trinity.explorer
Submodules
trinity.explorer.explorer module
The explorer module
trinity.explorer.runner_pool module
Runner pool for running tasks in parallel. Modified from ray.util.actor_pool.ActorPool.
- class trinity.explorer.runner_pool.RunnerPool(config: Config, models: List[InferenceModel], auxiliary_models: List[List[InferenceModel]] | None = None)[source]
Bases:
objectA pool of WorkflowRunner.
The RunnerPool will automatically handle the exceptions during the workflow and retry when the workflow fails or timeout. The number of max retries is set in config.explorer.max_retry_times and the max timeout is set in config.explorer.max_timeout.
- __init__(config: Config, models: List[InferenceModel], auxiliary_models: List[List[InferenceModel]] | None = None)[source]
- run_tasks(tasks: List[Task] | Task) None[source]
Schedule a list of tasks to run in the pool.
- Parameters:
tasks – A list of tasks.
- has_next()[source]
Returns whether there are any pending results to return.
- Returns:
True if there are any pending results not yet returned.
- get_next_unorder() List[Status][source]
Returns the next pending result unorder.
- Returns:
The return status of the next task.
- get_next() Status[source]
Returns the next pending result in order.
This returns the next task result, blocking for up to the specified timeout until it is available.
- Returns:
The return status of the next task.
trinity.explorer.workflow_runner module
The Workflow Runner Moudle.
- class trinity.explorer.workflow_runner.Status(ok: bool, metric: dict[str, float], message: str | None = None)[source]
Bases:
objectStatus of the task running result.
- ok: bool
- metric: dict[str, float]
- message: str | None = None
- __init__(ok: bool, metric: dict[str, float], message: str | None = None) None
Module contents
- class trinity.explorer.RunnerPool(config: Config, models: List[InferenceModel], auxiliary_models: List[List[InferenceModel]] | None = None)[source]
Bases:
objectA pool of WorkflowRunner.
The RunnerPool will automatically handle the exceptions during the workflow and retry when the workflow fails or timeout. The number of max retries is set in config.explorer.max_retry_times and the max timeout is set in config.explorer.max_timeout.
- __init__(config: Config, models: List[InferenceModel], auxiliary_models: List[List[InferenceModel]] | None = None)[source]
- run_tasks(tasks: List[Task] | Task) None[source]
Schedule a list of tasks to run in the pool.
- Parameters:
tasks – A list of tasks.
- has_next()[source]
Returns whether there are any pending results to return.
- Returns:
True if there are any pending results not yet returned.
- get_next_unorder() List[Status][source]
Returns the next pending result unorder.
- Returns:
The return status of the next task.
- get_next() Status[source]
Returns the next pending result in order.
This returns the next task result, blocking for up to the specified timeout until it is available.
- Returns:
The return status of the next task.