param predict,rm sess.run

eda/esmm/Model_pipline/dist_predict.py

@@ -190,11 +190,11 @@ if __name__ == "__main__":
     # df = spark.read.format("tfrecords").load(path+"nearby/part-r-00000")
     # df.show()
 
-    name = spark.sparkContext.parallelize([1,2,3,4,5])
-
-    test = name.repartition(5).map(lambda x: test_map(x))
-    print(test)
-    print(test.collect())
+    # name = spark.sparkContext.parallelize([1,2,3,4,5])
+    #
+    # test = name.repartition(5).map(lambda x: test_map(x))
+    # print(test)
+    # print(test.collect())
 
     tf.logging.set_verbosity(tf.logging.INFO)
     # te_files = [[path+"nearby/part-r-00000"],[path+"native/part-r-00000"]]