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opengradient / client / alpha

Package opengradient.client.alpha ​

Alpha Testnet features for OpenGradient SDK.

This module contains features that are only available on the Alpha Testnet, including on-chain ONNX model inference, workflow management, and ML model execution.

Classes ​

Alpha ​

Alpha Testnet features namespace.

This class provides access to features that are only available on the Alpha Testnet, including on-chain ONNX model inference, workflow deployment, and execution.

Constructor ​

python
def __init__(blockchain: `Web3`, wallet_account: `LocalAccount`, inference_hub_contract_address: str, api_url: str)

Methods ​

infer() ​

python
def infer(self, model_cid: str, inference_mode: `InferenceMode`, model_input: Dict[str, Union[str, int, float, List, `ndarray`]], max_retries: Optional[int] = None) ‑> `InferenceResult`

Perform inference on a model.

Arguments

  • model_cid (str): The unique content identifier for the model from IPFS.
  • inference_mode (InferenceMode): The inference mode.
  • model_input (Dict[str, Union[str, int, float, List, np.ndarray]]): The input data for the model.
  • max_retries (int, optional): Maximum number of retry attempts. Defaults to 5.

Returns

InferenceResult (InferenceResult): A dataclass object containing the transaction hash and model output. transaction_hash (str): Blockchain hash for the transaction model_output (Dict[str, np.ndarray]): Output of the ONNX model

Raises

  • OpenGradientError: If the inference fails.

new_workflow() ​

python
def new_workflow(self, model_cid: str, input_query: `HistoricalInputQuery`, input_tensor_name: str, scheduler_params: Optional[`SchedulerParams`] = None) ‑> str

Deploy a new workflow contract with the specified parameters.

This function deploys a new workflow contract on OpenGradient that connects an AI model with its required input data. When executed, the workflow will fetch the specified model, evaluate the input query to get data, and perform inference.

The workflow can be set to execute manually or automatically via a scheduler.

Arguments

  • model_cid (str): CID of the model to be executed from the Model Hub
  • input_query (HistoricalInputQuery): Input definition for the model inference, will be evaluated at runtime for each inference
  • input_tensor_name (str): Name of the input tensor expected by the model
  • scheduler_params (Optional[SchedulerParams]): Scheduler configuration for automated execution: - frequency: Execution frequency in seconds - duration_hours: How long the schedule should live for

Returns

str: Deployed contract address. If scheduler_params was provided, the workflow will be automatically executed according to the specified schedule.

Raises

  • Exception: If transaction fails or gas estimation fails

read_workflow_history() ​

python
def read_workflow_history(self, contract_address: str, num_results: int) ‑> List[`ModelOutput`]

Gets historical inference results from a workflow contract.

Retrieves the specified number of most recent inference results from the contract's storage, with the most recent result first.

Arguments

  • contract_address (str): Address of the deployed workflow contract
  • num_results (int): Number of historical results to retrieve

Returns

List[ModelOutput]: List of historical inference results

read_workflow_result() ​

python
def read_workflow_result(self, contract_address: str) ‑> `ModelOutput`

Reads the latest inference result from a deployed workflow contract.

Arguments

  • contract_address (str): Address of the deployed workflow contract

Returns

ModelOutput: The inference result from the contract

Raises

  • ContractLogicError: If the transaction fails
  • Web3Error: If there are issues with the web3 connection or contract interaction

run_workflow() ​

python
def run_workflow(self, contract_address: str) ‑> `ModelOutput`

Triggers the run() function on a deployed workflow contract and returns the result.

Arguments

  • contract_address (str): Address of the deployed workflow contract

Returns

ModelOutput: The inference result from the contract

Raises

  • ContractLogicError: If the transaction fails
  • Web3Error: If there are issues with the web3 connection or contract interaction

Variables ​

  • inference_abi : dict
  • precompile_abi : dict