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import json
from typing import Annotated, Optional
import typer
from huggingface_hub._inference_endpoints import InferenceEndpoint, InferenceEndpointScalingMetric
from huggingface_hub.errors import HfHubHTTPError
from ._cli_utils import TokenOpt, get_hf_api, typer_factory
ie_cli = typer_factory(help="Manage Hugging Face Inference Endpoints.")
catalog_app = typer_factory(help="Interact with the Inference Endpoints catalog.")
NameArg = Annotated[
str,
typer.Argument(help="Endpoint name."),
]
NameOpt = Annotated[
Optional[str],
typer.Option(help="Endpoint name."),
]
NamespaceOpt = Annotated[
Optional[str],
typer.Option(
help="The namespace associated with the Inference Endpoint. Defaults to the current user's namespace.",
),
]
def _print_endpoint(endpoint: InferenceEndpoint) -> None:
typer.echo(json.dumps(endpoint.raw, indent=2, sort_keys=True))
@ie_cli.command()
def ls(
namespace: NamespaceOpt = None,
token: TokenOpt = None,
) -> None:
"""Lists all Inference Endpoints for the given namespace."""
api = get_hf_api(token=token)
try:
endpoints = api.list_inference_endpoints(namespace=namespace, token=token)
except HfHubHTTPError as error:
typer.echo(f"Listing failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
typer.echo(
json.dumps(
{"items": [endpoint.raw for endpoint in endpoints]},
indent=2,
sort_keys=True,
)
)
@ie_cli.command(name="deploy")
def deploy(
name: NameArg,
repo: Annotated[
str,
typer.Option(
help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
),
],
framework: Annotated[
str,
typer.Option(
help="The machine learning framework used for the model (e.g. 'vllm').",
),
],
accelerator: Annotated[
str,
typer.Option(
help="The hardware accelerator to be used for inference (e.g. 'cpu').",
),
],
instance_size: Annotated[
str,
typer.Option(
help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
),
],
instance_type: Annotated[
str,
typer.Option(
help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
),
],
region: Annotated[
str,
typer.Option(
help="The cloud region in which the Inference Endpoint will be created (e.g. 'us-east-1').",
),
],
vendor: Annotated[
str,
typer.Option(
help="The cloud provider or vendor where the Inference Endpoint will be hosted (e.g. 'aws').",
),
],
*,
namespace: NamespaceOpt = None,
task: Annotated[
Optional[str],
typer.Option(
help="The task on which to deploy the model (e.g. 'text-classification').",
),
] = None,
token: TokenOpt = None,
min_replica: Annotated[
int,
typer.Option(
help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
),
] = 1,
max_replica: Annotated[
int,
typer.Option(
help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
),
] = 1,
scale_to_zero_timeout: Annotated[
Optional[int],
typer.Option(
help="The duration in minutes before an inactive endpoint is scaled to zero.",
),
] = None,
scaling_metric: Annotated[
Optional[InferenceEndpointScalingMetric],
typer.Option(
help="The metric reference for scaling.",
),
] = None,
scaling_threshold: Annotated[
Optional[float],
typer.Option(
help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
),
] = None,
) -> None:
"""Deploy an Inference Endpoint from a Hub repository."""
api = get_hf_api(token=token)
endpoint = api.create_inference_endpoint(
name=name,
repository=repo,
framework=framework,
accelerator=accelerator,
instance_size=instance_size,
instance_type=instance_type,
region=region,
vendor=vendor,
namespace=namespace,
task=task,
token=token,
min_replica=min_replica,
max_replica=max_replica,
scaling_metric=scaling_metric,
scaling_threshold=scaling_threshold,
scale_to_zero_timeout=scale_to_zero_timeout,
)
_print_endpoint(endpoint)
@catalog_app.command(name="deploy")
def deploy_from_catalog(
repo: Annotated[
str,
typer.Option(
help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
),
],
name: NameOpt = None,
namespace: NamespaceOpt = None,
token: TokenOpt = None,
) -> None:
"""Deploy an Inference Endpoint from the Model Catalog."""
api = get_hf_api(token=token)
try:
endpoint = api.create_inference_endpoint_from_catalog(
repo_id=repo,
name=name,
namespace=namespace,
token=token,
)
except HfHubHTTPError as error:
typer.echo(f"Deployment failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
def list_catalog(
token: TokenOpt = None,
) -> None:
"""List available Catalog models."""
api = get_hf_api(token=token)
try:
models = api.list_inference_catalog(token=token)
except HfHubHTTPError as error:
typer.echo(f"Catalog fetch failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
typer.echo(json.dumps({"models": models}, indent=2, sort_keys=True))
catalog_app.command(name="ls")(list_catalog)
ie_cli.command(name="list-catalog", help="List available Catalog models.", hidden=True)(list_catalog)
ie_cli.add_typer(catalog_app, name="catalog")
@ie_cli.command()
def describe(
name: NameArg,
namespace: NamespaceOpt = None,
token: TokenOpt = None,
) -> None:
"""Get information about an existing endpoint."""
