import time from dataclasses import dataclass, field from datetime import datetime from enum import Enum from typing import TYPE_CHECKING, Dict, Optional, Union from huggingface_hub.errors import InferenceEndpointError, InferenceEndpointTimeoutError from .inference._client import InferenceClient from .inference._generated._async_client import AsyncInferenceClient from .utils import get_session, logging, parse_datetime if TYPE_CHECKING: from .hf_api import HfApi logger = logging.get_logger(__name__) class InferenceEndpointStatus(str, Enum): PENDING = "pending" INITIALIZING = "initializing" UPDATING = "updating" UPDATE_FAILED = "updateFailed" RUNNING = "running" PAUSED = "paused" FAILED = "failed" SCALED_TO_ZERO = "scaledToZero" class InferenceEndpointType(str, Enum): PUBlIC = "public" PROTECTED = "protected" PRIVATE = "private" @dataclass class InferenceEndpoint: """ Contains information about a deployed Inference Endpoint. Args: name (`str`): The unique name of the Inference Endpoint. namespace (`str`): The namespace where the Inference Endpoint is located. repository (`str`): The name of the model repository deployed on this Inference Endpoint. status ([`InferenceEndpointStatus`]): The current status of the Inference Endpoint. url (`str`, *optional*): The URL of the Inference Endpoint, if available. Only a deployed Inference Endpoint will have a URL. framework (`str`): The machine learning framework used for the model. revision (`str`): The specific model revision deployed on the Inference Endpoint. task (`str`): The task associated with the deployed model. created_at (`datetime.datetime`): The timestamp when the Inference Endpoint was created. updated_at (`datetime.datetime`): The timestamp of the last update of the Inference Endpoint. type ([`InferenceEndpointType`]): The type of the Inference Endpoint (public, protected, private). raw (`Dict`): The raw dictionary data returned from the API. token (`str` or `bool`, *optional*): Authentication token for the Inference Endpoint, if set when requesting the API. Will default to the locally saved token if not provided. Pass `token=False` if you don't want to send your token to the server. Example: ```python >>> from huggingface_hub import get_inference_endpoint >>> endpoint = get_inference_endpoint("my-text-to-image") >>> endpoint InferenceEndpoint(name='my-text-to-image', ...) # Get status >>> endpoint.status 'running' >>> endpoint.url 'https://my-text-to-image.region.vendor.endpoints.huggingface.cloud' # Run inference >>> endpoint.client.text_to_image(...) # Pause endpoint to save $$$ >>> endpoint.pause() # ... # Resume and wait for deployment >>> endpoint.resume() >>> endpoint.wait() >>> endpoint.client.text_to_image(...) ``` """ # Field in __repr__ name: str = field(init=False) namespace: str repository: str = field(init=False) status: InferenceEndpointStatus = field(init=False) url: Optional[str] = field(init=False) # Other fields framework: str = field(repr=False, init=False) revision: str = field(repr=False, init=False) task: str = field(repr=False, init=False) created_at: datetime = field(repr=False, init=False) updated_at: datetime = field(repr=False, init=False) type: InferenceEndpointType = field(repr=False, init=False) # Raw dict from the API raw: Dict = field(repr=False) # Internal fields _token: Union[str, bool, None] = field(repr=False, compare=False) _api: "HfApi" = field(repr=False, compare=False) @classmethod def from_raw( cls, raw: Dict, namespace: str, token: Union[str, bool, None] = None, api: Optional["HfApi"] = None ) -> "InferenceEndpoint": """Initialize object from raw dictionary.""" if api is None: from .hf_api import HfApi api = HfApi() if token is None: token = api.token # All other fields are populated in __post_init__ return cls(raw=raw, namespace=namespace, _token=token, _api=api) def __post_init__(self) -> None: """Populate fields from raw dictionary.""" self._populate_from_raw() @property def client(self) -> InferenceClient: """Returns a client to make predictions on this Inference Endpoint. Returns: [`InferenceClient`]: an inference client pointing to the deployed endpoint. Raises: [`InferenceEndpointError`]: If the Inference Endpoint is not yet deployed. """ if self.url is None: raise InferenceEndpointError( "Cannot create a client for this Inference Endpoint as it is not yet deployed. " "Please wait for the Inference Endpoint to be deployed using `endpoint.wait()` and try again." ) return InferenceClient( model=self.url, token=self._token, # type: ignore[arg-type] # boolean token shouldn't be possible. In practice it's ok. ) @property def