start_time = time.time()
step_size = 4000
for start_idx in range(0, len(sentences), step_size):
translations_single_p = model.translate(sentences[start_idx:start_idx+step_size], source_lang='en', target_lang='de', show_progress_bar=True)
end_time = time.time()
print("Single-Process translation done after {:.2f} sec. {:.2f} sentences / second".format(end_time - start_time, len(sentences) / (end_time - start_time)))
######## Multi-Process-Translation
del model
model = EasyNMT(sys.argv[1])
# You can pass a target_devices parameter to the start_multi_process_pool() method to define how many processes to start
# and on which devices the processes should run
process_pool = model.start_multi_process_pool()
#Do some warm-up
model.translate_multi_process(process_pool, sentences[0:100], source_lang='en', target_lang='de', show_progress_bar=False)
# Start translation speed measure - Multi process
start_time = time.time()
translations_multi_p = model.translate_multi_process(process_pool, sentences, source_lang='en', target_lang='de', show_progress_bar=True)
end_time = time.time()
print("Multi-Process translation done after {:.2f} sec. {:.2f} sentences / second".format(end_time - start_time, len(sentences) / (end_time - start_time)))
model.stop_multi_process_pool(process_pool)
##Check that the resutls are equivalent
for idx in range(len(translations_single_p)):
continue
if row['dataset'] == 'SNLI' and snli < snli_max_sentences:
sentences.add(row['sentence1'])
snli += 1
if row['dataset'] == 'MNLI' and mnli < mnli_max_sentences:
sentences.add(row['sentence1'])
mnli += 1
if snli >= snli_max_sentences and mnli >= mnli_max_sentences:
break
print("Sentences:", len(sentences))
sentences = list(sentences)
######## Multi-Process-Translation
# You can pass a target_devices parameter to the start_multi_process_pool() method to define how many processes to start
# and on which devices the processes should run
process_pool = model.start_multi_process_pool(['cuda', 'cuda'])
#Do some warm-up
model.translate_multi_process(process_pool, sentences[0:100], source_lang='en', target_lang='de', show_progress_bar=False)
# Start translation speed measure - Multi process
start_time = time.time()
translations_multi_p = model.translate_multi_process(process_pool, sentences, source_lang='en', target_lang='de', show_progress_bar=True)
end_time = time.time()
print("Multi-Process translation done after {:.2f} sec. {:.2f} sentences / second".format(end_time - start_time, len(sentences) / (end_time - start_time)))
model.stop_multi_process_pool(process_pool)