mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 20:28:20 +03:00
fcbf899b08
* remove _convert_examples * fix test_gold, raise TypeError if tuples are used instead of Example's * throwing proper errors when the wrong type of objects are passed * fix deprectated format in tests * fix deprectated format in parser tests * fix tests for NEL, morph, senter, tagger, textcat * update regression tests with new Example format * use make_doc * more fixes to nlp.update calls * few more small fixes for rehearse and evaluate * only import ml_datasets if really necessary
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
from typing import Optional, Sequence, Union, Iterator
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import tqdm
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from pathlib import Path
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import srsly
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import cProfile
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import pstats
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import sys
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import itertools
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from wasabi import msg, Printer
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from ._app import app, Arg, Opt
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from ..language import Language
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from ..util import load_model
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@app.command("profile")
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def profile_cli(
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# fmt: off
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model: str = Arg(..., help="Model to load"),
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inputs: Optional[Path] = Arg(None, help="Location of input file. '-' for stdin.", exists=True, allow_dash=True),
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n_texts: int = Opt(10000, "--n-texts", "-n", help="Maximum number of texts to use if available"),
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# fmt: on
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):
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"""
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Profile a spaCy pipeline, to find out which functions take the most time.
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Input should be formatted as one JSON object per line with a key "text".
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It can either be provided as a JSONL file, or be read from sys.sytdin.
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If no input file is specified, the IMDB dataset is loaded via Thinc.
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"""
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profile(model, inputs=inputs, n_texts=n_texts)
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def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) -> None:
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if inputs is not None:
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inputs = _read_inputs(inputs, msg)
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if inputs is None:
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try:
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import ml_datasets
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except ImportError:
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msg.fail(
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"This command, when run without an input file, "
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"requires the ml_datasets library to be installed: "
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"pip install ml_datasets",
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exits=1,
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)
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n_inputs = 25000
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with msg.loading("Loading IMDB dataset via Thinc..."):
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imdb_train, _ = ml_datasets.imdb()
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inputs, _ = zip(*imdb_train)
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msg.info(f"Loaded IMDB dataset and using {n_inputs} examples")
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inputs = inputs[:n_inputs]
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with msg.loading(f"Loading model '{model}'..."):
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nlp = load_model(model)
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msg.good(f"Loaded model '{model}'")
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texts = list(itertools.islice(inputs, n_texts))
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cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
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s = pstats.Stats("Profile.prof")
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msg.divider("Profile stats")
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s.strip_dirs().sort_stats("time").print_stats()
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def parse_texts(nlp: Language, texts: Sequence[str]) -> None:
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for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16):
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pass
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def _read_inputs(loc: Union[Path, str], msg: Printer) -> Iterator[str]:
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if loc == "-":
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msg.info("Reading input from sys.stdin")
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file_ = sys.stdin
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file_ = (line.encode("utf8") for line in file_)
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else:
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input_path = Path(loc)
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if not input_path.exists() or not input_path.is_file():
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msg.fail("Not a valid input data file", loc, exits=1)
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msg.info(f"Using data from {input_path.parts[-1]}")
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file_ = input_path.open()
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for line in file_:
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data = srsly.json_loads(line)
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text = data["text"]
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yield text
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