Merge pull request #473 from yahoo/leewyang_parallel

add TFParallel.run() API

Author: leewyang

docs/source/tensorflowonspark.TFParallel.rst

@@ -0,0 +1,7 @@
+tensorflowonspark\.TFParallel module
+===================================
+
+.. automodule:: tensorflowonspark.TFParallel
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/source/tensorflowonspark.rst

@@ -14,6 +14,7 @@ Submodules
    tensorflowonspark.TFCluster
    tensorflowonspark.TFManager
    tensorflowonspark.TFNode
+   tensorflowonspark.TFParallel
    tensorflowonspark.TFSparkNode
    tensorflowonspark.dfutil
    tensorflowonspark.gpu_info

examples/mnist/keras/mnist_inference.py

@@ -21,16 +21,7 @@
 import tensorflow as tf
 
 
-def inference(it, num_workers, args):
-  from tensorflowonspark import util
-
-  # consume worker number from RDD partition iterator
-  for i in it:
-    worker_num = i
-  print("worker_num: {}".format(i))
-
-  # setup env for single-node TF
-  util.single_node_env()
+def inference(args, ctx):
 
   # load saved_model
   saved_model = tf.saved_model.load(args.export_dir, tags='serve')
@@ -48,14 +39,14 @@ def parse_tfr(example_proto):
 
   # define a new tf.data.Dataset (for inferencing)
   ds = tf.data.Dataset.list_files("{}/part-*".format(args.images_labels))
-  ds = ds.shard(num_workers, worker_num)
+  ds = ds.shard(ctx.num_workers, ctx.worker_num)
   ds = ds.interleave(tf.data.TFRecordDataset)
   ds = ds.map(parse_tfr)
   ds = ds.batch(10)
 
   # create an output file per spark worker for the predictions
   tf.io.gfile.makedirs(args.output)
-  output_file = tf.io.gfile.GFile("{}/part-{:05d}".format(args.output, worker_num), mode='w')
+  output_file = tf.io.gfile.GFile("{}/part-{:05d}".format(args.output, ctx.worker_num), mode='w')
 
   for batch in ds:
     predictions = predict(conv2d_input=batch[0])
@@ -70,6 +61,7 @@ def parse_tfr(example_proto):
 if __name__ == '__main__':
   from pyspark.context import SparkContext
   from pyspark.conf import SparkConf
+  from tensorflowonspark import TFParallel
 
   sc = SparkContext(conf=SparkConf().setAppName("mnist_inference"))
   executors = sc._conf.get("spark.executor.instances")
@@ -83,7 +75,5 @@ def parse_tfr(example_proto):
   args, _ = parser.parse_known_args()
   print("args: {}".format(args))
 
-  # Not using TFCluster... just running single-node TF instances on each executor
-  nodes = list(range(args.cluster_size))
-  nodeRDD = sc.parallelize(list(range(args.cluster_size)), args.cluster_size)
-  nodeRDD.foreachPartition(lambda worker_num: inference(worker_num, args.cluster_size, args))
+  # Running single-node TF instances on each executor
+  TFParallel.run(sc, inference, args, args.cluster_size)

examples/resnet/README.md

@@ -4,14 +4,16 @@ Original Source: https://github.com/tensorflow/models/tree/master/official/visio
 
 This code is based on the Image Classification model from the official [TensorFlow Models](https://github.com/tensorflow/models) repository.  This example already supports different forms of distribution via the `DistributionStrategy` API, so there isn't much additional work to convert it to TensorFlowOnSpark.
 
-Notes: 
+Notes:
 - This example assumes that Spark, TensorFlow, and TensorFlowOnSpark are already installed.
 - For simplicity, this just uses a single-node Spark Standalone installation.
 
