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Module: ml_model_id

MlModelId

Bases: BaseModel

Attributes:

Name Type Description
ml_type Optional[MLType]

MLType - The type of machine learning model
repo_id RitualRepoId

RitualRepoId - The repository id of the model
files List[str]

List[str] - The list of files that make up the model
Properties
Source code in src/infernet_ml/utils/specs/ml_model_id.py 
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class MlModelId(BaseModel):
    """
    ModelId: Base class for all models within Ritual's services.

    Attributes:
        ml_type: MLType - The type of machine learning model
        repo_id: RitualRepoId - The repository id of the model
        files: List[str] - The list of files that make up the model

    Properties:
        repo_id: str - The repository id of the model
        unique_id: str - The unique identifier of the model
        version: str - The version of the model

    """

    repo_id: RitualRepoId
    files: List[str] = []
    ml_type: Optional[MLType] = None

    @property
    def version(self) -> str | None:
        """
        Get the version of the model, as denoted by the version of the model's
        repository.

        Returns:
            str - The version of the model
        """
        return self.repo_id.version

    @classmethod
    def from_any(
        cls, model: MlModelId | str, ml_type: Optional[MLType] = None
    ) -> MlModelId:
        """
        Utility function to convert a string or MlModelId to an MlModelId.

        Args:
            model: MlModelId | str - The model id or unique identifier
            ml_type: MLType - The type of machine learning model

        Returns:

        """
        if isinstance(model, str):
            return cls.from_unique_id(model, ml_type)
        elif isinstance(model, MlModelId):
            if ml_type:
                model.ml_type = ml_type
            return model
        else:
            raise ValueError(f"Cannot convert data type: {type(model)} to MlModelId")

    @property
    def hf_repo_id(self) -> str:
        """
        Get the huggingface repository id of the model, if it is a huggingface model.

        Returns:
            str - The huggingface repository id of the model
        """
        return self.repo_id.hf_id

    @property
    def arweave_repo_id(self) -> str:
        """
        Get the arweave repository id of the model, as implemented in the
        RitualRepoId class.

        Returns:
            str - The arweave repository id of the model
        """
        return self.repo_id.ar_id

    @property
    def to_web3(self) -> bytes:
        """
        Get the web3 encoding of the model id.

        Returns:
            bytes - The web3 encoding of the model id
        """
        return encode(
            ["bytes", "string"],
            [
                self.repo_id.to_web3(),
                ",".join(self.files),
            ],
        )

    def to_local_dir(self, cache: Path = DEFAULT_CACHE_DIR) -> Path:
        return self.repo_id.to_local_dir(cache)

    @classmethod
    def from_web3(cls, encoding: bytes) -> MlModelId:
        """
        Create a ModelId from a web3 encoding.

        Args:
            encoding: bytes - The web3 encoding of the model id

        Returns:
            MlModelId - The model id
        """
        repo_b, files = decode(["bytes", "string"], encoding)
        repo = RitualRepoId.from_web3(repo_b)
        return cls(
            repo_id=repo,
            files=files.split(","),
        )

    @property
    def unique_id(self) -> str:
        """
        Get the unique identifier of the model. This has the format:
        <repo_id>:<files> where the repo_id is the unique identifier of the repository
        as implemented in the RitualRepoId class and the files are a comma-separated
        list of the files that make up the model.

        Returns:
            str - The unique identifier of the model
        """
        base = self.repo_id.to_unique_id()
        files_str = ",".join(self.files) if self.files else ""
        base = f"{base}:{files_str}" if files_str else base
        return base

    @classmethod
    def from_unique_id(
        cls, unique_id: str, ml_type: Optional[MLType] = None
    ) -> MlModelId:
        """
        Create a ModelId from a unique identifier.

