<p dir="auto">I am using a containerized env on CentOS with Anaconda, and a RTX 2080 Ti GPU card with 540 Tensor Core Processing Units.</p>
<p dir="auto">According to the docs: <a href="https://www.tensorflow.org/install/gpu" rel="nofollow">https://www.tensorflow.org/install/gpu</a><br>
it may not work due to lack of versioning support.</p>
<p dir="auto">Also - how do I leverage the NVIDIA TPUs/is there support for that?</p>
<p dir="auto"><strong>Edited:</strong> 17-DEC-2018 Nightly builds are CUDA 10<br>
<strong>Edited:</strong> 27-NOV-2018 Added 1.12 FINAL builds.<br>
<strong>Edited:</strong> 29-OCT-2018 Added 1.12 RC2 builds.<br>
<strong>Edited:</strong> 17-OCT-2018 Added 1.12 RC1 builds.</p>
<h2 dir="auto"><strong>Nightly Builds are now CUDA 10 as of 16-DEC-2018</strong></h2>
<ul dir="auto">
<li>[<strong>DONE</strong>] CUDA 10 in the nightly builds mid-DEC 2018 if testing goes well</li>
<li>CUDA 10 in official in mid-JAN-2019 as TF 1.13.</li>
</ul>
<p dir="auto"><a href="https://github.com/tensorflow/docs/pull/249" data-hovercard-type="pull_request" data-hovercard-url="/tensorflow/docs/pull/249/hovercard">Install instructions for CUDA 10</a> for a fresh system that should also work on an existing system if using <code class="notranslate">apt-get</code>.</p>
<p dir="auto">TensorFlow will be upgrading to <code class="notranslate">CUDA 10</code> as soon as possible.  As of TF 1.11 building TensorFlow from source works just fine with <code class="notranslate">CUDA 10</code> and possibly even before.  There is nothing special needed other than all of the <code class="notranslate">CUDA</code>, <code class="notranslate">cuDNN</code>, <code class="notranslate">NCCL</code> (optional), and <code class="notranslate">TensorRT</code> (optional) libraries.  If people have some builds feel free to link them here (Keep in mind if you download them to decided what risk you want to take based on the source.) as well as any issues.  Also<code class="notranslate"> NCCL</code> is now open source again and soon will be back to being automatically downloaded by <code class="notranslate">bazel</code> and included in the binary.</p>
<p dir="auto"><code class="notranslate">CUDA 10</code> would likely go into TF 1.13, which is not scheduled.  I will update this post as I have more info to share.  I hope to flip nightly builds to CUDA 10 in November but the TF 1.13 release will likely push to early Jan.</p>
<p dir="auto">I and some really cool people below made some binaries (even windows, see comments below) to help people out along with my really <a href="https://github.com/tfboyd/tf-tools/blob/master/install/cuda_10.md">bad "instructions"</a>.  I suspect the instructions linked in the comments below are better.</p>
<p dir="auto">Libraries used (rough list, similar to what I listed above)</p>
<ul dir="auto">
<li>Ubuntu 16.04 LTS on GCE from base Google Cloud Ubuntu image.</li>
<li>Python 2.7 because that is how I roll and someday I will change.</li>
<li>Install 410+ driver using apt-get</li>
<li>CUDA 10.0.130 from .tgz install (do not install the driver)</li>
<li>cuDNN-10.0 v7.3.0.29 rom tgz install</li>
<li>nccl 2.3.4 for CUDA 10 (I am not sure it matters I have mixed them up before)</li>
<li>TensorRT-5.0.0 for CUDA 10 cudnn 7.3 (TensorRT 5 was not stated as supported by Tensorflow when</li>
<li>Compute Capability: 3.7,5.2,6.0,6.1,7.0</li>
</ul>
<p dir="auto"><em><strong>TF 1.12.0 FINAL</strong></em></p>
<p dir="auto"><strong>Python 2.7 (Ubuntu 16.04)</strong></p>
<ul dir="auto">
<li><a href="https://storage.googleapis.com/tf-performance/tf_binary/tensorflow-1.12.0.a6d8ffa.AVX2.CUDA10-cp27-cp27mu-linux_x86_64.whl" rel="nofollow">AVX2 + CUDA 10</a></li>
</ul>
<p dir="auto"><strong>Python 3.5 (Ubuntu 16.04)</strong></p>
<ul dir="auto">
<li><a href="https://storage.googleapis.com/tf-performance/tf_binary/tensorflow-1.12.0.a6d8ffa.AVX2.CUDA10-cp35-cp35m-linux_x86_64.whl" rel="nofollow">AVX2 + CUDA 10</a></li>
</ul>
<p dir="auto"><strong>Dockerfiles</strong><br>
These are partial files and the apt-get commands should work on non-Docker systems as well assuming you have the NVIDIA apt-get repositories which should be the same as listed on <a href="https://www.tensorflow.org/install/gpu" rel="nofollow">tf.org</a>.</p>
<ul dir="auto">
<li><a href="https://github.com/tfboyd/tensorflow/blob/cuda_10_dockerfile/tensorflow/tools/dockerfiles/partials/nvidia-devel.partial.Dockerfile">Devel</a>  Confirmed by building TensorFlow at 1.12RC0.</li>
<li><a href="https://github.com/tfboyd/tensorflow/blob/cuda_10_dockerfile/tensorflow/tools/dockerfiles/partials/nvidia.partial.Dockerfile">Runtime</a></li>
</ul>