test rm sess.run

eda/esmm/Model_pipline/dist_predict.py

@@ -187,8 +187,8 @@ if __name__ == "__main__":
     # df = spark.read.format("tfrecords").load(path+"nearby/part-r-00000")
     # df.show()
 
-    name = spark.parallelize([1,2,3,4,5])
-    test = name.repartition(5).map(lambda x: test_map())
+    name = spark.createDataFrame([1,2,3,4,5],5)
+    test = name.map(lambda x: test_map())
     test.show()
 
     b = time.time()