我目前正在尝试从Packtpub上的“ TensorFlow机器学习项目”一书中运行示例脚本。我收到以下错误...
Py4JJavaError: An error occurred while calling o99.collectToPython.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2.0 failed 4 times, most recent failure: Lost task 0.3 in stage 2.0 (TID 5, spark-deeplearning-w-3.us-central1-a.c.deeplearnig-spark.internal, executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 334, in _batched
for item in iterator:
File "<string>", line 1, in <lambda>
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 83, in <lambda>
return lambda *a: toInternal(f(*a))
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
return f(*args, **kwargs)
File "/opt/conda/anaconda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 158, in resizeImageAsRow
File "/opt/conda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 121, in imageStructToArray
imType = imageType(imageRow)
File "/opt/conda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 111, in imageType
return sparkModeLookup[imageRow.mode]
KeyError: 16
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1890)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1877)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2111)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2060)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2049)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:365)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3257)
at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3254)
at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364)
at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363)
at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3254)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 377, in main
process()
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 372, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 345, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 141, in dump_stream
for obj in iterator:
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 334, in _batched
for item in iterator:
File "<string>", line 1, in <lambda>
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/worker.py", line 83, in <lambda>
return lambda *a: toInternal(f(*a))
File "/usr/lib/spark/python/lib/pyspark.zip/pyspark/util.py", line 99, in wrapper
return f(*args, **kwargs)
File "/opt/conda/anaconda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 158, in resizeImageAsRow
File "/opt/conda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 121, in imageStructToArray
imType = imageType(imageRow)
File "/opt/conda/lib/python3.6/site-packages/sparkdl/image/imageIO.py", line 111, in imageType
return sparkModeLookup[imageRow.mode]
KeyError: 16
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:452)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:81)
at org.apache.spark.sql.execution.python.PythonUDFRunner$$anon$1.read(PythonUDFRunner.scala:64)
at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:406)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage2.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:255)
at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:247)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:836)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:288)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
在我的gcp群集上运行此脚本时会发生这种情况...
from pyspark.sql import SparkSession
import pyspark.sql.functions as f
import sparkdl as dl
from pyspark.ml.image import ImageSchema
from pyspark.ml.classification import LogisticRegression
from pyspark.ml import Pipeline
spark = SparkSession.builder \
.appName("ImageClassification") \
.config("spark.executor.memory", "70g") \
.config("spark.driver.memory", "50g") \
.config("spark.memory.offHeap.enabled",True) \
.config("spark.memory.offHeap.size","16g") \
.getOrCreate()
dfbuses = ImageSchema.readImages('gs://car-buses/data/buses/').withColumn('label', f.lit(0))
dfcars = ImageSchema.readImages('gs://car-buses/data/cars/').withColumn('label', f.lit(1))
trainDFbuses, testDFbuses = dfbuses.randomSplit([0.60,0.40], seed = 123)
trainDFcars, testDFcars = dfcars.randomSplit([0.60,0.40], seed = 122)
trainDF = trainDFbuses.unionAll(trainDFcars)
testDF = testDFbuses.unionAll(testDFcars)
vectorizer = dl.DeepImageFeaturizer(inputCol="image", outputCol="features", modelName="InceptionV3")
logreg = LogisticRegression(maxIter=30, labelCol="label")
pipeline = Pipeline(stages=[vectorizer, logreg])
pipeline_model = pipeline.fit(trainDF)
上面脚本中包含的最后一行是发生错误的地方。接受训练的数据是形式为spark的数据框架，其中的label列是二进制分类，其分类为0（公共汽车）和1（汽车）...
+--------------------+-----+
| image|label|
+--------------------+-----+
|[gs://car-buses/d...| 0|
|[gs://car-buses/d...| 0|
|[gs://car-buses/d...| 0|
|[gs://car-buses/d...| 0|
|[gs://car-buses/d...| 0|
+--------------------+-----+
“图像”列是表格的一行...
Row(image=Row(origin='gs://car-buses/data/buses/images.jpeg', height=84, width=126, nChannels=3, mode=16, data=bytearray(b'\xd6\xde\xde\xd6\...
我正在运行的GCP集群有5个节点，其中1个主节点和4个从节点。下面是我在GCP CLI中运行的gcloud命令，以创建环境...
gcloud beta dataproc clusters create spark-deeplearning --image-version 1.4 --zone us-central1-a --master-machine-type n1-standard-4 --master-boot-disk-size 500 --worker-machine-type n1-standard-4 --num-workers 4 --worker-boot-disk-size 500 --metadata=MINICONDA_VERSION=4.3.30 --optional-components=ANACONDA,JUPYTER --enable-component-gateway --initialization-actions gs://initializations/creata_sparkdl_cluster.sh
我包含在上述gcloud命令中的脚本中的初始化文件是一个shell脚本，该脚本下载了必要的conda（如有必要，我可以将其上传到注释中）以及运行运行sparkdl和“- metadata = MINICONDA_VERSION = 4.3.30“使所有节点上的python版本保持一致。
我漫无目的地搜索了一个接近我的错误，但是我发现的唯一错误来自这个stack thread，它表示超出了“开销限制”。我使用的错误与此有所不同，仅提及阶段失败。
我怀疑错误可能出在我用于集群中库的版本中，但是我不确定。我还在具有相似依赖性的Ubuntu 16 VM上尝试了该示例，并且发生了相同的错误。
我要实现的目标是在Inception v3模型上使用sparkdl和tensorflowOnSpark拟合对象检测模型。