Apache spark 如何解决Elephas basic示例上的java错误？
我使用Pypark和Elephas，但目前不起作用。我尝试了Elephas'doc Github上给出的示例。请注意，在PySpark控制台中，我与Keras和Pandas的代码可以工作（但不使用PySpark库）。但是，在使用Elephas接口Keras和PySpark库时给出的示例不起作用，我根本不知道如何解决这个问题。我所有的PySpark配置都使用Python 3.7 以下是我的脚本内容和错误消息：Apache spark 如何解决Elephas basic示例上的java错误？,apache-spark,keras,elephas,Apache Spark,Keras,Elephas,我使用Pypark和Elephas，但目前不起作用。我尝试了Elephas'doc Github上给出的示例。请注意，在PySpark控制台中，我与Keras和Pandas的代码可以工作（但不使用PySpark库）。但是，在使用Elephas接口Keras和PySpark库时给出的示例不起作用，我根本不知道如何解决这个问题。我所有的PySpark配置都使用Python 3.7 以下是我的脚本内容和错误消息： from pyspark import SparkContext, SparkConf
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName('Elephas_App').setMaster('local[4]')
#ici local[4] indique qu'on execute l'application Elephas_app sur la machine locale seule et avec 4 coeurs
sc = SparkContext(conf=conf)
#Chargement des packages
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(128, input_dim=784))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=SGD())
##INTEGRATION ELEPHAS
from elephas.utils.rdd_utils import to_simple_rdd
rdd = to_simple_rdd(sc, x_train, y_train)
from elephas.spark_model import SparkModel
spark_model = SparkModel(model, frequency='epoch', mode='asynchronous')
spark_model.fit(rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1)
>>> Distribute load
Traceback (most recent call last):
File "/home/admin-tv/deeplearning/elephas_ann.py", line 100, in <module>
spark_model.fit(rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1)
File "/usr/local/lib/python3.7/dist-packages/elephas/spark_model.py", line 151, in fit
self._fit(rdd, epochs, batch_size, verbose, validation_split)
File "/usr/local/lib/python3.7/dist-packages/elephas/spark_model.py", line 182, in _fit
rdd.mapPartitions(worker.train).collect()
File "/opt/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 816, in collect
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.lang.IllegalArgumentException: Unsupported class file major version 55
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:166)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:148)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:136)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:237)
at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:49)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:517)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:500)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:134)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:134)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:134)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:500)
at org.apache.xbean.asm6.ClassReader.readCode(ClassReader.java:2175)
at org.apache.xbean.asm6.ClassReader.readMethod(ClassReader.java:1238)
at org.apache.xbean.asm6.ClassReader.accept(ClassReader.java:631)
at org.apache.xbean.asm6.ClassReader.accept(ClassReader.java:355)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:307)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:306)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:306)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2326)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2100)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
from pyspark import SparkContext, SparkConf
conf = SparkConf().setAppName('Elephas_App').setMaster('local[4]')
#ici local[4] indique qu'on execute l'application Elephas_app sur la machine locale seule et avec 4 coeurs
sc = SparkContext(conf=conf)
#Chargement des packages
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
model = Sequential()
model.add(Dense(128, input_dim=784))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(128))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer=SGD())
##INTEGRATION ELEPHAS
from elephas.utils.rdd_utils import to_simple_rdd
rdd = to_simple_rdd(sc, x_train, y_train)
from elephas.spark_model import SparkModel
spark_model = SparkModel(model, frequency='epoch', mode='asynchronous')
spark_model.fit(rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1)
>>> Distribute load
Traceback (most recent call last):
File "/home/admin-tv/deeplearning/elephas_ann.py", line 100, in <module>
spark_model.fit(rdd, epochs=20, batch_size=32, verbose=0, validation_split=0.1)
File "/usr/local/lib/python3.7/dist-packages/elephas/spark_model.py", line 151, in fit
self._fit(rdd, epochs, batch_size, verbose, validation_split)
File "/usr/local/lib/python3.7/dist-packages/elephas/spark_model.py", line 182, in _fit
rdd.mapPartitions(worker.train).collect()
File "/opt/spark/python/lib/pyspark.zip/pyspark/rdd.py", line 816, in collect
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
File "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", line 328, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.lang.IllegalArgumentException: Unsupported class file major version 55
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:166)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:148)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:136)
at org.apache.xbean.asm6.ClassReader.<init>(ClassReader.java:237)
at org.apache.spark.util.ClosureCleaner$.getClassReader(ClosureCleaner.scala:49)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:517)
