This is an automated email from the ASF dual-hosted git repository.
hui pushed a commit to branch mlnode/test
in repository https://gitbox.apache.org/repos/asf/iotdb.git
The following commit(s) were added to refs/heads/mlnode/test by this push:
new 3fa855d8da refactor factory
3fa855d8da is described below
commit 3fa855d8daaccd29ee601594c4d6b0e64480ad21
Author: Minghui Liu <[email protected]>
AuthorDate: Mon Apr 3 22:32:49 2023 +0800
refactor factory
---
mlnode/iotdb/mlnode/algorithm/enums.py | 22 +++++---
mlnode/iotdb/mlnode/algorithm/factory.py | 62 +++++++++++-----------
mlnode/iotdb/mlnode/algorithm/metric.py | 7 +++
.../mlnode/algorithm/models/forecast/__init__.py | 5 --
mlnode/iotdb/mlnode/client.py | 4 +-
mlnode/iotdb/mlnode/data_access/enums.py | 2 +-
mlnode/iotdb/mlnode/data_access/factory.py | 37 +++++--------
mlnode/iotdb/mlnode/process/manager.py | 4 +-
mlnode/iotdb/mlnode/process/task.py | 3 +-
mlnode/iotdb/mlnode/process/task_factory.py | 40 --------------
mlnode/iotdb/mlnode/process/trial.py | 25 ++++-----
mlnode/iotdb/mlnode/storage.py | 4 +-
mlnode/test/test_create_forecast_dataset.py | 5 +-
mlnode/test/test_create_forecast_model.py | 5 +-
mlnode/test/test_model_storage.py | 2 +-
mlnode/test/test_parse_training_request.py | 4 +-
thrift-mlnode/src/main/thrift/mlnode.thrift | 2 +-
17 files changed, 100 insertions(+), 133 deletions(-)
diff --git a/mlnode/iotdb/mlnode/algorithm/enums.py
b/mlnode/iotdb/mlnode/algorithm/enums.py
index 0f93cf056b..8d9c50640b 100644
--- a/mlnode/iotdb/mlnode/algorithm/enums.py
+++ b/mlnode/iotdb/mlnode/algorithm/enums.py
@@ -21,23 +21,33 @@ from enum import Enum
class ForecastTaskType(Enum):
"""
In multivariable time series forecasting tasks, the columns to be
predicted are called the endogenous variables
- and the columns that are independent or non-forecast are called the
exogenous variables (they might be helpful
+ and the columns that are independent or non-forecast are called the
exogenous variables (they might be helpful
to predict the endogenous variables). Both of them can appear in the input
time series.
-
+
ForecastTaskType.ENDOGENOUS: all input time series of input are endogenous
variables
ForecastTaskType.EXOGENOUS: the input time series is combined with
endogenous and exogenous variables
"""
ENDOGENOUS = "endogenous"
EXOGENOUS = "exogenous"
- def __str__(self):
+ def __str__(self) -> str:
return self.value
- def __hash__(self):
- return hash(self.value)
-
def __eq__(self, other: str) -> bool:
return self.value == other
def __hash__(self) -> int:
return hash(self.value)
+
+
+class ForecastModelType(Enum):
+ DLINEAR = "dlinear"
+ DLINEAR_INDIVIDUAL = "dlinear_individual"
+ NBEATS = "nbeats"
+
+ @classmethod
+ def values(cls) -> list[str]:
+ values = []
+ for item in list(cls):
+ values.append(item.value)
