apache spark

qhhrdooz  于 2021-05-27  发布在  Spark
关注(0)|答案(0)|浏览(413)

我遇到了一个名为spark\u tensorflow\u distributor的pypi包，其中有一个在spark集群上运行tensorflow作业的示例。
为了在spark群集上创建tensorflow作业，我执行了以下示例：

from spark_tensorflow_distributor import MirroredStrategyRunner

def train():
    import tensorflow_datasets as tfds
    import tensorflow as tf
    BUFFER_SIZE = 10000
    BATCH_SIZE = 64

    def make_datasets_unbatched():
        # Scaling MNIST data from (0, 255] to (0., 1.]
        def scale(image, label):
            image = tf.cast(image, tf.float32)
            image /= 255
            return image, label
        datasets, info = tfds.load(
            name='mnist',
            with_info=True,
            as_supervised=True,
        )
        return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE)

    def build_and_compile_cnn_model():
        model = tf.keras.Sequential([
            tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(10, activation='softmax'),
        ])
        model.compile(
            loss=tf.keras.losses.sparse_categorical_crossentropy,
            optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),
            metrics=['accuracy'],
        )
        return model

    GLOBAL_BATCH_SIZE = 64 * 8
    train_datasets = make_datasets_unbatched().batch(GLOBAL_BATCH_SIZE).repeat()
    options = tf.data.Options()
    options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.DATA
    train_datasets = train_datasets.with_options(options)
    multi_worker_model = build_and_compile_cnn_model()
    multi_worker_model.fit(x=train_datasets, epochs=10, steps_per_epoch=5)
    return tf.config.experimental.list_physical_devices('GPU')
%%time
MirroredStrategyRunner(num_slots=2).run(train)

但是，我得到以下错误：

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<timed eval> in <module>

~/anaconda3/lib/python3.8/site-packages/spark_tensorflow_distributor/mirrored_strategy_runner.py in run(self, train_fn,**kwargs)
    227         self._logger.info(
    228             'View Spark executor stderr logs to inspect training...')
--> 229         result = self.sc.parallelize(range(self._num_tasks), self._num_tasks) \
    230             .barrier() \
    231             .mapPartitions(spark_task_program) \

~/anaconda3/lib/python3.8/site-packages/pyspark/rdd.py in collect(self)
    887         """
    888         with SCCallSiteSync(self.context) as css:
--> 889             sock_info = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
    890         return list(_load_from_socket(sock_info, self._jrdd_deserializer))
    891 

~/anaconda3/lib/python3.8/site-packages/py4j/java_gateway.py in __call__(self, *args)
   1302 
   1303         answer = self.gateway_client.send_command(command)
-> 1304         return_value = get_return_value(
   1305             answer, self.gateway_client, self.target_id, self.name)
   1306 

~/anaconda3/lib/python3.8/site-packages/pyspark/sql/utils.py in deco(*a,**kw)
    129     def deco(*a,**kw):
    130         try:
--> 131             return f(*a,**kw)
    132         except py4j.protocol.Py4JJavaError as e:
    133             converted = convert_exception(e.java_exception)

~/anaconda3/lib/python3.8/site-packages/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    324             value = OUTPUT_CONVERTER[type](answer[2:], gateway_client)
    325             if answer[1] == REFERENCE_TYPE:
--> 326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
    328                     format(target_id, ".", name), value)

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Could not recover from a failed barrier ResultStage. Most recent failure reason: Stage failed because barrier task ResultTask(0, 1) finished unsuccessfully.
org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 605, in main
    process()
  File "/usr/local/spark/python/lib/pyspark.zip/pyspark/worker.py", line 595, in process
    out_iter = func(split_index, iterator)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/pyspark/rdd.py", line 2596, in pipeline_func
    return func(split, prev_func(split, iterator))
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/pyspark/rdd.py", line 2548, in func
    return f(iterator)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/spark_tensorflow_distributor/mirrored_strategy_runner.py", line 348, in wrapped_train_fn
    result = run_tensorflow_program(train_fn, use_custom_strategy,
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/spark_tensorflow_distributor/mirrored_strategy_runner.py", line 284, in _run_tensorflow_program
    return train_fn(**kwargs)
  File "<ipython-input-6-d99402e86a93>", line 41, in train
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 114, in _method_wrapper
    return dc.run_distribute_coordinator(
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_coordinator.py", line 859, in run_distribute_coordinator
    return _run_single_worker(worker_fn, strategy, cluster_spec, task_type,
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_coordinator.py", line 360, in _run_single_worker
    return worker_fn(strategy)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 115, in <lambda>
    lambda _: method(self, *args,**kwargs),
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py", line 1098, in fit
    tmp_logs = train_function(iterator)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 780, in __call__
    result = self._call(*args,**kwds)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 840, in _call
    return self._stateless_fn(*args,**kwds)
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 2829, in __call__
    return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1843, in _filtered_call
    return self._call_flat(
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1923, in _call_flat
    return self._build_call_outputs(self._inference_function.call(
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 545, in call
    outputs = execute.execute(
  File "/Users/vinhdiesal/anaconda3/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.UnknownError: 2 root error(s) found.
  (0) Unknown:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[node sequential/conv2d/Conv2D (defined at /Users/vinhdiesal/anaconda3/lib/python3.8/threading.py:932) ]]
     [[GroupCrossDeviceControlEdges_0/Identity_4/_39]]
  (1) Unknown:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
     [[node sequential/conv2d/Conv2D (defined at /Users/vinhdiesal/anaconda3/lib/python3.8/threading.py:932) ]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1008]

Errors may have originated from an input operation.
Input Source operations connected to node sequential/conv2d/Conv2D:
 cond_1/Identity (defined at <ipython-input-6-d99402e86a93>:41)

Input Source operations connected to node sequential/conv2d/Conv2D:
 cond_1/Identity (defined at <ipython-input-6-d99402e86a93>:41)

Function call stack:
train_function -> train_function

    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:503)
    at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:638)
    at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:621)
    at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:456)
    at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
    at scala.collection.Iterator.foreach(Iterator.scala:941)
    at scala.collection.Iterator.foreach$(Iterator.scala:941)
    at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28)
    at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62)
    at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49)
    at scala.collection.TraversableOnce.to(TraversableOnce.scala:315)
    at scala.collection.TraversableOnce.to$(TraversableOnce.scala:313)
    at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:307)
    at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:307)
    at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28)
    at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:294)
    at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:288)
    at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28)
    at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1004)
    at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2133)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
    at org.apache.spark.scheduler.Task.run(Task.scala:127)
    at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:444)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:447)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

    at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2023)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1972)
    at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1971)
    at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
    at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1971)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskCompletion(DAGScheduler.scala:1760)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2200)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2152)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2141)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:752)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2093)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2133)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2158)
    at org.apache.spark.rdd.RDD.$anonfun$collect$1(RDD.scala:1004)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:388)
    at org.apache.spark.rdd.RDD.collect(RDD.scala:1003)
    at org.apache.spark.api.python.PythonRDD$.collectAndServe(PythonRDD.scala:168)
    at org.apache.spark.api.python.PythonRDD.collectAndServe(PythonRDD.scala)
    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)

我遇到的问题是什么？如何解决？

apache-sparkpysparktensorflow

暂无答案！

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