Module `aiolirest.models.model_response`

HPE Machine Learning Inference Software (MLIS/Aioli)

HPE MLIS is Aioli – The AI On-line Inference Platform that enables easy deployment, tracking, and serving of your packaged models regardless of your preferred AI framework.

The version of the OpenAPI document: 1.0.0 Contact: community@determined-ai Generated by OpenAPI Generator (https://openapi-generator.tech)

Do not edit the class manually.

Expand source code

# coding: utf-8

"""
    HPE Machine Learning Inference Software (MLIS/Aioli)

    HPE MLIS is *Aioli* -- The AI On-line Inference Platform that enables easy deployment, tracking, and serving of your packaged models regardless of your preferred AI framework.

    The version of the OpenAPI document: 1.0.0
    Contact: community@determined-ai
    Generated by OpenAPI Generator (https://openapi-generator.tech)

    Do not edit the class manually.
"""  # noqa: E501


from __future__ import annotations
import pprint
import re  # noqa: F401
import json


from typing import Any, ClassVar, Dict, List, Optional
from pydantic import BaseModel, StrictInt, StrictStr
from pydantic import Field
try:
    from typing import Self
except ImportError:
    from typing_extensions import Self

class ModelResponse(BaseModel):
    """
    Provides a set of values that are returned by querying for models
    """ # noqa: E501
    description: Optional[StrictStr] = Field(default=None, description="The description of desribing the model")
    display_name: Optional[StrictStr] = Field(default=None, description="The name displayed that is human readable", alias="displayName")
    image: Optional[StrictStr] = Field(default=None, description="The default container image to execute the model (if available)")
    latest_version_id_str: Optional[StrictStr] = Field(default=None, description="The latest version of the model", alias="latestVersionIdStr")
    latest_version_size_in_bytes: Optional[StrictInt] = Field(default=None, description="The number of bytes for the latest model", alias="latestVersionSizeInBytes")
    metadata: Optional[Dict[str, StrictStr]] = Field(default=None, description="Extra argument in case more variables need to be stored.")
    format: Optional[StrictStr] = Field(default=None, description="The format of the model", alias="modelFormat")
    name: Optional[StrictStr] = Field(default=None, description="The name used in model specification")
    __properties: ClassVar[List[str]] = ["description", "displayName", "image", "latestVersionIdStr", "latestVersionSizeInBytes", "metadata", "modelFormat", "name"]

    model_config = {
        "populate_by_name": True,
        "validate_assignment": True
    }


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Self:
        """Create an instance of ModelResponse from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        _dict = self.model_dump(
            by_alias=True,
            exclude={
            },
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Dict) -> Self:
        """Create an instance of ModelResponse from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "description": obj.get("description"),
            "displayName": obj.get("displayName"),
            "image": obj.get("image"),
            "latestVersionIdStr": obj.get("latestVersionIdStr"),
            "latestVersionSizeInBytes": obj.get("latestVersionSizeInBytes"),
            "metadata": obj.get("metadata"),
            "modelFormat": obj.get("modelFormat"),
            "name": obj.get("name")
        })
        return _obj

Classes

class ModelResponse (**data: Any)

Provides a set of values that are returned by querying for models

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Expand source code

class ModelResponse(BaseModel):
    """
    Provides a set of values that are returned by querying for models
    """ # noqa: E501
    description: Optional[StrictStr] = Field(default=None, description="The description of desribing the model")
    display_name: Optional[StrictStr] = Field(default=None, description="The name displayed that is human readable", alias="displayName")
    image: Optional[StrictStr] = Field(default=None, description="The default container image to execute the model (if available)")
    latest_version_id_str: Optional[StrictStr] = Field(default=None, description="The latest version of the model", alias="latestVersionIdStr")
    latest_version_size_in_bytes: Optional[StrictInt] = Field(default=None, description="The number of bytes for the latest model", alias="latestVersionSizeInBytes")
    metadata: Optional[Dict[str, StrictStr]] = Field(default=None, description="Extra argument in case more variables need to be stored.")
    format: Optional[StrictStr] = Field(default=None, description="The format of the model", alias="modelFormat")
    name: Optional[StrictStr] = Field(default=None, description="The name used in model specification")
    __properties: ClassVar[List[str]] = ["description", "displayName", "image", "latestVersionIdStr", "latestVersionSizeInBytes", "metadata", "modelFormat", "name"]

    model_config = {
        "populate_by_name": True,
        "validate_assignment": True
    }


    def to_str(self) -> str:
        """Returns the string representation of the model using alias"""
        return pprint.pformat(self.model_dump(by_alias=True))

    def to_json(self) -> str:
        """Returns the JSON representation of the model using alias"""
        # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
        return json.dumps(self.to_dict())

    @classmethod
    def from_json(cls, json_str: str) -> Self:
        """Create an instance of ModelResponse from a JSON string"""
        return cls.from_dict(json.loads(json_str))

    def to_dict(self) -> Dict[str, Any]:
        """Return the dictionary representation of the model using alias.

