tasks.py

import json
from pathlib import Path
from typing import List

from functions import train_model


def train_model_task(config: Path, output_path: Path):
    """
    TODO: read the JSON config from `config` and pass its parameters (along with `output_path`)
    to the `train_model` function.
    """


def postprocess_task(result_files: List[Path], output_file: Path):
    results = []
    for result in result_files:
        with open(result) as f:
            result = json.load(f)
            results.append(result)
    results = sorted(results, key=lambda r: r["accuracy"], reverse=True)
    with open(output_file, "w") as f:
        print("Training results sorted by accuracy:", file=f)
        for result in results:
            learning_rate = result["parameters"]["learning_rate"]
            batch_size = result["parameters"]["batch_size"]
            accuracy = result["accuracy"]
            print(
                f"Learning rate={learning_rate}, batch_size={batch_size}: {accuracy * 100.0:.2f}%",
                file=f
            )