ensorFlow has grown to become one of the most loved and widely adopted ML platforms in the world. This community includes:
Researchers (e.g., to forecast earthquake aftershocks and detect breast cancer).
Developers (e.g., to build apps to identify diseased plants and to help people lose weight).
Companies (e.g., by eBay, Dropbox and AirBnB to improve their customer experience).
And many others.
In November, TensorFlow celebrated its 3rd birthday with a look back at the features added throughout the years. We’re excited about another major milestone, TensorFlow 2.0.
TensorFlow 2.0 will focus on simplicity and ease of use, featuring updates like:
Easy model building with Keras and eager execution.
Robust model deployment in production on any platform.
Powerful experimentation for research.
Simplifying the API by cleaning up deprecated APIs and reducing duplication.
Over the last few years, we’ve added a number of components to TensorFlow. With TensorFlow 2.0, these will be packaged together into a comprehensive platform that supports machine learning workflows from training through deployment. Let’s take a look at the new architecture of TensorFlow 2.0 using a simplified, conceptual diagram as shown below:
Note: Although the training part of this diagram focuses on the Python API, TensorFlow.js also supports training models. Other language bindings also exist with various degrees of support, including: Swift, R, and Julia.
Easy model building
In a recent blog post we announced that Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.
Here’s an example workflow (in the coming months, we will be working to update the guides linked below):
Load your data using tf.data. Training data is read using input pipelines which are created using tf.data. Feature characteristics, for example bucketing and feature crosses are described using tf.feature_column. Convenient input from in-memory data (for example, NumPy) is also supported.
Build, train and validate your model with tf.keras, or use Premade Estimators. Keras integrates tightly with the rest of TensorFlow so you can access TensorFlow’s features whenever you want. A set of standard packaged models (for example, linear or logistic regression, gradient boosted trees, random forests) are also available to use directly (implemented using the tf.estimator API). If you’re not looking to train a model from scratch, you’ll soon be able to use transfer learning to train a Keras or Estimator model using modules from TensorFlow Hub.
Run and debug with eager execution, then use tf.function for the benefits of graphs. TensorFlow 2.0 runs with eager execution by default for ease of use and smooth debugging. Additionally, the tf.function annotation transparently translates your Python programs into TensorFlow graphs. This process retains all the advantages of 1.x TensorFlow graph-based execution: Performance optimizations, remote execution and the ability to serialize, export and deploy easily, while adding the flexibility and ease of use of expressing programs in simple Python.
Use Distribution Strategies for distributed training. For large ML training tasks, the Distribution Strategy API makes it easy to distribute and train models on different hardware configurations without changing the model definition. Since TensorFlow provides support for a range of hardware accelerators like CPUs, GPUs, and TPUs, you can enable training workloads to be distributed to single-node/multi-accelerator as well as multi-node/multi-accelerator configurations, including TPU Pods. Although this API supports a variety of cluster configurations, templates to deploy training on Kubernetes clusters in on-prem or cloud environments are provided.
Export to SavedModel. TensorFlow will standardize on SavedModel as an interchange format for TensorFlow Serving, TensorFlow Lite, TensorFlow.js, TensorFlow Hub, and more.
Robust model deployment in production
for more visit :
https://medium.com/tensorflow/whats-coming-in-tensorflow-2-0-d3663832e9b8?linkId=62351082&fbclid=IwAR17At-YLut-nz0FAnZL317N1gSrR0hHXqQzHmmzHxx6Em4zoe3JxZWGHH0
