Module aiolirest.models.trained_model_registry_request

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.

# 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, StrictBool, StrictStr
from pydantic import Field
try:
    from typing import Self
except ImportError:
    from typing_extensions import Self

class TrainedModelRegistryRequest(BaseModel):
    """
    Provides the metadata that describes how to access a named model registry to enable download of a trained model for deployment.
    """ # noqa: E501
    access_key: Optional[StrictStr] = Field(default=None, description="The access key, username or team name for the registry. * `s3` - The access key/username. * `ngc` - The optional NGC team name.", alias="accessKey")
    bucket: Optional[StrictStr] = Field(default=None, description="The bucket or organization name.  * `s3` - The required S3 bucket name. * `ngc` - The required NGC org name.")
    endpoint_url: Optional[StrictStr] = Field(default=None, description="The registry endpoint (host): * `s3` - The S3 registry endpoint. Required. * `openllm` - The huggingface.co-compatible endpoint (default https://huggingface.co). * `ngc` - The NVIDIA NGC-compatible api endpoint (default https://api.ngc.nvidia.com).", alias="endpointUrl")
    insecure_https: Optional[StrictBool] = Field(default=None, description="For https endpoints, the server certificate will be accepted without validation.", alias="insecureHttps")
    modified_at: Optional[StrictStr] = Field(default=None, description="Date-time of last modification of the registry. This is a read-only field and is automatically updated.", alias="modifiedAt")
    name: StrictStr = Field(description="The name of the registry.  Must begin with a letter, but may contain letters, numbers, and hyphen.")
    secret_key: StrictStr = Field(description="The secret key is the password, secret key, or access token for the registry.  * `s3` - The secret key for the S3 bucket. * `openllm` -  The access token for huggingface.co and is supplied to the launched container via the `HF_TOKEN` environment variable.bucket. * `ngc` - The requied NVIDIA NGC apikey.", alias="secretKey")
    type: StrictStr = Field(description="The type of this model registry. Must be one of the values: (s3, http, openllm, ngc). * `s3` - Configuration to enable access to an s3 bucket. * `openllm` - Configuration to enable direct download of OpenLLM models from huggingface.co.   Provide your access token in the `secretKey` field. * `ngc` -  Configuration to enable direct download from the NVIDA NGC: AI Development Catalog. * `http` - Not yet supported.  Configuration to enable model download from a protected http endpoint that requires login.")
    __properties: ClassVar[List[str]] = ["accessKey", "bucket", "endpointUrl", "insecureHttps", "modifiedAt", "name", "secretKey", "type"]

    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 TrainedModelRegistryRequest 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 TrainedModelRegistryRequest from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "accessKey": obj.get("accessKey"),
            "bucket": obj.get("bucket"),
            "endpointUrl": obj.get("endpointUrl"),
            "insecureHttps": obj.get("insecureHttps"),
            "modifiedAt": obj.get("modifiedAt"),
            "name": obj.get("name"),
            "secretKey": obj.get("secretKey"),
            "type": obj.get("type")
        })
        return _obj

Classes

class TrainedModelRegistryRequest (**data: Any)

Provides the metadata that describes how to access a named model registry to enable download of a trained model for deployment.

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 TrainedModelRegistryRequest(BaseModel):
    """
    Provides the metadata that describes how to access a named model registry to enable download of a trained model for deployment.
    """ # noqa: E501
    access_key: Optional[StrictStr] = Field(default=None, description="The access key, username or team name for the registry. * `s3` - The access key/username. * `ngc` - The optional NGC team name.", alias="accessKey")
    bucket: Optional[StrictStr] = Field(default=None, description="The bucket or organization name.  * `s3` - The required S3 bucket name. * `ngc` - The required NGC org name.")
    endpoint_url: Optional[StrictStr] = Field(default=None, description="The registry endpoint (host): * `s3` - The S3 registry endpoint. Required. * `openllm` - The huggingface.co-compatible endpoint (default https://huggingface.co). * `ngc` - The NVIDIA NGC-compatible api endpoint (default https://api.ngc.nvidia.com).", alias="endpointUrl")
    insecure_https: Optional[StrictBool] = Field(default=None, description="For https endpoints, the server certificate will be accepted without validation.", alias="insecureHttps")
    modified_at: Optional[StrictStr] = Field(default=None, description="Date-time of last modification of the registry. This is a read-only field and is automatically updated.", alias="modifiedAt")
    name: StrictStr = Field(description="The name of the registry.  Must begin with a letter, but may contain letters, numbers, and hyphen.")
    secret_key: StrictStr = Field(description="The secret key is the password, secret key, or access token for the registry.  * `s3` - The secret key for the S3 bucket. * `openllm` -  The access token for huggingface.co and is supplied to the launched container via the `HF_TOKEN` environment variable.bucket. * `ngc` - The requied NVIDIA NGC apikey.", alias="secretKey")
    type: StrictStr = Field(description="The type of this model registry. Must be one of the values: (s3, http, openllm, ngc). * `s3` - Configuration to enable access to an s3 bucket. * `openllm` - Configuration to enable direct download of OpenLLM models from huggingface.co.   Provide your access token in the `secretKey` field. * `ngc` -  Configuration to enable direct download from the NVIDA NGC: AI Development Catalog. * `http` - Not yet supported.  Configuration to enable model download from a protected http endpoint that requires login.")
    __properties: ClassVar[List[str]] = ["accessKey", "bucket", "endpointUrl", "insecureHttps", "modifiedAt", "name", "secretKey", "type"]

    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 TrainedModelRegistryRequest 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 TrainedModelRegistryRequest from a dict"""
        if obj is None:
            return None

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

        _obj = cls.model_validate({
            "accessKey": obj.get("accessKey"),
            "bucket": obj.get("bucket"),
            "endpointUrl": obj.get("endpointUrl"),
            "insecureHttps": obj.get("insecureHttps"),
            "modifiedAt": obj.get("modifiedAt"),
            "name": obj.get("name"),
            "secretKey": obj.get("secretKey"),
            "type": obj.get("type")
        })
        return _obj

Ancestors

• pydantic.main.BaseModel

Class variables

var access_key : Optional[str]

var bucket : Optional[str]

var endpoint_url : Optional[str]

var insecure_https : Optional[bool]

var model_computed_fields

var model_config

var model_fields

var modified_at : Optional[str]

var name : str

var secret_key : str

var type : str

Static methods

def from_dict(obj: Dict) ‑> Self

Create an instance of TrainedModelRegistryRequest from a dict

Expand source code

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

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

    _obj = cls.model_validate({
        "accessKey": obj.get("accessKey"),
        "bucket": obj.get("bucket"),
        "endpointUrl": obj.get("endpointUrl"),
        "insecureHttps": obj.get("insecureHttps"),
        "modifiedAt": obj.get("modifiedAt"),
        "name": obj.get("name"),
        "secretKey": obj.get("secretKey"),
        "type": obj.get("type")
    })
    return _obj

def from_json(json_str: str) ‑> Self

Create an instance of TrainedModelRegistryRequest from a JSON string

Expand source code

@classmethod
def from_json(cls, json_str: str) -> Self:
    """Create an instance of TrainedModelRegistryRequest 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))