Merge pull request #496 from yahoo/leewyang_gpu_fix

detect TF version w/o importing

Author: leewyang

tensorflowonspark/TFNode.py

@@ -16,12 +16,14 @@
 
 import getpass
 import logging
+import pkg_resources
 
 from packaging import version
 from six.moves.queue import Empty
 from . import compat, marker
 
 logger = logging.getLogger(__name__)
+TF_VERSION = pkg_resources.get_distribution('tensorflow').version
 
 
 def hdfs_path(ctx, path):
@@ -79,11 +81,10 @@ def start_cluster_server(ctx, num_gpus=1, rdma=False):
     A tuple of (cluster_spec, server)
   """
   import os
-  import tensorflow as tf
   import time
   from . import gpu_info
 
-  if version.parse(tf.__version__) >= version.parse("2.0.0"):
+  if version.parse(TF_VERSION) >= version.parse("2.0.0"):
     raise Exception("DEPRECATED: Use higher-level APIs like `tf.keras` or `tf.estimator`")
 
   logging.info("{0}: ======== {1}:{2} ========".format(ctx.worker_num, ctx.job_name, ctx.task_index))
@@ -115,6 +116,9 @@ def start_cluster_server(ctx, num_gpus=1, rdma=False):
         # Set GPU device to use for TensorFlow
         os.environ['CUDA_VISIBLE_DEVICES'] = gpus_to_use
 
+        # Import tensorflow after gpu allocation
+        import tensorflow as tf
+
         # Create a cluster from the parameter server and worker hosts.
         cluster = tf.train.ClusterSpec(cluster_spec)
 

tensorflowonspark/TFSparkNode.py

@@ -12,24 +12,26 @@
 import logging
 import multiprocessing
 import os
+import pkg_resources
 import platform
 import socket
 import subprocess
 import sys
 import uuid
 import time
 import traceback
+from packaging import version
 from threading import Thread
 
 from . import TFManager
 from . import TFNode
-from . import compat
 from . import gpu_info
 from . import marker
 from . import reservation
 from . import util
 
 logger = logging.getLogger(__name__)
+TF_VERSION = pkg_resources.get_distribution('tensorflow').version
 
 
 class TFNodeContext:
@@ -137,15 +139,12 @@ def run(fn, tf_args, cluster_meta, tensorboard, log_dir, queues, background):
     A nodeRDD.mapPartitions() function.
   """
   def _mapfn(iter):
-    import tensorflow as tf
-    from packaging import version
-
     # Note: consuming the input iterator helps Pyspark re-use this worker,
     for i in iter:
       executor_id = i
 
     # check that there are enough available GPUs (if using tensorflow-gpu) before committing reservation on this node
-    if compat.is_gpu_available():
+    if gpu_info.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)
 
@@ -227,7 +226,7 @@ def _mapfn(iter):
         raise Exception("Unable to find 'tensorboard' in: {}".format(search_path))
 
       # launch tensorboard
-      if version.parse(tf.__version__) >= version.parse('2.0.0'):
+      if version.parse(TF_VERSION) >= version.parse('2.0.0'):
         tb_proc = subprocess.Popen([pypath, tb_path, "--reload_multifile=True", "--logdir=%s" % logdir, "--port=%d" % tb_port], env=os.environ)
       else:
         tb_proc = subprocess.Popen([pypath, tb_path, "--logdir=%s" % logdir, "--port=%d" % tb_port], env=os.environ)
@@ -296,7 +295,7 @@ def _mapfn(iter):
       os.environ['TF_CONFIG'] = tf_config
 
     # reserve GPU(s) again, just before launching TF process (in case situation has changed)
-    if compat.is_gpu_available():
+    if gpu_info.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/gpu_info.py

@@ -42,6 +42,15 @@ def _get_gpu():
   return gpu
 
 
+def is_gpu_available():
+  """Determine if GPUs are available on the host"""
+  try:
+    subprocess.check_output(["nvidia-smi", "--list-gpus"])
+    return True
+  except Exception:
+    return False
+
+
 def get_gpus(num_gpu=1, worker_index=-1):
   """Get list of free GPUs according to nvidia-smi.
 

tensorflowonspark/pipeline.py

@@ -23,14 +23,15 @@
 import argparse
 import copy
 import logging
+import pkg_resources
 import sys
-import tensorflow as tf
 
 from . import TFCluster, util
 from packaging import version
 
 
 logger = logging.getLogger(__name__)
+TF_VERSION = pkg_resources.get_distribution('tensorflow').version
 
