error.args[1:]
-                if error.response.status_code == 503:
-                    # If Model is unavailable, either raise a TimeoutError...
-                    if timeout is not None and time.time() - t0 > timeout:
-                        raise InferenceTimeoutError(
-                            f"Model not loaded on the server: {request_parameters.url}. Please retry with a higher timeout (current:"
-                            f" {self.timeout}).",
-                            request=error.request,
-                            response=error.response,
-                        ) from error
-                    # ...or wait 1s and retry
-                    logger.info(f"Waiting for model to be loaded on the server: {error}")
-                    time.sleep(1)
-                    if "X-wait-for-model" not in request_parameters.headers and request_parameters.url.startswith(
-                        INFERENCE_ENDPOINT
-                    ):
-                        request_parameters.headers["X-wait-for-model"] = "1"
-                    if timeout is not None:
-                        timeout = max(self.timeout - (time.time() - t0), 1)  # type: ignore
-                    continue
                 raise
 
     def audio_classification(
@@ -3261,6 +3237,13 @@ def zero_shot_image_classification(
         response = self._inner_post(request_parameters)
         return ZeroShotImageClassificationOutputElement.parse_obj_as_list(response)
 
+    @_deprecate_method(
+        version="0.33.0",
+        message=(
+            "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)."
+            " Use `HfApi.list_models(..., inference_provider='...')` to list warm models per provider."
+        ),
+    )
     def list_deployed_models(
         self, frameworks: Union[None, str, Literal["all"], List[str]] = None
     ) -> Dict[str, List[str]]:
@@ -3444,6 +3427,13 @@ def health_check(self, model: Optional[str] = None) -> bool:
         response = get_session().get(url, headers=build_hf_headers(token=self.token))
         return response.status_code == 200
 
+    @_deprecate_method(
+        version="0.33.0",
+        message=(
+            "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)."
+            " Use `HfApi.model_info` to get the model status both with HF Inference API and external providers."
+        ),
+    )
     def get_model_status(self, model: Optional[str] = None) -> ModelStatus:
         """
         Get the status of a model hosted on the HF Inference API.

src/huggingface_hub/inference/_generated/_async_client.py

@@ -22,7 +22,6 @@
 import base64
 import logging
 import re
-import time
 import warnings
 from typing import TYPE_CHECKING, Any, AsyncIterable, Dict, List, Literal, Optional, Set, Union, overload
 
@@ -299,8 +298,6 @@ async def _inner_post(
         if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers:
             request_parameters.headers["Accept"] = "image/png"
 
-        t0 = time.time()
-        timeout = self.timeout
         while True:
             with _open_as_binary(request_parameters.data) as data_as_binary:
                 # Do not use context manager as we don't want to close the connection immediately when returning
@@ -331,27 +328,6 @@ async def _inner_post(
                 except aiohttp.ClientResponseError as error:
                     error.response_error_payload = response_error_payload
                     await session.close()
-                    if response.status == 422 and request_parameters.task != "unknown":
-                        error.message += f". Make sure '{request_parameters.task}' task is supported by the model."
-                    if response.status == 503:
-                        # If Model is unavailable, either raise a TimeoutError...
-                        if timeout is not None and time.time() - t0 > timeout:
-                            raise InferenceTimeoutError(
-                                f"Model not loaded on the server: {request_parameters.url}. Please retry with a higher timeout"
-                                f" (current: {self.timeout}).",
-                                request=error.request,
-                                response=error.response,
-                            ) from error
-                        # ...or wait 1s and retry
-                        logger.info(f"Waiting for model to be loaded on the server: {error}")
-                        if "X-wait-for-model" not in request_parameters.headers and request_parameters.url.startswith(
-                            INFERENCE_ENDPOINT
-                        ):
-                            request_parameters.headers["X-wait-for-model"] = "1"
-                        await asyncio.sleep(1)
-                        if timeout is not None:
-                            timeout = max(self.timeout - (time.time() - t0), 1)  # type: ignore
-                        continue
                     raise error
                 except Exception:
                     await session.close()
@@ -3325,6 +3301,13 @@ async def zero_shot_image_classification(
         response = await self._inner_post(request_parameters)
         return ZeroShotImageClassificationOutputElement.parse_obj_as_list(response)
 
