ads.aqua.common package

Submodules

ads.aqua.common.utils

ads.aqua.common.enums

class ads.aqua.common.enums.ConfigFolder

Bases: ExtendedEnum

ARTIFACT = 'artifact'

CONFIG = 'config'

class ads.aqua.common.enums.CustomInferenceContainerTypeFamily

Bases: ExtendedEnum

AQUA_TEI_CONTAINER_FAMILY = 'odsc-tei-serving'

class ads.aqua.common.enums.DataScienceResource

Bases: ExtendedEnum

MODEL = 'datasciencemodel'

MODEL_DEPLOYMENT = 'datasciencemodeldeployment'

class ads.aqua.common.enums.EvaluationContainerTypeFamily

Bases: ExtendedEnum

AQUA_EVALUATION_CONTAINER_FAMILY = 'odsc-llm-evaluate'

class ads.aqua.common.enums.FineTuningContainerTypeFamily

Bases: ExtendedEnum

AQUA_FINETUNING_CONTAINER_FAMILY = 'odsc-llm-fine-tuning'

class ads.aqua.common.enums.HuggingFaceTags

Bases: ExtendedEnum

TEXT_GENERATION_INFERENCE = 'text-generation-inference'

class ads.aqua.common.enums.InferenceContainerParamType

Bases: ExtendedEnum

PARAM_TYPE_LLAMA_CPP = 'LLAMA_CPP_PARAMS'

PARAM_TYPE_TGI = 'TGI_PARAMS'

PARAM_TYPE_VLLM = 'VLLM_PARAMS'

class ads.aqua.common.enums.InferenceContainerType

Bases: ExtendedEnum

CONTAINER_TYPE_LLAMA_CPP = 'llama-cpp'

CONTAINER_TYPE_TGI = 'tgi'

CONTAINER_TYPE_VLLM = 'vllm'

class ads.aqua.common.enums.InferenceContainerTypeFamily

Bases: ExtendedEnum

AQUA_LLAMA_CPP_CONTAINER_FAMILY = 'odsc-llama-cpp-serving'

AQUA_TGI_CONTAINER_FAMILY = 'odsc-tgi-serving'

AQUA_VLLM_CONTAINER_FAMILY = 'odsc-vllm-serving'

AQUA_VLLM_LLAMA4_CONTAINER_FAMILY = 'odsc-vllm-serving-llama4'

AQUA_VLLM_OPENAI_CONTAINER_FAMILY = 'odsc-vllm-serving-openai'

AQUA_VLLM_V1_CONTAINER_FAMILY = 'odsc-vllm-serving-v1'

class ads.aqua.common.enums.ModelFormat

Bases: ExtendedEnum

GGUF = 'GGUF'

SAFETENSORS = 'SAFETENSORS'

UNKNOWN = 'UNKNOWN'

class ads.aqua.common.enums.Platform

Bases: ExtendedEnum

ARM_CPU = 'ARM_CPU'

NVIDIA_GPU = 'NVIDIA_GPU'

class ads.aqua.common.enums.PredictEndpoints

Bases: ExtendedEnum

CHAT_COMPLETIONS_ENDPOINT = '/v1/chat/completions'

EMBEDDING_ENDPOINT = '/v1/embedding'

FORECAST = 'v1/forecast'

RESPONSES = '/v1/responses'

TEXT_COMPLETIONS_ENDPOINT = '/v1/completions'

class ads.aqua.common.enums.Resource

Bases: ExtendedEnum

JOB = 'jobs'

JOBRUN = 'jobruns'

MODEL = 'models'

MODEL_DEPLOYMENT = 'modeldeployments'

MODEL_VERSION_SET = 'model-version-sets'

class ads.aqua.common.enums.RqsAdditionalDetails

Bases: ExtendedEnum

CREATED_BY = 'createdBy'

DESCRIPTION = 'description'

METADATA = 'metadata'

MODEL_VERSION_SET_ID = 'modelVersionSetId'

MODEL_VERSION_SET_NAME = 'modelVersionSetName'

PROJECT_ID = 'projectId'

VERSION_LABEL = 'versionLabel'

class ads.aqua.common.enums.Tags

Bases: ExtendedEnum

AQUA_EVALUATION = 'aqua_evaluation'

AQUA_EVALUATION_MODEL_ID = 'evaluation_model_id'

AQUA_FINE_TUNED_MODEL_TAG = 'aqua_fine_tuned_model'

AQUA_FINE_TUNE_MODEL_VERSION = 'fine_tune_model_version'

AQUA_FINE_TUNING = 'aqua_finetuning'

AQUA_MODEL_ID_TAG = 'aqua_model_id'

AQUA_MODEL_NAME_TAG = 'aqua_model_name'

AQUA_SERVICE_MODEL_TAG = 'aqua_service_model'