 
 # TensorFlowOnSpark Params
@@ -370,7 +371,7 @@ def __init__(self, train_fn, tf_args, export_fn=None):
     self.train_fn = train_fn
     self.args = Namespace(tf_args)
 
-    master_node = 'chief' if version.parse(tf.__version__) >= version.parse("2.0.0") else None
+    master_node = 'chief' if version.parse(TF_VERSION) >= version.parse("2.0.0") else None
     self._setDefault(input_mapping={},
                      cluster_size=1,
                      num_ps=0,
@@ -413,7 +414,7 @@ def _fit(self, dataset):
     cluster.shutdown(grace_secs=self.getGraceSecs())
 
     if self.export_fn:
-      if version.parse(tf.__version__) < version.parse("2.0.0"):
+      if version.parse(TF_VERSION) < version.parse("2.0.0"):
         # For TF1.x, run export function, if provided
         assert local_args.export_dir, "Export function requires --export_dir to be set"
         logging.info("Exporting saved_model (via export_fn) to: {}".format(local_args.export_dir))
@@ -480,7 +481,7 @@ def _transform(self, dataset):
 
     tf_args = self.args.argv if self.args.argv else local_args
 
-    _run_model = _run_model_tf1 if version.parse(tf.__version__) < version.parse("2.0.0") else _run_model_tf2
+    _run_model = _run_model_tf1 if version.parse(TF_VERSION) < version.parse("2.0.0") else _run_model_tf2
     rdd_out = dataset.select(input_cols).rdd.mapPartitions(lambda it: _run_model(it, local_args, tf_args))
 
     # convert to a DataFrame-friendly format
@@ -516,7 +517,7 @@ def _run_model_tf1(iterator, args, tf_args):
   output_tensor_names = [tensor for tensor, col in sorted(args.output_mapping.items())]
 
   # if using a signature_def_key, get input/output tensor info from the requested signature
-  if version.parse(tf.__version__) < version.parse("2.0.0") and args.signature_def_key:
+  if version.parse(TF_VERSION) < version.parse("2.0.0") and args.signature_def_key:
     assert args.export_dir, "Inferencing with signature_def_key requires --export_dir argument"
     logging.info("===== loading meta_graph_def for tag_set ({0}) from saved_model: {1}".format(args.tag_set, args.export_dir))
     meta_graph_def = get_meta_graph_def(args.export_dir, args.tag_set)
@@ -534,6 +535,7 @@ def _run_model_tf1(iterator, args, tf_args):
     sess = global_sess
   else:
     # otherwise, create new session and load graph from disk
+    import tensorflow as tf
     tf.reset_default_graph()
     sess = tf.Session(graph=tf.get_default_graph())
     if args.export_dir:
@@ -584,6 +586,8 @@ def _run_model_tf2(iterator, args, tf_args):
   """mapPartitions function (for TF2.x) to run single-node inferencing from a saved_model, using input/output mappings."""
   single_node_env(tf_args)
 
+  import tensorflow as tf
+
   logger.info("===== input_mapping: {}".format(args.input_mapping))
   logger.info("===== output_mapping: {}".format(args.output_mapping))
   input_tensor_names = [tensor for col, tensor in sorted(args.input_mapping.items())]

tensorflowonspark/util.py

@@ -13,7 +13,7 @@
 import subprocess
 import errno
 from socket import error as socket_error
-from . import compat, gpu_info
+from . import gpu_info
 
 logger = logging.getLogger(__name__)
 
@@ -28,7 +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 compat.is_gpu_available() and num_gpus > 0:
+  if gpu_info.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/README.md

@@ -16,7 +16,7 @@ Note: the tests that use Spark will require a local Spark Standalone cluster (vs
     export SPARK_HOME=<path_to_Spark>
     export TFoS_HOME=<path_to_TFoS>
     export PYTHONPATH=${SPARK_HOME}/python
-    export SPARK_CLASSPATH=<path_to_tensorflow-hadoop-*.jar>
+    export SPARK_CLASSPATH=${TFoS_HOME}/lib/tensorflow-hadoop-1.0-SNAPSHOT.jar
 ```
 2a. Run script to automatically start Spark Standalone cluster, run all tests, and shutdown the cluster, OR
 ```