replicate-python/replicate/training.py at main · hatgit/replicate-python · GitHub

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from typing import (

TYPE_CHECKING,

Any,

Dict,

List,

Literal,

Optional,

Tuple,

TypedDict,

Union,

overload,

)

from typing_extensions import NotRequired, Unpack

from replicate.files import upload_file

from replicate.identifier import ModelVersionIdentifier

from replicate.json import encode_json

from replicate.model import Model

from replicate.pagination import Page

from replicate.resource import Namespace, Resource

from replicate.version import Version

try:

from pydantic import v1 as pydantic # type: ignore

except ImportError:

import pydantic # type: ignore

if TYPE_CHECKING:

from replicate.client import Client

class Training(Resource):

"""

A training made for a model hosted on Replicate.

"""

_client: "Client" = pydantic.PrivateAttr()

id: str

"""The unique ID of the training."""

model: str

"""An identifier for the model used to create the prediction, in the form `owner/name`."""

version: Union[str, Version]

"""The version of the model used to create the training."""

destination: Optional[str]

"""The model destination of the training."""

status: Literal["starting", "processing", "succeeded", "failed", "canceled"]

"""The status of the training."""

input: Optional[Dict[str, Any]]

"""The input to the training."""

output: Optional[Any]

"""The output of the training."""

logs: Optional[str]

"""The logs of the training."""

error: Optional[str]

"""The error encountered during the training, if any."""

created_at: Optional[str]

"""When the training was created."""

started_at: Optional[str]

"""When the training was started."""

completed_at: Optional[str]

"""When the training was completed, if finished."""

urls: Optional[Dict[str, str]]

"""

URLs associated with the training.

The following keys are available:

- `get`: A URL to fetch the training.

- `cancel`: A URL to cancel the training.

"""

def cancel(self) -> None:

"""Cancel a running training"""

self._client.trainings.cancel(self.id)

def reload(self) -> None:

"""

Load the training from the server.

"""

updated = self._client.trainings.get(self.id)

for name, value in updated.dict().items():

setattr(self, name, value)

class Trainings(Namespace):

"""

Namespace for operations related to trainings.

"""

def list(self, cursor: Union[str, "ellipsis", None] = ...) -> Page[Training]: # noqa: F821

"""

List your trainings.

Parameters:

cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.

Returns:

Page[Training]: A page of trainings.

Raises:

ValueError: If `cursor` is `None`.

"""

if cursor is None:

raise ValueError("cursor cannot be None")

resp = self._client._request(

"GET", "/v1/trainings" if cursor is ... else cursor

)

obj = resp.json()

obj["results"] = [

_json_to_training(self._client, result) for result in obj["results"]

]

return Page[Training](**obj)

async def async_list(

self,

cursor: Union[str, "ellipsis", None] = ..., # noqa: F821

) -> Page[Training]:

"""

List your trainings.

Parameters:

cursor: The cursor to use for pagination. Use the value of `Page.next` or `Page.previous`.

Returns:

Page[Training]: A page of trainings.

Raises:

ValueError: If `cursor` is `None`.

"""

if cursor is None:

raise ValueError("cursor cannot be None")

resp = await self._client._async_request(

"GET", "/v1/trainings" if cursor is ... else cursor

)

obj = resp.json()

obj["results"] = [

_json_to_training(self._client, result) for result in obj["results"]

]

return Page[Training](**obj)

def get(self, id: str) -> Training:

"""

Get a training by ID.

Args:

id: The ID of the training.

Returns:

Training: The training object.

"""

resp = self._client._request(

"GET",

f"/v1/trainings/{id}",

)

return _json_to_training(self._client, resp.json())

async def async_get(self, id: str) -> Training:

"""

Get a training by ID.

Args:

id: The ID of the training.

Returns:

Training: The training object.

"""

resp = await self._client._async_request(

"GET",

f"/v1/trainings/{id}",

)

return _json_to_training(self._client, resp.json())

class CreateTrainingParams(TypedDict):

"""Parameters for creating a training."""

destination: Union[str, Tuple[str, str], "Model"]

webhook: NotRequired[str]

webhook_completed: NotRequired[str]

webhook_events_filter: NotRequired[List[str]]

@overload

def create( # pylint: disable=too-many-arguments

self,

version: str,

input: Dict[str, Any],

destination: str,

webhook: Optional[str] = None,

webhook_events_filter: Optional[List[str]] = None,

**kwargs,

) -> Training:

...

