Discarding the master and slave lingo

docs/source/conf.py

@@ -56,8 +56,8 @@
 # source_suffix = ['.rst', '.md']
 source_suffix = '.rst'
 
-# The master toctree document.
-master_doc = 'index'
+# The main toctree document.
+main_doc = 'index'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -138,7 +138,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'TensorFlowOnSpark.tex', 'TensorFlowOnSpark Documentation',
+    (main_doc, 'TensorFlowOnSpark.tex', 'TensorFlowOnSpark Documentation',
      'Lee Yang', 'manual'),
 ]
 
@@ -148,7 +148,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'tensorflowonspark', 'TensorFlowOnSpark Documentation',
+    (main_doc, 'tensorflowonspark', 'TensorFlowOnSpark Documentation',
      [author], 1)
 ]
 
@@ -159,7 +159,7 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'TensorFlowOnSpark', 'TensorFlowOnSpark Documentation',
+    (main_doc, 'TensorFlowOnSpark', 'TensorFlowOnSpark Documentation',
      author, 'TensorFlowOnSpark', 'One line description of project.',
      'Miscellaneous'),
 ]

examples/mnist/estimator/mnist_estimator.py

@@ -184,5 +184,5 @@ def main(args, ctx):
   args = parser.parse_args()
   print("args:", args)
 
-  cluster = TFCluster.run(sc, main, args, args.cluster_size, args.num_ps, tensorboard=args.tensorboard, input_mode=TFCluster.InputMode.TENSORFLOW, log_dir=args.model, master_node='master')
+  cluster = TFCluster.run(sc, main, args, args.cluster_size, args.num_ps, tensorboard=args.tensorboard, input_mode=TFCluster.InputMode.TENSORFLOW, log_dir=args.model, main_node='main')
   cluster.shutdown()

examples/mnist/keras/mnist_mlp_estimator.py

@@ -150,13 +150,13 @@ def train_input_fn():
 
   if args.input_mode == 'tf':
     # for TENSORFLOW mode, each node will load/train/infer entire dataset in memory per original example
-    cluster = TFCluster.run(sc, main_fun, args, args.cluster_size, args.num_ps, args.tensorboard, TFCluster.InputMode.TENSORFLOW, log_dir=args.model_dir, master_node='master')
+    cluster = TFCluster.run(sc, main_fun, args, args.cluster_size, args.num_ps, args.tensorboard, TFCluster.InputMode.TENSORFLOW, log_dir=args.model_dir, main_node='main')
     cluster.shutdown()
   else:  # 'spark'
     # for SPARK mode, just use CSV format as an example
     images = sc.textFile(args.images).map(lambda ln: [float(x) for x in ln.split(',')])
     labels = sc.textFile(args.labels).map(lambda ln: [float(x) for x in ln.split(',')])
     dataRDD = images.zip(labels)
-    cluster = TFCluster.run(sc, main_fun, args, args.cluster_size, args.num_ps, args.tensorboard, TFCluster.InputMode.SPARK, log_dir=args.model_dir, master_node='master')
+    cluster = TFCluster.run(sc, main_fun, args, args.cluster_size, args.num_ps, args.tensorboard, TFCluster.InputMode.SPARK, log_dir=args.model_dir, main_node='main')
     cluster.train(dataRDD, args.epochs)
     cluster.shutdown()

examples/mnist/spark/mnist_dist.py

@@ -101,7 +101,7 @@ def rdd_generator():
     summary_writer = tf.summary.FileWriter("tensorboard_%d" % worker_num, graph=tf.get_default_graph())
 
     hooks = [tf.train.StopAtStepHook(last_step=args.steps)] if args.mode == "train" else []
-    with tf.train.MonitoredTrainingSession(master=server.target,
+    with tf.train.MonitoredTrainingSession(main=server.target,
                                            is_chief=(task_index == 0),
                                            scaffold=tf.train.Scaffold(init_op=init_op, summary_op=summary_op, saver=saver),
                                            checkpoint_dir=logdir,

examples/mnist/tf/mnist_dist.py

@@ -136,7 +136,7 @@ def build_model(graph, x):
       tf.gfile.MkDir(output_dir)
       output_file = tf.gfile.Open("{}/part-{:05d}".format(output_dir, task_index), mode='w')
 
-    with tf.train.MonitoredTrainingSession(master=server.target,
+    with tf.train.MonitoredTrainingSession(main=server.target,
                                            is_chief=(task_index == 0),
                                            scaffold=tf.train.Scaffold(init_op=init_op, summary_op=summary_op, saver=saver),
                                            checkpoint_dir=model_dir,

examples/wide_deep/census_main.py

@@ -155,5 +155,5 @@ def main_fun(argv, ctx):
   num_workers = args.cluster_size - args.num_ps
   print("===== num_executors={}, num_workers={}, num_ps={}".format(args.cluster_size, num_workers, args.num_ps))
 
-  cluster = TFCluster.run(sc, main_fun, remainder, args.cluster_size, args.num_ps, False, TFCluster.InputMode.TENSORFLOW, master_node='master')
+  cluster = TFCluster.run(sc, main_fun, remainder, args.cluster_size, args.num_ps, False, TFCluster.InputMode.TENSORFLOW, main_node='main')
   cluster.shutdown()