def test_deepfm_train(self):
model_def = "deepfm_functional_api.deepfm_functional_api.custom_model"
self._create_pserver(model_def, 2)
db, test_db = get_frappe_dataset(self._batch_size)
self._create_worker(1)
worker_results = self._worker_train(
0, train_db=db, test_db=test_db, stop_step=100
)
acc = max([r[1] for r in worker_results])
self.assertLess(0.65, acc)
def _test_deepfm_train(self, num_ps, num_worker, stop_step):
model_def = "deepfm_functional_api.deepfm_functional_api.custom_model"
self._create_pserver(model_def, num_ps)
db, test_db = get_frappe_dataset(self._batch_size)
self._create_worker(num_worker)
threads = []
for w in range(num_worker):
t = Thread(target=self._worker_train,
args=(w, db, test_db, stop_step))
t.start()
threads.append(t)
for t in threads:
t.join()
def test_train_acceleration_with_embedding(self):
model_def = "deepfm_functional_api.deepfm_functional_api.custom_model"
self._create_pserver(model_def, 2)
db, test_db = get_frappe_dataset(self._batch_size)
self._create_worker(1)
worker_results = self._worker_train(
0,
train_db=db,
test_db=test_db,
stop_step=100,
use_tf_function=True,
)
acc = max([r[1] for r in worker_results])
self.assertLess(0.65, acc)
self._close_channels()
