def _persist_metadata(self, path, dump_flattened_stories=False):
# type: (Text) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
def _persist_metadata(self, path, dump_flattened_stories=False):
# type: (Text) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [
utils.module_path_from_instance(p) for p in self.policies
]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
def test_agent_with_model_server(tmpdir, zipped_moodbot_model,
moodbot_domain, moodbot_metadata):
fingerprint = 'somehash'
model_endpoint_config = EndpointConfig.from_dict(
{"url": 'http://server.com/model/default_core@latest'}
)
# mock a response that returns a zipped model
with io.open(zipped_moodbot_model, 'rb') as f:
responses.add(responses.GET,
model_endpoint_config.url,
headers={"ETag": fingerprint},
body=f.read(),
content_type='application/zip',
stream=True)
agent = rasa_core.agent.load_from_server(
model_server=model_endpoint_config)
assert agent.fingerprint == fingerprint
assert agent.domain.as_dict() == moodbot_domain.as_dict()
agent_policies = {utils.module_path_from_instance(p)
for p in agent.policy_ensemble.policies}
moodbot_policies = set(moodbot_metadata["policy_names"])
assert agent_policies == moodbot_policies
def _persist_metadata(self,
path: Text,
dump_flattened_stories: bool = False) -> None:
"""Persists the domain specification to storage."""
# make sure the directory we persist exists
domain_spec_path = os.path.join(path, 'metadata.json')
training_data_path = os.path.join(path, 'stories.md')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self._training_events_from_trackers(
self.training_trackers)
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"python": ".".join([str(s) for s in sys.version_info[:3]]),
"max_histories": self._max_histories(),
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names,
"trained_at": self.date_trained
}
self._add_package_version_info(metadata)
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
# if there are lots of stories, saving flattened stories takes a long
# time, so this is turned off by default
if dump_flattened_stories:
training.persist_data(self.training_trackers, training_data_path)
async def test_agent_with_model_server_in_thread(model_server, tmpdir,
zipped_moodbot_model,
moodbot_domain,
moodbot_metadata):
model_endpoint_config = EndpointConfig.from_dict({
"url":
model_server.make_url('/model'),
"wait_time_between_pulls":
2
})
agent = Agent()
agent = await rasa_core.agent.load_from_server(
agent, model_server=model_endpoint_config)
await asyncio.sleep(3)
assert agent.fingerprint == "somehash"
assert agent.domain.as_dict() == moodbot_domain.as_dict()
agent_policies = {
utils.module_path_from_instance(p)
for p in agent.policy_ensemble.policies
}
moodbot_policies = set(moodbot_metadata["policy_names"])
assert agent_policies == moodbot_policies
assert model_server.app.number_of_model_requests == 1
jobs.kill_scheduler()
def _persist_metadata(self, path, max_history):
# type: (Text, Optional[int]) -> None
"""Persists the domain specification to storage."""
# make sure the directory we persist to exists
domain_spec_path = os.path.join(path, 'policy_metadata.json')
utils.create_dir_for_file(domain_spec_path)
policy_names = [utils.module_path_from_instance(p)
for p in self.policies]
training_events = self.training_metadata.get("events", {})
action_fingerprints = self._create_action_fingerprints(training_events)
metadata = {
"action_fingerprints": action_fingerprints,
"rasa_core": rasa_core.__version__,
"max_history": max_history,
"ensemble_name": self.__module__ + "." + self.__class__.__name__,
"policy_names": policy_names
}
utils.dump_obj_as_json_to_file(domain_spec_path, metadata)
def persistence_info(self):
return {"type": utils.module_path_from_instance(self),
"initial_value": self.initial_value}
def persistence_info(self):
return {"type": utils.module_path_from_instance(self),
"initial_value": self.initial_value}
def persistence_info(self):
return {**{"type": utils.module_path_from_instance(self),
"initial_value": self.initial_value}, **{k: getattr(self, k) for k in self._additional}}
