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spaCy
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Update the train script, fixing GPU memory leak

This commit is contained in:
Matthew Honnibal 2017-05-19 18:15:50 -05:00
parent 836fe1d880
commit 3376d4d6e8
1 changed files with 30 additions and 19 deletions
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49
spacy/cli/train.py
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@ -17,7 +17,7 @@ from .. import displacy
def train(language, output_dir, train_data, dev_data, n_iter, n_sents, def train(language, output_dir, train_data, dev_data, n_iter, n_sents,
use_gpu, tagger, parser, ner, parser_L1): use_gpu, no_tagger, no_parser, no_entities, parser_L1):
output_path = util.ensure_path(output_dir) output_path = util.ensure_path(output_dir)
train_path = util.ensure_path(train_data) train_path = util.ensure_path(train_data)
dev_path = util.ensure_path(dev_data) dev_path = util.ensure_path(dev_data)
@ -44,9 +44,11 @@ def train(language, output_dir, train_data, dev_data, n_iter, n_sents,
'lang': language, 'lang': language,
'features': lang.Defaults.tagger_features} 'features': lang.Defaults.tagger_features}
gold_train = list(read_gold_json(train_path, limit=n_sents)) gold_train = list(read_gold_json(train_path, limit=n_sents))
gold_dev = list(read_gold_json(dev_path, limit=n_sents)) if dev_path else None gold_dev = list(read_gold_json(dev_path, limit=n_sents))
train_model(lang, gold_train, gold_dev, output_path, n_iter, use_gpu=use_gpu) train_model(lang, gold_train, gold_dev, output_path, n_iter,
no_tagger=no_tagger, no_parser=no_parser, no_entities=no_entities,
use_gpu=use_gpu)
if gold_dev: if gold_dev:
scorer = evaluate(lang, gold_dev, output_path) scorer = evaluate(lang, gold_dev, output_path)
print_results(scorer) print_results(scorer)
@ -65,34 +67,43 @@ def train_config(config):
def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg): def train_model(Language, train_data, dev_data, output_path, n_iter, **cfg):
print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %") print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
nlp = Language(pipeline=['token_vectors', 'tags', 'dependencies']) pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
if cfg.get('no_tagger') and 'tags' in pipeline:
pipeline.remove('tags')
if cfg.get('no_parser') and 'dependencies' in pipeline:
pipeline.remove('dependencies')
if cfg.get('no_entities') and 'entities' in pipeline:
pipeline.remove('entities')
print(pipeline)
nlp = Language(pipeline=pipeline)
dropout = util.env_opt('dropout', 0.0) dropout = util.env_opt('dropout', 0.0)
# TODO: Get spaCy using Thinc's trainer and optimizer # TODO: Get spaCy using Thinc's trainer and optimizer
with nlp.begin_training(train_data, **cfg) as (trainer, optimizer): with nlp.begin_training(train_data, **cfg) as (trainer, optimizer):
for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=True)): for itn, epoch in enumerate(trainer.epochs(n_iter, gold_preproc=False)):
losses = defaultdict(float) losses = defaultdict(float)
to_render = []
for i, (docs, golds) in enumerate(epoch): for i, (docs, golds) in enumerate(epoch):
state = nlp.update(docs, golds, drop=dropout, sgd=optimizer) nlp.update(docs, golds, drop=dropout, sgd=optimizer)
losses['dep_loss'] += state.get('parser_loss', 0.0) for doc in docs:
losses['tag_loss'] += state.get('tag_loss', 0.0) doc.tensor = None
to_render.insert(0, nlp(docs[-1].text)) doc._py_tokens = []
to_render[0].user_data['title'] = "Batch %d" % i
with Path('/tmp/entities.html').open('w') as file_:
html = displacy.render(to_render[:5], style='ent', page=True)
file_.write(html)
with Path('/tmp/parses.html').open('w') as file_:
html = displacy.render(to_render[:5], style='dep', page=True)
file_.write(html)
if dev_data: if dev_data:
with nlp.use_params(optimizer.averages): with nlp.use_params(optimizer.averages):
dev_scores = trainer.evaluate(dev_data).scores dev_scores = trainer.evaluate(dev_data, gold_preproc=False).scores
else: else:
dev_scores = defaultdict(float) dev_scores = defaultdict(float)
print_progress(itn, losses, dev_scores) print_progress(itn, losses, dev_scores)
with (output_path / 'model.bin').open('wb') as file_: with (output_path / 'model.bin').open('wb') as file_:
dill.dump(nlp, file_, -1) dill.dump(nlp, file_, -1)
#nlp.to_disk(output_path, tokenizer=False)
def _render_parses(i, to_render):
to_render[0].user_data['title'] = "Batch %d" % i
with Path('/tmp/entities.html').open('w') as file_:
html = displacy.render(to_render[:5], style='ent', page=True)
file_.write(html)
with Path('/tmp/parses.html').open('w') as file_:
html = displacy.render(to_render[:5], style='dep', page=True)
file_.write(html)
def evaluate(Language, gold_tuples, path): def evaluate(Language, gold_tuples, path):