Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX, AVX2 warning message occurs when the “version of TensorFlow you have installed was not compiled to take advantage of certain CPU instructions (Advanced Vector Extensions – AVX and AVX2) that could speed up some operations.”
How to fix the warning?
Here are the ways to fix the “Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX, AVX2” warning message.
- Install Tensorflow from the source
- Using a pre-built TensorFlow binary with AVX and AVX2 support
- Upgrade your CPU
- Using a Docker Image
- Check for TensorFlow Updates
Solution 1: Install Tensorflow from the source
To get the best performance, you can build TensorFlow from the source on your machine, enabling these extensions. This is a more advanced option and might take some time and expertise.
By building TensorFlow from the source, it will allow you to compile TensorFlow with AVX and AVX2 support.
To install TensorFlow from the source, follow these steps:
- Install the required dependencies, such as Python and its dependencies, tools, and TensorFlow’s dependencies.
- Clone the TensorFlow repository from GitHub.
- Configure TensorFlow with the appropriate options to enable AVX and AVX2 support.
- Build and install TensorFlow.
Solution 2: Using a pre-built TensorFlow binary with AVX and AVX2 support
Using pre-built TensorFlow binaries optimized for your hardware can save you the time and complexity of building from source.
Here’s how you can proceed with some of the options:
Anaconda often provides optimized libraries through its package manager conda. You can install TensorFlow from the Anaconda repository, which may have been compiled with AVX and AVX2 support.
To install TensorFlow using Anaconda, you can use the following command:
conda install tensorflow
Intel’s Optimized TensorFlow
Intel provides a TensorFlow distribution optimized for Intel hardware, including CPUs that support AVX and AVX2.
You can install it using pip:
pip install intel-tensorflow
Or, if you are using Anaconda, you can install it via the conda package manager as well:
conda install intel-tensorflow
Solution 3: Upgrade your CPU
Upgrading to a CPU that supports AVX and AVX2 can be an excellent long-term solution, especially if you plan to run applications that benefit from these instruction sets.
Modern CPUs provide a lot of low-level instructions besides the usual arithmetic and logic, known as extensions, e.g., SSE2, SSE4, AVX, etc.
Here are some factors to consider:
Socket Type: Ensure the new CPU is compatible with your current motherboard. If it’s not, you may also need to upgrade your motherboard.
- BIOS Update: Even if the CPU is technically compatible with the motherboard, you may need a BIOS update to ensure smooth operation.
RAM Compatibility: Newer CPUs sometimes support only newer types of RAM. Make sure to check whether you’ll need to upgrade this as well.
Solution 4: Using a Docker Image
Docker images are available that contain versions of TensorFlow compiled with various optimizations. You could consider using one of these if it suits your workflow.
Solution 5: Check for TensorFlow Updates
Future versions of TensorFlow may offer better support for these types of optimizations out of the box. Keeping your TensorFlow installation up-to-date might eventually resolve this issue.
I hope this one of the five solutions will work for you!