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| <title>DL TensorFlow - Apache Ignite</title> |
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| <section id="machine-learning" class="page-section"> |
| <h1 class="first">Deep Learning With TensorFlow</h1> |
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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> |
| <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: |
| |
| <ul class="page-list" style="margin-bottom: 20px;"> |
| <li> |
| <strong>Unlimited Capacity</strong> - Ignite is used as a distributed database with |
| unlimited capacity which 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 calculation, Ignite will be |
| capable of restarting the process from the point of failure. |
| </li> |
| </ul> |
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| <p><a href="https://apacheignite.readme.io/docs/tensor-flow" target="_blank" rel="noopener">Read more</a></p> |
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