Use distutils.version.StrictVersion for version comparisons

samples/outreach/blogs/blog_custom_estimators.py

@@ -22,12 +22,14 @@ import sys
 
 import six.moves.urllib.request as request
 
+from distutils.version import StrictVersion
+
 tf.logging.set_verbosity(tf.logging.INFO)
 
 # Check that we have correct TensorFlow version installed
 tf_version = tf.__version__
 tf.logging.info("TensorFlow version: {}".format(tf_version))
-assert "1.4" <= tf_version, "TensorFlow r1.4 or later is needed"
+assert StrictVersion("1.4") <= StrictVersion(tf_version), "TensorFlow r1.4 or later is needed"
 
 # Windows users: You only need to change PATH, rest is platform independent
 PATH = "/tmp/tf_custom_estimators"

samples/outreach/blogs/blog_estimators_dataset.py

@@ -22,10 +22,12 @@ import os
 import six.moves.urllib.request as request
 import tensorflow as tf
 
+from distutils.version import StrictVersion
+
 # Check that we have correct TensorFlow version installed
 tf_version = tf.__version__
 print("TensorFlow version: {}".format(tf_version))
-assert "1.4" <= tf_version, "TensorFlow r1.4 or later is needed"
+assert StrictVersion("1.4") <= StrictVersion(tf_version), "TensorFlow r1.4 or later is needed"
 
 # Windows users: You only need to change PATH, rest is platform independent
 PATH = "/tmp/tf_dataset_and_estimator_apis"

tutorials/rnn/ptb/ptb_word_lm.py

@@ -69,6 +69,8 @@ import util
 
 from tensorflow.python.client import device_lib
 
+from distutils.version import StrictVersion
+
 flags = tf.flags
 logging = tf.logging
 
@@ -436,7 +438,7 @@ def get_config():
     raise ValueError("Invalid model: %s", FLAGS.model)
   if FLAGS.rnn_mode:
     config.rnn_mode = FLAGS.rnn_mode
-  if FLAGS.num_gpus != 1 or tf.__version__ < "1.3.0" :
+  if FLAGS.num_gpus != 1 or StrictVersion(tf.__version__) < StrictVersion("1.3.0") :
     config.rnn_mode = BASIC
   return config
 
@@ -489,7 +491,7 @@ def main(_):
     for name, model in models.items():
       model.export_ops(name)
     metagraph = tf.train.export_meta_graph()
-    if tf.__version__ < "1.1.0" and FLAGS.num_gpus > 1:
+    if StrictVersion(tf.__version__) < StrictVersion("1.1.0") and FLAGS.num_gpus > 1:
       raise ValueError("num_gpus > 1 is not supported for TensorFlow versions "
                        "below 1.1.0")
     soft_placement = False