def train_new(path_model):
gpu_id = (1 if torch.cuda.is_available() else -1)
x, y = get_data(gpu_id)
model = Model(
nn_class='test_fnet_model.DummyModel',
nn_kwargs={'some_param': SOME_PARAM_TEST_VAL},
)
model.to_gpu(gpu_id)
for idx in range(4):
_ = model.train_on_batch(x, y)
model.save(path_model)
def train_new(path_model):
gpu_id = 1 if torch.cuda.is_available() else -1
x, y = get_data(gpu_id)
model = Model(
nn_class="fnet.nn_modules.dummy.DummyModel",
nn_kwargs={"some_param": SOME_PARAM_TEST_VAL},
)
model.to_gpu(gpu_id)
for idx in range(4):
_ = model.train_on_batch(x, y)
model.save(path_model)