本文整理汇总了Python中pyspark.context.SparkContext.newAPIHadoopFile方法的典型用法代码示例。如果您正苦于以下问题：Python SparkContext.newAPIHadoopFile方法的具体用法？Python SparkContext.newAPIHadoopFile怎么用？Python SparkContext.newAPIHadoopFile使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.context.SparkContext的用法示例。
在下文中一共展示了SparkContext.newAPIHadoopFile方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: print
# 需要导入模块: from pyspark.context import SparkContext [as 别名]
# 或者: from pyspark.context.SparkContext import newAPIHadoopFile [as 别名]
parser.add_argument("-m", "--model", help="HDFS path to save/load model during train/inference", default="mnist_model")
parser.add_argument("-n", "--cluster_size", help="number of nodes in the cluster", type=int, default=num_executors)
parser.add_argument("-o", "--output", help="HDFS path to save test/inference output", default="predictions")
parser.add_argument("-r", "--readers", help="number of reader/enqueue threads", type=int, default=1)
parser.add_argument("-s", "--steps", help="maximum number of steps", type=int, default=1000)
parser.add_argument("-tb", "--tensorboard", help="launch tensorboard process", action="store_true")
parser.add_argument("-X", "--mode", help="train|inference", default="train")
parser.add_argument("-c", "--rdma", help="use rdma connection", default=False)
args = parser.parse_args()
print("args:",args)
print("{0} ===== Start".format(datetime.now().isoformat()))
if args.format == "tfr":
images = sc.newAPIHadoopFile(args.images, "org.tensorflow.hadoop.io.TFRecordFileInputFormat",
keyClass="org.apache.hadoop.io.BytesWritable",
valueClass="org.apache.hadoop.io.NullWritable")
def toNumpy(bytestr):
example = tf.train.Example()
example.ParseFromString(bytestr)
features = example.features.feature
image = numpy.array(features['image'].int64_list.value)
label = numpy.array(features['label'].int64_list.value)
return (image, label)
dataRDD = images.map(lambda x: toNumpy(str(x[0])))
else:
if args.format == "csv":
images = sc.textFile(args.images).map(lambda ln: [int(x) for x in ln.split(',')])
labels = sc.textFile(args.labels).map(lambda ln: [float(x) for x in ln.split(',')])
else: # args.format == "pickle":
images = sc.pickleFile(args.images)