chore: import upstream snapshot with attribution

spacy/cli/train.py

@@ -0,0 +1,103 @@
+import logging
+import sys
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+import typer
+from wasabi import msg
+
+from .. import util
+from ..training.initialize import init_nlp
+from ..training.loop import train as train_nlp
+from ._util import (
+    Arg,
+    Opt,
+    app,
+    import_code,
+    parse_config_overrides,
+    setup_gpu,
+    show_validation_error,
+)
+
+
+@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 training.
+    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://spacy.io/api/cli#train
+    """
+    if verbose:
+        util.logger.setLevel(logging.DEBUG)
+    overrides = parse_config_overrides(ctx.args)
+    import_code(code_path)
+    train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)
+
+
+def train(
+    config_path: Union[str, Path],
+    output_path: Optional[Union[str, Path]] = None,
+    *,
+    use_gpu: int = -1,
+    overrides: Dict[str, Any] = util.SimpleFrozenDict(),
+):
+    config_path = util.ensure_path(config_path)
+    output_path = util.ensure_path(output_path)
+    # 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 not output_path:
+        msg.info("No output directory provided")
+    else:
+        if not output_path.exists():
+            output_path.mkdir(parents=True)
+            msg.good(f"Created output directory: {output_path}")
+        msg.info(f"Saving to output directory: {output_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(nlp, output_path, use_gpu=use_gpu, stdout=sys.stdout, stderr=sys.stderr)