+ return values
diff --git a/mlnode/iotdb/mlnode/algorithm/factory.py
b/mlnode/iotdb/mlnode/algorithm/factory.py
index 37f81c1e68..0488a6dfae 100644
--- a/mlnode/iotdb/mlnode/algorithm/factory.py
+++ b/mlnode/iotdb/mlnode/algorithm/factory.py
@@ -16,10 +16,11 @@
# under the License.
#
import torch.nn as nn
-from iotdb.mlnode.algorithm.models.forecast import *
-from iotdb.mlnode.algorithm.enums import ForecastTaskType
-from iotdb.mlnode.algorithm.models.forecast import support_forecasting_models
-from iotdb.mlnode.algorithm.models.forecast.dlinear import dlinear
+
+from iotdb.mlnode.algorithm.enums import ForecastModelType, ForecastTaskType
+from iotdb.mlnode.algorithm.models.forecast.dlinear import (dlinear,
+ dlinear_individual)
+from iotdb.mlnode.algorithm.models.forecast.nbeats import nbeats
from iotdb.mlnode.exception import BadConfigValueError
@@ -35,7 +36,7 @@ def _common_config(**kwargs):
# Common forecasting task configs
-support_common_configs = {
+_forecasting_model_default_config_dict = {
# multivariable forecasting with all endogenous variables, current support
this only
ForecastTaskType.ENDOGENOUS: _common_config(
input_vars=1,
@@ -47,20 +48,6 @@ support_common_configs = {
}
-def is_model(model_name: str) -> bool:
- """
- Check if a model name exists
- """
- return model_name in support_forecasting_models
-
-
-def list_model() -> list:
- """
- List support forecasting model
- """
- return support_forecasting_models
-
-
def create_forecast_model(
model_name,
input_len=96,
@@ -88,13 +75,13 @@ def create_forecast_model(
model: torch.nn.Module
model_config: dict of model configurations
"""
- if not is_model(model_name):
- raise BadConfigValueError('model_name', model_name, f'It should be one
of {list_model()}')
- if forecast_task_type not in support_common_configs.keys():
+ if model_name not in ForecastModelType.values():
+ raise BadConfigValueError('model_name', model_name, f'It should be one
of {ForecastModelType.values()}')
+ if forecast_task_type not in _forecasting_model_default_config_dict.keys():
raise BadConfigValueError('forecast_task_type', forecast_task_type,
- f'It should be one of
{list(support_common_configs.keys())}')
+ f'It should be one of
{list(_forecasting_model_default_config_dict.keys())}')
- common_config = support_common_configs[forecast_task_type]
+ common_config = _forecasting_model_default_config_dict[forecast_task_type]
common_config['input_len'] = input_len
common_config['pred_len'] = pred_len
common_config['input_vars'] = input_vars
@@ -113,16 +100,29 @@ def create_forecast_model(
if not output_vars > 0:
raise BadConfigValueError('output_vars', output_vars,
'Number of output variables should be
positive')
- if forecast_task_type == ForecastTaskType.ENDOGENOUS:
+ if forecast_task_type is ForecastTaskType.ENDOGENOUS:
if input_vars != output_vars:
raise BadConfigValueError('forecast_task_type', forecast_task_type,
'Number of input/output variables should
be '
'the same in endogenous forecast')
- create_fn = eval(model_name)
- model, model_config = create_fn(
- common_config=common_config,
- **kwargs
- )
- model_config['model_name'] = model_name
+ if model_name == ForecastModelType.DLINEAR.value:
+ model, model_config = dlinear(
+ common_config=common_config,
+ **kwargs
+ )
+ elif model_name == ForecastModelType.DLINEAR_INDIVIDUAL.value:
+ model, model_config = dlinear_individual(
+ common_config=common_config,
+ **kwargs
+ )
+ elif model_name == ForecastModelType.NBEATS.value:
+ model, model_config = nbeats(
+ common_config=common_config,
+ **kwargs
+ )
+ else:
+ raise BadConfigValueError('model_name', model_name, f'It should be one
of {ForecastModelType.values()}')
+
+ model_config['model_name'] = model_name
return model, model_config
diff --git a/mlnode/iotdb/mlnode/algorithm/metric.py
b/mlnode/iotdb/mlnode/algorithm/metric.py
index e623642191..41519dc956 100644
--- a/mlnode/iotdb/mlnode/algorithm/metric.py
+++ b/mlnode/iotdb/mlnode/algorithm/metric.py
@@ -67,3 +67,10 @@ class MAPE(Metric):
class MSPE(Metric):
def calculate(self, pred, ground_truth):
return np.mean(np.square((pred - ground_truth) / ground_truth))
+
+
+def build_metrics(metric_names: list[str]) -> dict[str, Metric]:
+ metrics_dict = {}
+ for metric_name in metric_names:
+ metrics_dict[metric_name] = eval(metric_name)()
