<p dir="auto"><em>Please make sure that this is a build/installation issue. As per our <a href="https://github.com/tensorflow/tensorflow/blob/master/ISSUES.md">GitHub Policy</a>, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template</em></p>
<p dir="auto"><strong>System information</strong></p>
<ul dir="auto">
<li>OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10</li>
<li>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: N/A</li>
<li>TensorFlow installed from (source or binary): binary</li>
<li>TensorFlow version: 1.13.0rc0</li>
<li>Python version: 3.6.8</li>
<li>Installed using virtualenv? pip? conda?: pip</li>
<li>Bazel version (if compiling from source): N/A</li>
<li>GCC/Compiler version (if compiling from source): N/A</li>
<li>CUDA/cuDNN version: CUDA 10.0, CUDNN 7.4.1</li>
<li>GPU model and memory: GeForce GTX 980 Ti</li>
</ul>
<p dir="auto"><strong>Describe the problem</strong></p>
<p dir="auto">The 1.13 release candidate requires a minimum compute capability of 6.0 (so it ignores lower-level devices).  Is this an intentional change that we can expect to see in the full release? Or just an artifact of the release candidate build?</p>
<p dir="auto"><strong>Provide the exact sequence of commands / steps that you executed before running into the problem</strong></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="from tensorflow.python.client import device_lib
device_lib.list_local_devices()"><pre class="notranslate"><code class="notranslate">from tensorflow.python.client import device_lib
device_lib.list_local_devices()
</code></pre></div>
<p dir="auto"><strong>Any other info / logs</strong><br>
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="2019-01-30 14:29:07.690292: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-01-30 14:29:07.883552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 980 Ti major: 5 minor: 2 memoryClockRate(GHz): 1.291
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-30 14:29:07.888807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1483] Ignoring visible gpu device (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2) with Cuda compute capability 5.2. The minimum required Cuda capability is 6.0.
2019-01-30 14:29:07.894771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-30 14:29:07.898191: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2019-01-30 14:29:07.901776: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
[name: &quot;/device:CPU:0&quot;
device_type: &quot;CPU&quot;
memory_limit: 268435456
locality {
}
incarnation: 8036038049545966896
]"><pre class="notranslate"><code class="notranslate">2019-01-30 14:29:07.690292: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-01-30 14:29:07.883552: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce GTX 980 Ti major: 5 minor: 2 memoryClockRate(GHz): 1.291
pciBusID: 0000:01:00.0
totalMemory: 6.00GiB freeMemory: 4.97GiB
2019-01-30 14:29:07.888807: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1483] Ignoring visible gpu device (device: 0, name: GeForce GTX 980 Ti, pci bus id: 0000:01:00.0, compute capability: 5.2) with Cuda compute capability 5.2. The minimum required Cuda capability is 6.0.
2019-01-30 14:29:07.894771: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-01-30 14:29:07.898191: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2019-01-30 14:29:07.901776: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 8036038049545966896
]
</code></pre></div>
<p dir="auto">I noticed that tensorflow always takes about ~2min before it actually starts to compute. I've been trying to find out, why this happens, and nothing really worked so far.</p>
<p dir="auto"><a href="https://www.tensorflow.org/install/install_windows" rel="nofollow">Tensorflow site</a> says, I should use CUDA® Toolkit 9.0 and cuDNN v7.0. I have CUDA 9.0, so I downloaded CuDNN 7.0.5 for CUDA 9.0 and pasted the files to *C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0*, overwriting the ones form cuDNN 7.1.2, which I tested earlier. To make sure, I pip-installed tensorflow-gpu into a fresh anaconda env. See install <a href="https://pastebin.com/rjiV1s3b" rel="nofollow">here</a>. The issue is still the same.</p>
<p dir="auto">CUDA works, since it prints the  <em>'Hello, TensorFlow!'</em>, when I use the official test example, but before that it takes like 2minutes every time!</p>
<p dir="auto">When I tested this with <a href="https://drive.google.com/drive/folders/1lVK_ABvVHzVYKs7X5SUhcZFBgKpC41Qw" rel="nofollow">another wheel</a> (<a href="http://www.python36.com/install-tensorflow-gpu-windows/" rel="nofollow">which is linked in this tutorial</a>, I did not compile it myself.) on cuda 9.1/cudnn7.0.5, I had the same issues. A NVIDIA employee <a href="https://stackoverflow.com/questions/49770217/why-does-cuda-initialisation-take-so-long-python-vscode-anaconda-tensorflow" rel="nofollow">on stackoverflow</a> suggested, I may be hitting a lengthy JIT compile step, because the GTX 1080 has compute capability of 6.1, which the wheel I used may not be compiled for.</p>
<p dir="auto">So I tried to find wheels for tensorflow with compute capability 6.1 for windows, but <a href="https://github.com/fo40225/tensorflow-windows-wheel/tree/master/1.5.0/py36/GPU/cuda91cudnn7avx2">the only one I found</a> and tested produced the same problem.</p>
