(tf_random_seed=None,
                    single_core=False,
                    iterations_per_loop=1000,
                    save_checkpoints_steps=5000,
                    keep_checkpoint_max=1000)
return tf.contrib.tpu.RunConfig(
      model_dir=FLAGS.model_dir,
      tf_random_seed=tf_random_seed,
      save_checkpoints_steps=save_checkpoints_steps,
      keep_checkpoint_max=keep_checkpoint_max,
      cluster=_get_cluster(),
      tpu_config=tpu_config)
Return `RunConfig` for TPUs.
Return `RunConfig` for TPUs.
Return RunConfig for TPUs .
def _get_run_config(tf_random_seed=None,
                    single_core=False,
                    iterations_per_loop=1000,
                    save_checkpoints_steps=5000,
                    keep_checkpoint_max=1000):
  """Return `RunConfig` for TPUs."""
  tpu_config = tf.contrib.tpu.TPUConfig(
      num_shards=1 if single_core else None,  # None = all cores.
      iterations_per_loop=iterations_per_loop)
  return tf.contrib.tpu.RunConfig(
      model_dir=FLAGS.model_dir,
      tf_random_seed=tf_random_seed,
      save_checkpoints_steps=save_checkpoints_steps,
      keep_checkpoint_max=keep_checkpoint_max,
      cluster=_get_cluster(),
      tpu_config=tpu_config)
def _get_run_config ( tf_random_seed = None , single_core = False , iterations_per_loop = 1000 , save_checkpoints_steps = 5000 , keep_checkpoint_max = 1000 ) : tpu_config = tf . contrib . tpu . TPUConfig ( num_shards = 1 if single_core else None , # None = all cores. iterations_per_loop = iterations_per_loop ) return tf . contrib . tpu . RunConfig ( model_dir = FLAGS . model_dir , tf_random_seed = tf_random_seed , save_checkpoints_steps = save_checkpoints_steps , keep_checkpoint_max = keep_checkpoint_max , cluster = _get_cluster ( ) , tpu_config = tpu_config )