Dive deep into the eager execution runtime of TensorFlow with Software Engineer Alex Passos in this 45-minute technical presentation from the TensorFlow team's internal training sessions. Explore key concepts such as reloj generation, Python context, tensor handling, execution modes, kernel operations, code generation, and multidevice calls. Gain insights into the TensorFlow Executor, host vs device memory, and solutions to common challenges. Enhance your understanding of TensorFlow's inner workings and improve your ability to leverage its capabilities in your machine learning projects.
Overview
Syllabus
Introduction
Defining reloj
Generating reloj
Python context
Python vs C
Eager code
tensor handle
execution
Execution in Python
Eager execute
Sync mode
Calling a kernel
Class kernel
Kernels
Code generation
Summary
Async mode
Function calls
Function library runtime
Multidevice call
Rendezvous
Tensorflow Executor
Highlights
Host vs device memory
TF in 32 problem
Solution
Taught by
TensorFlow
