SolutionYour CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

Table of Contents

SolutionYour CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

problem causes

Solution

1. Compile TensorFlow source code

2. Install a lower version of TensorFlow

in conclusion

Sample code:

AVX instruction set

AVX2 instruction set

Performance advantages and application scenarios

Solution Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

When you run TensorFlow code, you may encounter the following error message:

plaintextCopy codeYour CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

This error means that your CPU supports the AVX and AVX2 instruction sets, but the TensorFlow binary you are using has not been compiled to support these instruction sets. In this blog post, we will explain how to solve this problem.

Problem reason

TensorFlow is installed using precompiled binaries by default. These binaries are compiled for compatibility with multiple CPU architectures. For example, some binaries may not use the AVX and AVX2 instruction sets because these instruction sets were introduced in newer processors. If your CPU supports the AVX and AVX2 instruction sets, but you use TensorFlow binaries that do not support these instruction sets, the above error will occur.

Solution

To solve this problem, you have two options:

1. Compile TensorFlow source code

This option requires some extra steps, but ensures that the version of TensorFlow you use is optimized for your hardware.

  1. First, you need to install the Bazel build tools. For specific installation steps, please refer to Bazel official documentation.
  2. Download the TensorFlow source code. You can get the source code via git clone or download the tarball file.
  3. Switch to the TensorFlow source code directory and execute the following commands to configure the build process:
plaintextCopy code./configure
  1. Execute the following command to start the compilation process of TensorFlow:
plaintextCopy codebazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
  1. After compilation is completed, execute the following command to generate the pip installation package:
plaintextCopy codebazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
  1. Finally, install the generated pip package:
plaintextCopy codepip install /tmp/tensorflow_pkg/tensorflow-<version>.whl
  1. ???? here is the version number of TensorFlow.

2. Install a lower version of TensorFlow

If you don’t want to compile the TensorFlow source code, you can also choose to install a lower version of TensorFlow that does not use the AVX and AVX2 instruction sets. You can install a specific version of TensorFlow with the following command:

plaintextCopy codepip install tensorflow==<version>

The ???? here is the TensorFlow version number you want to install. Please note that when choosing this option, you may miss out on some of the latest version features and optimizations.

Conclusion

In this article, we describe how to resolve the “Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2” error encountered when running TensorFlow code. You can choose to compile the TensorFlow source code to optimize it for your hardware, or install a lower version of TensorFlow that does not use the AVX and AVX2 instruction sets.

Sample code:

pythonCopy codeimport tensorflow as tf
# Check the TensorFlow version and print it out
print("TensorFlow version:", tf.__version__)
# Check the CPU characteristics of the current system
from tensorflow.python.platform import build_info
print("CPU supported instructions:", build_info.detect_cpu_features())
# Define a simple neural network model
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])
#Import the MNIST handwritten digits data set
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Preprocess the data into floating point numbers between 0 and 1
x_train, x_test = x_train / 255.0, x_test / 255.0
# Compile and train the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

In this example code, we first imported the TensorFlow library and checked the currently used TensorFlow version and the instruction set supported by the CPU. We then define a simple neural network model for handwritten digit recognition. Next, we imported the MNIST dataset and preprocessed the data. Finally, we compile and train the model using the compilation options and training data. You can choose different TensorFlow versions according to your needs. If you want to use optimized binaries, you can compile the TensorFlow source code as mentioned before. If you want to use a lower version of TensorFlow, you can install a specific version by modifying the ??pip install tensorflow==?? statement in the code.

AVX (Advanced Vector Extensions) and AVX2 are instruction set architectures introduced by Intel. They are all designed to improve the floating point computing performance of the CPU. AVX and AVX2 will be introduced in detail below.

AVX instruction set

The AVX instruction set was first introduced in the Intel Sandy Bridge processor architecture. It introduced a 256-bit wide SIMD (Single Instruction, Multiple Data) register that can process 8 single-precision floating-point numbers or 4 double-precision floating-point numbers simultaneously. The AVX instruction set has great advantages in vector calculations and parallel computing, and can accelerate applications involving floating point operations. The AVX instruction set provides some new instructions, such as VADDPS (corresponding to single-precision floating-point addition), VMULPS (corresponding to single-precision floating-point multiplication), etc. These instructions allow programmers to process multiple data elements simultaneously with a single instruction rather than executing them one at a time. The AVX instruction set also introduces some advanced features such as the Fused Multiply-Add (FMA) instruction. The FMA instruction can perform multiplication and accumulation operations in a single instruction, thereby improving computing performance.

AVX2 instruction set

The AVX2 instruction set was introduced in the Intel Haswell processor architecture and is an extended and improved version of the AVX instruction set. The AVX2 instruction set introduces more SIMD instructions, which can provide higher computing performance. The AVX2 instruction set introduces 256-bit and 128-bit wide integer SIMD instructions, allowing parallel calculations on integer data. The AVX2 instruction set provides a series of integer addition, subtraction, multiplication and logical operation instructions, as well as instructions for packing and unpacking integer data, which can process multiple integer data simultaneously in one instruction. The AVX2 instruction set also provides richer control flow instructions, such as vector comparison and conditional selection instructions, which can easily implement more complex program logic.

Performance advantages and application scenarios

The AVX and AVX2 instruction sets provide powerful hardware support for large-scale parallel data processing, which can significantly improve computing performance. Therefore, they are widely used in many fields, especially in fields such as scientific computing, data analysis, and machine learning that require parallel computing. In machine learning and deep learning, using the AVX and AVX2 instruction sets can accelerate key computing steps such as matrix operations, convolution calculations, and vector operations, thereby increasing the speed of training and inference. In summary, the AVX and AVX2 instruction sets provide more efficient vector computing and parallel computing capabilities by introducing wider SIMD registers and richer instructions, which can significantly improve computing performance in applications that require large-scale parallel data processing.

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