@overload

def create(

self,

model: Union[str, Tuple[str, str], "Model"],

version: Union[str, Version],

input: Optional[Dict[str, Any]] = None,

**params: Unpack["Trainings.CreateTrainingParams"],

) -> Training:

...

def create( # type: ignore

self,

*args,

model: Optional[Union[str, Tuple[str, str], "Model"]] = None,

version: Optional[Union[str, Version]] = None,

input: Optional[Dict[str, Any]] = None,

**params: Unpack["Trainings.CreateTrainingParams"],

) -> Training:

"""

Create a new training using the specified model version as a base.

"""

url = None

# Support positional arguments for backwards compatibility

if args:

if shorthand := args[0] if len(args) > 0 else None:

url = _create_training_url_from_shorthand(shorthand)

input = args[1] if len(args) > 1 else input

if len(args) > 2:

params["destination"] = args[2]

if len(args) > 3:

params["webhook"] = args[3]

if len(args) > 4:

params["webhook_completed"] = args[4]

if len(args) > 5:

params["webhook_events_filter"] = args[5]

elif model and version:

url = _create_training_url_from_model_and_version(model, version)

elif model is None and isinstance(version, str):

url = _create_training_url_from_shorthand(version)

if not url:

raise ValueError("model and version or shorthand version must be specified")

body = _create_training_body(input, **params)

resp = self._client._request(

"POST",

url,

json=body,

)

return _json_to_training(self._client, resp.json())

async def async_create(

self,

model: Union[str, Tuple[str, str], "Model"],

version: Union[str, Version],

input: Dict[str, Any],

**params: Unpack["Trainings.CreateTrainingParams"],

) -> Training:

"""

Create a new training using the specified model version as a base.

Args:

version: The ID of the base model version that you're using to train a new model version.

input: The input to the training.

destination: The desired model to push to in the format `{owner}/{model_name}`. This should be an existing model owned by the user or organization making the API request.

webhook: The URL to send a POST request to when the training is completed. Defaults to None.

webhook_completed: The URL to receive a POST request when the prediction is completed.

webhook_events_filter: The events to send to the webhook. Defaults to None.

Returns:

The training object.

"""

url = _create_training_url_from_model_and_version(model, version)

body = _create_training_body(input, **params)

resp = await self._client._async_request(

"POST",

url,

json=body,

)

return _json_to_training(self._client, resp.json())

def cancel(self, id: str) -> Training:

"""

Cancel a training.

Args:

id: The ID of the training to cancel.

Returns:

Training: The canceled training object.

"""

resp = self._client._request(

"POST",

f"/v1/trainings/{id}/cancel",

)

return _json_to_training(self._client, resp.json())

async def async_cancel(self, id: str) -> Training:

"""

Cancel a training.

Args:

id: The ID of the training to cancel.

Returns:

Training: The canceled training object.

"""

resp = await self._client._async_request(

"POST",

f"/v1/trainings/{id}/cancel",

)

return _json_to_training(self._client, resp.json())

def _create_training_body(

input: Optional[Dict[str, Any]] = None,

destination: Optional[Union[str, Tuple[str, str], "Model"]] = None,

webhook: Optional[str] = None,

webhook_completed: Optional[str] = None,

webhook_events_filter: Optional[List[str]] = None,

) -> Dict[str, Any]:

body = {}

if input is not None:

body["input"] = encode_json(input, upload_file=upload_file)

if destination is None:

raise ValueError(

"A destination must be provided as a positional or keyword argument."

)

if isinstance(destination, Model):

destination = f"{destination.owner}/{destination.name}"

elif isinstance(destination, tuple):

destination = f"{destination[0]}/{destination[1]}"

body["destination"] = destination

if webhook is not None:

body["webhook"] = webhook

if webhook_completed is not None:

body["webhook_completed"] = webhook_completed

if webhook_events_filter is not None:

body["webhook_events_filter"] = webhook_events_filter

return body

def _create_training_url_from_shorthand(ref: str) -> str:

owner, name, version_id = ModelVersionIdentifier.parse(ref)

return f"/v1/models/{owner}/{name}/versions/{version_id}/trainings"

def _create_training_url_from_model_and_version(

model: Union[str, Tuple[str, str], "Model"],

version: Union[str, "Version"],

) -> str:

if isinstance(model, Model):

owner, name = model.owner, model.name

elif isinstance(model, tuple):

owner, name = model[0], model[1]

elif isinstance(model, str):

owner, name, _ = ModelVersionIdentifier.parse(model)

else:

raise ValueError(

"model must be a Model, a tuple of (owner, name), or a string in the format 'owner/name'"

)

if isinstance(version, Version):

version_id = version.id

else:

version_id = version

return f"/v1/models/{owner}/{name}/versions/{version_id}/trainings"

def _json_to_training(client: "Client", json: Dict[str, Any]) -> Training:

training = Training(**json)

training._client = client

return training

replicate-python/replicate/training.py at main · hatgit/replicate-python · GitHub

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