AQUA_TAG = 'OCI_AQUA'

BASE_MODEL_CUSTOM = 'aqua_custom_base_model'

LICENSE = 'license'

MODEL_ARTIFACT_FILE = 'model_file'

MODEL_DEPLOY_PREDICT_ENDPOINT = 'model_deploy_predict_endpoint'

MODEL_FORMAT = 'model_format'

MULTIMODEL_TYPE_TAG = 'aqua_multimodel'

ORGANIZATION = 'organization'

READY_TO_FINE_TUNE = 'ready_to_fine_tune'

READY_TO_IMPORT = 'ready_to_import'

STACKED_MODEL_TYPE_TAG = 'aqua_stacked_model'

TASK = 'task'

class ads.aqua.common.enums.TextEmbeddingInferenceContainerParams

Bases: ExtendedEnum

Contains a subset of params that are required for enabling model deployment in OCI Data Science. More options are available at https://huggingface.co/docs/text-embeddings-inference/en/cli_arguments

MODEL_ID = 'model-id'

PORT = 'port'

ads.aqua.common.entities

class ads.aqua.common.entities.AquaComputeTarget(*, id: str | None = None, name: str | None = None, compartment_id: str | None = None, description: str | None = None, lifecycle_state: str | None = None, lifecycle_details: str | None = None, compute_configuration_details: ~ads.aqua.common.entities.ComputeConfigurationDetails | None = <factory>)

Bases: Serializable

Represents the specification of Aqua compute target.

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.

compartment_id: str | None

compute_configuration_details: ComputeConfigurationDetails | None

description: str | None

classmethod from_oci(oci_compute_target) → Self

Converts oci.data_science.models.ComputeTarget to AquaComputeTarget.

id: str | None

lifecycle_details: str | None

lifecycle_state: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class ads.aqua.common.entities.AquaComputeTargetSummary(*, id: str | None = None, name: str | None = None, compartment_id: str | None = None, lifecycle_state: str | None = None, freeform_tags: Dict | None = None, defined_tags: Dict | None = None)

Bases: Serializable

Represents the specification of compute target.

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.

compartment_id: str | None

defined_tags: Dict | None

freeform_tags: Dict | None

classmethod from_oci_summary(oci_compute_target) → Self

Converts oci.data_science.models.ComputeTargetSummary to AquaComputeTargetSummary.

id: str | None

lifecycle_state: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class ads.aqua.common.entities.AquaMultiModelRef(*, model_id: str, model_name: str | None = None, gpu_count: int | None = None, model_task: str | None = None, env_var: dict | None = <factory>, params: dict | None = None, artifact_location: str | None = None, fine_tune_weights: ~typing.List[~ads.aqua.common.entities.LoraModuleSpec] | None = None, **extra_data: ~typing.Any)

Bases: Serializable

Lightweight model descriptor used for multi-model deployment.

This class holds essential details required to fetch model metadata and deploy individual models as part of a multi-model deployment group.

model_id

The unique identifier (OCID) of the base model.

Type:

str

model_name

Optional name for the model.

Type:

Optional[str]

gpu_count

Number of GPUs required to allocate for this model during deployment.

Type:

Optional[int]

model_task

The machine learning task this model performs (e.g., text-generation, summarization). Supported values are listed in MultiModelSupportedTaskType.

Type:

Optional[str]

env_var

Optional dictionary of environment variables to inject into the runtime environment of the model container.

Type:

Optional[Dict[str, Any]]

params

Optional dictionary of container-specific inference parameters to override. These are typically framework-level flags required by the runtime backend. For example, in vLLM containers, valid params may include: –tensor-parallel-size, –enforce-eager, –max-model-len, etc.

Type:

Optional[Dict[str, Any]]

artifact_location

Relative path or URI of the model artifact inside the multi-model group folder.

Type:

Optional[str]

fine_tune_weights

List of fine-tuned weight artifacts (e.g., LoRA modules) associated with this model.

Type:

Optional[List[LoraModuleSpec]]

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.

class Config

Bases: object

extra = 'allow'

protected_namespaces = ()

all_model_ids() → List[str]

Returns all model OCIDs associated with this reference, including fine-tuned weights.

Returns:

A list containing the base model OCID and any fine-tuned module OCIDs.

Return type:

List[str]

artifact_location: str | None

env_var: dict | None

classmethod extract_params_from_env_var(values: Dict[str, Any]) → Dict[str, Any]

A model-level validator that extracts PARAMS from the env_var dictionary and injects them into the params field as a dictionary.

