Posted by the TensorFlow Team
Thanks to everyone who joined our inaugural virtual ML Community Day! It was so great to get the community together and hear incredible talks like how JAX and TPUs make AlphaFold possible from the DeepMind team, and how Edge Impulse makes it easy for developers to work with TinyML using TensorFlow.
We also celebrated TensorFlow’s 6th birthday! The TensorFlow ecosystem has come a long way in 6 years, and we love seeing what you all achieve with our tools. From using machine learning to help advance access to human rights information, to creating a custom, TensorFlow-powered drumming arm.
In this article are a few of the updates and topics we shared during the event. You can watch the keynote below, and you can find recordings of every talk on the TensorFlow YouTube channel.
TensorFlow 2.7 is here! This release offers performance and usability improvements, including TFLite use of XNNPack for mobile inference performance boosts, training improvements on GPUs, and a dramatic improvement in debugging efficiency in Keras and TF.
Keras has been modularized as a separate pip package on top of TensorFlow (installed by default) and now lives in a separate GitHub repository. This will make it much easier for the community to contribute to the development of Keras. We welcome your PRs!
The Responsible AI team also announced v0.4 of our Language Interpretability Tool (LIT). LIT is an open-source platform for visualization and understanding of NLP models. This new release includes new interpretability techniques like TCAV, Targeted Concept activation Vector. TCAV is an interpretability method for ML models that shows the importance of high level conceptsfor a predicted class.
We recently launched on-device training in TensorFlow Lite. When deploying TensorFlow Lite machine learning model to a mobile app, you may want to enable the model to be improved or personalized based on input from the device or end user. Using on-device training techniques allows you to update a model without data leaving your users’ devices, improving user privacy, and without requiring users to update the device software. It’s currently available on Android.
And we continue to work on making performance better on TensorFlow Lite. As mentioned above, XNNPACK, a library for faster floating point ops, is now turned on by default in TensorFlow Lite. This allows your models to run on an average 2.3x faster on the CPU.
Find all the talks here
You can find all of the content in this playlist, and for your convenience here are direct links to each of the sessions also:
- Simplified machine learning with Google On-Device MLManage MLOps and deploy ML to production with the new and improved TFX
- Building fair, ethical and responsible AI with the Responsible AI Toolkit
- Intro to JAX: Accelerating Machine Learning Research
- Simplified distributed training with tf.distribute parameter servers
- Cloud TPU v4: Fast, flexible, and easy-to-use
- Chip Floorplanning with Deep Reinforcement Learning
- How to get involved in machine learning