Module aiolirest.models.resources_template
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, StrictStr
from pydantic import Field
from aiolirest.models.configuration_resources import ConfigurationResources
try:
from typing import Self
except ImportError:
from typing_extensions import Self
class ResourcesTemplate(BaseModel):
"""
Provides a named set of values to configure resource parameters for a packaged model.
""" # noqa: E501
description: Optional[StrictStr] = Field(default=None, description="Description of the template.")
name: StrictStr = Field(description="The template name. Must begin with a letter, but may contain letters, numbers, underscore, and hyphen.")
resources: Optional[ConfigurationResources] = None
__properties: ClassVar[List[str]] = ["description", "name", "resources"]
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 ResourcesTemplate 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,
)
# override the default output from pydantic by calling `to_dict()` of resources
if self.resources:
_dict['resources'] = self.resources.to_dict()
return _dict
@classmethod
def from_dict(cls, obj: Dict) -> Self:
"""Create an instance of ResourcesTemplate 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"),
"name": obj.get("name"),
"resources": ConfigurationResources.from_dict(obj.get("resources")) if obj.get("resources") is not None else None
})
return _obj
Classes
class ResourcesTemplate (**data: Any)
-
Provides a named set of values to configure resource parameters for a packaged model.
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 allowself
as a field name.Expand source code
class ResourcesTemplate(BaseModel): """ Provides a named set of values to configure resource parameters for a packaged model. """ # noqa: E501 description: Optional[StrictStr] = Field(default=None, description="Description of the template.") name: StrictStr = Field(description="The template name. Must begin with a letter, but may contain letters, numbers, underscore, and hyphen.") resources: Optional[ConfigurationResources] = None __properties: ClassVar[List[str]] = ["description", "name", "resources"] 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 ResourcesTemplate 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, ) # override the default output from pydantic by calling `to_dict()` of resources if self.resources: _dict['resources'] = self.resources.to_dict() return _dict @classmethod def from_dict(cls, obj: Dict) -> Self: """Create an instance of ResourcesTemplate 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"), "name": obj.get("name"), "resources": ConfigurationResources.from_dict(obj.get("resources")) if obj.get("resources") is not None else None }) return _obj
Ancestors
- pydantic.main.BaseModel
Class variables
var description : Optional[str]
var model_computed_fields
var model_config
var model_fields
var name : str
var resources : Optional[ConfigurationResources]
Static methods
def from_dict(obj: Dict) ‑> Self
-
Create an instance of ResourcesTemplate from a dict
Expand source code
@classmethod def from_dict(cls, obj: Dict) -> Self: """Create an instance of ResourcesTemplate 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"), "name": obj.get("name"), "resources": ConfigurationResources.from_dict(obj.get("resources")) if obj.get("resources") is not None else None }) return _obj
def from_json(json_str: str) ‑> Self
-
Create an instance of ResourcesTemplate from a JSON string
Expand source code
@classmethod def from_json(cls, json_str: str) -> Self: """Create an instance of ResourcesTemplate 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 valueNone
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, ) # override the default output from pydantic by calling `to_dict()` of resources if self.resources: _dict['resources'] = self.resources.to_dict() 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))