Update multiprocessing example
@@ -34,21 +34,24 @@ def main(output_dir, model='en_core_web_sm', n_jobs=4, batch_size=1000,
data, _ = thinc.extra.datasets.imdb()
texts, _ = zip(*data[-limit:])
partitions = partition_all(batch_size, texts)
- items = ((i, [nlp(text) for text in texts], output_dir) for i, texts
- in enumerate(partitions))
- Parallel(n_jobs=n_jobs)(delayed(transform_texts)(*item) for item in items)
+ executor = Parallel(n_jobs=n_jobs)
+ do = delayed(transform_texts)
+ tasks = (do(nlp, i, batch, output_dir)
+ for i, batch in enumerate(partitions))
+ executor(tasks)
-def transform_texts(batch_id, docs, output_dir):
+def transform_texts(nlp, batch_id, texts, output_dir):
+ print(nlp.pipe_names)
out_path = Path(output_dir) / ('%d.txt' % batch_id)
if out_path.exists(): # return None in case same batch is called again
return None
print('Processing batch', batch_id)
with out_path.open('w', encoding='utf8') as f:
- for doc in docs:
+ for doc in nlp.pipe(texts):
f.write(' '.join(represent_word(w) for w in doc if not w.is_space))
f.write('\n')
- print('Saved {} texts to {}.txt'.format(len(docs), batch_id))
+ print('Saved {} texts to {}.txt'.format(len(texts), batch_id))
def represent_word(word):
