def main()

in utils/model_registry.py [0:0]

• 40 lines of code
• 6 McCabe index (conditional complexity)

def main():
    parser = argparse.ArgumentParser(
        description=__doc__,
        # Preserves whitespace in the help text.
        formatter_class=argparse.RawTextHelpFormatter,
    )
    parser.add_argument(
        "--no_cache", action="store_true", help="Do not cache the TaskCluster calls"
    )
    parser.add_argument("--clear_cache", action="store_true", help="Clears the TaskCluster cache")
    parser.add_argument(
        "--upload",
        action="store_true",
        help="When set to true, the artifacts are uploaded to GCS. Otherwise they are stored at to data/model-registry/",
    )
    parser.add_argument(
        "--overwrite_runs",
        action="store_true",
        help="By default only missing training runs are created. This recreates everything.",
    )
    args = parser.parse_args()

    cache = None
    if not args.no_cache:
        print(f"Using the cache {CACHE_FILE}")
        cache = shelve.open(str(CACHE_FILE))

    if args.clear_cache:
        print(f"Clearing the cache {CACHE_FILE}")
        if cache is None:
            cache = shelve.open(str(CACHE_FILE))
            cache.clear()
            cache.close()
            cache = None
        else:
            cache.clear()

    runs_by_training_pair = get_training_runs_by_langpair(cache)
    print_training_runs_tree(runs_by_training_pair)

    # Saves out the training runs depending on the --upload argument:
    #   - data/model-registry/training-runs/{name}-{langpair}.json
    #   - gs://{BUCKET}/models/{langpair}/{name}.json
    build_json_for_training_runs(runs_by_training_pair, args.overwrite_runs, args.upload, cache)

    # Saves a reference of all the listings:
    #   - gs://{BUCKET}/models/listing.json
    if args.upload:
        save_training_run_listing(runs_by_training_pair)

    if cache is not None:
        cache.close()