在Spark Pipeline中部署Tensorflow/Keras模型

pip install tensorflow
pip install keras

from pyspark.sql import SparkSession
import tensorflow as tf
from tensorflow import keras

spark = SparkSession.builder.appName("TensorFlowKeras").getOrCreate()
data = spark.read.format("csv").option("header", "true").load("data.csv")
# 进行数据预处理操作

model = keras.Sequential()
model.add(keras.layers.Dense(64, activation='relu', input_dim=100))
model.add(keras.layers.Dense(64, activation='relu'))
model.add(keras.layers.Dense(10, activation='softmax'))

# 将数据转换为TensorFlow/Keras所需的格式
train_data = data.rdd.map(lambda row: (row.features, row.label))
train_data = train_data.toDF(["features", "label"])

# 使用Spark的分布式计算能力进行训练和评估
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_data, epochs=10, batch_size=32)

model.save("model")

class TensorFlowModelTransformer(keras.Model, spark.ml.Transformer):
    def __init__(self, model_path):
        super(TensorFlowModelTransformer, self).__init__()
        self.model_path = model_path
        self.model = keras.models.load_model(model_path)

    def _transform(self, dataset):
        # 进行模型推理操作
        predictions = self.model.predict(dataset)
        return predictions

# 创建模型转换器
model_transformer = TensorFlowModelTransformer("model")

# 将模型转换器添加到Spark Pipeline中
pipeline = spark.ml.Pipeline(stages=[model_transformer])
pipeline_model = pipeline.fit(data)
predictions = pipeline_model.transform(data)