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XLA is a compiler for accelerating the runtime of TensorFlow Models.
tf.config.optimizer.set_jit
Tracing When executing an XLA-enabled function (like xla_generate() for the first time, it will internally
try to infer the computational graph, which is time-consuming. But following executions will be fast as
the graph won't be generated again
AI workload specific compiler for linear algebra that can accelerate TensorFlow models with potentially
no source code changes.
XLA comes packaged inside the tensorflow library, and it can be triggered with the jit_compile argument
in any graph-creating function such as tf.function.
When using Keras methods like fit() and predict(), you can enable XLA simply by passing the jit_compile
argument to model.compile()
Eager execution refers to a programming environment that evaluates operations immediately. Python
implements eager execution
TensorFlow 2.0 has a feature called AutoGraph which lets you write TensorFlow graph code using
regular Python code syntax by using the decorator @tf.function
Additional Reading
The TensorFlow XLA guide provides an introduction to XLA, explaining its concepts, features, and benefits. It also includes instructions on how to use XLA with TensorFlow and how to enable XLA compilation for improved performance.
https://www.tensorflow.org/xla
TensorFlow XLA GitHub Repository
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/compiler/xla
TensorFlow XLA Code Labs: TensorFlow provides code labs that offer hands-on exercises and tutorials for XLA.
https://www.tensorflow.org/resources/code-labs