mirror of https://github.com/explosion/spaCy.git
69 lines
2.3 KiB
Python
69 lines
2.3 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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import plac
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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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import thinc.extra.datasets
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from wasabi import msg
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from ..util import load_model
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@plac.annotations(
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model=("Model to load", "positional", None, str),
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inputs=("Location of input file. '-' for stdin.", "positional", None, str),
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n_texts=("Maximum number of texts to use if available", "option", "n", int),
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)
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def profile(model, inputs=None, n_texts=10000):
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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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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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n_inputs = 25000
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with msg.loading("Loading IMDB dataset via Thinc..."):
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imdb_train, _ = thinc.extra.datasets.imdb()
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inputs, _ = zip(*imdb_train)
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msg.info("Loaded IMDB dataset and using {} examples".format(n_inputs))
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inputs = inputs[:n_inputs]
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with msg.loading("Loading model '{}'...".format(model)):
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nlp = load_model(model)
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msg.good("Loaded model '{}'".format(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, texts):
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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, msg):
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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("Using data from {}".format(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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