+    @_deprecate_method(
+        version="0.33.0",
+        message=(
+            "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)."
+            " Use `HfApi.list_models(..., inference_provider='...')` to list warm models per provider."
+        ),
+    )
     async def list_deployed_models(
         self, frameworks: Union[None, str, Literal["all"], List[str]] = None
     ) -> Dict[str, List[str]]:
@@ -3554,6 +3537,13 @@ async def health_check(self, model: Optional[str] = None) -> bool:
             response = await client.get(url, proxy=self.proxies)
             return response.status == 200
 
+    @_deprecate_method(
+        version="0.33.0",
+        message=(
+            "HF Inference API is getting revamped and will only support warm models in the future (no cold start allowed)."
+            " Use `HfApi.model_info` to get the model status both with HF Inference API and external providers."
+        ),
+    )
     async def get_model_status(self, model: Optional[str] = None) -> ModelStatus:
         """
         Get the status of a model hosted on the HF Inference API.

src/huggingface_hub/inference/_providers/hf_inference.py

@@ -38,14 +38,7 @@ def _prepare_url(self, api_key: str, mapped_model: str) -> str:
         # hf-inference provider can handle URLs (e.g. Inference Endpoints or TGI deployment)
         if mapped_model.startswith(("http://", "https://")):
             return mapped_model
-
-        return (
-            # Feature-extraction and sentence-similarity are the only cases where we handle models with several tasks.
-            f"{self.base_url}/pipeline/{self.task}/{mapped_model}"
-            if self.task in ("feature-extraction", "sentence-similarity")
-            # Otherwise, we use the default endpoint
-            else f"{self.base_url}/models/{mapped_model}"
-        )
+        return f"{self.base_url}/models/{mapped_model}"
 
     def _prepare_payload(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
         if isinstance(inputs, bytes):

tests/cassettes/TestInferenceClient.test_sentence_similarity[hf-inference,sentence-similarity].yaml

@@ -17,7 +17,7 @@ interactions:
       X-Amzn-Trace-Id:
       - 0434ff33-56fe-49db-9380-17b81e41f756
     method: POST
-    uri: https://router.huggingface.co/hf-inference/pipeline/sentence-similarity/sentence-transformers/all-MiniLM-L6-v2
+    uri: https://router.huggingface.co/hf-inference/models/sentence-transformers/all-MiniLM-L6-v2
   response:
     body:
       string: '[0.7785724997520447,0.4587624967098236,0.29062220454216003]'

tests/cassettes/test_async_sentence_similarity.yaml

@@ -3,7 +3,7 @@ interactions:
     body: null
     headers: {}
     method: POST
-    uri: https://router.huggingface.co/hf-inference/pipeline/sentence-similarity/sentence-transformers/all-MiniLM-L6-v2
+    uri: https://router.huggingface.co/hf-inference/models/sentence-transformers/all-MiniLM-L6-v2
   response:
     body:
       string: '[0.7785724997520447,0.4587624967098236,0.29062220454216003]'

tests/test_inference_async_client.py

@@ -300,6 +300,7 @@ def test_sync_vs_async_signatures() -> None:
 
 
 @pytest.mark.asyncio
+@pytest.mark.skip("Deprecated (get_model_status)")
 async def test_get_status_too_big_model() -> None:
     model_status = await AsyncInferenceClient(token=False).get_model_status("facebook/nllb-moe-54b")
     assert model_status.loaded is False
@@ -309,6 +310,7 @@ async def test_get_status_too_big_model() -> None:
 
 
 @pytest.mark.asyncio
+@pytest.mark.skip("Deprecated (get_model_status)")
 async def test_get_status_loaded_model() -> None:
     model_status = await AsyncInferenceClient(token=False).get_model_status("bigscience/bloom")
     assert model_status.loaded is True
@@ -318,18 +320,21 @@ async def test_get_status_loaded_model() -> None:
 
 
 @pytest.mark.asyncio
+@pytest.mark.skip("Deprecated (get_model_status)")
 async def test_get_status_unknown_model() -> None:
     with pytest.raises(ClientResponseError):
         await AsyncInferenceClient(token=False).get_model_status("unknown/model")
 
 
 @pytest.mark.asyncio
+@pytest.mark.skip("Deprecated (get_model_status)")
 async def test_get_status_model_as_url() -> None:
     with pytest.raises(NotImplementedError):
         await AsyncInferenceClient(token=False).get_model_status("https://unkown/model")
 