<p dir="auto">Am I doing something wrong here, or do I just have to accept the 2min delay everytime I start my tensorflow/keras scripts?</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>:<br>
Code:</li>
</ul>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="import time
start_time = time.time()
import tensorflow as tf
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
timer = time.time()
print(timer - start_time)"><pre class="notranslate"><code class="notranslate">import time
start_time = time.time()
import tensorflow as tf
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
timer = time.time()
print(timer - start_time)
</code></pre></div>
<p dir="auto">Output:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="(tf_clean) C:\python_code\test&gt;C:/anaconda/envs/tf_clean/python.exe c:/python_code/test/tf_test.py
2018-04-18 14:36:04.376661: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this
TensorFlow binary was not compiled to use: AVX2
2018-04-18 14:36:04.689661: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1344] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.60GiB
2018-04-18 14:36:04.699485: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1423] Adding visible gpu devices: 0
2018-04-18 14:38:12.227561: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:911] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-04-18 14:38:12.234504: I T:\src\github\tens2018-04-18 14:38:12.237156: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:930] 0:   N
2018-04-18 14:38:12.240997: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1041] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6379 MB memory) -&gt; physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -&gt; device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1
2018-04-18 14:38:12.548288: I T:\src\github\tensorflow\tensorflow\core\common_runtime\direct_session.cc:297] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -&gt; device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.559262: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0
b: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.564847: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] b: (Const)/job:localhost/replica:0/task:0/device:GPU:0
a: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.570545: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] a: (Const)/job:localhost/replica:0/task:0/device:GPU:0
[[22. 28.]
 [49. 64.]]
129.14624643325806"><pre class="notranslate"><code class="notranslate">(tf_clean) C:\python_code\test&gt;C:/anaconda/envs/tf_clean/python.exe c:/python_code/test/tf_test.py
2018-04-18 14:36:04.376661: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this
TensorFlow binary was not compiled to use: AVX2
2018-04-18 14:36:04.689661: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1344] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.60GiB
2018-04-18 14:36:04.699485: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1423] Adding visible gpu devices: 0
2018-04-18 14:38:12.227561: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:911] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-04-18 14:38:12.234504: I T:\src\github\tens2018-04-18 14:38:12.237156: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:930] 0:   N
2018-04-18 14:38:12.240997: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1041] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6379 MB memory) -&gt; physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -&gt; device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1
2018-04-18 14:38:12.548288: I T:\src\github\tensorflow\tensorflow\core\common_runtime\direct_session.cc:297] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -&gt; device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1
MatMul: (MatMul): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.559262: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] MatMul: (MatMul)/job:localhost/replica:0/task:0/device:GPU:0
b: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.564847: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] b: (Const)/job:localhost/replica:0/task:0/device:GPU:0
a: (Const): /job:localhost/replica:0/task:0/device:GPU:0
2018-04-18 14:38:12.570545: I T:\src\github\tensorflow\tensorflow\core\common_runtime\placer.cc:884] a: (Const)/job:localhost/replica:0/task:0/device:GPU:0
[[22. 28.]
 [49. 64.]]
129.14624643325806
</code></pre></div>
<ul dir="auto">
<li>
<p dir="auto"><strong>OS Platform and Distribution</strong>:<br>
Windows 10 Education (Version 10.0.16299 Build 16299)<br>
Intel(R) Core(TM) i5-7500 CPU @ 3.40GHz, 3408 MHz, 4 Cores</p>
</li>
<li>
<p dir="auto"><strong>TensorFlow installed from (source or binary)</strong>:<br>
binary</p>
</li>
<li>
<p dir="auto"><strong>TensorFlow version</strong>:<br>
tensorflow-gpu 1.5.0, 1.7.0</p>
</li>
<li>
<p dir="auto"><strong>Python version</strong>:<br>
3.5.5 &amp; 3.6 (via anaconda, conda 4.5.1.)</p>
</li>
<li>
<p dir="auto"><strong>Bazel Version</strong>:<br>
N/A</p>
</li>
<li>
<p dir="auto"><strong>CUDA/cuDNN version</strong>:<br>
Tested combinations:<br>
CUDA 9.0 and CuDNN 7.1.2 (tested on tensorflow 1.5.0, 1.7.0 and 1.8.0-dev20180329)<br>
CUDA 9.1 and CuDNN 7.0.5 (tested on tensorflow 1.5.0 and 1.7.0)</p>
</li>
<li>
<p dir="auto"><strong>GPU model and memory</strong>:<br>
NVIDIA GeForce GTX 1080 (GP104-400) [Hewlett-Packard], 8192 MBytes of GDDR5X SDRAM [Micron]</p>