        Args:
            unique_id: str - The unique identifier of the model
            ml_type: MLType - The type of machine learning model

        Returns:
            MlModelId - The model id
        """
        parts = unique_id.split(":")
        base = parts[0]
        repo_id = RitualRepoId.from_unique_id(base)
        files = parts[1].split(",") if len(parts) > 1 else []
        return MlModelId(
            ml_type=ml_type,
            repo_id=repo_id,
            files=files,
        )

    @classmethod
    def __hash__(self) -> int:
        return hash(self.unique_id)

    def __eq__(self, other: object) -> bool:
        if not isinstance(other, MlModelId):
            return False
        return self.unique_id == other.unique_id

arweave_repo_id: str property

Get the arweave repository id of the model, as implemented in the RitualRepoId class.

Returns:

Type Description
str

str - The arweave repository id of the model

hf_repo_id: str property

Get the huggingface repository id of the model, if it is a huggingface model.

Returns:

Type Description
str

str - The huggingface repository id of the model

to_web3: bytes property

Get the web3 encoding of the model id.

Returns:

Type Description
bytes

bytes - The web3 encoding of the model id

unique_id: str property

Get the unique identifier of the model. This has the format: : where the repo_id is the unique identifier of the repository as implemented in the RitualRepoId class and the files are a comma-separated list of the files that make up the model.

Returns:

Type Description
str

str - The unique identifier of the model

version: str | None property

Get the version of the model, as denoted by the version of the model's repository.

Returns:

Type Description
str | None

str - The version of the model

from_any(model, ml_type=None) classmethod

Utility function to convert a string or MlModelId to an MlModelId.

Parameters:

Name Type Description Default
model MlModelId | str

MlModelId | str - The model id or unique identifier

 required
ml_type Optional[MLType]

MLType - The type of machine learning model

 None
Source code in src/infernet_ml/utils/specs/ml_model_id.py 
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@classmethod
def from_any(
    cls, model: MlModelId | str, ml_type: Optional[MLType] = None
) -> MlModelId:
    """
    Utility function to convert a string or MlModelId to an MlModelId.

    Args:
        model: MlModelId | str - The model id or unique identifier
        ml_type: MLType - The type of machine learning model

    Returns:

    """
    if isinstance(model, str):
        return cls.from_unique_id(model, ml_type)
    elif isinstance(model, MlModelId):
        if ml_type:
            model.ml_type = ml_type
        return model
    else:
        raise ValueError(f"Cannot convert data type: {type(model)} to MlModelId")

from_unique_id(unique_id, ml_type=None) classmethod

Create a ModelId from a unique identifier.

Parameters:

Name Type Description Default
unique_id str

str - The unique identifier of the model

 required
ml_type Optional[MLType]

MLType - The type of machine learning model

 None

Returns:

Type Description
MlModelId

MlModelId - The model id
Source code in src/infernet_ml/utils/specs/ml_model_id.py 
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@classmethod
def from_unique_id(
    cls, unique_id: str, ml_type: Optional[MLType] = None
) -> MlModelId:
    """
    Create a ModelId from a unique identifier.

    Args:
        unique_id: str - The unique identifier of the model
        ml_type: MLType - The type of machine learning model

    Returns:
        MlModelId - The model id
    """
    parts = unique_id.split(":")
    base = parts[0]
    repo_id = RitualRepoId.from_unique_id(base)
    files = parts[1].split(",") if len(parts) > 1 else []
    return MlModelId(
        ml_type=ml_type,
        repo_id=repo_id,
        files=files,
    )

from_web3(encoding) classmethod

Create a ModelId from a web3 encoding.

Parameters:

Name Type Description Default
encoding bytes

bytes - The web3 encoding of the model id

 required

Returns:

Type Description
MlModelId

MlModelId - The model id
Source code in src/infernet_ml/utils/specs/ml_model_id.py 
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@classmethod
def from_web3(cls, encoding: bytes) -> MlModelId:
    """
    Create a ModelId from a web3 encoding.

    Args:
        encoding: bytes - The web3 encoding of the model id

    Returns:
        MlModelId - The model id
    """
    repo_b, files = decode(["bytes", "string"], encoding)
    repo = RitualRepoId.from_web3(repo_b)
    return cls(
        repo_id=repo,
        files=files.split(","),
    )