def get_trainings_queue():
"""
swagger_from_file: yaml/training_queue.yml
"""
if not qa_models_integrator.is_environment_ready():
return {
"msg": "Environment resources not ready, please try again later."
}, 503
response = qa_models_integrator.get_training_requests(formatted=True)
return response
def get_prediction(prediction_id):
"""
swagger_from_file: yaml/prediction_completed.yml
"""
if not qa_models_integrator.is_environment_ready():
return {
"msg": "Environment resources not ready, please try again later."
}, 503
prediction = qa_models_integrator.get_prediction(prediction_id)
if not prediction:
return {"msg": "prediction request id not found"}, 404
return prediction
def get_training(training_id):
"""
swagger_from_file: yaml/training_completed.yml
"""
if not qa_models_integrator.is_environment_ready():
return {
"msg": "Environment resources not ready, please try again later."
}, 503
training = qa_models_integrator.get_training(training_id)
if not training:
return {"msg": "training request id not found"}, 404
return training
def delete_training(training_id):
"""
swagger_from_file: yaml/training_delete.yml
"""
if not qa_models_integrator.is_environment_ready():
return {
"msg": "Environment resources not ready, please try again later."
}, 503
result_successful = qa_models_integrator.delete_training(training_id)
if result_successful is None:
return {"msg": "training request id not found"}, 404
if not result_successful:
return {"msg": "something went wrong deleting, try again later"}, 503
return "deleted successfully"
def test_model_dir():
"""
swagger_from_file: yaml/test_resources_status.yml
"""
actual_models_dir_present = os.path.isdir('actual_models')
bert_model_dir_present = os.path.isdir('actual_models/bert-model')
to_rtn = {
"environment_ready":
qa_models_integrator.is_environment_ready(),
"actual_models_dir":
actual_models_dir_present,
"bert-model":
bert_model_dir_present,
"root_files":
os.listdir(),
"actual_models_files":
os.listdir('actual_models') if actual_models_dir_present else []
}
return to_rtn
def do_training():
"""
swagger_from_file: yaml/training_request.yml
"""
if not qa_models_integrator.is_environment_ready():
return {
"msg": "Environment resources not ready, please try again later."
}, 503
documents_param = get_param(request.json, 'data', required=True)
model_types_param = get_param(request.json, 'models', required=True)
missing_params = check_params([documents_param, model_types_param])
if missing_params:
return {"missing_required_params": missing_params}, 400
prediction_request = qa_models_integrator.do_training(
documents_param['value'], model_types_param['value'])
return prediction_request