def __init__(self):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dir = self.config.paths['datasources']
self.mindsdb_native = NativeInterface()
def __init__(self, ray_based):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dbw = DatabaseWrapper()
self.predictor_cache = {}
self.ray_based = ray_based
def __init__(self):
self.config = Config()
self.fs_store = FsSotre()
self.company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
self.dbw = DatabaseWrapper()
self.storage_dir = self.config['paths']['custom_models']
os.makedirs(self.storage_dir, exist_ok=True)
self.model_cache = {}
self.mindsdb_native = NativeInterface()
self.dbw = DatabaseWrapper()
def run(self):
'''
running at subprocess due to
ValueError: signal only works in main thread
this is work for celery worker here?
'''
import mindsdb_native
import setproctitle
try:
setproctitle.setproctitle('mindsdb_native_process')
except Exception:
pass
config = Config()
fs_store = FsSotre()
company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
name, from_data, to_predict, kwargs, datasource_id = self._args
mdb = mindsdb_native.Predictor(name=name, run_env={'trigger': 'mindsdb'})
predictor_record = Predictor.query.filter_by(company_id=company_id, name=name).first()
predictor_record.datasource_id = datasource_id
predictor_record.to_predict = to_predict
predictor_record.version = mindsdb_native.__version__
predictor_record.data = {
'name': name,
'status': 'training'
}
#predictor_record.datasource_id = ... <-- can be done once `learn` is passed a datasource name
session.commit()
to_predict = to_predict if isinstance(to_predict, list) else [to_predict]
data_source = getattr(mindsdb_native, from_data['class'])(*from_data['args'], **from_data['kwargs'])
try:
mdb.learn(
from_data=data_source,
to_predict=to_predict,
**kwargs
)
except Exception:
pass
fs_store.put(name, f'predictor_{company_id}_{predictor_record.id}', config['paths']['predictors'])
model_data = mindsdb_native.F.get_model_data(name)
predictor_record = Predictor.query.filter_by(company_id=company_id, name=name).first()
predictor_record.data = model_data
session.commit()
DatabaseWrapper().register_predictors([model_data])
def run_learn(name, from_data, to_predict, kwargs, datasource_id):
import mindsdb_native
import mindsdb_datasources
import mindsdb
create_process_mark('learn')
config = Config()
fs_store = FsSotre()
company_id = os.environ.get('MINDSDB_COMPANY_ID', None)
mdb = mindsdb_native.Predictor(name=name, run_env={'trigger': 'mindsdb'})
predictor_record = Predictor.query.filter_by(company_id=company_id,
name=name).first()
predictor_record.datasource_id = datasource_id
predictor_record.to_predict = to_predict
predictor_record.native_version = mindsdb_native.__version__
predictor_record.mindsdb_version = mindsdb_version
predictor_record.learn_args = {'to_predict': to_predict, 'kwargs': kwargs}
predictor_record.data = {'name': name, 'status': 'training'}
session.commit()
to_predict = to_predict if isinstance(to_predict, list) else [to_predict]
data_source = getattr(mindsdb_datasources,
from_data['class'])(*from_data['args'],
**from_data['kwargs'])
try:
mdb.learn(from_data=data_source, to_predict=to_predict, **kwargs)
except Exception as e:
log = logging.getLogger('mindsdb.main')
log.error(f'Predictor learn error: {e}')
predictor_record.data = {'name': name, 'status': 'error'}
session.commit()
delete_process_mark('learn')
return
fs_store.put(name, f'predictor_{company_id}_{predictor_record.id}',
config['paths']['predictors'])
model_data = mindsdb_native.F.get_model_data(name)
predictor_record = Predictor.query.filter_by(company_id=company_id,
name=name).first()
predictor_record.data = model_data
session.commit()
DatabaseWrapper().register_predictors([model_data])
delete_process_mark('learn')