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TensorFlow and High Level APIs

TensorFlowTensorFlow

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:

  • 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, v2TensorFlow, v2
Martin Wicke of Google shows the new TensorFlow v2 conversion script
  • 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.

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