+ return metrics_dict
diff --git a/mlnode/iotdb/mlnode/algorithm/models/forecast/__init__.py
b/mlnode/iotdb/mlnode/algorithm/models/forecast/__init__.py
index 8be9fa80b5..2a1e720805 100644
--- a/mlnode/iotdb/mlnode/algorithm/models/forecast/__init__.py
+++ b/mlnode/iotdb/mlnode/algorithm/models/forecast/__init__.py
@@ -15,8 +15,3 @@
# specific language governing permissions and limitations
# under the License.
#
-
-from iotdb.mlnode.algorithm.models.forecast.dlinear import dlinear,
dlinear_individual
-from iotdb.mlnode.algorithm.models.forecast.nbeats import nbeats
-
-support_forecasting_models = ['dlinear', 'dlinear_individual', 'nbeats']
diff --git a/mlnode/iotdb/mlnode/client.py b/mlnode/iotdb/mlnode/client.py
index ecfe75859f..1560b0507a 100644
--- a/mlnode/iotdb/mlnode/client.py
+++ b/mlnode/iotdb/mlnode/client.py
@@ -71,7 +71,7 @@ class MLNodeClient(object):
model_id: str,
is_auto: bool,
model_configs: dict,
- query_expressions: list = [],
+ query_expressions: list,
query_filter: str = '') -> None:
req = TCreateTrainingTaskReq(
modelId=model_id,
@@ -123,7 +123,7 @@ class DataNodeClient(object):
self.__client = IMLNodeInternalRPCService.Client(protocol)
def fetch_timeseries(self,
- query_expressions: list = [],
+ query_expressions: list,
query_filter: str = None,
fetch_size: int = DEFAULT_FETCH_SIZE,
timeout: int = DEFAULT_TIMEOUT) ->
TFetchTimeseriesResp:
diff --git a/mlnode/iotdb/mlnode/data_access/enums.py
b/mlnode/iotdb/mlnode/data_access/enums.py
index aad21d2c8c..a5d415fc0e 100644
--- a/mlnode/iotdb/mlnode/data_access/enums.py
+++ b/mlnode/iotdb/mlnode/data_access/enums.py
@@ -43,4 +43,4 @@ class DataSourceType(Enum):
return hash(self.value)
def __eq__(self, other: str) -> bool:
- return self.value == other
\ No newline at end of file
+ return self.value == other
diff --git a/mlnode/iotdb/mlnode/data_access/factory.py
b/mlnode/iotdb/mlnode/data_access/factory.py
index 2e9a3342f7..b2d75f18fa 100644
--- a/mlnode/iotdb/mlnode/data_access/factory.py
+++ b/mlnode/iotdb/mlnode/data_access/factory.py
@@ -24,27 +24,17 @@ from iotdb.mlnode.data_access.offline.source import
(FileDataSource,
ThriftDataSource)
from iotdb.mlnode.exception import BadConfigValueError, MissingConfigError
-support_forecasting_dataset = {
- DatasetType.TIMESERIES: TimeSeriesDataset,
- DatasetType.WINDOW: WindowDataset
-}
-
-support_forecasting_datasource = {
- DataSourceType.FILE: FileDataSource,
- DataSourceType.THRIFT: ThriftDataSource
-}
-
-def _dataset_config(**kwargs):
+def _dataset_common_config(**kwargs):
return {
'time_embed': 'h',
**kwargs
}
-support_dataset_configs = {
- DatasetType.TIMESERIES: _dataset_config(),
- DatasetType.WINDOW: _dataset_config(
+_dataset_default_config_dict = {
+ DatasetType.TIMESERIES: _dataset_common_config(),
+ DatasetType.WINDOW: _dataset_common_config(
input_len=96,
pred_len=96,
)
@@ -71,9 +61,8 @@ def create_forecast_dataset(
dataset: torch.nn.Module
dataset_config: dict of dataset configurations
"""
- if dataset_type not in support_forecasting_dataset.keys():
- raise BadConfigValueError('dataset_type', dataset_type,
- f'It should be one of
{list(support_forecasting_dataset.keys())}')
+ if dataset_type not in list(DatasetType):