async_client(self) -> AsyncInferenceClient: """Returns a client to make predictions on this Inference Endpoint. Returns: [`AsyncInferenceClient`]: an asyncio-compatible inference client pointing to the deployed endpoint. Raises: [`InferenceEndpointError`]: If the Inference Endpoint is not yet deployed. """ if self.url is None: raise InferenceEndpointError( "Cannot create a client for this Inference Endpoint as it is not yet deployed. " "Please wait for the Inference Endpoint to be deployed using `endpoint.wait()` and try again." ) return AsyncInferenceClient( model=self.url, token=self._token, # type: ignore[arg-type] # boolean token shouldn't be possible. In practice it's ok. ) def wait(self, timeout: Optional[int] = None, refresh_every: int = 5) -> "InferenceEndpoint": """Wait for the Inference Endpoint to be deployed. Information from the server will be fetched every 1s. If the Inference Endpoint is not deployed after `timeout` seconds, a [`InferenceEndpointTimeoutError`] will be raised. The [`InferenceEndpoint`] will be mutated in place with the latest data. Args: timeout (`int`, *optional*): The maximum time to wait for the Inference Endpoint to be deployed, in seconds. If `None`, will wait indefinitely. refresh_every (`int`, *optional*): The time to wait between each fetch of the Inference Endpoint status, in seconds. Defaults to 5s. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. Raises: [`InferenceEndpointError`] If the Inference Endpoint ended up in a failed state. [`InferenceEndpointTimeoutError`] If the Inference Endpoint is not deployed after `timeout` seconds. """ if timeout is not None and timeout < 0: raise ValueError("`timeout` cannot be negative.") if refresh_every <= 0: raise ValueError("`refresh_every` must be positive.") start = time.time() while True: if self.status == InferenceEndpointStatus.FAILED: raise InferenceEndpointError( f"Inference Endpoint {self.name} failed to deploy. Please check the logs for more information." ) if self.status == InferenceEndpointStatus.UPDATE_FAILED: raise InferenceEndpointError( f"Inference Endpoint {self.name} failed to update. Please check the logs for more information." ) if self.status == InferenceEndpointStatus.RUNNING and self.url is not None: # Verify the endpoint is actually reachable response = get_session().get(self.url, headers=self._api._build_hf_headers(token=self._token)) if response.status_code == 200: logger.info("Inference Endpoint is ready to be used.") return self if timeout is not None: if time.time() - start > timeout: raise InferenceEndpointTimeoutError("Timeout while waiting for Inference Endpoint to be deployed.") logger.info(f"Inference Endpoint is not deployed yet ({self.status}). Waiting {refresh_every}s...") time.sleep(refresh_every) self.fetch() def fetch(self) -> "InferenceEndpoint": """Fetch latest information about the Inference Endpoint. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. """ obj = self._api.get_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token) # type: ignore [arg-type] self.raw = obj.raw self._populate_from_raw() return self def update( self, *, # Compute update accelerator: Optional[str] = None, instance_size: Optional[str] = None, instance_type: Optional[str] = None, min_replica: Optional[int] = None, max_replica: Optional[int] = None, scale_to_zero_timeout: Optional[int] = None, # Model update repository: Optional[str] = None, framework: Optional[str] = None, revision: Optional[str] = None, task: Optional[str] = None, custom_image: Optional[Dict] = None, secrets: Optional[Dict[str, str]] = None, ) -> "InferenceEndpoint": """Update the Inference Endpoint. This method allows the update of either the compute configuration, the deployed model, or both. All arguments are optional but at least one must be provided. This is an alias for [`HfApi.update_inference_endpoint`]. The current object is mutated in place with the latest data from the server. Args: accelerator (`str`, *optional*): The hardware accelerator to be used for inference (e.g. `"cpu"`). instance_size (`str`, *optional*): The size or type of the instance to be used for hosting the model (e.g. `"x4"`). instance_type (`str`, *optional*): The cloud instance type where the Inference Endpoint will be deployed (e.g. `"intel-icl"`). min_replica (`int`, *optional*): The minimum number of replicas (instances) to keep running for the Inference Endpoint. max_replica (`int`, *optional*): The maximum number of replicas (instances) to scale to for the Inference Endpoint. scale_to_zero_timeout (`int`, *optional*): The duration in minutes before an inactive endpoint is scaled to zero. repository (`str`, *optional*): The name of the model repository associated with the Inference Endpoint (e.g. `"gpt2"`). framework (`str`, *optional*): The machine learning framework used for the model (e.g. `"custom"`). revision (`str`, *optional*): The specific model revision to deploy on the Inference Endpoint (e.g. `"6c0e6080953db56375760c0471a8c5f2929baf11"`). task (`str`, *optional*): The task on which to deploy the model (e.g. `"text-classification"`). custom_image (`Dict`, *optional*): A custom Docker image to use for the Inference Endpoint. This is useful if you want to deploy an Inference Endpoint running on the `text-generation-inference` (TGI) framework (see examples). secrets (`Dict[str, str]`, *optional*): Secret values to inject in the container environment. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. """ # Make API call obj = self._api.update_inference_endpoint( name=self.name, namespace=self.namespace, accelerator=accelerator, instance_size=instance_size, instance_type=instance_type, min_replica=min_replica, max_replica=max_replica, scale_to_zero_timeout=scale_to_zero_timeout, repository=repository, framework=framework, revision=revision, task=task, custom_image=custom_image, secrets=secrets, token=self._token, # type: ignore [arg-type] ) # Mutate current object self.raw = obj.raw self._populate_from_raw() return self def pause(self) -> "InferenceEndpoint": """Pause the Inference Endpoint. A paused Inference Endpoint will not be charged. It can be resumed at any time using [`InferenceEndpoint.resume`]. This is different than scaling the Inference Endpoint to zero with [`InferenceEndpoint.scale_to_zero`], which would be automatically restarted when a request is made to it. This is an alias for [`HfApi.pause_inference_endpoint`]. The current object is mutated in place with the latest data from the server. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. """ obj = self._api.pause_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token) # type: ignore [arg-type] self.raw = obj.raw self._populate_from_raw() return self def resume(self, running_ok: bool = True) -> "InferenceEndpoint": """Resume the Inference Endpoint. This is an alias for [`HfApi.resume_inference_endpoint`]. The current object is mutated in place with the latest data from the server. Args: running_ok (`bool`, *optional*): If `True`, the method will not raise an error if the Inference Endpoint is already running. Defaults to `True`. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. """ obj = self._api.resume_inference_endpoint( name=self.name, namespace=self.namespace, running_ok=running_ok, token=self._token ) # type: ignore [arg-type] self.raw = obj.raw self._populate_from_raw() return self def scale_to_zero(self) -> "InferenceEndpoint": """Scale Inference Endpoint to zero. An Inference Endpoint scaled to zero will not be charged. It will be resume on the next request to it, with a cold start delay. This is different than pausing the Inference Endpoint with [`InferenceEndpoint.pause`], which would require a manual resume with [`InferenceEndpoint.resume`]. This is an alias for [`HfApi.scale_to_zero_inference_endpoint`]. The current object is mutated in place with the latest data from the server. Returns: [`InferenceEndpoint`]: the same Inference Endpoint, mutated in place with the latest data. """ obj = self._api.scale_to_zero_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token) # type: ignore [arg-type] self.raw = obj.raw self._populate_from_raw() return self def delete(self) -> None: """Delete the Inference Endpoint. This operation is not reversible. If you don't want to be charged for an Inference Endpoint, it is preferable to pause it with [`InferenceEndpoint.pause`] or scale it to zero with [`InferenceEndpoint.scale_to_zero`]. This is an alias for [`HfApi.delete_inference_endpoint`]. """ self._api.delete_inference_endpoint(name=self.name, namespace=self.namespace, token=self._token) # type: ignore [arg-type] def _populate_from_raw(self) -> None: """Populate fields from raw dictionary. Called in __post_init__ + each time the Inference Endpoint is updated. """ # Repr fields self.name = self.raw["name"] self.repository = self.raw["model"]["repository"] self.status = self.raw["status"]["state"] self.url = self.raw["status"].get("url") # Other fields self.framework = self.raw["model"]["framework"] self.revision = self.raw["model"]["revision"] self.task = self.raw["model"]["task"] self.created_at = parse_datetime(self.raw["status"]["createdAt"]) self.updated_at = parse_datetime(self.raw["status"]["updatedAt"]) self.type = self.raw["type"]