pyflink1.14.0读取模型实时预测
时间: 2023-12-15 07:03:50 浏览: 22
PyFlink 1.14.0可以通过使用Python API和Flink SQL来读取模型进行实时预测。
使用Python API:
1. 加载模型
```python
import tensorflow as tf
model = tf.keras.models.load_model('path/to/model')
```
2. 定义UDF函数来进行模型预测
```python
from pyflink.table.udf import udf, DataTypes
import numpy as np
@udf(input_types=[DataTypes.FLOAT(), DataTypes.FLOAT()], result_type=DataTypes.FLOAT())
def predict(x1, x2):
x = np.array([x1, x2])
y = model.predict(x.reshape(1, -1))
return float(y[0])
```
3. 使用UDF函数进行实时预测
```python
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, DataTypes
env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)
t_env.register_function("predict", predict)
source = env.from_elements((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
t_env.from_data_stream(source, ['x1', 'x2']) \
.select("x1, x2, predict(x1, x2) as y") \
.execute_and_collect()
```
使用Flink SQL:
1. 将模型转换为UDF函数
```python
import tensorflow as tf
model = tf.keras.models.load_model('path/to/model')
def predict(x1, x2):
x = np.array([x1, x2])
y = model.predict(x.reshape(1, -1))
return float(y[0])
from pyflink.table import ScalarFunction
class Predict(ScalarFunction):
def eval(self, x1, x2):
return predict(x1, x2)
t_env.create_temporary_function("predict", Predict())
```
2. 使用Flink SQL进行实时预测
```python
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment, DataTypes
env = StreamExecutionEnvironment.get_execution_environment()
t_env = StreamTableEnvironment.create(env)
source = env.from_elements((1.0, 2.0), (3.0, 4.0), (5.0, 6.0))
t_env.from_data_stream(source, ['x1', 'x2']) \
.select("x1, x2, predict(x1, x2) as y") \
.execute_and_collect()
```