TensorFlow, the machine learning (ML) platform developed by Google, is about to get a version 2.0 with a slew of new features. With the open source software library used by enterprises from eBay to Intel, SAP to Twitter, many data scientists will be watching closely – and hoping it’s not too buggy!
The open source platform underpins a wide range of applications: TensorFlow’s team cites applications to forecast earthquake aftershocks, identify diseased plants and help improve customers experience. It’s not hugely easy to use however – and the new release will focus on simplicity, its team said.
This will be music to the ears of many. ML is hard: from moving models to production, to tracking which parameters, code, and data went into each experiment. Many enterprise users have built their own internal platforms (e.g. Facebook, Google and Uber with FBLearner Flow, TFX, and Michelangelo respectively) to manage data preparation, model training and deployment.
(A public preview is coming “early this year” and after 2.0’s release the team will provide 12 months of security patches to the last 1.x release, they said).
One immediate complaint from those watching though: a strong proprietary GCP focus. It’s a common gripe: as as San Francisco-based Databricks’ Matei Zaharia puts it in a blog last year: “Typical ML platforms only support a small set of built-in algorithms, or a single ML library, and they are tied to each company’s infrastructure. Users cannot easily leverage new ML libraries, or share their work with a wider community.”
All examples are for Google Cloud. Feels unfair to be promoting as a universal solution when all or almost all examples are for Google Cloud. Where in the official documentation is the Distributed/Multi-Worker Kubernetes example for a bare metal GPU cluster?
The mood music from TensorFlow is that interoperability will improve.
Updates will include easy model building with Keras and cleaning up deprecated APIs, while reducing duplication, TensorFlow’s team said.
“With the rapid evolution of ML, the platform has grown enormously and now supports a diverse mix of users with a diverse mix of needs. With TensorFlow 2.0, we have an opportunity to clean up and modularize the platform.”
A major push by the team is on improving compatibility and parity across platforms and components by standardising exchange formats and aligning APIs, they added.
Deployment Three Ways
With TensorFlow 2.0, once you’ve trained and saved your model, you can execute it directly in your application or serve it via three deployment libraries.
To simplify the migration to TensorFlow 2.0 meanwhile, there will be a conversion tool which updates TensorFlow 1.x Python code to use TensorFlow 2.0 compatible APIs, or flags cases where code cannot be converted automatically.
This article is from the CBROnline archive: some formatting and images may not be present.
Join Our Newsletter
Want more on technology leadership?
Sign up for Tech Monitor's weekly newsletter, Changelog, for the latest insight and analysis delivered straight to your inbox.