+ raise BadConfigValueError('dataset_type', dataset_type, f'It should be
one of {list(DatasetType)}')
if source_type == DataSourceType.FILE:
if 'filename' not in kwargs.keys():
@@ -86,17 +75,19 @@ def create_forecast_dataset(
raise MissingConfigError('query_filter')
datasource = ThriftDataSource(kwargs['query_expressions'],
kwargs['query_filter'])
else:
- raise BadConfigValueError('source_type', source_type,
- f"It should be one of
{list(support_forecasting_datasource)}")
-
- dataset_fn = support_forecasting_dataset[dataset_type]
- dataset_config = support_dataset_configs[dataset_type]
+ raise BadConfigValueError('source_type', source_type, f"It should be
one of {list(DataSourceType)}")
+ dataset_config = _dataset_default_config_dict[dataset_type]
for k, v in kwargs.items():
if k in dataset_config.keys():
dataset_config[k] = v
- dataset = dataset_fn(datasource, **dataset_config)
+ if dataset_type == DatasetType.TIMESERIES:
+ dataset = TimeSeriesDataset(datasource, **dataset_config)
+ elif dataset_type == DatasetType.WINDOW:
+ dataset = WindowDataset(datasource, **dataset_config)
+ else:
+ raise BadConfigValueError('dataset_type', dataset_type, f'It should be
one of {list(DatasetType)}')
if 'input_vars' in kwargs.keys() and dataset.get_variable_num() !=
kwargs['input_vars']:
raise BadConfigValueError('input_vars', kwargs['input_vars'],
diff --git a/mlnode/iotdb/mlnode/process/manager.py
b/mlnode/iotdb/mlnode/process/manager.py
index 0af0353973..88214a19bc 100644
--- a/mlnode/iotdb/mlnode/process/manager.py
+++ b/mlnode/iotdb/mlnode/process/manager.py
@@ -23,7 +23,6 @@ from torch.utils.data import Dataset
from iotdb.mlnode.log import logger
from iotdb.mlnode.process.task import ForecastingTrainingTask
-from iotdb.mlnode.process.task_factory import create_task
class TaskManager(object):
@@ -44,13 +43,14 @@ class TaskManager(object):
task_configs: dict) -> ForecastingTrainingTask:
model_id = task_configs['model_id']
self.__pid_info[model_id] = self.__shared_resource_manager.dict()
- return create_task(
+ task = ForecastingTrainingTask(
task_configs,
model_configs,
model,
dataset,
self.__pid_info
)
+ return task
def submit_training_task(self, task: ForecastingTrainingTask) -> None:
if task is not None:
diff --git a/mlnode/iotdb/mlnode/process/task.py
b/mlnode/iotdb/mlnode/process/task.py
index 85d5b5d2cf..cf067cb165 100644
--- a/mlnode/iotdb/mlnode/process/task.py
+++ b/mlnode/iotdb/mlnode/process/task.py
@@ -97,4 +97,5 @@ class ForecastingTrainingTask(_BasicTask):
else:
self.trial.start()
except Exception as e:
- logger.exception(e)
+ logger.warn(e)
+ raise e
diff --git a/mlnode/iotdb/mlnode/process/task_factory.py
b/mlnode/iotdb/mlnode/process/task_factory.py
deleted file mode 100644
index 083b84eba2..0000000000
--- a/mlnode/iotdb/mlnode/process/task_factory.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements. See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership. The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License. You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied. See the License for the
-# specific language governing permissions and limitations
-# under the License.
