TensorFlow 1.13.0-rc2 releases!

2 min read

After the TensorFlow 1.13.0-rc0 release last month, the TensorFlow team is out with another update 1.13.0-rc2, unveiling major features and updates. The new release explores minor bug fixes, improvements, and other changes.

Let’s have a look at the noteworthy features in TensorFlow 1.13.0-rc2.

Major Improvements

TensorFlow Lite has moved from contrib to core.
TensorFlow GPU binaries are built against CUDA 10 and TensorRT 5.0.
There’s newly added support for Python3.7 on all operating systems.
NCCL has been moved to core.

Behavioral and other changes

Conversion of python floating types to uint32/64 in tf.constant is not allowed.
The gain argument of convolutional orthogonal initializers has consistent behavior with the tf.initializers.orthogonal initializer.
Subclassed Keras models can be saved via tf.contrib.saved_model.save_keras_model.
LinearOperator.matmul now returns a new LinearOperator.
Performance of GPU cumsum/cumprod has improved by up to 300x.
Support has been added for weight decay in most TPU embedding optimizers, including AdamW and MomentumW.
Tensorflow/contrib/lite has been moved to tensorflow/lite.
An experimental Java API is added to inject TensorFlow Lite delegates.
Support has been added for strings in TensorFlow Lite Java API.
All the occurences of tf.contrib.estimator.DNNLinearCombinedEstimator has been replaced with tf.estimator.DNNLinearCombinedEstimator.
Regression_head has been updated to the new Head API for Canned Estimator V2.
XLA HLO graphs can be rendered as SVG/HTML.

Bug Fixes

Documentation has been updated with the details regarding the rounding mode used in quantize_and_dequantize_v2.
OpenSSL compatibility has been fixed by avoiding EVP_MD_CTX_destroy.
CUDA dependency has been upgraded to 10.0.
All occurences of tf.contrib.estimator.InMemoryEvaluatorHook and tf.contrib.estimator.make_stop_at_checkpoint_step_hook have been replaced with tf.estimator.experimental.InMemoryEvaluatorHook and tf.estimator.experimental.make_stop_at_checkpoint_step_hook.
tf.data.Dataset.make_one_shot_iterator() has been deprecated in V1, removed from V2, and tf.compat.v1.data.make_one_shot_iterator() has instead been added.
keep_prob is deprecated and Dropout now takes rate argument.
NUMA-aware MapAndBatch dataset has been added.
Apache Ignite Filesystem plugin has been added to support accessing Apache IGFS.

For more information, check out the official TensorFlow 1.13.0-rc2 release notes