Let's see how to read data in LIBSVM format using the read API and the load() method by specifying the format of the data (that is, libsvm) as follows:

# Creating DataFrame from libsvm dataset
 myDF = spark.read.format("libsvm").load("C:/Exp//mnist.bz2")

The preceding MNIST dataset can be downloaded from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/mnist.bz2. This will essentially return a DataFrame and the content can be seen by calling the show() method as follows:

myDF.show() 

The output is as follows:

You can also specify other options such as how many features of the raw dataset you want to give to your DataFrame as follows:

myDF= spark.read.format("libsvm")
           .option("numFeatures", "780")
           .load("data/Letterdata_libsvm.data")

Now if you want to create an RDD from the same dataset, you can use the MLUtils API from pyspark.mllib.util as follows:

Creating RDD from the libsvm data file
myRDD = MLUtils.loadLibSVMFile(spark.sparkContext, "data/Letterdata_libsvm.data")

Now you can save the RDD in your preferred location as follows:

myRDD.saveAsTextFile("data/myRDD")