 #### Run the Single-Node Application
 
-First, make sure that you can run the example per the [original instructions](https://github.com/tensorflow/models/tree/68c3c65596b8fc624be15aef6eac3dc8952cbf23/official/vision/image_classification).  For now, we'll just use the CIFAR-10 dataset.  After cloning the [tensorflow/models](https://github.com/tensorflow/models) repository and downloading the dataset, you should be able to run the training as follows:
+First, make sure that you can run the example per the [original instructions](https://github.com/tensorflow/models/tree/68c3c65596b8fc624be15aef6eac3dc8952cbf23/official/vision/image_classification).  For now, we'll just use the CIFAR-10 dataset.  After cloning the [tensorflow/models](https://github.com/tensorflow/models) repository (checking out the `v2.0` tag with `git checkout v2.0`), and downloading the dataset, you should be able to run the training as follows:
 ```
+# Note: these instructions have been tested with the `v2.0` tag of tensorflow/models.
+
 export TENSORFLOW_MODELS=/path/to/tensorflow/models
 export CIFAR_DATA=/path/to/cifar
 export PYTHONPATH=${PYTHONPATH}:${TENSORFLOW_MODELS}

tensorflowonspark/TFNode.py

@@ -21,6 +21,7 @@
 
 logger = logging.getLogger(__name__)
 
+
 def hdfs_path(ctx, path):
   """Convenience function to create a Tensorflow-compatible absolute HDFS path from relative paths
 

tensorflowonspark/TFParallel.py

@@ -0,0 +1,64 @@
+# Copyright 2019 Yahoo Inc / Verizon Media
+# 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 nested_scopes
+from __future__ import print_function
+
+import logging
+from . import TFSparkNode
+from . import gpu_info, util
+
+logger = logging.getLogger(__name__)
+
+
+def run(sc, map_fn, tf_args, num_executors):
+  """Runs the user map_fn as parallel, independent instances of TF on the Spark executors.
+
+  Args:
+    :sc: SparkContext
+    :map_fun: user-supplied TensorFlow "main" function
+    :tf_args: ``argparse`` args, or command-line ``ARGV``.  These will be passed to the ``map_fun``.
+    :num_executors: number of Spark executors.  This should match your Spark job's ``--num_executors``.
+
+  Returns:
+    None
+  """
+
+  # get default filesystem from spark
+  defaultFS = sc._jsc.hadoopConfiguration().get("fs.defaultFS")
+  # strip trailing "root" slash from "file:///" to be consistent w/ "hdfs://..."
+  if defaultFS.startswith("file://") and len(defaultFS) > 7 and defaultFS.endswith("/"):
+    defaultFS = defaultFS[:-1]
+
+  def _run(it):
+    from pyspark import BarrierTaskContext
+
+    for i in it:
+      worker_num = i
+
+    # use BarrierTaskContext to get placement of all nodes
+    ctx = BarrierTaskContext.get()
+    tasks = ctx.getTaskInfos()
+    nodes = [t.address for t in tasks]
+
+    # use the placement info to help allocate GPUs
+    num_gpus = tf_args.num_gpus if 'num_gpus' in tf_args else 1
+    util.single_node_env(num_gpus=num_gpus, worker_index=worker_num, nodes=nodes)
+
+    # run the user map_fn
+    ctx = TFSparkNode.TFNodeContext()
+    ctx.defaultFS = defaultFS
+    ctx.worker_num = worker_num
+    ctx.executor_id = worker_num
+    ctx.num_workers = len(nodes)
+
+    map_fn(tf_args, ctx)
+
+    # return a dummy iterator (since we have to use mapPartitions)
+    return [0]
+
+  nodeRDD = sc.parallelize(list(range(num_executors)), num_executors)
+  nodeRDD.barrier().mapPartitions(_run).collect()

tensorflowonspark/TFSparkNode.py

@@ -46,7 +46,7 @@ class TFNodeContext:
     :working_dir: the current working directory for local filesystems, or YARN containers.
     :mgr: TFManager instance for this Python worker.
   """
-  def __init__(self, executor_id, job_name, task_index, cluster_spec, defaultFS, working_dir, mgr):
+  def __init__(self, executor_id=0, job_name='', task_index=0, cluster_spec={}, defaultFS='file://', working_dir='.', mgr=None):
     self.worker_num = executor_id       # for backwards-compatibility
     self.executor_id = executor_id
     self.job_name = job_name

tensorflowonspark/reservation.py

@@ -190,9 +190,8 @@ def _listen(self, sock):
   def get_server_ip(self):
     return os.getenv(TFOS_SERVER_HOST) if os.getenv(TFOS_SERVER_HOST) else util.get_ip_address()
 
-
   def start_listening_socket(self):
-    port_number = int(os.getenv(TFOS_SERVER_PORT)) if  os.getenv(TFOS_SERVER_PORT) else 0
+    port_number = int(os.getenv(TFOS_SERVER_PORT)) if os.getenv(TFOS_SERVER_PORT) else 0
     server_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
     server_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
     server_sock.bind(('', port_number))

tensorflowonspark/util.py

@@ -18,7 +18,7 @@
 logger = logging.getLogger(__name__)
 
 
-def single_node_env(num_gpus=1):
+def single_node_env(num_gpus=1, worker_index=-1, nodes=[]):
   """Setup environment variables for Hadoop compatibility and GPU allocation"""
   import tensorflow as tf
   # ensure expanded CLASSPATH w/o glob characters (required for Spark 2.1 + JNI)
@@ -29,9 +29,19 @@ def single_node_env(num_gpus=1):
       os.environ['CLASSPATH'] = classpath + os.pathsep + hadoop_classpath
       os.environ['TFOS_CLASSPATH_UPDATED'] = '1'
 
-  # reserve GPU, if requested
-  if tf.test.is_built_with_cuda():
-    gpus_to_use = gpu_info.get_gpus(num_gpus)
+  if tf.test.is_built_with_cuda() and num_gpus > 0:
+    # reserve GPU(s), if requested
+    if worker_index >= 0 and len(nodes) > 0:
+      # compute my index relative to other nodes on the same host, if known
+      my_addr = nodes[worker_index]
+      my_host = my_addr.split(':')[0]
+      local_peers = [n for n in nodes if n.startswith(my_host)]
+      my_index = local_peers.index(my_addr)
+    else:
+      # otherwise, just use global worker index
+      my_index = worker_index
+
+    gpus_to_use = gpu_info.get_gpus(num_gpus, my_index)
     logger.info("Using gpu(s): {0}".format(gpus_to_use))
     os.environ['CUDA_VISIBLE_DEVICES'] = gpus_to_use
   else: