spaCy/spacy/cli/init_pipeline.py

from typing import Optional
import logging
from pathlib import Path
from wasabi import msg
import typer

from .. import util
from ..training.initialize import init_nlp
from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code, setup_gpu


@init_cli.command(
    "nlp",
    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
    hidden=True,
)
def init_pipeline_cli(
    # fmt: off
    ctx: typer.Context,  # This is only used to read additional arguments
    config_path: Path = Arg(..., help="Path to config file", exists=True),
    output_path: Path = Arg(..., help="Output directory for the prepared data"),
    code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
    verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
    use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
    # fmt: on
):
    util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
    overrides = parse_config_overrides(ctx.args)
    import_code(code_path)
    setup_gpu(use_gpu)
    with show_validation_error(config_path):
        config = util.load_config(config_path, overrides=overrides)
    with show_validation_error(hint_fill=False):
        nlp = init_nlp(config, use_gpu=use_gpu, silent=False)
    nlp.to_disk(output_path)
    msg.good(f"Saved initialized pipeline to {output_path}")