def __init__()

in optimum/runs_base.py [0:0]

• 45 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, run_config: dict):
        """Initialize the Run class holding methods to perform inference and evaluation given a config.

        A run compares a transformers model and an optimized model on latency/throughput, model size, and provided metrics.

        Args:
            run_config (dict): Parameters to use for the run. See [`~utils.runs.RunConfig`] for the expected keys.
        """
        RunConfig(**run_config)  # validate the data (useful if used as standalone)

        self.task = run_config["task"]

        if run_config["quantization_approach"] == "static":
            self.static_quantization = True
        else:
            self.static_quantization = False

        search_space = {"batch_size": run_config["batch_sizes"], "input_length": run_config["input_lengths"]}

        self.study = optuna.create_study(
            directions=["maximize", "minimize"],
            sampler=optuna.samplers.GridSampler(search_space),
        )

        cpu_info = subprocess.check_output([cpu_info_command()], shell=True).decode("utf-8")

        optimum_hash = None
        if "dev" in optimum_version.__version__:
            optimum_hash = subprocess.check_output(
                "git ls-remote https://github.com/huggingface/optimum.git HEAD | awk '{ print $1}'", shell=True
            )
            optimum_hash = optimum_hash.decode("utf-8").strip("\n")

        self.return_body = {
            "model_name_or_path": run_config["model_name_or_path"],
            "task": self.task,
            "task_args": run_config["task_args"],
            "dataset": run_config["dataset"],
            "quantization_approach": run_config["quantization_approach"],
            "operators_to_quantize": run_config["operators_to_quantize"],
            "node_exclusion": run_config["node_exclusion"],
            "aware_training": run_config["aware_training"],
            "per_channel": run_config["per_channel"],
            "calibration": run_config["calibration"],
            "framework": run_config["framework"],
            "framework_args": run_config["framework_args"],
            "hardware": cpu_info,  # is this ok?
            "versions": {
                "transformers": transformers.__version__,
                "optimum": optimum_version.__version__,
                "optimum_hash": optimum_hash,
            },
            "evaluation": {
                "time": [],
                "others": {"baseline": {}, "optimized": {}},
            },
            "max_eval_samples": run_config["max_eval_samples"],
            "time_benchmark_args": run_config["time_benchmark_args"],
        }