Fixing the CI with the new huggingface-hub version (#3329)

* fixing the CI

* fix it for the api docs

Author: bcdurak

docs/mocked_libs.json

@@ -128,6 +128,7 @@
     "hvac",
     "hvac.exceptions",
     "huggingface_hub",
+    "huggingface_hub.errors",
     "huggingface_hub.utils",
     "keras",
     "kfp",

src/zenml/integrations/huggingface/services/huggingface_deployment.py

@@ -13,17 +13,18 @@
 #  permissions and limitations under the License.
 """Implementation of the Hugging Face Deployment service."""
 
-from typing import Any, Generator, Optional, Tuple
+from typing import Any, Dict, Generator, Optional, Tuple
 
 from huggingface_hub import (
     InferenceClient,
     InferenceEndpoint,
     InferenceEndpointError,
     InferenceEndpointStatus,
+    InferenceEndpointType,
     create_inference_endpoint,
     get_inference_endpoint,
 )
-from huggingface_hub.utils import HfHubHTTPError
+from huggingface_hub.errors import HfHubHTTPError
 from pydantic import Field
 
 from zenml.client import Client
@@ -138,30 +139,67 @@ def inference_client(self) -> InferenceClient:
         """
         return self.hf_endpoint.client
 
+    def _validate_endpoint_configuration(self) -> Dict[str, str]:
+        """Validates the configuration to provision a Huggingface service.
+
+        Raises:
+            ValueError: if there is a missing value in the configuration
+
+        Returns:
+            The validated configuration values.
+        """
+        configuration = {}
+        missing_keys = []
+
+        for k, v in {
+            "repository": self.config.repository,
+            "framework": self.config.framework,
+            "accelerator": self.config.accelerator,
+            "instance_size": self.config.instance_size,
+            "instance_type": self.config.instance_type,
+            "region": self.config.region,
+            "vendor": self.config.vendor,
+            "endpoint_type": self.config.endpoint_type,
+        }.items():
+            if v is None:
+                missing_keys.append(k)
+            else:
+                configuration[k] = v
+
+        if missing_keys:
+            raise ValueError(
+                f"Missing values in the Huggingface Service "
+                f"configuration: {', '.join(missing_keys)}"
+            )
+
+        return configuration
+
     def provision(self) -> None:
         """Provision or update remote Hugging Face deployment instance.
 
         Raises:
-            Exception: If any unexpected error while creating inference endpoint.
+            Exception: If any unexpected error while creating inference
+                endpoint.
         """
         try:
-            # Attempt to create and wait for the inference endpoint
+            validated_config = self._validate_endpoint_configuration()
+
             hf_endpoint = create_inference_endpoint(
                 name=self._generate_an_endpoint_name(),
-                repository=self.config.repository,
-                framework=self.config.framework,
-                accelerator=self.config.accelerator,
-                instance_size=self.config.instance_size,
-                instance_type=self.config.instance_type,
-                region=self.config.region,
-                vendor=self.config.vendor,
+                repository=validated_config["repository"],
+                framework=validated_config["framework"],
+                accelerator=validated_config["accelerator"],
+                instance_size=validated_config["instance_size"],
+                instance_type=validated_config["instance_type"],
+                region=validated_config["region"],
+                vendor=validated_config["vendor"],
                 account_id=self.config.account_id,
                 min_replica=self.config.min_replica,
                 max_replica=self.config.max_replica,
                 revision=self.config.revision,
                 task=self.config.task,
                 custom_image=self.config.custom_image,
-                type=self.config.endpoint_type,
+                type=InferenceEndpointType(validated_config["endpoint_type"]),
                 token=self.get_token(),
                 namespace=self.config.namespace,
             ).wait(timeout=POLLING_TIMEOUT)
@@ -172,21 +210,25 @@ def provision(self) -> None:
             )
             # Catch-all for any other unexpected errors
             raise Exception(
-                f"An unexpected error occurred while provisioning the Hugging Face inference endpoint: {e}"
+                "An unexpected error occurred while provisioning the "
+                f"Hugging Face inference endpoint: {e}"
             )
 
         # Check if the endpoint URL is available after provisioning
         if hf_endpoint.url:
             logger.info(
-                f"Hugging Face inference endpoint successfully deployed and available. Endpoint URL: {hf_endpoint.url}"
+                "Hugging Face inference endpoint successfully deployed "
+                f"and available. Endpoint URL: {hf_endpoint.url}"
             )
         else:
             logger.error(
-                "Failed to start Hugging Face inference endpoint service: No URL available, please check the Hugging Face console for more details."
+                "Failed to start Hugging Face inference endpoint "
+                "service: No URL available, please check the Hugging "
+                "Face console for more details."
             )
 
     def check_status(self) -> Tuple[ServiceState, str]:
-        """Check the the current operational state of the Hugging Face deployment.
+        """Check the current operational state of the Hugging Face deployment.
 
         Returns:
             The operational state of the Hugging Face deployment and a message
@@ -196,26 +238,29 @@ def check_status(self) -> Tuple[ServiceState, str]:
         try:
             status = self.hf_endpoint.status
             if status == InferenceEndpointStatus.RUNNING:
-                return (ServiceState.ACTIVE, "")
+                return ServiceState.ACTIVE, ""
 
             elif status == InferenceEndpointStatus.SCALED_TO_ZERO:
                 return (
                     ServiceState.SCALED_TO_ZERO,
-                    "Hugging Face Inference Endpoint is scaled to zero, but still running. It will be started on demand.",
+                    "Hugging Face Inference Endpoint is scaled to zero, but "
+                    "still running. It will be started on demand.",
                 )
 
             elif status == InferenceEndpointStatus.FAILED:
                 return (
                     ServiceState.ERROR,
-                    "Hugging Face Inference Endpoint deployment is inactive or not found",
+                    "Hugging Face Inference Endpoint deployment is inactive "
+                    "or not found",
                 )
             elif status == InferenceEndpointStatus.PENDING:
-                return (ServiceState.PENDING_STARTUP, "")
-            return (ServiceState.PENDING_STARTUP, "")
+                return ServiceState.PENDING_STARTUP, ""
+            return ServiceState.PENDING_STARTUP, ""
         except (InferenceEndpointError, HfHubHTTPError):
             return (
                 ServiceState.INACTIVE,
-                "Hugging Face Inference Endpoint deployment is inactive or not found",
+                "Hugging Face Inference Endpoint deployment is inactive or "
+                "not found",
             )
 
     def deprovision(self, force: bool = False) -> None:
@@ -253,15 +298,13 @@ def predict(self, data: "Any", max_new_tokens: int) -> "Any":
             )
         if self.prediction_url is not None:
             if self.hf_endpoint.task == "text-generation":
-                result = self.inference_client.task_generation(
+                return self.inference_client.text_generation(
                     data, max_new_tokens=max_new_tokens
                 )
-        else:
-            # TODO: Add support for all different supported tasks
-            raise NotImplementedError(
-                "Tasks other than text-generation is not implemented."
-            )
-        return result
+        # TODO: Add support for all different supported tasks
+        raise NotImplementedError(
+            "Tasks other than text-generation is not implemented."
+        )
 
     def get_logs(
         self, follow: bool = False, tail: Optional[int] = None