任何人都知道为什么我一直得到这个错误在Jupyter笔记本电脑？？？我一直试图加载我的Tensorflow模型到Apache Spark vis SparlFlow，但我似乎不知道如何得到过去这个错误。任何帮助将不胜感激。
第一个木星细胞：

from sparkflow.graph_utils import build_graph
from sparkflow.tensorflow_async import SparkAsyncDL
import tensorflow as tf
from pyspark.ml.feature import VectorAssembler, OneHotEncoder
from pyspark.ml.pipeline import Pipeline

 from pyspark.sql import SparkSession
    from tensorflow.keras import layers
    from tensorflow.keras import losses

第二木星细胞：

def lstm_model(X_train, y_train):
    # Reshapes to input neuron
    inputs= keras.Input(shape = (X_train.shape[1], 1))\
    #Training Layers
    x_1 = layers.LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))(inputs)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.LSTM(units = 50, return_sequences = True)(x_1)
    x_1 = layers.Dropout(0.2)(x_1)
    x_1 = layers.Flatten()(x_1)

    # 1 output neuron for each column prediction
    output = Dense(units=1)(x_1)
    return losses.MeanSquaredError(y_train,output)

第三木星细胞：

def dataframe_input(pandas_dataframe):

    train_data = pandas_dataframe[self.column_name].values

    # Reshaping to a 2D array
    train_data = train_data.reshape(-1,1)
    print(train_data.dtype)
    print(type(train_data))
    print(train_data.shape) 

    # Feature Scaling
    scaler = MinMaxScaler(feature_range=(0, 1))
    scaled_train_data =scaler.fit_transform(train_data)

    # Initialzing each x_train and y_train datasets for each column
    X_train = []
    y_train = []

    # Appending scaled training data to each dataset
    for i in range(self.timesteps, len(train_data)):
        X_train.append(scaled_train_data[i - self.timesteps:i, 0])
        y_train.append(scaled_train_data[i, 0])

    # Numpy array creation, Keras requires numpy arrays for Inputs
    X_train, y_train = np.array(X_train, dtype=int), np.array(y_train)
    print(X_train.shape)
    print(X_train.dtype)

    # Reshaping to a 3D matrix (970, 30, 1)
    #X_train = np.reshape(X_train, (X_train[0], X_train[1], 1))
    print(X_train.shape)
    return X_train, y_train

第四木星细胞（我得到的错误）：

# Spark Session

# In order to use APIs of SQL, HIVE, and Streaming, no need to create separate contexts as sparkSession includes all the APIs.

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .getOrCreate()

# Reading CSVto a Spark DataFrame

df = spark.read.option("inferSchema", "true").csv('"../csv_test_files/stats.csv"')

# Convert the Spark dataframe into a Pandas Dataframe

pandas_dataframe = df.select("*").toPandas()

# Get the input and ouput data for passing to the model

X_train, y_train = dataframe_input(pandas_dataframe)

错误输出：

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-25-5143cc437b69> in <module>
      3 spark = SparkSession \
      4     .builder \
----> 5     .appName("Python Spark SQL basic example") \
      6     .getOrCreate()
      7 

~/anaconda3/lib/python3.7/site-packages/pyspark/sql/session.py in getOrCreate(self)
    171                     for key, value in self._options.items():
    172                         sparkConf.set(key, value)
--> 173                     sc = SparkContext.getOrCreate(sparkConf)
    174                     # This SparkContext may be an existing one.
    175                     for key, value in self._options.items():

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in getOrCreate(cls, conf)
    365         with SparkContext._lock:
    366             if SparkContext._active_spark_context is None:
--> 367                 SparkContext(conf=conf or SparkConf())
    368             return SparkContext._active_spark_context
    369 

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in __init__(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer, conf, gateway, jsc, profiler_cls)
    134         try:
    135             self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
--> 136                           conf, jsc, profiler_cls)
    137         except:
    138             # If an error occurs, clean up in order to allow future SparkContext creation:

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer, conf, jsc, profiler_cls)
    196 
    197         # Create the Java SparkContext through Py4J
--> 198         self._jsc = jsc or self._initialize_context(self._conf._jconf)
    199         # Reset the SparkConf to the one actually used by the SparkContext in JVM.
    200         self._conf = SparkConf(_jconf=self._jsc.sc().conf())

~/anaconda3/lib/python3.7/site-packages/pyspark/context.py in _initialize_context(self, jconf)
    304         Initialize SparkContext in function to allow subclass specific initialization
    305         """
--> 306         return self._jvm.JavaSparkContext(jconf)
    307 
    308     @classmethod

~/anaconda3/lib/python3.7/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1523         answer = self._gateway_client.send_command(command)
   1524         return_value = get_return_value(
-> 1525             answer, self._gateway_client, None, self._fqn)
   1526 
   1527         for temp_arg in temp_args:

~/anaconda3/lib/python3.7/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling None.org.apache.spark.api.java.JavaSparkContext.
: java.net.BindException: Can't assign requested address: Service 'sparkDriver' failed after 16 retries (on a random free port)! Consider explicitly setting the appropriate binding address for the service 'sparkDriver' (for example spark.driver.bindAddress for SparkDriver) to the correct binding address.
    at java.base/sun.nio.ch.Net.bind0(Native Method)
    at java.base/sun.nio.ch.Net.bind(Net.java:461)
    at java.base/sun.nio.ch.Net.bind(Net.java:453)
    at java.base/sun.nio.ch.ServerSocketChannelImpl.bind(ServerSocketChannelImpl.java:227)
    at io.netty.channel.socket.nio.NioServerSocketChannel.doBind(NioServerSocketChannel.java:128)
    at io.netty.channel.AbstractChannel$AbstractUnsafe.bind(AbstractChannel.java:558)
    at io.netty.channel.DefaultChannelPipeline$HeadContext.bind(DefaultChannelPipeline.java:1283)
    at io.netty.channel.AbstractChannelHandlerContext.invokeBind(AbstractChannelHandlerContext.java:501)
    at io.netty.channel.AbstractChannelHandlerContext.bind(AbstractChannelHandlerContext.java:486)
    at io.netty.channel.DefaultChannelPipeline.bind(DefaultChannelPipeline.java:989)
    at io.netty.channel.AbstractChannel.bind(AbstractChannel.java:254)
    at io.netty.bootstrap.AbstractBootstrap$2.run(AbstractBootstrap.java:364)
    at io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:163)
    at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:403)
    at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:463)
    at io.netty.util.concurrent.SingleThreadEventExecutor$5.run(SingleThreadEventExecutor.java:858)
    at io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:138)
    at java.base/java.lang.Thread.run(Thread.java:834)