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<title>TensorFlow - Apache Ignite</title>
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content="Acceleration of TensorFlow workloads with Apache Ignite in-memory computing platform."/>
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<h1><strong>Deep Learning</strong> With TensorFlow and Apache Ignite</h1>
</div>
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<p>
TensorFlow is an open-source software library for high-performance numerical computation that is
used mostly for deep learning and other computationally intensive machine learning tasks.
Its flexible architecture allows easy deployment of computation across a variety of platforms
(CPUs, GPUs, TPUs).
</p>
<img class="img-responsive diagram-right" src="/images/tensorflow-1.png" alt="Deep Learning With TensorFlow and Apache Ignite" />
<p>
TensorFlow and Apache Ignite® can be used together to provide a full toolset needed to work with operational
and historical data, perform data analysis, and build complex mathematical models based
on neural networks.
</p>
<p>
Technically, TensorFlow uses Ignite as a data source for neural
network training, inference, and all other computations supported getting the following
advantages:</p>
<ul class="page-list" >
<li>
<strong>Unlimited Capacity</strong> - Ignite is used as a distributed database with
unlimited capacity that is capable of holding petabytes of data needed for deep learning
tasks of TensorFlow.</li>
<li>
<strong>Faster Performance</strong> - There will be minimal or zero data movement over the
network if TensorFlow workers are deployed on the same machines with Ignite nodes. Each
TensorFlow worker will work with an Ignite node local to it.
</li>
<li>
<strong>Fault Tolerance</strong> - In case of a failure during a calculation, Ignite will be
capable of restarting the process from the point of failure.
</li>
</ul>
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<div class="title display-6">Learn More</div>
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<p>
<a href="https://apacheignite.readme.io/docs/tensor-flow" target="docs">
Apache Ignite for TensorFlow Documentation <i class="fas fa-angle-double-right"></i>
</a>
</p>
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<p>
<a href="/features/machinelearning.html">
Apache Ignite Machine Learning <i class="fas fa-angle-double-right"></i>
</a>
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