The x86 instruction set architecture for microprocessors from Intel and AMD supports Advanced Vector Extensions (AVX), which were first proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge processor shipping in Q1 2011 and later by AMD with the Bulldozer processor shipping in Q3 2011.

New features, new commands, and a new coding structure are offered by AVX.

TensorFlow’s shared library generated this alert message. The shared library does not contain the kind of instructions that your CPU could use, as the message indicates.

Your CPU Supports Instructions that this Tensorflow Binary was not Compiled to use: AVX2

What Motivates This Warn?

The binaries for TensorFlow 1.6 use AVX instructions, which might not work on older CPUs. As a result, the AVX won’t work on older CPUs, while the user will need to build TensorFlow from source for newer CPUs.

You can find all the details you require about this warning in the sections below. A technique for removing this warning is also provided for future use.

What is the AVX Used for?

The FMA (Fused multiply-add), a floating-point multiply-add operation, was specifically introduced by the AVX. This entire operation is completed in a single step. This effortlessly speeds up a lot of processes.

Additionally, it makes the dot-product, matrix multiply, convolution, and other algebraic operations faster and simpler to use. And for every machine-learning training, these are all the commonly used and fundamental operations. The AVX and FMA-capable CPUs will be much faster than the older models. It’s a good point, but the warning does state that your CPU supports AVX.

Why isn’t it Applied Automatically?

This is so because the CPU extensions are not built into the TensorFlow default distribution. It mentions the AVX, AVX2, FMA, and other CPU extensions.

The default builds that are currently available do not have the instructions that cause this problem enabled by default. To make this more compatible with as many CPUs as possible, they are not enabled.

These extensions are also much slower on the CPU than the GPU, to compare. Small-scale machine learning uses the CPU, whereas medium- or large-scale machine learning training is more likely to use the GPU.

Correcting the Warning!

These alerts are merely straightforward messages. These alerts are intended to inform you of how TensorFlow was built from source. TensorFlow may run faster on the computer if it is built from scratch. Therefore, the only thing that these warnings are alerting you to is the TensorFlow source buildup.

You can disregard these AVX support warnings if your computer has a GPU. because a GPU device will be used to dispatch the most expensive ones. You can simply ignore this error by adding the following if you don’t want to see it any longer:

Set the mapping object for the OS module and import it into the main programme code.

For turning off the warning

os import

Os.Environment.TF CPP MIN LOG LEVEL = “2”

Use the export command in the bash shell instead if you’re using a Unix system.

TF CPP MIN LOG LEVEL = 2 export