        This has the following differences from calling pydantic's
        `self.model_dump(by_alias=True)`:

        * `None` is only added to the output dict for nullable fields that
          were set at model initialization. Other fields with value `None`
          are ignored.
        """
        _dict = self.model_dump(
            by_alias=True,
            exclude={
            },
            exclude_none=True,
        )
        return _dict

    @classmethod
    def from_dict(cls, obj: Dict) -> Self:
        """Create an instance of ModelResponse from a dict"""
        if obj is None:
            return None

        if not isinstance(obj, dict):
            return cls.model_validate(obj)

        _obj = cls.model_validate({
            "description": obj.get("description"),
            "displayName": obj.get("displayName"),
            "image": obj.get("image"),
            "latestVersionIdStr": obj.get("latestVersionIdStr"),
            "latestVersionSizeInBytes": obj.get("latestVersionSizeInBytes"),
            "metadata": obj.get("metadata"),
            "modelFormat": obj.get("modelFormat"),
            "name": obj.get("name")
        })
        return _obj

Ancestors

pydantic.main.BaseModel

Class variables

var description : Optional[str]
var display_name : Optional[str]
var format : Optional[str]
var image : Optional[str]
var latest_version_id_str : Optional[str]
var latest_version_size_in_bytes : Optional[int]
var metadata : Optional[Dict[str, str]]
var model_computed_fields
var model_config
var model_fields
var name : Optional[str]

Static methods

def from_dict(obj: Dict) ‑> Self

Create an instance of ModelResponse from a dict

Expand source code

@classmethod
def from_dict(cls, obj: Dict) -> Self:
    """Create an instance of ModelResponse from a dict"""
    if obj is None:
        return None

    if not isinstance(obj, dict):
        return cls.model_validate(obj)

    _obj = cls.model_validate({
        "description": obj.get("description"),
        "displayName": obj.get("displayName"),
        "image": obj.get("image"),
        "latestVersionIdStr": obj.get("latestVersionIdStr"),
        "latestVersionSizeInBytes": obj.get("latestVersionSizeInBytes"),
        "metadata": obj.get("metadata"),
        "modelFormat": obj.get("modelFormat"),
        "name": obj.get("name")
    })
    return _obj

def from_json(json_str: str) ‑> Self

Create an instance of ModelResponse from a JSON string

Expand source code

@classmethod
def from_json(cls, json_str: str) -> Self:
    """Create an instance of ModelResponse from a JSON string"""
    return cls.from_dict(json.loads(json_str))

Methods

def model_post_init(self: BaseModel, context: Any, /) ‑> None

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Args

self: The BaseModel instance.
context: The context.

Expand source code

def init_private_attributes(self: BaseModel, context: Any, /) -> None:
    """This function is meant to behave like a BaseModel method to initialise private attributes.

    It takes context as an argument since that's what pydantic-core passes when calling it.

    Args:
        self: The BaseModel instance.
        context: The context.
    """
    if getattr(self, '__pydantic_private__', None) is None:
        pydantic_private = {}
        for name, private_attr in self.__private_attributes__.items():
            default = private_attr.get_default()
            if default is not PydanticUndefined:
                pydantic_private[name] = default
        object_setattr(self, '__pydantic_private__', pydantic_private)

def to_dict(self) ‑> Dict[str, Any]

Return the dictionary representation of the model using alias.

This has the following differences from calling pydantic's self.model_dump(by_alias=True):

None is only added to the output dict for nullable fields that were set at model initialization. Other fields with value None are ignored.

Expand source code

def to_dict(self) -> Dict[str, Any]:
    """Return the dictionary representation of the model using alias.

    This has the following differences from calling pydantic's
    `self.model_dump(by_alias=True)`:

    * `None` is only added to the output dict for nullable fields that
      were set at model initialization. Other fields with value `None`
      are ignored.
    """
    _dict = self.model_dump(
        by_alias=True,
        exclude={
        },
        exclude_none=True,
    )
    return _dict

def to_json(self) ‑> str

Returns the JSON representation of the model using alias

Expand source code

def to_json(self) -> str:
    """Returns the JSON representation of the model using alias"""
    # TODO: pydantic v2: use .model_dump_json(by_alias=True, exclude_unset=True) instead
    return json.dumps(self.to_dict())

def to_str(self) ‑> str

Returns the string representation of the model using alias

Expand source code

def to_str(self) -> str:
    """Returns the string representation of the model using alias"""
    return pprint.pformat(self.model_dump(by_alias=True))