-#
-
-
-from iotdb.mlnode.process.task import ForecastingTrainingTask
-
-support_task_types = {
- 'forecast': ForecastingTrainingTask
-}
-
-
-def create_task(task_configs, model_configs, model, dataset, task_trial_map):
- task_class = task_configs["task_class"]
- if task_class not in support_task_types:
- raise RuntimeError(f'Unknown task type: ({task_class}), which'
- f' should be one of {support_task_types.keys()}')
- task_fn = support_task_types[task_class]
- task = task_fn(
- task_configs,
- model_configs,
- model,
- dataset,
- task_trial_map
- )
- return task
diff --git a/mlnode/iotdb/mlnode/process/trial.py
b/mlnode/iotdb/mlnode/process/trial.py
index 9852e3ffb4..105dfeaf37 100644
--- a/mlnode/iotdb/mlnode/process/trial.py
+++ b/mlnode/iotdb/mlnode/process/trial.py
@@ -23,7 +23,7 @@ import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
-from iotdb.mlnode.algorithm.metric import MAE, MSE, all_metrics
+from iotdb.mlnode.algorithm.metric import all_metrics, build_metrics
from iotdb.mlnode.client import client_manager
from iotdb.mlnode.log import logger
from iotdb.mlnode.storage import model_storage
@@ -122,6 +122,7 @@ class ForecastingTrainingTrial(BasicTrial):
self.confignode_client = client_manager.borrow_config_node_client()
self.criterion = torch.nn.MSELoss()
self.optimizer = torch.optim.Adam(self.model.parameters(),
lr=self.learning_rate)
+ self.metrics_dict = build_metrics(self.metric_names)
def _build_dataloader(self):
return DataLoader(
@@ -148,8 +149,10 @@ class ForecastingTrainingTrial(BasicTrial):
# decoder input
dec_inp = torch.zeros_like(batch_y[:, -self.pred_len:, :]).float()
outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
+
outputs = outputs[:, -self.pred_len:]
batch_y = batch_y[:, -self.pred_len:]
+
loss = self.criterion(outputs, batch_y)
train_loss.append(loss.item())
@@ -161,7 +164,6 @@ class ForecastingTrainingTrial(BasicTrial):
self.optimizer.step()
train_loss = np.average(train_loss)
- # TODO: manage these training output
logger.info('Epoch: {0} cost time: {1} | Train Loss: {2:.7f}'
.format(epoch + 1, time.time() - epoch_time, train_loss))
return train_loss
@@ -169,7 +171,7 @@ class ForecastingTrainingTrial(BasicTrial):
def _validate(self, epoch):
self.model.eval()
val_loss = []
- metrics_dict = {name: [] for name in self.metric_names}
+ metrics_value_dict = {name: [] for name in self.metric_names}
for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in
enumerate(self.dataloader):
batch_x = batch_x.float().to(self.device)
batch_y = batch_y.float().to(self.device)
@@ -185,26 +187,25 @@ class ForecastingTrainingTrial(BasicTrial):
batch_y = batch_y[:, -self.pred_len:]
loss = self.criterion(outputs, batch_y)
-
val_loss.append(loss.item())
+
for name in self.metric_names:
- metric = eval(name)()
+ metric = self.metrics_dict[name]
value = metric(outputs.detach().cpu().numpy(),
batch_y.detach().cpu().numpy())
- metrics_dict[name].append(value)
+ metrics_value_dict[name].append(value)
- for name, value_list in metrics_dict.items():
- metrics_dict[name] = np.average(value_list)
+ for name, value_list in metrics_value_dict.items():
+ metrics_value_dict[name] = np.average(value_list)
- # TODO: handle some exception
self.datanode_client.record_model_metrics(
model_id=self.model_id,
trial_id=self.trial_id,
- metrics=list(metrics_dict.keys()),
- values=list(metrics_dict.values())
+ metrics=list(metrics_value_dict.keys()),
+ values=list(metrics_value_dict.values())
)
val_loss = np.average(val_loss)
logger.info('Epoch: {0} Vali Loss: {1:.7f}'.format(epoch + 1,
val_loss))
- return val_loss, metrics_dict
+ return val_loss, metrics_value_dict
def start(self) -> float:
try:
diff --git a/mlnode/iotdb/mlnode/storage.py b/mlnode/iotdb/mlnode/storage.py
index 68392be53b..ae93cc991b 100644
--- a/mlnode/iotdb/mlnode/storage.py
+++ b/mlnode/iotdb/mlnode/storage.py
@@ -26,6 +26,7 @@ from pylru import lrucache
from iotdb.mlnode.config import descriptor
from iotdb.mlnode.exception import ModelNotExistError
+from iotdb.mlnode.log import logger
class ModelStorage(object):
@@ -34,7 +35,8 @@ class ModelStorage(object):
if not os.path.exists(self.__model_dir):
try:
os.mkdir(self.__model_dir)
- except PermissionError as e: # TODO: handle storage permission
+ except PermissionError as e:
+ logger.error(e)
raise e
self.__model_cache =
lrucache(descriptor.get_config().get_mn_model_storage_cache_size())
