TensorFlow 2.0 is now available!

TensorFlow 2.0 makes development of ML applications much easier. With tight integration of Keras into TensorFlow, eager execution by default, and Pythonic function execution, TensorFlow 2.0 makes the experience of developing applications as familiar as possible for Python developers.

For researchers pushing the boundaries of ML, they have invested heavily in TensorFlow’s low-level API: It is possible to now export all ops that are used internally, and the framework provides inheritable interfaces for crucial concepts such as variables and checkpoints.

This allows you to build onto the internals of TensorFlow without having to rebuild TensorFlow.

New performance improvements

TensorFlow 2.0 offers many performance improvements on GPUs. TensorFlow 2.0 delivers up to 3x faster training performance using mixed precision on Volta and Turing GPUs with a few lines of code, used for example in ResNet-50 and BERT. TensorFlow 2.0 is tightly integrated with TensorRT and uses an improved API to deliver better usability and high performance during inference on NVIDIA T4 Cloud GPUs on Google Cloud.
“Machine learning on NVIDIA GPUs and systems allows developers to solve problems that seemed impossible just a few years ago,” – Kari Briski, Senior Director of Accelerated Computing Software Product Management at NVIDIA.

Not just for Python developers

Also, ML isn’t just for Python developers — using TensorFlow.js, training and inference is available to JavaScript developers, and we continue to invest in Swift as a language for building models with the Swift for TensorFlow library.

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