mnist_spark.py

# Copyright 2017 Yahoo Inc.
# Licensed under the terms of the Apache 2.0 license.
# Please see LICENSE file in the project root for terms.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from pyspark.context import SparkContext
from pyspark.conf import SparkConf
from pyspark.streaming import StreamingContext

import argparse
import numpy
from datetime import datetime

from tensorflowonspark import TFCluster
import mnist_dist

sc = SparkContext(conf=SparkConf().setAppName("mnist_streaming"))
ssc = StreamingContext(sc, 60)
executors = sc._conf.get("spark.executor.instances")
num_executors = int(executors) if executors is not None else 1
num_ps = 1

parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", help="number of records per batch", type=int, default=100)
parser.add_argument("--epochs", help="number of epochs", type=int, default=1)
parser.add_argument("--format", help="example format: (csv|csv2|pickle|tfr)", choices=["csv", "csv2", "pickle", "tfr"], default="stream")
parser.add_argument("--images", help="HDFS path to MNIST images in parallelized format")
parser.add_argument("--model", help="HDFS path to save/load model during train/inference", default="mnist_model")
parser.add_argument("--cluster_size", help="number of nodes in the cluster", type=int, default=num_executors)
parser.add_argument("--output", help="HDFS path to save test/inference output", default="predictions")
parser.add_argument("--steps", help="maximum number of steps", type=int, default=1000)
parser.add_argument("--tensorboard", help="launch tensorboard process", action="store_true")
parser.add_argument("--mode", help="train|inference", default="train")
parser.add_argument("--rdma", help="use rdma connection", default=False)
args = parser.parse_args()
print("args:", args)

print("{0} ===== Start".format(datetime.now().isoformat()))


def parse(ln):
  lbl, img = ln.split('|')
  image = [int(x) for x in img.split(',')]
  label = numpy.zeros(10)
  label[int(lbl)] = 1.0
  return (image, label)


stream = ssc.textFileStream(args.images)
imageRDD = stream.map(lambda ln: parse(ln))

cluster = TFCluster.run(sc, mnist_dist.map_fun, args, args.cluster_size, num_ps, args.tensorboard, TFCluster.InputMode.SPARK)
if args.mode == "train":
  cluster.train(imageRDD)
else:
  labelRDD = cluster.inference(imageRDD)
  labelRDD.saveAsTextFiles(args.output)

ssc.start()
cluster.shutdown(ssc)

print("{0} ===== Stop".format(datetime.now().isoformat()))