Introduction

TensorFlow is a popular open-source library developed by Google for machine learning and deep learning tasks. With its powerful capabilities, it is widely used for building and deploying machine learning models. TensorFlow 2.x introduces an eager execution mode by default, making it more user-friendly compared to its predecessors. However, one challenge arises when trying to run TensorFlow on CPUs without Advanced Vector Extensions (AVX), which are used for optimizing computations. This article delves into the details of running TensorFlow 2.0 on CPUs without AVX support and provides insights into handling potential challenges.

Understanding AVX in TensorFlow

AVX (Advanced Vector Extensions) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD. AVX allows for improved performance in computation-heavy applications such as scientific simulations and machine learning by enabling the use of SIMD (Single Instruction, Multiple Data) operations.

Since TensorFlow 2.0 leverages these hardware extensions to speed up numerical computations, running it on CPUs that don't support AVX might pose some difficulties. Fortunately, it is possible to build TensorFlow from source for a specific environment without AVX, albeit at a possible performance cost.

Building TensorFlow 2.0 Without AVX

Prerequisites

Before proceeding with the build, ensure that the following prerequisites are fulfilled:

1. Bazel: Google's build tool, which is required to compile TensorFlow.
2. Python: TensorFlow 2.0 supports Python 3.5 to 3.8.
3. NumPy: A package for scientific computing with Python.
4. GCC: A GNU Compiler Collection version that supports C++14.

Step-by-step Guide

1. Clone TensorFlow Repository

• Execution Speed: The lack of AVX optimization might lead to slower execution speed, especially for intensive computation.
• Compatibility: Custom builds are closely tied to specific hardware and software configurations, which may limit portability.
• Maintenance: Custom software builds may require additional maintenance effort to keep them updated.