</li>
<li>
<p dir="auto"><strong>Exact command to reproduce</strong>:<br>
See: <em>Have I written custom code...</em></p>
</li>
</ul>
<p dir="auto">=================================================================<br>
EDIT:</p>
<p dir="auto">Threadstarter here, hello.</p>
<blockquote>
<p dir="auto">Could you try with the latest nightly?<br>
<a href="https://files.pythonhosted.org/packages/67/c0/e68a4f0400340b54c887703baa8eee188042c3d65a0cf535dda71abffbc2/tf_nightly_gpu-1.13.0.dev20190205-cp37-cp37m-win_amd64.whl" rel="nofollow">https://files.pythonhosted.org/packages/67/c0/e68a4f0400340b54c887703baa8eee188042c3d65a0cf535dda71abffbc2/tf_nightly_gpu-1.13.0.dev20190205-cp37-cp37m-win_amd64.whl</a></p>
</blockquote>
<p dir="auto"><strong>This works!</strong> I checked with that wheel, and then with <code class="notranslate">tf-nightly-gpu-2.0-preview</code> on PYPI, which also worked.<br>
I initially wanted to use the anaconda cudatoolkit and cudnn packages, but currently, cudnn is only available up to version 7.3.1 on anaconda-cloud. Tensorflow 2.0 however, is compiled with 7.4.1, so I had to do this the oldschool way, and download the setups from Nvidia.<br>
Soon, though...<a href="https://imgur.com/a/A2jZizt" rel="nofollow">soon</a>.</p>
<p dir="auto">For everyone, here's what I did, as a guide:</p>
<h3 dir="auto">How to install Tensorflow Nightly 2.0 GPU in Anaconda on Windows 10 x64</h3>
<p dir="auto">• I installed these CUDA/CuDnn Versions:<br>
– cuda_10.0.130_win10_network (Nvidia CUDA Download: <a href="https://developer.nvidia.com/cuda-toolkit" rel="nofollow">https://developer.nvidia.com/cuda-toolkit</a>)<br>
– cuDNN v7.4.1 (Nov 8, 2018), for CUDA 10.0 (Nvidia CuDnn Download: <a href="https://developer.nvidia.com/cudnn" rel="nofollow">https://developer.nvidia.com/cudnn</a>)<br>
– Don't forget to check, whether the Cuda setup has correctly written itself to the PATH system variable.<br>
– Reboot.<br>
• Now make a new environment in Anaconda and activate it:<br>
– <code class="notranslate">conda create --name tf2-nightly-gpu python=3.6</code><br>
– <code class="notranslate">activate tf2-nightly-gpu</code><br>
• Now, with the new env still activated, install the latest Tensorflow 2.0 nightly GPU build from PYPI:<br>
– <code class="notranslate">pip install tf-nightly-gpu-2.0-preview</code><br>
• For machine learning in Jupyter notebook (or Jupyter Lab) , you need these as well:<br>
– <code class="notranslate">conda install nb_conda matplotlib scipy Pillow pandas scikit-learn</code><br>
• Check, if your GPU is recognized by Tensorflow. Open the Anaconda prompt, activate the new environment and type <code class="notranslate">python</code>, then press Enter. Now type:<br>
<code class="notranslate">import tensorflow as tf</code><br>
<code class="notranslate">tf.test.is_gpu_available(cuda_only=False,min_cuda_compute_capability=None) </code><br>
• Output should be something like this:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="(tf2-nightly-gpu) C:\Users\___&gt;python
&gt;&gt;&gt; import tensorflow as tf
&gt;&gt;&gt; tf.test.is_gpu_available(cuda_only=False,min_cuda_compute_capability=None)
2019-03-19 17:46:25.722209: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-03-19 17:46:25.729724: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll
2019-03-19 17:46:25.922934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1551] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.61GiB
2019-03-19 17:46:25.938231: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1674] Adding visible gpu devices: 0
2019-03-19 17:46:26.539185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1082] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-19 17:46:26.546009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1088]      0
2019-03-19 17:46:26.550123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1101] 0:   N
2019-03-19 17:46:26.554188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1222] Created TensorFlow device (/device:GPU:0 with 6360 MB memory) -&gt; physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
True"><pre class="notranslate"><code class="notranslate">(tf2-nightly-gpu) C:\Users\___&gt;python
&gt;&gt;&gt; import tensorflow as tf
&gt;&gt;&gt; tf.test.is_gpu_available(cuda_only=False,min_cuda_compute_capability=None)
2019-03-19 17:46:25.722209: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-03-19 17:46:25.729724: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library nvcuda.dll
2019-03-19 17:46:25.922934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1551] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.61GiB
2019-03-19 17:46:25.938231: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1674] Adding visible gpu devices: 0
2019-03-19 17:46:26.539185: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1082] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-03-19 17:46:26.546009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1088]      0
2019-03-19 17:46:26.550123: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1101] 0:   N
2019-03-19 17:46:26.554188: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1222] Created TensorFlow device (/device:GPU:0 with 6360 MB memory) -&gt; physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
True
</code></pre></div>
<p dir="auto">• Done.</p>