TF2.1 release compatibility

tensorflowonspark/TFNode.py

@@ -19,7 +19,7 @@
 
 from packaging import version
 from six.moves.queue import Empty
-from . import marker
+from . import compat, marker
 
 logger = logging.getLogger(__name__)
 
@@ -90,7 +90,7 @@ def start_cluster_server(ctx, num_gpus=1, rdma=False):
   cluster_spec = ctx.cluster_spec
   logging.info("{0}: Cluster spec: {1}".format(ctx.worker_num, cluster_spec))
 
-  if tf.test.is_built_with_cuda() and num_gpus > 0:
+  if compat.is_gpu_available() and num_gpus > 0:
     # compute my index relative to other nodes placed on the same host (for GPU allocation)
     my_addr = cluster_spec[ctx.job_name][ctx.task_index]
     my_host = my_addr.split(':')[0]

tensorflowonspark/TFSparkNode.py

@@ -23,6 +23,7 @@
 
 from . import TFManager
 from . import TFNode
+from . import compat
 from . import gpu_info
 from . import marker
 from . import reservation
@@ -144,7 +145,7 @@ def _mapfn(iter):
       executor_id = i
 
     # check that there are enough available GPUs (if using tensorflow-gpu) before committing reservation on this node
-    if tf.test.is_built_with_cuda():
+    if compat.is_gpu_available():
       num_gpus = tf_args.num_gpus if 'num_gpus' in tf_args else 1
       gpus_to_use = gpu_info.get_gpus(num_gpus)
 
@@ -295,7 +296,7 @@ def _mapfn(iter):
       os.environ['TF_CONFIG'] = tf_config
 
     # reserve GPU(s) again, just before launching TF process (in case situation has changed)
-    if tf.test.is_built_with_cuda():
+    if compat.is_gpu_available():
       # compute my index relative to other nodes on the same host (for GPU allocation)
       my_addr = cluster_spec[job_name][task_index]
       my_host = my_addr.split(':')[0]

tensorflowonspark/compat.py

@@ -20,3 +20,10 @@ def disable_auto_shard(options):
     options.experimental_distribute.auto_shard = False
   else:
     options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
+
+
+def is_gpu_available():
+  if version.parse(tf.__version__) < version.parse('2.1.0'):
+    return tf.test.is_built_with_cuda()
+  else:
+    return len(tf.config.list_logical_devices('GPU')) > 0

tensorflowonspark/util.py

@@ -13,14 +13,13 @@
 import subprocess
 import errno
 from socket import error as socket_error
-from . import gpu_info
+from . import compat, gpu_info
 
 logger = logging.getLogger(__name__)
 
 
 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)
   if 'HADOOP_PREFIX' in os.environ and 'TFOS_CLASSPATH_UPDATED' not in os.environ:
       classpath = os.environ['CLASSPATH']
@@ -29,7 +28,7 @@ def single_node_env(num_gpus=1, worker_index=-1, nodes=[]):
       os.environ['CLASSPATH'] = classpath + os.pathsep + hadoop_classpath
       os.environ['TFOS_CLASSPATH_UPDATED'] = '1'
 
-  if tf.test.is_built_with_cuda() and num_gpus > 0:
+  if compat.is_gpu_available() 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

test/test_pipeline.py

@@ -94,6 +94,7 @@ def _spark_train(args, ctx):
       import tensorflow as tf
       from tensorflowonspark import TFNode
 
+      tf.compat.v1.disable_eager_execution()
       tf.compat.v1.reset_default_graph()
       strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()