diff --git a/examples/src/main/python/gpu/nubma_example.py b/examples/src/main/python/gpu/nubma_example.py
deleted file mode 100644
index 689d772a..00000000
--- a/examples/src/main/python/gpu/nubma_example.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import numpy as np 
-import numba
-import math
-import time
-from numba import cuda, vectorize, jit
-from pyspark.sql import SparkSession
-
-@cuda.jit('(float32[:], float32[:])')
-def func(inp, out):
-    i = cuda.grid(1)
-    if i < out.size:
-        out[i] = inp[i] ** 2
-
-
-def gpu_work(xs):
-    inp = np.asarray(list(xs), dtype=np.float32)
-    out = np.zeros_like(inp)
-    block_size = 32 * 4
-    grid_size = (inp.size + block_size - 1)
-    func[grid_size, block_size](inp, out)
-    return out
-
-spark = SparkSession\
-    .builder\
-    .getOrCreate()
-
-sc = spark.sparkContext
-
-# detect if there is an available GPU
-if cuda.is_available():
-    rdd = sc.parallelize(list(range(100)))
-    print("Partitions", rdd.getNumPartitions())
-
-    rdd = rdd.mapPartitions(gpu_work)
-    print(rdd.collect())
-
-
-    # compare cpu and gpu run times
-
-    @vectorize(["float32(float32, float32)", "float64(float64, float64)"], target='cpu')
-    def cpu_some_trig(x, y):
-        return math.cos(x) + math.sin(y)
-
-    @vectorize(["float32(float32, float32)", "float64(float64, float64)"], target='cuda')
-    def cuda_some_trig(x, y):
-        return math.cos(x) + math.sin(y)
-
-
-    nelem = 10 ** 8
-
-    xs = np.random.random(nelem).astype(np.float32)
-    ys = np.random.random(nelem).astype(np.float32)
-
-    start_time = time.time()
-    cpu_some_trig(xs, ys)
-    cpu_elapsed_time = time.time() - start_time
-
-    start_time = time.time()
-    cuda_some_trig(xs, ys)
-    gpu_elapsed_time = time.time() - start_time
-
-    print("CPU time: {}".format(cpu_elapsed_time))
-    print("GPU time: {}".format(gpu_elapsed_time))
-    print("GPU performed {}% faster than CPU".format((cpu_elapsed_time-gpu_elapsed_time)*100))
-
-else:
-    print("No CUDA devices detected. Are you running this on a GPU enabled VM?")