async def train(request):
# if not set will use the default project name, e.g. "default"
project = request.args.get("project", None)
# if set will not generate a model name but use the passed one
model_name = request.args.get("model", None)
try:
model_config, data = extract_data_and_config(request)
except Exception as e:
raise ErrorResponse(
500,
"ServerError",
"An unexpected error occurred.",
details={"error": str(e)},
)
data_file = dump_to_data_file(data)
try:
path_to_model = await data_router.start_train_process(
data_file, project, RasaNLUModelConfig(model_config), model_name
)
zipped_path = utils.zip_folder(path_to_model)
return await response.file(zipped_path)
except MaxWorkerProcessError as e:
raise ErrorResponse(
403,
"NoFreeProcess",
"No process available for training.",
details={"error": str(e)},
)
except InvalidProjectError as e:
raise ErrorResponse(
404,
"ProjectNotFound",
"Project '{}' not found.".format(project),
details={"error": str(e)},
)
except TrainingException as e:
raise ErrorResponse(
500,
"ServerError",
"An unexpected error occurred.",
details={"error": str(e)},
)
async def test_project_with_model_server(trained_nlu_model):
fingerprint = "somehash"
model_endpoint = EndpointConfig("http://server.com/models/nlu/tags/latest")
zip_path = zip_folder(trained_nlu_model)
# mock a response that returns a zipped model
with io.open(zip_path, "rb") as f:
responses.add(
responses.GET,
model_endpoint.url,
headers={
"ETag": fingerprint,
"filename": "my_model_xyz.zip"
},
body=f.read(),
content_type="application/zip",
stream=True,
)
project = await load_from_server(model_server=model_endpoint)
assert project.fingerprint == fingerprint
def zipped_nlu_model():
spacy_config_path = "sample_configs/config_pretrained_embeddings_spacy.yml"
cfg = config.load(spacy_config_path)
trainer = Trainer(cfg)
td = training_data.load_data(DEFAULT_DATA_PATH)
trainer.train(td)
trainer.persist("test_models",
project_name="test_model_pretrained_embeddings")
model_dir_list = os.listdir(TEST_MODEL_PATH)
# directory name of latest model
model_dir = sorted(model_dir_list)[-1]
# path of that directory
model_path = os.path.join(TEST_MODEL_PATH, model_dir)
zip_path = zip_folder(model_path)
return zip_path
def train(self, request):
# if not set will use the default project name, e.g. "default"
project = parameter_or_default(request, "project", default=None)
# if set will not generate a model name but use the passed one
model_name = parameter_or_default(request, "model", default=None)
try:
model_config, data = self.extract_data_and_config(request)
except Exception as e:
request.setResponseCode(400)
returnValue(json_to_string({"error": "{}".format(e)}))
data_file = dump_to_data_file(data)
request.setHeader('Content-Type', 'application/zip')
try:
request.setResponseCode(200)
request.setHeader("Content-Disposition", "attachment")
path_to_model = yield self.data_router.start_train_process(
data_file, project, RasaNLUModelConfig(model_config),
model_name)
zipped_path = utils.zip_folder(path_to_model)
zip_content = io.open(zipped_path, 'r+b').read()
return returnValue(zip_content)
except MaxTrainingError as e:
request.setResponseCode(403)
returnValue(json_to_string({"error": "{}".format(e)}))
except InvalidProjectError as e:
request.setResponseCode(404)
returnValue(json_to_string({"error": "{}".format(e)}))
except TrainingException as e:
request.setResponseCode(500)
returnValue(json_to_string({"error": "{}".format(e)}))