def _predict(self, queries: List[Query]) -> List[Prediction]:
# Pass queries to model, set null predictions if it errors
try:
predictions = self._model_inst.predict([x.query for x in queries])
except:
logger.error('Error while making predictions:')
logger.error(traceback.format_exc())
predictions = [None for x in range(len(queries))]
# Transform predictions, adding associated worker & query ID
predictions = [
Prediction(x, query.id, self._worker_id)
for (x, query) in zip(predictions, queries)
]
return predictions
def make_predictions(queries: List[Any], task: str,
py_model_class: Type[BaseModel], proposal: Proposal,
fine_tune_dataset_path, params: Params) -> List[Any]:
inference_cache: InferenceCache = InferenceCache()
worker_id = 'local'
# print('Queries: {}'.format(queries))
# Worker load best trained model's parameters
model_inst = None
_print_header('Loading trained model...')
model_inst = py_model_class(**proposal.knobs)
if task == 'question_answering_covid19' and fine_tune_dataset_path is not None:
model_inst.load_parameters(fine_tune_dataset_path)
elif task != 'question_answering_covid19':
model_inst.load_parameters(params)
# Inference worker tells predictor that it is free
inference_cache.add_worker(worker_id)
# Predictor receives queries
queries = [Query(x) for x in queries]
# Predictor checks free workers
worker_ids = inference_cache.get_workers()
assert worker_id in worker_ids
# Predictor sends query to worker
inference_cache.add_queries_for_worker(worker_id, queries)
# Worker receives query
queries_at_worker = inference_cache.pop_queries_for_worker(
worker_id, len(queries))
assert len(queries_at_worker) == len(queries)
# Worker makes prediction on queries
_print_header('Making predictions with trained model...')
predictions = model_inst.predict([x.query for x in queries_at_worker])
predictions = [
Prediction(x, query.id, worker_id)
for (x, query) in zip(predictions, queries_at_worker)
]
# Worker sends predictions to predictor
inference_cache.add_predictions_for_worker(worker_id, predictions)
# Predictor receives predictions
predictions_at_predictor = []
for query in queries:
prediction = inference_cache.take_prediction_for_worker(
worker_id, query.id)
assert prediction is not None
predictions_at_predictor.append(prediction)
ensemble_method = get_ensemble_method(task)
print(f'Ensemble method: {ensemble_method}')
out_predictions = []
for prediction in predictions_at_predictor:
prediction = prediction.prediction
_assert_jsonable(
prediction,
Exception('Each `prediction` should be JSON serializable'))
out_prediction = ensemble_method([prediction])
out_predictions.append(out_prediction)
print('Predictions: {}'.format(out_predictions))
return (out_predictions, model_inst)