<p dir="auto">Hi,</p>
<p dir="auto">Does tensorflow version r1.9 support Intel Xeon E5645 CPU?</p>
<p dir="auto">Thanks !<br>
Alan</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>