<p dir="auto">Please go to Stack Overflow for help and support:</p>
<p dir="auto"><a href="https://stackoverflow.com/questions/tagged/tensorflow" rel="nofollow">https://stackoverflow.com/questions/tagged/tensorflow</a></p>
<p dir="auto">If you open a GitHub issue, here is our policy:</p>
<ol dir="auto">
<li>It must be a bug, a feature request, or a significant problem with documentation (for small docs fixes please send a PR instead).</li>
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<li>It shouldn't be a TensorBoard issue. Those go <a href="https://github.com/tensorflow/tensorboard/issues">here</a>.</li>
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<p dir="auto"><strong>Here's why we have that policy</strong>: TensorFlow developers respond to issues. We want to focus on work that benefits the whole community, e.g., fixing bugs and adding features. Support only helps individuals. GitHub also notifies thousands of people when issues are filed. We want them to see you communicating an interesting problem, rather than being redirected to Stack Overflow.</p>
<hr>
<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>:</li>
<li><strong>OS Platform and Distribution (e.g., Linux Ubuntu 16.04)</strong>:</li>
<li><strong>Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device</strong>:</li>
<li><strong>TensorFlow installed from (source or binary)</strong>:</li>
<li><strong>TensorFlow version (use command below)</strong>:</li>
<li><strong>Python version</strong>:</li>
<li><strong>Bazel version (if compiling from source)</strong>:</li>
<li><strong>GCC/Compiler version (if compiling from source)</strong>:</li>
<li><strong>CUDA/cuDNN version</strong>:</li>
<li><strong>GPU model and memory</strong>:</li>
<li><strong>Exact command to reproduce</strong>:</li>
</ul>
<p dir="auto">You can collect some of this information using our environment capture script:</p>
<p dir="auto"><a href="https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh">https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh</a></p>
<p dir="auto">You can obtain the TensorFlow version with:</p>
<div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python -c &quot;import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)&quot;"><pre class="notranslate">python -c <span class="pl-s"><span class="pl-pds">"</span>import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)<span class="pl-pds">"</span></span></pre></div>
<h3 dir="auto">Describe the problem</h3>
<p dir="auto">Describe the problem clearly here. Be sure to convey here why it's a bug in TensorFlow or a feature request.</p>
<h3 dir="auto">Source code / logs</h3>
<p dir="auto">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. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.</p>
<p dir="auto">In my programe, I use  f.nn.embedding_lookup as follow:</p>
<p dir="auto">embedding = tf.get_variable("embedding", [200000, 128], tf.float32, initializer=tf.        random_normal_initializer(stddev=0.1), trainable=True,partitioner=tf.fixed_size_partitioner(10))</p>
<p dir="auto">word_embedding = tf.nn.embedding_lookup(embedding, query_tensor)<br>
while(1):<br>
sess.run(word_embedding )</p>
<p dir="auto">when I start 30 worker，each worker qps is 3000.<br>
but when I test tf.nn.embedding_lookup_sparse, each worker qps is 110000</p>
<p dir="auto">how to solve this problem,any suggestion welcome</p>