Accelerated linear algebra (XLA) is a domain-specific compiler for linear algebra. According to https://www.tensorflow.org/performance/xla/, it is still in the experimental stage and is used to optimize TensorFlow computations. It can provide improvements in execution speed, memory usage, and portability on the server and mobile platforms. It provides two-way JIT (Just In Time) compilation or AoT (Ahead of Time) compilation. Using XLA, you can produce platform-dependent binary files (for a large number of platforms like x64, ARM, and so on), which can be optimized for both memory and speed.
Using XLA to enhance computational performance
by Amita Kapoor, Antonio Gulli
TensorFlow 1.x Deep Learning Cookbook
- Preface
- TensorFlow - An Introduction
- Introduction
- Installing TensorFlow
- Hello world in TensorFlow
- Understanding the TensorFlow program structure
- Working with constants, variables, and placeholders
- Performing matrix manipulations using TensorFlow
- Using a data flow graph
- Migrating from 0.x to 1.x
- Using XLA to enhance computational performance
- Invoking CPU/GPU devices
- TensorFlow for Deep Learning
- Different Python packages required for DNN-based problems
- Regression
- Neural Networks - Perceptron
- Convolutional Neural Networks
- Advanced Convolutional Neural Networks
- Introduction
- Creating a ConvNet for Sentiment Analysis
- Inspecting what filters a VGG pre-built network has learned
- Classifying images with VGGNet, ResNet, Inception, and Xception
- Recycling pre-built Deep Learning models for extracting features
- Very deep InceptionV3 Net used for Transfer Learning
- Generating music with dilated ConvNets, WaveNet, and NSynth
- Answering questions about images (Visual Q&A)
- Classifying videos with pre-trained nets in six different ways
- Recurrent Neural Networks
- Introduction
- Neural machine translation - training a seq2seq RNN
- Neural machine translation - inference on a seq2seq RNN
- All you need is attention - another example of a seq2seq RNN
- Learning to write as Shakespeare with RNNs
- Learning to predict future Bitcoin value with RNNs
- Many-to-one and many-to-many RNN examples
- Unsupervised Learning
- Autoencoders
- Reinforcement Learning
- Mobile Computation
- Introduction
- Installing TensorFlow mobile for macOS and Android
- Playing with TensorFlow and Android examples
- Installing TensorFlow mobile for macOS and iPhone
- Optimizing a TensorFlow graph for mobile devices
- Profiling a TensorFlow graph for mobile devices
- Transforming a TensorFlow graph for mobile devices
- Generative Models and CapsNet
- Distributed TensorFlow and Cloud Deep Learning
- Introduction
- Working with TensorFlow and GPUs
- Playing with Distributed TensorFlow: multiple GPUs and one CPU
- Playing with Distributed TensorFlow: multiple servers
- Training a Distributed TensorFlow MNIST classifier
- Working with TensorFlow Serving and Docker
- Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
- Running Distributed TensorFlow on Google CloudML
- Running Distributed TensorFlow on Microsoft Azure
- Running Distributed TensorFlow on Amazon AWS
- Learning to Learn with AutoML (Meta-Learning)
- TensorFlow Processing Units
Using XLA to enhance computational performance
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