diff --git a/mlnode/test/test_create_forecast_dataset.py
b/mlnode/test/test_create_forecast_dataset.py
index 69307e77a1..49e2d177e8 100644
--- a/mlnode/test/test_create_forecast_dataset.py
+++ b/mlnode/test/test_create_forecast_dataset.py
@@ -16,12 +16,11 @@
# under the License.
#
import os
-import torch
+
import requests
-from iotdb.mlnode.data_access.factory import create_forecast_dataset
-from iotdb.mlnode.exception import BadConfigValueError, MissingConfigError
from iotdb.mlnode.data_access.enums import DatasetType, DataSourceType
+from iotdb.mlnode.data_access.factory import create_forecast_dataset
def test_create_dataset():
diff --git a/mlnode/test/test_create_forecast_model.py
b/mlnode/test/test_create_forecast_model.py
index 0e4e479c83..08c0fd10fb 100644
--- a/mlnode/test/test_create_forecast_model.py
+++ b/mlnode/test/test_create_forecast_model.py
@@ -17,9 +17,9 @@
#
import torch
+from iotdb.mlnode.algorithm.enums import ForecastTaskType
from iotdb.mlnode.algorithm.factory import create_forecast_model
from iotdb.mlnode.exception import BadConfigValueError
-from iotdb.mlnode.algorithm.enums import ForecastTaskType
def test_create_forecast_model():
@@ -34,7 +34,7 @@ def test_create_forecast_model():
model, model_config = create_forecast_model(model_name='nbeats',
kernel_size=25, d_model=64)
assert model_config['d_model'] == 64
- assert 'kernel_size' not in model_config # config kernel_size not belongs
to nbeats model
+ assert 'kernel_size' not in model_config # config kernel_size not belongs
to nbeats model
def test_bad_config_model1():
@@ -68,6 +68,7 @@ def test_bad_config_model4():
except BadConfigValueError as e:
print(e) # ('forecast_task_type', 'dummy_task')
+
def test_bad_config_model5():
try:
model, models = create_forecast_model(model_name='dlinear',
diff --git a/mlnode/test/test_model_storage.py
b/mlnode/test/test_model_storage.py
index 8e2cbf4623..1b18f974aa 100644
--- a/mlnode/test/test_model_storage.py
+++ b/mlnode/test/test_model_storage.py
@@ -23,8 +23,8 @@ import time
import torch.nn as nn
from iotdb.mlnode.config import config
-from iotdb.mlnode.storage import model_storage
from iotdb.mlnode.exception import ModelNotExistError
+from iotdb.mlnode.storage import model_storage
class ExampleModel(nn.Module):
diff --git a/mlnode/test/test_parse_training_request.py
b/mlnode/test/test_parse_training_request.py
index 8fe193f53d..bafa53695d 100644
--- a/mlnode/test/test_parse_training_request.py
+++ b/mlnode/test/test_parse_training_request.py
@@ -15,8 +15,8 @@
# specific language governing permissions and limitations
# under the License.
#
-from iotdb.mlnode.parser import parse_training_request
from iotdb.mlnode.exception import MissingConfigError, WrongTypeConfigError
+from iotdb.mlnode.parser import parse_training_request
from iotdb.thrift.mlnode.ttypes import TCreateTrainingTaskReq
@@ -26,7 +26,7 @@ def test_parse_training_request():
model_configs = {
'task_class': 'forecast_training_task',
'source_type': 'thrift',
- 'dataset_type': 'window', # or use DatasetType.WINDOW,
+ 'dataset_type': 'window', # or use DatasetType.WINDOW,
'filename': 'ETTh1.csv',
'time_embed': 'h',
'input_len': 96,
diff --git a/thrift-mlnode/src/main/thrift/mlnode.thrift
b/thrift-mlnode/src/main/thrift/mlnode.thrift
index 916022e973..abadc79576 100644
--- a/thrift-mlnode/src/main/thrift/mlnode.thrift
+++ b/thrift-mlnode/src/main/thrift/mlnode.thrift
@@ -31,7 +31,7 @@ struct TCreateTrainingTaskReq {
struct TDeleteModelReq {
1: required string modelId
- 2: optional string trailId
+ 2: optional string trialId
}
struct TForecastReq {
