TensorFlow and High Level APIs

I got a chance to watch this great presentation on the upcoming release of TensorFlow v2 by Martin Wicke. He goes over the big changes - and there’s a lot - plus how you can upgrade your earlier versions of TensorFlow to the new one. Let’s hope the new version is faster than before! My video notes are below:

TensorFlow

• Since it’s release, TensorFlow (TF) has grown into a vibrant community
• Learned a lot on how people used TF
• Realized using TF can be painful
• You can do everything in TF but what is the best way
• TF 2.0 alpha is just been released
• Do ‘pip install -U –pre tensorflow’
• Adopted tf.keras as high-level API (SWEET!)
• Includes eager execution by default
• TF 2 is a major release that removes duplicate functionality, makes the APIs consistent, and makes it compatible in different TF versions
• New flexibilities: full low-level API, internal operations are accessible now (tf.raw_ops), and inheritable interfaces for variables, checkpoints, and layers
• How do I upgrade to TensorFlow 2
• Google is starting the process of converting the largest codebase ever
• Will provide migration guides and best practices
• Two scripts will be shipped: backward compatibility and a conversion script
• The reorganization of API causes a lot of function name changes

TensorFlow v2

• Release candidate in ‘Spring 2019’ < might be a bit flexible in the timeline
• All on GitHub and project tracker
• Needs user testing, please go download it
• Karmel Allison is an Engineering manager for TF and will show off high-level APIs
• TF adopted Keras
• Implemented Keras and optimized in TF as tf.keras
• Keras built from the ground up to be pythonic and simple
• Tf.keras was built for small models, whereas in Google they need HUGE model building
• Focused on production-ready estimators
• How do you bridge the gap from simple vs scalable API
• Debug with Eager, easy to review Numpy array
• TF also consolidated many APIs into Keras
• There’s one set of Optimizers now, fully scalable
• One set of Metrics and Losses now
• One set of Layers
• Took care of RNN layers in TF, there is one version of GRE and LSTM layers and selects the right CPU/GPU at runtime
• Easier configurable data parsing now (WOW, I have to check this out)
• TensorBoard is now integrated into Keras
• TF distribute strategy for distributing work with Keras
• Can add or change distribution strategy with a few lines of code
• TF Models can be exported to SavedModel using the Keras function (and reloaded too)
• Coming soon: multi-node sync
• Coming soon: TPU’s

There’s a lot in this 22 minute video about TensorFlow v2. Must watch.