How to Fix the “Your CPU Supports Instructions That This TensorFlow Binary Was Not Compiled to Use: avx20” Error

If you’re trying to run TensorFlow on your computer and come across the error message “your cpu supports instructions that this TensorFlow binary was not compiled to use: avx20,” don’t worry. This error message is relatively common and can be fixed by following simple steps. This article will explain what the error message means, why it occurs, and how to fix it.

What is the “avx20” Error?

The “avx20” error message occurs when TensorFlow, a popular open-source machine learning library, cannot use the Advanced Vector Extensions (AVX) 2.0 instructions on your computer’s CPU. AVX2 is a set of instructions that can speed up some mathematical operations in TensorFlow, but not all CPUs support it.

Why Does the “avx20” Error Occur?

The “avx20” error message occurs because the TensorFlow binary you’re trying to use was compiled without AVX2 support. This means the TensorFlow binary you’re using doesn’t have the necessary instructions to run on a CPU supporting AVX2. If your CPU supports AVX2, compile TensorFlow from the source or use a pre-compiled binary with AVX2 support.

How to Fix the “avx20” Error?

There are several ways to fix the “avx20” error message, depending on your level of expertise and the operating system you’re using. Here are some methods you can try:

Method 1: Upgrade Your CPU

If you’re running TensorFlow on an older computer with a CPU that doesn’t support AVX2, you may need to upgrade your CPU to fix the error message. This method is the most effective but also the most expensive.

Method 2: Use a Pre-compiled TensorFlow Binary with AVX2 Support

If you’re using a pre-compiled binary of TensorFlow, check to see if a version includes AVX2 support. Many pre-compiled binaries are available with AVX2 support, which can be downloaded from the TensorFlow website or GitHub. You must download the appropriate binary for your operating system and CPU architecture.

Method 3: Compile TensorFlow from Source with AVX2 Support

If you’re comfortable compiling software from a source, you can compile TensorFlow with AVX2 support. This method requires some knowledge of the command line and can be time-consuming. However, it’s the most flexible method because you can customize TensorFlow to suit your needs.

Here’s how to compile TensorFlow from the source with AVX2 support:

1. Install the required dependencies: TensorFlow has several dependencies, such as Bazel and NumPy. You’ll need to install these dependencies before compiling TensorFlow from the source. The TensorFlow website provides detailed instructions for installing these dependencies.

• Clone the TensorFlow repository: Use Git to clone the TensorFlow repository onto your computer. You can do this by running the following command in your terminal: Git clone https://github.com/tensorflow/tensorflow.git 
• Configure TensorFlow: Use the ‘configure’ script to configure TensorFlow with the appropriate options for your system. This script will prompt you to enter information such as the path to your Python interpreter and the location of your CUDA installation (if you’re using a GPU). To enable AVX2 support, include the ‘–copt=-march=native’ flag when running the ‘configure’ script: ./configure 

1. Build TensorFlow: Use the bazel build tool to build TensorFlow from the source. You’ll need to specify the appropriate flags to enable AVX2 support. Here’s an example command to build TensorFlow with AVX2 help: bazel build –config=opt –copt=-march=native //tensorflow/tools/pip_package:build_pip_package 
2. Install TensorFlow: Once you’ve built TensorFlow, you can create a Python package by running the following command: bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg 
3. This will create a ‘.whl’ file in the ‘/tmp/tensorflow_pkg’ directory. You can install this package using pip.

Pros and Cons of Each Method

Each method for fixing the “avx20” Error has advantages and disadvantages. Here’s a quick summary:

Upgrading Your CPU

Pros:

• The most effective method for fixing the error message.
• Can improve overall system performance.

Cons:

• The most expensive method.
• It may require significant time and effort to replace the CPU.

Using a Pre-compiled TensorFlow Binary with AVX2 Support

Pros:

• Relatively easy to do.
• No need to compile TensorFlow from the source.

Cons:

• It may not be available for all operating systems or CPU architectures.
• It may provide a different level of customization than compiling from source.

Compiling TensorFlow from Source with AVX2 Support

Pros:

• The most flexible method.
• It can be customized to suit your needs.

Cons:

• Requires some knowledge of the command line and the build process.
• It can be time-consuming.

Conclusion

The “your cpu supports instructions that this TensorFlow binary was not compiled to use: avx20” error message is a common issue for users of TensorFlow. 

However, it can be fixed by upgrading your CPU, using a pre-compiled TensorFlow binary with AVX2 support, or compiling TensorFlow from a source with AVX2 support. Each method has advantages and disadvantages, so choose the one that’s right for you.