at org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply(ClosureCleaner.scala:500)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:134)
at scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply(HashMap.scala:134)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:236)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap$$anon$1.foreach(HashMap.scala:134)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn(ClosureCleaner.scala:500)
at org.apache.xbean.asm6.ClassReader.readCode(ClassReader.java:2175)
at org.apache.xbean.asm6.ClassReader.readMethod(ClassReader.java:1238)
at org.apache.xbean.asm6.ClassReader.accept(ClassReader.java:631)
at org.apache.xbean.asm6.ClassReader.accept(ClassReader.java:355)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:307)
at org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply(ClosureCleaner.scala:306)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:306)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:162)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2326)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2100)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:990)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:385)
at org.apache.spark.rdd.RDD.collect(RDD.scala:989)
at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:166)
at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:566)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
>>分配负载
回溯（最近一次呼叫最后一次）：
文件“/home/admin tv/deeplearning/elephas_ann.py”，第100行，in
spark_model.fit（rdd，epochs=20，batch_size=32，verbose=0，validation_split=0.1）
文件“/usr/local/lib/python3.7/dist-packages/elephas/spark_model.py”，第151行，适合
自适配（rdd、年代、批量大小、详细、验证分割）
文件“/usr/local/lib/python3.7/dist-packages/elephas/spark\u model.py”，第182行，in\u-fit
映射分区（worker.train.collect（））
文件“/opt/spark/python/lib/pyspark.zip/pyspark/rdd.py”，第816行，在collect中
文件“/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py”，第1257行，in_u调用__
文件“/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py”，第328行，在get_return_值中
py4j.protocol.Py4JJavaError:调用z:org.apache.spark.api.python.PythonRDD.collectAndServe时出错。
：java.lang.IllegalArgumentException:不支持的类文件主版本55
位于org.apache.xbean.asm6.ClassReader（ClassReader.java:166）
位于org.apache.xbean.asm6.ClassReader（ClassReader.java:148）
位于org.apache.xbean.asm6.ClassReader（ClassReader.java:136）
位于org.apache.xbean.asm6.ClassReader（ClassReader.java:237）
位于org.apache.spark.util.ClosureCleaner$.getClassReader（ClosureCleaner.scala:49）
在org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply上（ClosureCleaner.scala:517）
在org.apache.spark.util.FieldAccessFinder$$anon$3$$anonfun$visitMethodInsn$2.apply上（ClosureCleaner.scala:500）
在scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply处（TraversableLike.scala:733）
位于scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply（HashMap.scala:134）
位于scala.collection.mutable.HashMap$$anon$1$$anonfun$foreach$2.apply（HashMap.scala:134）
位于scala.collection.mutable.HashTable$class.foreachEntry（HashTable.scala:236）
位于scala.collection.mutable.HashMap.foreachEntry（HashMap.scala:40）
位于scala.collection.mutable.HashMap$$anon$1.foreach（HashMap.scala:134）
位于scala.collection.TraversableLike$WithFilter.foreach（TraversableLike.scala:732）
在org.apache.spark.util.FieldAccessFinder$$anon$3.visitMethodInsn上（ClosureCleaner.scala:500）
位于org.apache.xbean.asm6.ClassReader.readCode（ClassReader.java:2175）
位于org.apache.xbean.asm6.ClassReader.readMethod（ClassReader.java:1238）
位于org.apache.xbean.asm6.ClassReader.accept（ClassReader.java:631）
位于org.apache.xbean.asm6.ClassReader.accept（ClassReader.java:355）
在org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply（ClosureCleaner.scala:307）
在org.apache.spark.util.ClosureCleaner$$anonfun$org$apache$spark$util$ClosureCleaner$$clean$14.apply（ClosureCleaner.scala:306）
位于scala.collection.immutable.List.foreach（List.scala:392）
位于org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean（ClosureCleaner.scala:306）
位于org.apache.spark.util.ClosureCleaner$.clean（ClosureCleaner.scala:162）
位于org.apache.spark.SparkContext.clean（SparkContext.scala:2326）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2100）
位于org.apache.spark.SparkContext.runJob（SparkContext.scala:2126）
位于org.apache.spark.rdd.rdd$$anonfun$collect$1.apply（rdd.scala:990）
位于org.apache.spark.rdd.RDDOperationScope$.withScope（RDDOperationScope.scala:151）
位于org.apache.spark.rdd.RDDOperationScope$.withScope（RDDOperationScope.scala:112）
位于org.apache.spark.rdd.rdd.withScope（rdd.scala:385）
位于org.apache.spark.rdd.rdd.collect（rdd.scala:989）
位于org.apache.spark.api.PythonRDD$.collectAndServe（PythonRDD.scala:166）
位于org.apache.spark.api.python.PythonRDD.collectAndServe（PythonRDD.scala）
位于java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0（本机方法）
位于java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke（NativeMethodAccessorImpl.java:62）
位于java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke（DelegatingMethodAccessorImpl.java:43）
位于java.base/java.lang.reflect.Method.invoke（Method.java:566）
位于py4j.reflection.MethodInvoker.invoke（MethodInvoker.java:244）
位于py4j.reflection.ReflectionEngine.invoke（ReflectionEngine.java:357）
Java 11不适用于Spark 2.4，显然它适用于PySpark 3，请查看。经过一些研究，我切换到Java 8并删除了我的Java 11安装。然后，我手动重写了python2.7下的所有安装。现在我认为它起作用了。 我还得把剧本改编得更适合我的x_系列和y_系列。 我使用keras的predict（）函数得到了一个我认为是一致的数组 Java11不适用于Spark 2.4，显然它适用于PySpark 3，请查看