python - Py4JJavaError：调用 None.org.apache.spark.api.java.JavaSparkContext 时出错

Question

任何人都知道为什么我会在 Jupyter Notebooks 中遇到此错误？？？我一直在尝试将我的 Tensorflow 模型加载到 Apache Spark vis SparlFlow 中，但我似乎无法弄清楚如何克服这个错误。任何帮助将非常感激。

第一个 Jupyter 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

第二个 Jupyter 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)

第三个 Jupyter 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

第四个 Jupyter 单元（我得到错误的地方）：

# 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)

标签： pythonapache-sparktensorflowpysparkjupyter-notebook

Answer 1

好像你有太多正在运行的 SparkSessions。在默认配置中，您只能有 16 次，因为有 16 次重试才能为 Spark 的作业概览页面获取端口。

这可能是因为您在一个繁忙的集群上工作，有许多用户在运行作业，或者，例如，因为您有很多运行 SparkSessions 的 Jupyter 笔记本。

根据您使用的资源管理器，有不同的方法可以检查当前打开了多少 SparkSession。

为了避免该问题，您还可以增加重试次数以查找 Spark 在创建 SparkSession 时创建的未使用端口。为此，您必须将 config 参数设置spark.port.maxRetries为更大的值（另请参见此处：https ://spark.apache.org/docs/latest/configuration.html ）：

spark = SparkSession.builder.config('spark.port.maxRetries', 100).getOrCreate()