This is useful for backward compatibility where users pass CLI-style parameters via environment variables, e.g.: env_var = { “PARAMS”: “–max-model-len 65536 –enable-streaming” }

If params is already set, values from PARAMS in env_var are added only if they do not override existing keys.

fine_tune_weights: List[LoraModuleSpec] | None

gpu_count: int | None

model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_id: str

model_name: str | None

model_task: str | None

params: dict | None

class ads.aqua.common.entities.ComputeConfigurationDetails(*, compute_type: str | None = None, instance_configuration: ~ads.aqua.common.entities.InstanceConfiguration | None = <factory>)

Bases: Serializable

Represents the specification of compute configuration of compute target.

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.

compute_type: str | None

classmethod from_oci(oci_compute_configuration) → Self

instance_configuration: InstanceConfiguration | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ads.aqua.common.entities.ComputeRank(*, cost: int | None = None, performance: int | None = None)

Bases: Serializable

Represents the cost and performance rankings for a specific compute shape. These rankings help compare different shapes based on their relative pricing and computational capabilities.

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.

cost: int | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

performance: int | None

class ads.aqua.common.entities.ComputeShapeSummary(*, available: bool | None = False, core_count: int | None = None, memory_in_gbs: int | None = None, name: str | None = None, shape_series: str | None = None, gpu_specs: GPUSpecs | None = None)

Bases: Serializable

Represents a compute shape’s specification including CPU, memory, and (if applicable) GPU configuration.

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.

available: bool | None

core_count: int | None

gpu_specs: GPUSpecs | None

memory_in_gbs: int | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

classmethod populate_gpu_specs(model: ComputeShapeSummary) → ComputeShapeSummary

Attempts to populate GPU specs if the shape is GPU-based and no GPU specs are explicitly set.

Logic: - If shape_series includes ‘GPU’ and gpu_specs is None:

• Tries to parse the shape name to extract GPU count (e.g., from ‘VM.GPU.A10.2’).

• Fallback is based on suffix numeric group (e.g., ‘.2’ → gpu_count=2).

• If extraction fails, logs debug-level error but does not raise.

Returns:

The updated model instance.

Return type:

ComputeShapeSummary

shape_series: str | None

class ads.aqua.common.entities.ComputeTargetDetails(*, compute_target_id: str | None, gpu_count: int | None, ocpus: float | None, memory_in_gbs: float | None)

Bases: Serializable

Represents the specification of compute target details for creating Aqua 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.

compute_target_id: str | None

gpu_count: int | None

memory_in_gbs: float | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

ocpus: float | None

class ads.aqua.common.entities.ContainerPath(*, full_path: str, path: str | None = None, name: str | None = None, version: str | None = None)

Bases: Serializable

Represents a parsed container path, extracting the path, name, and version.

This model is designed to parse a container path string of the format ‘<image_path>:<version>’. It extracts the following components: - path: The full path up to the version. - name: The last segment of the path, representing the image name. - version: The version number following the final colon.

Example Usage:

>>> container = ContainerPath(full_path="iad.ocir.io/ociodscdev/odsc-llm-evaluate:0.1.2.9")
>>> container.path
'iad.ocir.io/ociodscdev/odsc-llm-evaluate'
>>> container.name
'odsc-llm-evaluate'
>>> container.version
'0.1.2.9'

>>> container = ContainerPath(full_path="custom-scheme://path/to/versioned-model:2.5.1")
>>> container.path
'custom-scheme://path/to/versioned-model'
>>> container.name
'versioned-model'
>>> container.version
'2.5.1'

full_path

The complete container path string to be parsed.

Type:

str

path

The full path up to the version (e.g., ‘iad.ocir.io/ociodscdev/odsc-llm-evaluate’).

Type:

Optional[str]

name

The image name, which is the last segment of path (e.g., ‘odsc-llm-evaluate’).

Type:

Optional[str]

version

The version number following the final colon in the path (e.g., ‘0.1.2.9’).

Type:

Optional[str]

validate(values: Any) → Any

Validates and parses the full_path, extracting path, name, and version.

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.

class Config

Bases: object

extra = 'ignore'

protected_namespaces = ()

full_path: str

model_config: ClassVar[ConfigDict] = {'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

path: str | None

classmethod validate(values: Any) → Any

Validates and parses the full container path, extracting the image path, image name, and version.

Parameters:

values (dict) – The dictionary of values being validated, containing ‘full_path’.

Returns:

Updated values dictionary with extracted ‘path’, ‘name’, and ‘version’.

Return type:

dict

version: str | None

class ads.aqua.common.entities.ContainerSpec

Bases: object

Class to hold to hold keys within the container spec.

CLI_PARM = 'cliParam'

CONTAINER_SPEC = 'containerSpec'

ENV_VARS = 'envVars'

EVALUATION_CONFIGURATION = 'evaluationConfiguration'

HEALTH_CHECK_PORT = 'healthCheckPort'

RESTRICTED_PARAMS = 'restrictedParams'

SERVER_PORT = 'serverPort'

class ads.aqua.common.entities.GPUShapesIndex(*, shapes: ~typing.Dict[str, ~ads.aqua.common.entities.GPUSpecs] = <factory>)

Bases: Serializable

Represents the index of GPU shapes.

shapes(Dict[str, GPUSpecs])

Type:

A mapping of compute shape names to their GPU specifications.

