def accelerate()

in src/lighteval/main_accelerate.py [0:0]

• 61 lines of code
• 1 McCabe index (conditional complexity)

def accelerate(  # noqa C901
    # === general ===
    model_args: Annotated[
        str,
        Argument(
            help="Model arguments in the form key1=value1,key2=value2,... or path to yaml config file (see examples/model_configs/transformers_model.yaml)"
        ),
    ],
    tasks: Annotated[str, Argument(help="Comma-separated list of tasks to evaluate on.")],
    # === Common parameters ===
    vision_model: Annotated[
        bool, Option(help="Use vision model for evaluation.", rich_help_panel=HELP_PANEL_NAME_4)
    ] = False,
    dataset_loading_processes: Annotated[
        int, Option(help="Number of processes to use for dataset loading.", rich_help_panel=HELP_PANEL_NAME_1)
    ] = 1,
    custom_tasks: Annotated[
        Optional[str], Option(help="Path to custom tasks directory.", rich_help_panel=HELP_PANEL_NAME_1)
    ] = None,
    num_fewshot_seeds: Annotated[
        int, Option(help="Number of seeds to use for few-shot evaluation.", rich_help_panel=HELP_PANEL_NAME_1)
    ] = 1,
    load_responses_from_details_date_id: Annotated[
        Optional[str], Option(help="Load responses from details directory.", rich_help_panel=HELP_PANEL_NAME_1)
    ] = None,
    # === saving ===
    output_dir: Annotated[
        str, Option(help="Output directory for evaluation results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = "results",
    results_path_template: Annotated[
        str | None,
        Option(
            help="Template path for where to save the results, you have access to 3 variables, `output_dir`, `org` and `model`. for example a template can be `'{output_dir}/1234/{org}+{model}'`",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = None,
    push_to_hub: Annotated[
        bool, Option(help="Push results to the huggingface hub.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    push_to_tensorboard: Annotated[
        bool, Option(help="Push results to tensorboard.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    public_run: Annotated[
        bool, Option(help="Push results and details to a public repo.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    results_org: Annotated[
        Optional[str], Option(help="Organization to push results to.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = None,
    save_details: Annotated[
        bool, Option(help="Save detailed, sample per sample, results.", rich_help_panel=HELP_PANEL_NAME_2)
    ] = False,
    wandb: Annotated[
        bool,
        Option(
            help="Push results to wandb. This will only work if you have wandb installed and logged in. We use env variable to configure wandb. see here: https://docs.wandb.ai/guides/track/environment-variables/",
            rich_help_panel=HELP_PANEL_NAME_2,
        ),
    ] = False,
    # === debug ===
    max_samples: Annotated[
        Optional[int], Option(help="Maximum number of samples to evaluate on.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = None,
    job_id: Annotated[
        int, Option(help="Optional job id for future reference.", rich_help_panel=HELP_PANEL_NAME_3)
    ] = 0,