api = get_hf_api(token=token)
try:
endpoint = api.get_inference_endpoint(name=name, namespace=namespace, token=token)
except HfHubHTTPError as error:
typer.echo(f"Fetch failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
@ie_cli.command()
def update(
name: NameArg,
namespace: NamespaceOpt = None,
repo: Annotated[
Optional[str],
typer.Option(
help="The name of the model repository associated with the Inference Endpoint (e.g. 'openai/gpt-oss-120b').",
),
] = None,
accelerator: Annotated[
Optional[str],
typer.Option(
help="The hardware accelerator to be used for inference (e.g. 'cpu').",
),
] = None,
instance_size: Annotated[
Optional[str],
typer.Option(
help="The size or type of the instance to be used for hosting the model (e.g. 'x4').",
),
] = None,
instance_type: Annotated[
Optional[str],
typer.Option(
help="The cloud instance type where the Inference Endpoint will be deployed (e.g. 'intel-icl').",
),
] = None,
framework: Annotated[
Optional[str],
typer.Option(
help="The machine learning framework used for the model (e.g. 'custom').",
),
] = None,
revision: Annotated[
Optional[str],
typer.Option(
help="The specific model revision to deploy on the Inference Endpoint (e.g. '6c0e6080953db56375760c0471a8c5f2929baf11').",
),
] = None,
task: Annotated[
Optional[str],
typer.Option(
help="The task on which to deploy the model (e.g. 'text-classification').",
),
] = None,
min_replica: Annotated[
Optional[int],
typer.Option(
help="The minimum number of replicas (instances) to keep running for the Inference Endpoint.",
),
] = None,
max_replica: Annotated[
Optional[int],
typer.Option(
help="The maximum number of replicas (instances) to scale to for the Inference Endpoint.",
),
] = None,
scale_to_zero_timeout: Annotated[
Optional[int],
typer.Option(
help="The duration in minutes before an inactive endpoint is scaled to zero.",
),
] = None,
scaling_metric: Annotated[
Optional[InferenceEndpointScalingMetric],
typer.Option(
help="The metric reference for scaling.",
),
] = None,
scaling_threshold: Annotated[
Optional[float],
typer.Option(
help="The scaling metric threshold used to trigger a scale up. Ignored when scaling metric is not provided.",
),
] = None,
token: TokenOpt = None,
) -> None:
"""Update an existing endpoint."""
api = get_hf_api(token=token)
try:
endpoint = api.update_inference_endpoint(
name=name,
namespace=namespace,
repository=repo,
framework=framework,
revision=revision,
task=task,
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,
scaling_metric=scaling_metric,
scaling_threshold=scaling_threshold,
token=token,
)
except HfHubHTTPError as error:
typer.echo(f"Update failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
@ie_cli.command()
def delete(
name: NameArg,
namespace: NamespaceOpt = None,
yes: Annotated[
bool,
typer.Option("--yes", help="Skip confirmation prompts."),
] = False,
token: TokenOpt = None,
) -> None:
"""Delete an Inference Endpoint permanently."""
if not yes:
confirmation = typer.prompt(f"Delete endpoint '{name}'? Type the name to confirm.")
if confirmation != name:
typer.echo("Aborted.")
raise typer.Exit(code=2)
api = get_hf_api(token=token)
try:
api.delete_inference_endpoint(name=name, namespace=namespace, token=token)
except HfHubHTTPError as error:
typer.echo(f"Delete failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
typer.echo(f"Deleted '{name}'.")
@ie_cli.command()
def pause(
name: NameArg,
namespace: NamespaceOpt = None,
token: TokenOpt = None,
) -> None:
"""Pause an Inference Endpoint."""
api = get_hf_api(token=token)
try:
endpoint = api.pause_inference_endpoint(name=name, namespace=namespace, token=token)
except HfHubHTTPError as error:
typer.echo(f"Pause failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
@ie_cli.command()
def resume(
name: NameArg,
namespace: NamespaceOpt = None,
fail_if_already_running: Annotated[
bool,
typer.Option(
"--fail-if-already-running",
help="If `True`, the method will raise an error if the Inference Endpoint is already running.",
),
] = False,
token: TokenOpt = None,
) -> None:
"""Resume an Inference Endpoint."""
api = get_hf_api(token=token)
try:
endpoint = api.resume_inference_endpoint(
name=name,
namespace=namespace,
token=token,
running_ok=not fail_if_already_running,
)
except HfHubHTTPError as error:
typer.echo(f"Resume failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
@ie_cli.command()
def scale_to_zero(
name: NameArg,
namespace: NamespaceOpt = None,
token: TokenOpt = None,
) -> None:
"""Scale an Inference Endpoint to zero."""
api = get_hf_api(token=token)
try:
endpoint = api.scale_to_zero_inference_endpoint(name=name, namespace=namespace, token=token)
except HfHubHTTPError as error:
typer.echo(f"Scale To Zero failed: {error}")
raise typer.Exit(code=error.response.status_code) from error
_print_endpoint(endpoint)
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