尝试从 S3 加载模型时，无法从链中的任何提供商加载 AWS 凭证 - 错误

Posted 2023-04-15

【中文标题】尝试从 S3 加载模型时，无法从链中的任何提供商加载 AWS 凭证 - 错误

【英文标题】：Unable to load AWS credentials from any provider in the chain - error - when trying to load model from S3

【发布时间】：2019-09-28 06:34:37

【问题描述】：

我有一个 MLLib 模型保存在 S3 上的一个文件夹中，例如 bucket-name/test-model。现在，我有一个火花集群（假设现在在一台机器上）。我正在运行以下命令来加载模型：

pyspark --packages com.amazonaws:aws-java-sdk:1.7.4,org.apache.hadoop:hadoop-aws:2.7.3

那么，

sc.setSystemProperty("com.amazonaws.services.s3.enableV4", "true")
hadoopConf = sc._jsc.hadoopConfiguration()
hadoopConf.set("fs.s3a.awsAccessKeyId", AWS_ACCESS_KEY)
hadoopConf.set("fs.s3a.awsSecretAccessKey", AWS_SECRET_KEY)
hadoopConf.set("fs.s3a.endpoint", "s3.us-east-1.amazonaws.com")
hadoopConf.set("com.amazonaws.services.s3a.enableV4", "true")
hadoopConf.set("fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
from pyspark.ml.classification import RandomForestClassifier, RandomForestClassificationModel
m1 = RandomForestClassificationModel.load('s3a://test-bucket/test-model')

我收到以下错误：

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/user/.local/lib/python3.6/site-packages/pyspark/ml/util.py", line 362, in load
    return cls.read().load(path)
  File "/home/user/.local/lib/python3.6/site-packages/pyspark/ml/util.py", line 300, in load
    java_obj = self._jread.load(path)
  File "/home/user/.local/lib/python3.6/site-packages/pyspark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__
  File "/home/user/.local/lib/python3.6/site-packages/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/home/user/.local/lib/python3.6/site-packages/pyspark/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 o35.load.
: com.amazonaws.AmazonClientException: Unable to load AWS credentials from any provider in the chain
    at com.amazonaws.auth.AWSCredentialsProviderChain.getCredentials(AWSCredentialsProviderChain.java:117)
    at com.amazonaws.services.s3.AmazonS3Client.invoke(AmazonS3Client.java:3521)
    at com.amazonaws.services.s3.AmazonS3Client.headBucket(AmazonS3Client.java:1031)
    at com.amazonaws.services.s3.AmazonS3Client.doesBucketExist(AmazonS3Client.java:994)
    at org.apache.hadoop.fs.s3a.S3AFileSystem.initialize(S3AFileSystem.java:297)
    at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2669)
    at org.apache.hadoop.fs.FileSystem.access$200(FileSystem.java:94)
    at org.apache.hadoop.fs.FileSystem$Cache.getInternal(FileSystem.java:2703)
    at org.apache.hadoop.fs.FileSystem$Cache.get(FileSystem.java:2685)
    at org.apache.hadoop.fs.FileSystem.get(FileSystem.java:373)
    at org.apache.hadoop.fs.Path.getFileSystem(Path.java:295)
    at org.apache.hadoop.mapred.FileInputFormat.singleThreadedListStatus(FileInputFormat.java:258)
    at org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:229)
    at org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:315)
    at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:204)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
    at org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:49)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:253)
    at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:251)
    at scala.Option.getOrElse(Option.scala:121)
    at org.apache.spark.rdd.RDD.partitions(RDD.scala:251)
    at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1343)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.take(RDD.scala:1337)
    at org.apache.spark.rdd.RDD$$anonfun$first$1.apply(RDD.scala:1378)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.first(RDD.scala:1377)
    at org.apache.spark.ml.util.DefaultParamsReader$.loadMetadata(ReadWrite.scala:615)
    at org.apache.spark.ml.tree.EnsembleModelReadWrite$.loadImpl(treeModels.scala:427)
    at org.apache.spark.ml.classification.RandomForestClassificationModel$RandomForestClassificationModelReader.load(RandomForestClassifier.scala:316)
    at org.apache.spark.ml.classification.RandomForestClassificationModel$RandomForestClassificationModelReader.load(RandomForestClassifier.scala:306)
    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)

老实说，这些代码行来自网络，我不知道如何将 MLLib 模型存储和加载到 S3。这里的任何帮助将不胜感激，对我来说，下一步是在一组机器上做同样的事情。因此，任何提醒也将不胜感激。

【问题讨论】：

尝试在 spark-env.sh 中添加 export AWS_ACCESS_KEY_ID="XXX" export AWS_SECRET_ACCESS_KEY="YYYYY"

【参考方案1】：

您为 s3a 连接器使用了错误的属性名称。

见https://hadoop.apache.org/docs/current3/hadoop-aws/tools/hadoop-aws/#Authentication_properties

具体来说：

fs.s3a.access.key您的访问密钥 fs.s3a.secret.key你的密钥

特别注意

小写 access 和 key、secret 和 key 之间有点/句点

mixedCaseOptions 来自 s3n 连接器，该连接器已过时并且早已从 hadoop 代码库中删除。 s3a 连接器将直接忽略它们

【讨论】：

s3a 连接器的正确属性名称是什么？

它们是我链接到的文档中的那些。在这里复制它们只能确保堆栈溢出帖子最终充满过时的事实

【参考方案2】：

AWS Java 开发工具包有一个凭证解析逻辑/链来正确解析与 AWS 服务交互时使用的 AWS 凭证。

见http://docs.aws.amazon.com/sdk-for-java/v1/developer-guide/credentials.html

此错误表示 SDK 无法在 SDK 查看的任何位置找到凭据。确保凭据至少存在于上述链接中提到的位置之一。

首先，填充环境变量 AWS_ACCESS_KEY_ID 和 AWS_SECRET_ACCESS_KEY。适用于 Java 的 AWS 开发工具包使用 EnvironmentVariableCredentialsProvider 类来加载这些凭证。

【参考方案3】：

这段代码帮了我大忙。

首先，定义 AWS 凭证：

config = configparser.ConfigParser()

config.read_file(open('aws/dl.cfg'))

os.environ["AWS_ACCESS_KEY_ID"]= config['default']['AWS_ACCESS_KEY_ID']
os.environ["AWS_SECRET_ACCESS_KEY"]= config['default']['AWS_SECRET_ACCESS_KEY']

然后，像这样开始一个会话：

spark = SparkSession \
.builder \
.config("spark.jars.packages", "org.apache.hadoop:hadoop-aws:2.7.0") \
.config("spark.hadoop.fs.s3a.impl","org.apache.hadoop.fs.s3a.S3AFileSystem") \
.config("spark.hadoop.fs.s3a.awsAccessKeyId", os.environ['AWS_ACCESS_KEY_ID']) \
.config("spark.hadoop.fs.s3a.awsSecretAccessKey", os.environ['AWS_SECRET_ACCESS_KEY']) \
.getOrCreate()