def toNumpy(bytestr):
example = tf.train.Example()
example.ParseFromString(bytestr)
features = example.features.feature
image = numpy.array(features['image'].int64_list.value)
label = numpy.array(features['label'].int64_list.value)
return (image, label)
dataRDD = images.map(lambda x: toNumpy(str(x[0])))
else:
if args.format == "csv":
images = sc.textFile(args.images).map(lambda ln: [int(x) for x in ln.split(',')])
labels = sc.textFile(args.labels).map(lambda ln: [float(x) for x in ln.split(',')])
else: # args.format == "pickle":
images = sc.pickleFile(args.images)
labels = sc.pickleFile(args.labels)
print("zipping images and labels")
dataRDD = images.zip(labels)
cluster = TFCluster.reserve(sc, args.cluster_size, num_ps, args.tensorboard, TFCluster.InputMode.SPARK)
cluster.start(mnist_dist.map_fun, args)
if args.mode == "train":
cluster.train(dataRDD, args.epochs)
else:
labelRDD = cluster.inference(dataRDD)
labelRDD.saveAsTextFile(args.output)
cluster.shutdown()
print("{0} ===== Stop".format(datetime.now().isoformat()))
from inception import inception_eval
from inception.imagenet_data import ImagenetData
print("argv:", argv)
sys.argv = argv
FLAGS = tf.app.flags.FLAGS
FLAGS._parse_flags()
print("FLAGS:", FLAGS.__dict__['__flags'])
dataset = ImagenetData(subset=FLAGS.subset)
assert dataset.data_files()
if tf.gfile.Exists(FLAGS.eval_dir):
tf.gfile.DeleteRecursively(FLAGS.eval_dir)
tf.gfile.MakeDirs(FLAGS.eval_dir)
cluster_spec, server = TFNode.start_cluster_server(ctx, 1, FLAGS.rdma)
inception_eval.evaluate(dataset)
if __name__ == '__main__':
sc = SparkContext(conf=SparkConf().setAppName("grid_imagenet_eval"))
num_executors = int(sc._conf.get("spark.executor.instances"))
num_ps = 0
cluster = TFCluster.reserve(sc, num_executors, num_ps, False,
TFCluster.InputMode.TENSORFLOW)
cluster.start(main_fun, sys.argv)
cluster.shutdown()
parser.add_argument("--input_mode",
help="method to ingest data: (spark|tf)",
choices=["spark", "tf"],
default="tf")
parser.add_argument("--tensorboard",
help="launch tensorboard process",
action="store_true")
(args, rem) = parser.parse_known_args()
input_mode = TFCluster.InputMode.SPARK if args.input_mode == 'spark' else TFCluster.InputMode.TENSORFLOW
print("{0} ===== Start".format(datetime.now().isoformat()))
sc = SparkContext(
conf=SparkConf().setAppName('imagenet_distributed_train'))
num_executors = int(sc._conf.get("spark.executor.instances"))
num_ps = 1
cluster = TFCluster.reserve(sc, num_executors, num_ps, args.tensorboard,
input_mode)
cluster.start(main_fun, sys.argv)
if input_mode == TFCluster.InputMode.SPARK:
dataRDD = sc.newAPIHadoopFile(
args.input_data,
"org.tensorflow.hadoop.io.TFRecordFileInputFormat",
keyClass="org.apache.hadoop.io.BytesWritable",
valueClass="org.apache.hadoop.io.NullWritable")
cluster.train(dataRDD, args.epochs)
cluster.shutdown()
print("{0} ===== Stop".format(datetime.now().isoformat()))
helper.display_image_predictions(random_test_features,
random_test_labels,
random_test_predictions)
test_model()
# def main():
def main_fun(argv, ctx):
pass
if __name__ == '__main__':
# tf.app.run()
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--tensorboard",
help="launch tensorboard process",
action="store_true")
args, rem = parser.parse_known_args()
sc = SparkContext(conf=SparkConf().setAppName("your_app_name"))
num_executors = int(sc._conf.get("spark.executor.instances"))
num_ps = 1
tensorboard = True
cluster = TFCluster.reserve(sc, num_executors, num_ps, tensorboard,
TFCluster.InputMode.TENSORFLOW)
cluster.start(main_fun, sys.argv)
cluster.shutdown()
sync_optimizer=optimizer if FLAGS.sync_replicas else None)
if __name__ == '__main__':
import argparse
sc = SparkContext(conf=SparkConf().setAppName("train_image_classifier"))
executors = sc._conf.get("spark.executor.instances")
num_executors = int(executors) if executors is not None else 1
parser = argparse.ArgumentParser()
parser.add_argument("--num_ps_tasks",
help="number of PS nodes",
type=int,
default=0)
parser.add_argument("--tensorboard",
help="launch tensorboard process",
action="store_true")
parser.add_argument("--cluster_size",
help="number of nodes in the cluster",
type=int,
default=num_executors)
(args, rem) = parser.parse_known_args()
assert (num_executors > args.num_ps_tasks)
cluster = TFCluster.reserve(sc, args.cluster_size, args.num_ps_tasks,
args.tensorboard,
TFCluster.InputMode.TENSORFLOW)
cluster.start(main_fun, sys.argv)
cluster.shutdown()