 
 @pytest.mark.asyncio
+@pytest.mark.skip("Deprecated (list_deployed_models)")
 async def test_list_deployed_models_single_frameworks() -> None:
     models_by_task = await AsyncInferenceClient().list_deployed_models("text-generation-inference")
     assert isinstance(models_by_task, dict)

tests/test_inference_client.py

@@ -869,6 +869,7 @@ def test_accept_header_image(self, get_session_mock: MagicMock, bytes_to_image_m
 
 
 class TestModelStatus(TestBase):
+    @expect_deprecation("get_model_status")
     def test_too_big_model(self) -> None:
         client = InferenceClient(token=False)
         model_status = client.get_model_status("facebook/nllb-moe-54b")
@@ -877,6 +878,7 @@ def test_too_big_model(self) -> None:
         assert model_status.compute_type == "cpu"
         assert model_status.framework == "transformers"
 
+    @expect_deprecation("get_model_status")
     def test_loaded_model(self) -> None:
         client = InferenceClient(token=False)
         model_status = client.get_model_status("bigscience/bloom")
@@ -885,28 +887,33 @@ def test_loaded_model(self) -> None:
         assert isinstance(model_status.compute_type, dict)  # e.g. {'gpu': {'gpu': 'a100', 'count': 8}}
         assert model_status.framework == "text-generation-inference"
 
+    @expect_deprecation("get_model_status")
     def test_unknown_model(self) -> None:
         client = InferenceClient()
         with pytest.raises(HfHubHTTPError):
             client.get_model_status("unknown/model")
 
+    @expect_deprecation("get_model_status")
     def test_model_as_url(self) -> None:
         client = InferenceClient()
         with pytest.raises(NotImplementedError):
             client.get_model_status("https://unkown/model")
 
 
 class TestListDeployedModels(TestBase):
+    @expect_deprecation("list_deployed_models")
     @patch("huggingface_hub.inference._client.get_session")
     def test_list_deployed_models_main_frameworks_mock(self, get_session_mock: MagicMock) -> None:
         InferenceClient().list_deployed_models()
         assert len(get_session_mock.return_value.get.call_args_list) == len(MAIN_INFERENCE_API_FRAMEWORKS)
 
+    @expect_deprecation("list_deployed_models")
     @patch("huggingface_hub.inference._client.get_session")
     def test_list_deployed_models_all_frameworks_mock(self, get_session_mock: MagicMock) -> None:
         InferenceClient().list_deployed_models("all")
         assert len(get_session_mock.return_value.get.call_args_list) == len(ALL_INFERENCE_API_FRAMEWORKS)
 
+    @expect_deprecation("list_deployed_models")
     def test_list_deployed_models_single_frameworks(self) -> None:
         models_by_task = InferenceClient().list_deployed_models("text-generation-inference")
         assert isinstance(models_by_task, dict)

utils/generate_async_inference_client.py

@@ -175,8 +175,6 @@ def _rename_to_AsyncInferenceClient(code: str) -> str:
         if request_parameters.task in TASKS_EXPECTING_IMAGES and "Accept" not in request_parameters.headers:
             request_parameters.headers["Accept"] = "image/png"
 
-        t0 = time.time()
-        timeout = self.timeout
         while True:
             with _open_as_binary(request_parameters.data) as data_as_binary:
                 # Do not use context manager as we don't want to close the connection immediately when returning
@@ -205,25 +203,6 @@ def _rename_to_AsyncInferenceClient(code: str) -> str:
                 except aiohttp.ClientResponseError as error:
                     error.response_error_payload = response_error_payload
                     await session.close()
-                    if response.status == 422 and request_parameters.task != "unknown":
-                        error.message += f". Make sure '{request_parameters.task}' task is supported by the model."
-                    if response.status == 503:
-                        # If Model is unavailable, either raise a TimeoutError...
-                        if timeout is not None and time.time() - t0 > timeout:
-                            raise InferenceTimeoutError(
-                                f"Model not loaded on the server: {request_parameters.url}. Please retry with a higher timeout"
-                                f" (current: {self.timeout}).",
-                                request=error.request,
-                                response=error.response,
-                            ) from error
-                        # ...or wait 1s and retry
-                        logger.info(f"Waiting for model to be loaded on the server: {error}")
-                        if "X-wait-for-model" not in request_parameters.headers and request_parameters.url.startswith(INFERENCE_ENDPOINT):
-                            request_parameters.headers["X-wait-for-model"] = "1"
-                        await asyncio.sleep(1)
-                        if timeout is not None:
-                            timeout = max(self.timeout - (time.time() - t0), 1)  # type: ignore
-                        continue
                     raise error
                 except Exception:
                     await session.close()