<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>:<br>
Ubuntu 18.04 Bionic Beaver<br>
AMD Phenom x4 II 965</li>
<li><strong>TensorFlow installed from (source or binary)</strong>: binary; docker image tensorflow-1.8.0-gpu-py3</li>
<li><strong>TensorFlow version (use command below)</strong>: 1.18.0</li>
<li><strong>Python version</strong>: 3.5</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>:<br>
CUDA 9.0<br>
CuDNN 7.0.5</li>
<li><strong>GPU model and memory</strong>:<br>
Geforce GTX 760 (compute capability 3.0) 2GB</li>
<li><strong>Exact command to reproduce</strong>:<br>
<code class="notranslate">import tensorflow as tf</code> on tensorflow-gpu on system without AVX</li>
</ul>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">Jupyter Kernel crashes when doing <code class="notranslate">import tensorflow as tf</code>using a system that does not have AVX.</p>
<p dir="auto">Similarly if i enter a python shell and do <code class="notranslate">import tensorflow as tf</code> i get an <code class="notranslate">Illegal Instruction (core dumped)</code> error and it exits the shell.</p>
<p dir="auto">I have confirmed this is due to the AVX instruction because when i use the docker image Tensorflow-1.15.0-gpu-py3 I am able to successfully import tensorflow.</p>
<p dir="auto"><em>Unfortunately</em>, tensorflow 1.15.0 requires compute capability 3.5 or higher which is extremely frusterating because i cannot find a build of tensorflow online that supports compute capaibility 3.0 but does not use AVX instructions.</p>
<p dir="auto">Why do i need a CPU that supports AVX just to import tensorflow when i dont actually need the AVX instruction since i have a supported GPU (at least it is supported on all tensorflow versions other than 1.15.0).</p>
<p dir="auto">Is there a way around this or do i need to build tensorflow from source to not use AVX but to allow compute capability 3.0? I tried using tensorflow-gpu-1.18.0-devel-py to build a new version from source but it did not even ask me what compute capabilities i wanted to build for and so i just let it build but it took over 24 hours and it was still going.</p>
<p dir="auto">Can someone either provide a tensorflow-gpu release with CUDA 9.0 and CuDNN 7 support that does not use AVX and supports compute capability 3.0 or at least tell me the best way to obtain such a build (since apparently building using the docker-devel image wont work).</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>