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TensorFlow 1.0 Released

| by Alex Giamas on Mar 05, 2017. Estimated reading time: 1 minute |

Google recently announced TensorFlow version 1.0. Python API is now stable and experimental APIs for Java and Go have been added. XLA, the new domain-specific compiler, delivers 7.3x performance increase in 8 GPUs setup and 58x for 64 GPUs for Inception-v3 neural network model. A new high level API can help with constructing convoluted neural networks, compute Evaluation-related metrics and loss functions operations. Keras can also be integrated with TensorFlow using a build-in module. Keras is a high-level Python neural networks library aiming to abstract deep learning for fast experimentation.

Shortly after the announcement, Google also announced tf.transform, a library for data preprocessing with TensorFlow. Based on Apache Beam, tf.transform can help avoiding 'training-serving skew', the problem having data in production differ from data used to train the underlying model.

In addition to these improvements, a command line debugger has been added, Python 3 docker images and easier installation via pip package management. A side effect of these improvements is that there are some backwards-incompatible changes which can be addressed via the migration guide and the conversion script.

Users can deploy TensorFlow on their own infrastructure or use Cloud Machine Learning, Google’s PaaS TensorFlow offering. Developers can start from introductory content or more advanced examples.

TensorFlow, in a little over a year, is already used in over 6000 open-source repositories in GitHub. More information is available in the videos from TensorFlow Developer Summit, covering recent updates and interesting use cases.

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