<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>: No</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>: Linux Ubuntu 16.04</li>
<li><strong>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device</strong>: n/a</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: binary (Docker tensorflow/tensorflow)</li>
<li><strong>TensorFlow version (use command below)</strong>: 1.10.1 (from core/public/version.h)</li>
<li><strong>Python version</strong>:2.7.12</li>
<li><strong>Bazel version (if compiling from source)</strong>: n/a</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>: n/a</li>
<li><strong>CUDA/cuDNN version</strong>: n/a</li>
<li><strong>GPU model and memory</strong>: n/a</li>
<li><strong>Exact command to reproduce</strong>:<br>
# docker run -it tensorflow/tensorflow bash<br>
root@xxxxxxx:/notebooks# python2.7 -c "import tensorflow as tf"<br>
Illegal instruction</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">This is a request to update documentation to include minimal system requirements, especially the description for Docker images that include prebuilt binaries.</p>
<p dir="auto">For example, the main Docker image on DockerHub (tensorflow/tensorflow, 19M downloads) <a href="https://github.com/tensorflow/tensorflow/issues/17411" data-hovercard-type="issue" data-hovercard-url="/tensorflow/tensorflow/issues/17411/hovercard">currently does not operate on CPU's without AVX support</a>.  This includes CPUs sold as recently as 4 years ago.</p>
<p dir="auto">This requirement should be stated in the Docker image description displayed in DockerHub.  Perhaps also include a link to an alternative docker image that users with non-AVX CPUs may utilize to build from source.</p>
<h3 dir="auto">Source code / logs</h3>
<p dir="auto">n/a</p>
<p dir="auto">As announced in release notes, TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets. This means on any CPU that do not have these instruction sets either CPU or GPU version of TF will fail to load with any of the following errors:</p>
<ul dir="auto">
<li><code class="notranslate">ImportError: DLL load failed:</code></li>
<li>A crash with return code 132</li>
</ul>
<p dir="auto">Our recommendation is to build TF from sources on these systems.</p>
<h3 dir="auto">System information</h3>
<ul dir="auto">
<li><strong>Have I written custom code (as opposed to using a stock example script provided in TensorFlow)</strong>: No</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>: ubuntu/windows/macos</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: binary</li>
<li><strong>TensorFlow version (use command below)</strong>: 1.6 and up</li>
<li><strong>Python version</strong>:  2.7, 3.3, 3.4, 3.5, 3.6 and any newer</li>
<li><strong>Bazel version (if compiling from source)</strong>: n/a</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>: n/a</li>
<li><strong>CUDA/cuDNN version</strong>: any</li>
<li><strong>GPU model and memory</strong>: any</li>
<li><strong>Exact command to reproduce</strong>: python -c "import tensorflow as tf"</li>
</ul>