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.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

shapes: Dict[str, GPUSpecs]

class ads.aqua.common.entities.GPUSpecs(*, gpu_count: int | None = None, gpu_memory_in_gbs: int | None = None, gpu_type: str | None = None, quantization: ~typing.List[str] | None = <factory>, cpu_count: int | None = None, cpu_memory_in_gbs: int | None = None, ranking: ~ads.aqua.common.entities.ComputeRank | None = None)

Bases: Serializable

Represents the specifications and capabilities of a GPU-enabled compute shape. Includes details about GPU and CPU resources, supported quantization formats, and relative rankings for cost and performance.

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.

cpu_count: int | None

cpu_memory_in_gbs: int | None

gpu_count: int | None

gpu_memory_in_gbs: int | None

gpu_type: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

quantization: List[str] | None

ranking: ComputeRank | None

class ads.aqua.common.entities.InstanceConfiguration(*, instance_shape: str | None = None, capacity_reservation_id: str | None = None)

Bases: Serializable

Represents the specification of instance shape of compute target.

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.

capacity_reservation_id: str | None

instance_shape: str | None

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ads.aqua.common.entities.LoraModuleSpec(*, model_id: str, model_name: str | None = None, model_path: str | None = None, **extra_data: Any)

Bases: BaseModel

Descriptor for a LoRA (Low-Rank Adaptation) module used in fine-tuning base models.

This class is used to define a single fine-tuned module that can be loaded during multi-model deployment alongside a base model.

model_id

The OCID of the fine-tuned model registered in the OCI Model Catalog.

Type:

str

model_name

The unique name used to route inference requests to this model variant.

Type:

Optional[str]

model_path

The relative path within the artifact pointing to the LoRA adapter weights.

Type:

Optional[str]

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.

model_config: ClassVar[ConfigDict] = {'extra': 'allow', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_id: str

model_name: str | None

model_path: str | None

classmethod validate_lora_module(data: dict) → dict

Validates that required structure exists for a LoRA module.

class ads.aqua.common.entities.ModelConfigResult(*, config: Dict[str, Any] | None = None, model_details: Model | None = None)

Bases: BaseModel

Represents the result of getting the AQUA model configuration. .. attribute:: model_details

A dictionary containing model details extracted from OCI.

type:

Dict[str, Any]

config

A dictionary of the loaded configuration.

Type:

Dict[str, Any]

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.

class Config

Bases: object

arbitrary_types_allowed = True

extra = 'ignore'

protected_namespaces = ()

config: Dict[str, Any] | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'ignore', 'protected_namespaces': ()}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_details: Model | None

ads.aqua.common.errors

aqua.exception

This module contains the set of Aqua exceptions.

exception ads.aqua.common.errors.AquaCLIError(reason, status=None, service_payload=None)

Bases: AquaError

Exception raised when AQUA CLI encounter error.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exit_code = 1

exception ads.aqua.common.errors.AquaConfigError(reason, status=404, service_payload=None)

Bases: AquaError

Exception raised when config for AQUA is invalid.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exit_code = 10

exception ads.aqua.common.errors.AquaError(reason: str, status: int, service_payload: dict | None = None)

Bases: Exception

The base exception from which all exceptions raised by Aqua will inherit.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exit_code = 1

exception ads.aqua.common.errors.AquaFileExistsError(reason, status=400, service_payload=None)

Bases: AquaError, FileExistsError

Exception raised when file already exists in resource.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaFileNotFoundError(reason, status=404, service_payload=None)

Bases: AquaError, FileNotFoundError

Exception raised for missing target file.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaMissingKeyError(reason, status=400, service_payload=None)

Bases: AquaError

Exception raised when missing metadata in resource.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaRecommendationError(reason, status=400, service_payload=None)

Bases: AquaError

Exception raised for models incompatible with shape recommendation tool.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaResourceAccessError(reason, status=404, service_payload=None)

Bases: AquaError

Exception raised when file already exists in resource.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaRuntimeError(reason, status=400, service_payload=None)

Bases: AquaError, RuntimeError

Exception raised for generic errors at runtime.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

exception ads.aqua.common.errors.AquaValueError(reason, status=403, service_payload=None)

Bases: AquaError, ValueError

Exception raised for unexpected values.

Initializes an AquaError.

Parameters:

• reason (str) – User friendly error message.

• status (int) – Http status code that are going to raise.

• service_payload (dict) – Payload to contain more details related to the error.

class ads.aqua.common.errors.ExitCode

Bases: ExtendedEnum

COMMON_ERROR = 1

INVALID_CONFIG = 10

SUCCESS = 0