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| <section class="blog__header"><h1>Entries tagged [two]</h1></section> |
| <div class="blog__content"> |
| <main class="blog_main"> |
| <section class="blog__posts"> |
| <article class="post"> |
| <div class="post__header"> |
| <h2><a href="/blog/apache-ignite-2-9-released.html">Apache Ignite 2.9 Released: Cluster snapshots and tracing</a></h2> |
| <div> |
| November 5, 2020 by Denis Magda: <a href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-2-9-released.html">Facebook</a><span>, </span |
| ><a href="http://twitter.com/home?status=Apache Ignite 2.9 Released: Cluster snapshots and tracing%20https://ignite.apache.org/blog/apache-ignite-2-9-released.html">Twitter</a> |
| </div> |
| </div> |
| <div class="post__content"> |
| <p>As of October 23, 2020, Apache Ignite 2.9 is available. Like every other Ignite release, release 2.9 includes many changes. Let's take a look at the major features of release 2.9.</p> |
| <p></p> |
| <h3>Cluster Snapshots</h3> |
| <p></p> |
| <p> |
| Ignite 2.9 provides the ability to create full cluster snapshots for deployments that use Ignite Persistence. Snapshots can be taken online, when the cluster is active and accessible to users. An Ignite snapshot includes a |
| cluster-wide copy of all data records that exist at the moment the snapshot is started. All snapshots are consistent — in terms of concurrent, cluster-wide operations as well as in terms of ongoing changes in Ignite |
| Persistence data, index, schema, binary metadata, marshaller, and other files on nodes. See <a href="https://ignite.apache.org/docs/latest/persistence/snapshots">Ignite documentation</a> to learn about this feature. |
| </p> |
| </div> |
| <div class="post__footer"><a class="more" href="/blog/apache-ignite-2-9-released.html">↓ Read all</a></div> |
| </article> |
| <article class="post"> |
| <div class="post__header"> |
| <h2><a href="/blog/apache-ignite-2-7-deep.html">Apache Ignite 2.7: Deep Learning and Extended Languages Support</a></h2> |
| <div> |
| December 13, 2018 by Denis Magda: <a href="http://www.facebook.com/sharer.php?u=https://ignite.apache.org/blog/apache-ignite-2-7-deep.html">Facebook</a><span>, </span |
| ><a href="http://twitter.com/home?status=Apache Ignite 2.7: Deep Learning and Extended Languages Support%20https://ignite.apache.org/blog/apache-ignite-2-7-deep.html">Twitter</a> |
| </div> |
| </div> |
| <div class="post__content"> |
| <h3>Deep Learning With TensorFlow</h3> |
| <p></p> |
| <p> |
| Even though it was natural to provide machine learning algorithms in Ignite out of the box, another direction was taken for deep learning capabilities. Primarily because machine learning approaches have already been |
| adopted in businesses from big to small -- while deep learning is still being used for narrow and specific use cases. |
| </p> |
| <p> |
| Thus, Ignite 2.7 can boast about an <a href="https://ignite.apache.org/features/tensorflow.html" target="_blank">official integration</a> with TensorFlow deep learning framework that gives a way to use Ignite as a |
| distributed storage for TensorFlow calculations. With Ignite, data scientists can store unlimited data sets across a cluster, gain performance improvements and rely on fault-tolerance of both products if an algorithm fails |
| in the middle of an execution. |
| </p> |
| </div> |
| <div class="post__footer"><a class="more" href="/blog/apache-ignite-2-7-deep.html">↓ Read all</a></div> |
| </article> |
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