mirror of https://github.com/explosion/spaCy.git
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
import plac
|
|
import tqdm
|
|
from pathlib import Path
|
|
import srsly
|
|
import cProfile
|
|
import pstats
|
|
import sys
|
|
import itertools
|
|
import thinc.extra.datasets
|
|
from wasabi import msg
|
|
|
|
from ..util import load_model
|
|
|
|
|
|
@plac.annotations(
|
|
model=("Model to load", "positional", None, str),
|
|
inputs=("Location of input file. '-' for stdin.", "positional", None, str),
|
|
n_texts=("Maximum number of texts to use if available", "option", "n", int),
|
|
)
|
|
def profile(model, inputs=None, n_texts=10000):
|
|
"""
|
|
Profile a spaCy pipeline, to find out which functions take the most time.
|
|
Input should be formatted as one JSON object per line with a key "text".
|
|
It can either be provided as a JSONL file, or be read from sys.sytdin.
|
|
If no input file is specified, the IMDB dataset is loaded via Thinc.
|
|
"""
|
|
if inputs is not None:
|
|
inputs = _read_inputs(inputs, msg)
|
|
if inputs is None:
|
|
n_inputs = 25000
|
|
with msg.loading("Loading IMDB dataset via Thinc..."):
|
|
imdb_train, _ = thinc.extra.datasets.imdb()
|
|
inputs, _ = zip(*imdb_train)
|
|
msg.info(f"Loaded IMDB dataset and using {n_inputs} examples")
|
|
inputs = inputs[:n_inputs]
|
|
with msg.loading(f"Loading model '{model}'..."):
|
|
nlp = load_model(model)
|
|
msg.good(f"Loaded model '{model}'")
|
|
texts = list(itertools.islice(inputs, n_texts))
|
|
cProfile.runctx("parse_texts(nlp, texts)", globals(), locals(), "Profile.prof")
|
|
s = pstats.Stats("Profile.prof")
|
|
msg.divider("Profile stats")
|
|
s.strip_dirs().sort_stats("time").print_stats()
|
|
|
|
|
|
def parse_texts(nlp, texts):
|
|
for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16):
|
|
pass
|
|
|
|
|
|
def _read_inputs(loc, msg):
|
|
if loc == "-":
|
|
msg.info("Reading input from sys.stdin")
|
|
file_ = sys.stdin
|
|
file_ = (line.encode("utf8") for line in file_)
|
|
else:
|
|
input_path = Path(loc)
|
|
if not input_path.exists() or not input_path.is_file():
|
|
msg.fail("Not a valid input data file", loc, exits=1)
|
|
msg.info(f"Using data from {input_path.parts[-1]}")
|
|
file_ = input_path.open()
|
|
for line in file_:
|
|
data = srsly.json_loads(line)
|
|
text = data["text"]
|
|
yield text
|