TensorFlow 2.4.0 Now Available
Software has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML.
Engineering Computing News
Engineering Computing Resources
December 28, 2020
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
The newest version of TensorFlow brings major features, improvements, bug fixes and other changes.
Major Features and Improvements
- tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. See the tutorial to learn more.
- MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Check out the detailed tutorial on Multi-worker training with Keras.
- Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide.
- Adds support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.
- A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.
- Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.
- TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.
- TFLite Profiler for Android is available. See the detailed guide.
- TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.
Sources: Press materials received from the company and additional information gleaned from the company’s website.