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Update CLI

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This commit is contained in:
Ines Montani 2020-09-28 11:06:07 +02:00
parent 553bfea641
commit 2fdb7285a0
1 changed files with 30 additions and 14 deletions
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44
spacy/cli/train.py
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@ -32,8 +32,9 @@ def train_cli(
config_path: Path = Arg(..., help="Path to config file", exists=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"),
init_path: Optional[Path] = Opt(None, "--init", "-i", help="Path to already initialized pipeline directory, e.g. created with 'spacy init pipeline' (will speed up training)"),
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"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
"""
@ -61,26 +62,38 @@ def train_cli(
msg.info("Using CPU")
config = util.load_config(config_path, overrides=overrides, interpolate=False)
msg.divider("Initializing pipeline")
# TODO: add warnings / --initialize (?) argument
if output_path is None:
nlp = init_pipeline(config)
else:
init_path = output_path / "model-initial"
if must_initialize(config, init_path):
nlp = init_pipeline(config)
nlp.to_disk(init_path)
msg.good(f"Saved initialized pipeline to {init_path}")
else:
nlp = util.load_model(init_path)
msg.good(f"Loaded initialized pipeline from {init_path}")
nlp = init_nlp(config, output_path, init_path)
msg.divider("Training pipeline")
train(nlp, output_path, use_gpu=use_gpu)
def init_nlp(
config: Config, output_path: Optional[Path], init_path: Optional[Path]
) -> None:
if init_path is not None:
nlp = util.load_model(init_path)
# TODO: how to handle provided pipeline that needs to be reinitialized?
msg.good(f"Loaded initialized pipeline from {init_path}")
return nlp
if output_path is not None:
output_init_path = output_path / "model-initial"
if must_initialize(config, output_init_path):
msg.warn("TODO:")
nlp = init_pipeline(config)
nlp.to_disk(init_path)
msg.good(f"Saved initialized pipeline to {output_init_path}")
else:
nlp = util.load_model(output_init_path)
msg.good(f"Loaded initialized pipeline from {output_init_path}")
return nlp
msg.warn("TODO:")
return init_pipeline(config)
def train(
nlp: Language, output_path: Optional[Path] = None, *, use_gpu: int = -1
) -> None:
# TODO: random seed, GPU allocator
# Create iterator, which yields out info after each optimization step.
config = nlp.config.interpolate()
if config["training"]["seed"] is not None:
@ -112,6 +125,9 @@ def train(
raw_text=raw_text,
exclude=frozen_components,
)
msg.info(f"Pipeline: {nlp.pipe_names}")
if frozen_components:
msg.info(f"Frozen components: {frozen_components}")
msg.info(f"Initial learn rate: {optimizer.learn_rate}")
with nlp.select_pipes(disable=frozen_components):
print_row, finalize_logger = train_logger(nlp)