mirror of
https://github.com/explosion/spaCy.git
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89 lines
3.9 KiB
Python
89 lines
3.9 KiB
Python
from typing import Optional
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from pathlib import Path
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from wasabi import msg
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from thinc.api import Config
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import typer
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import logging
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from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error
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from ._util import import_code, setup_gpu
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from ..language import Language
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from ..training.loop import train
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from ..training.initialize import init_nlp, must_reinitialize
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from .. import util
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@app.command(
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"train", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
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)
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def train_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"),
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code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
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# fmt: on
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):
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"""
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Train or update a spaCy pipeline. Requires data in spaCy's binary format. To
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convert data from other formats, use the `spacy convert` command. The
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config file includes all settings and hyperparameters used during traing.
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To override settings in the config, e.g. settings that point to local
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paths or that you want to experiment with, you can override them as
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command line options. For instance, --training.batch_size 128 overrides
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the value of "batch_size" in the block "[training]". The --code argument
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lets you pass in a Python file that's imported before training. It can be
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used to register custom functions and architectures that can then be
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referenced in the config.
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DOCS: https://nightly.spacy.io/api/cli#train
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"""
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util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
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verify_cli_args(config_path, output_path)
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overrides = parse_config_overrides(ctx.args)
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import_code(code_path)
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setup_gpu(use_gpu)
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with show_validation_error(config_path):
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config = util.load_config(config_path, overrides=overrides, interpolate=False)
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msg.divider("Initializing pipeline")
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with show_validation_error(config_path, hint_fill=False):
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nlp = init_pipeline(config, output_path, use_gpu=use_gpu)
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msg.divider("Training pipeline")
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train(nlp, output_path, use_gpu=use_gpu, silent=False)
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def init_pipeline(
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config: Config, output_path: Optional[Path], *, use_gpu: int = -1
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) -> Language:
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init_kwargs = {"use_gpu": use_gpu}
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if output_path is not None:
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init_path = output_path / "model-initial"
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if not init_path.exists():
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msg.info(f"Initializing the pipeline in {init_path}")
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nlp = init_nlp(config, **init_kwargs)
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nlp.to_disk(init_path)
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msg.good(f"Saved initialized pipeline to {init_path}")
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else:
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nlp = util.load_model(init_path)
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if must_reinitialize(config, nlp.config):
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msg.warn("Config has changed: need to re-initialize pipeline")
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nlp = init_nlp(config, **init_kwargs)
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nlp.to_disk(init_path)
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msg.good(f"Re-initialized pipeline in {init_path}")
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else:
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msg.good(f"Loaded initialized pipeline from {init_path}")
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return nlp
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return init_nlp(config, **init_kwargs)
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def verify_cli_args(config_path: Path, output_path: Optional[Path] = None) -> None:
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# Make sure all files and paths exists if they are needed
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if not config_path or not config_path.exists():
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msg.fail("Config file not found", config_path, exits=1)
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if output_path is not None:
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if not output_path.exists():
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output_path.mkdir()
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msg.good(f"Created output directory: {output_path}")
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