<p dir="auto"><em>Please make sure that this is a bug. 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:bug_template</em></p>
<p dir="auto"><strong>System information</strong></p>
<ul dir="auto">
<li>Have I written custom code (as opposed to using a stock example script provided in TensorFlow):</li>
<li>OS Platform and Distribution (e.g., Linux Ubuntu 16.04):</li>
<li>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:</li>
<li>TensorFlow installed from (source or binary):</li>
<li>TensorFlow version (use command below):</li>
<li>Python version: 3.6</li>
<li>Bazel version (if compiling from source):</li>
<li>GCC/Compiler version (if compiling from source):</li>
<li>CUDA/cuDNN version:</li>
<li>GPU model and memory:</li>
</ul>
<p dir="auto">You can collect some of this information using our environment capture<br>
<a href="https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh">script</a><br>
You can also obtain the TensorFlow version with: 1. TF 1.0: <code class="notranslate">python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"</code> 2. TF 2.0: <code class="notranslate">python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"</code></p>
<p dir="auto"><strong>Describe the current behavior</strong><br>
Line 76 in tensorflow/python/platform/self_check.py<br>
except OSError:<br>
raise ImportError(<br>
"Could not find %r. TensorFlow requires that this DLL be "<br>
"installed in a directory that is named in your %%PATH%% "<br>
"environment variable. Download and install CUDA %s from "<br>
"this URL: <a href="https://developer.nvidia.com/cuda-90-download-archive" rel="nofollow">https://developer.nvidia.com/cuda-90-download-archive</a>"<br>
% (build_info.cudart_dll_name, build_info.cuda_version_number))</p>
<p dir="auto">The URL directs you to version 9 but the current dll checks for version 10.</p>
<p dir="auto"><strong>Describe the expected behavior</strong></p>
<p dir="auto"><strong>Code to reproduce the issue</strong><br>
Provide a reproducible test case that is the bare minimum necessary to generate the problem.</p>
<p dir="auto"><strong>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>
<p dir="auto">TensorFlow 1.x has over 2000 endpoints total including over 500 endpoints in the root namespace. As number of symbols grows, it is important to maintain a clear structure to aid discoverability.</p>
<p dir="auto">Certain API symbol placements could be improved:</p>
<ul dir="auto">
<li>Some namespaces were created recently and might not contain all the corresponding symbols. For e.g. <code class="notranslate">tf.math</code> namespace was added recently. Symbols such as <code class="notranslate">tf.round</code> are not in <code class="notranslate">tf.math</code> namespace even though logically they belong in that namespace.</li>
<li>Some symbols are included in the root namespace even though they are rarely used (for e.g. <code class="notranslate">tf.zeta</code>).</li>
<li>Some symbols currently start with a prefix that could really be replaced by introducing a subnamespace (for e.g. <code class="notranslate">tf.string_strip</code> vs <code class="notranslate">tf.strings.strip</code>, <code class="notranslate">tf.sparse_maximum</code> vs <code class="notranslate">tf.sparse.maximum</code>).</li>
<li>Certain deep hierarchies seem redundant and could be flattened (for e.g. <code class="notranslate">tf.saved_model.signature_constants.CLASSIFY_INPUTS</code> could be moved to <code class="notranslate">tf.saved_model.CLASSIFY_INPUTS</code>).</li>
<li>To keep clear structure and reduce duplication, we want to collect all layers, losses and metrics under the <code class="notranslate">tf.keras</code> namespace.</li>
<li>In general, we want to balance flatness and browsability. Flat hierarchies allow for shorter endpoint names that are easy to remember (for e.g. <code class="notranslate">tf.add</code> vs <code class="notranslate">tf.math.add</code>). At the same time subnamespaces support easier browsability (for e.g. <code class="notranslate">tf.math</code> namespace would contain all math functions making it easier to discover available symbols).</li>
</ul>
<p dir="auto">Additional information about API symbol renames in TensorFlow 2.0 can be found in the RFC <a href="https://github.com/tensorflow/community/blob/master/rfcs/20180827-api-names.md">here</a>.</p>