from typing import Optional from pathlib import Path from wasabi import msg import typer import logging import sys from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error from ._util import import_code, setup_gpu from ..training.loop import train from ..training.initialize import init_nlp from .. import util @app.command( "train", context_settings={"allow_extra_args": True, "ignore_unknown_options": True} ) def train_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, allow_dash=True), output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"), 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 ): """ Train or update a spaCy pipeline. Requires data in spaCy's binary format. To convert data from other formats, use the `spacy convert` command. The config file includes all settings and hyperparameters used during traing. To override settings in the config, e.g. settings that point to local paths or that you want to experiment with, you can override them as command line options. For instance, --training.batch_size 128 overrides the value of "batch_size" in the block "[training]". The --code argument lets you pass in a Python file that's imported before training. It can be used to register custom functions and architectures that can then be referenced in the config. DOCS: https://nightly.spacy.io/api/cli#train """ util.logger.setLevel(logging.DEBUG if verbose else logging.INFO) # Make sure all files and paths exists if they are needed if not config_path or (str(config_path) != "-" and not config_path.exists()): msg.fail("Config file not found", config_path, exits=1) if output_path is not None and not output_path.exists(): output_path.mkdir(parents=True) msg.good(f"Created output directory: {output_path}") 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, interpolate=False) msg.divider("Initializing pipeline") with show_validation_error(config_path, hint_fill=False): nlp = init_nlp(config, use_gpu=use_gpu) msg.good("Initialized pipeline") msg.divider("Training pipeline") train(nlp, output_path, use_gpu=use_gpu, stdout=sys.stdout, stderr=sys.stderr)