def register_models(app):
global MODELS
global MODELS_LOADED
if MODELS_LOADED:
return
try:
for name, model in loading.get_available_models("v2").items():
MODELS[name] = wrapper.ModelWrapper(name, model, app)
except Exception as e:
LOG.warning("Error loading models: %s", e)
if MODELS:
MODELS_LOADED = True
return
LOG.warning("No models found using V2 namespace, trying with V1. This is "
"DEPRECATED and it is done only to try to preserve backards "
"compatibility, but may lead to unexpected behaviour. You "
"should move to the new namespace as soon as possible. Please "
"refer to the documentation to get more details.")
try:
for name, model in loading.get_available_models("v1").items():
MODELS[name] = wrapper.ModelWrapper(name, model, app)
except Exception as e:
LOG.warning("Error loading models: %s", e)
if not MODELS:
LOG.info("No models found with V2 or V1 namespace, loading test model")
MODELS["deepaas-test"] = wrapper.ModelWrapper("deepaas-test",
test.TestModel(), app)
MODELS_LOADED = True
def register_models(app):
global MODELS
global MODELS_LOADED
if MODELS_LOADED:
return
try:
for name, model in loading.get_available_models("v2").items():
MODELS[name] = wrapper.ModelWrapper(name, model, app)
except Exception as e:
LOG.warning("Error loading models: %s", e)
if MODELS:
MODELS_LOADED = True
return
if not MODELS:
LOG.info("No models found in V2, loading test model")
MODELS["deepaas-test"] = wrapper.ModelWrapper(
"deepaas-test",
test.TestModel(),
app
)
MODELS_LOADED = True
async def test_dummy_model_with_wrapper(self, m_clean):
w = v2_wrapper.ModelWrapper("foo", v2_test.TestModel(), self.app)
task = w.predict()
await task
ret = task.result()['output']
self.assertDictEqual(
{
'date': '2019-01-1',
'labels': [{
'label': 'foo',
'probability': 1.0
}]
}, ret)
task = w.train(sleep=0)
await task
ret = task.result()
self.assertIsNone(ret['output'])
meta = w.get_metadata()
self.assertIn("description", meta)
self.assertIn("id", meta)
self.assertIn("name", meta)
pargs = w.get_predict_args()
targs = w.get_train_args()
for arg, val in itertools.chain(pargs.items(), targs.items()):
self.assertIsInstance(val, fields.Field)
def test_dummy_model(self):
m = v2_test.TestModel()
self.assertDictEqual(
{'date': '2019-01-1',
'labels': [{'label': 'foo', 'probability': 1.0}]},
m.predict()
)
self.assertIsNone(m.train())
meta = m.get_metadata()
self.assertIn("description", meta)
self.assertIn("id", meta)
self.assertIn("name", meta)
self.assertEqual("Alvaro Lopez Garcia", meta["author"])
pargs = m.get_predict_args()
targs = m.get_train_args()
for arg, val in itertools.chain(pargs.items(), targs.items()):
self.assertIsInstance(val, fields.Field)