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Commit ab7463d

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Merge pull request #205 from eclipse/treo/redirect_to_gitbook
Redirect docs to gitbook
2 parents f7c6e07 + 7c14cf6 commit ab7463d

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‎_includes/nav.html

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<ul class="navbar-nav ml-auto">
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<li class="nav-item nav-button nav-cta">
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<a class="nav-item btn btn-rounded btn-outline-warning" href="/docs/{{site.versionString}}/deeplearning4j-quickstart">Quickstart</a>
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<a class="nav-item btn btn-rounded btn-outline-warning" href="https://deeplearning4j.konduit.ai/getting-started/quickstart">Quickstart</a>
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</li>
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<li class="nav-item">
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<a class="nav-link dropdown-none" href="/docs/{{site.versionString}}/">Guide</a>
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<a class="nav-link dropdown-none" href="https://deeplearning4j.konduit.ai/">Guide</a>
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</li>
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<li class="nav-item">
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</li>
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<li class="nav-item">
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<a class="nav-link dropdown-none" href="/tutorials/setup">Tutorials</a>
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<a class="nav-link dropdown-none" href="https://deeplearning4j.konduit.ai/getting-started/tutorials">Tutorials</a>
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</li>
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<li class="nav-item">
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<a class="nav-link dropdown-none" href="/support">Support</a>
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</li>
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<li class="nav-item">
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<a class="nav-link dropdown-none" href="/release-notes">{{site.latestVersion}}</a>
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<a class="nav-link dropdown-none" href="https://deeplearning4j.konduit.ai/getting-started/release-notes">{{site.latestVersion}}</a>
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</li>
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</ul>
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</div>

‎_layouts/redirect.html

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<!DOCTYPE html>
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<html lang="en">
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<head>
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{% include head.html %}
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<meta http-equiv="refresh" content="2;url={{page.redirectTo}}" />
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<script>
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window.location.replace("{{page.redirectTo}}");
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</script>
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</head>
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<body>
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<div id="preloader">
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<div id="preloader-inner"></div>
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</div><!--/preloader-->
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{% if page.topbar != "hide" %}
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{% include topbar.html %}
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{% endif %}
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<!-- Site Overlay -->
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<div class="site-overlay"></div>
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{% include nav.html %}
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<div class="page-titles title-dark pt30 pb20 mb70">
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<div class="container">
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<div class="row">
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<div class=" col-md-6">
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<h4><span>{{ page.title }}</span></h4>
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</div>
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<div class=" col-md-6 mb0">
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</div>
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</div>
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</div>
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</div>
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<div class="container mb70">
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{{ content }}
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</div>
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{% include footer.html %}
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</body>
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</html>

‎about.md

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title: About Eclipse Deeplearning4j
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short_title: About
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description: Facts and introduction to Eclipse Deeplearning4j, the top JVM deep learning framework.
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layout: default
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layout: redirect
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redirectTo: https://deeplearning4j.konduit.ai/
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---
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## About Eclipse Deeplearning4j
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The documentation has moved to a new location. You will be automatically redirected.
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[Go to About Eclipse Deeplearning4j without waiting for the redirect](https://deeplearning4j.konduit.ai/)
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Eclipse Deeplearning4j is an open-source, distributed deep-learning project in Java and Scala spearheaded by the people at [Konduit](https://konduit.ai/), a San Francisco-based business intelligence and enterprise software firm. We're a team of data scientists, deep-learning specialists, Java systems engineers and semi-sentient robots.
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There are a lot of knobs to turn when you're training a distributed deep-learning network. We've done our best to explain them, so that Eclipse Deeplearning4j can serve as a DIY tool for Java, Scala and Clojure programmers working on Hadoop and other file systems.
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## Media
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Deeplearning4j has been featured in [Wired](http://www.wired.com/2014/06/skymind-deep-learning/), [GigaOM](http://gigaom.com/2014/06/02/a-startup-called-skymind-launches-pushing-open-source-deep-learning/), [Businessweek](http://www.businessweek.com/articles/2014-06-03/teaching-smaller-companies-how-to-probe-deep-learning-on-their-own), [Venturebeat](http://venturebeat.com/2014/06/02/skymind-launches-with-open-source-plug-and-play-deep-learning-features-for-your-app/), [The Wall Street Journal](http://blogs.wsj.com/cio/2014/06/03/the-morning-download-apple-relies-on-ecosystem-for-innovation/), [Fusion](http://fusion.net/story/177825/privacy-conscious-siris-that-dont-give-up-your-secrets-are-coming/) and [Java Magazine](http://oraclejavamagazine-digital.com/javamagazine/may_june_2015?sub_id=DJ9kzXBnuXELe#pg58).
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## Cite Eclipse Deeplearning4j
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If you plan to publish an academic paper and wish to cite Deeplearning4j, please use this format:
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Eclipse Deeplearning4j Development Team. Deeplearning4j: Open-source distributed deep learning for the JVM, Apache Software Foundation License 2.0. [http://deeplearning4j.org](http://deeplearning4j.org)
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## Supporters
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Profiling supported by [YourKit](https://www.yourkit.com/).
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‎cpu.md

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title: CPU and AVX
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short_title: CPU
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description: CPU and AVX support in ND4J/Deeplearning4j
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layout: default
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layout: redirect
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redirectTo: https://deeplearning4j.konduit.ai/config/backends/cpu
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---
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# ND4J - CPU (nd4j-native) AVX Configuration
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The documentation has moved to a new location. You will be automatically redirected.
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[Go to CPU and AVX without waiting for the redirect](https://deeplearning4j.konduit.ai/config/backends/cpu)
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### What is AVX, and why does it matter?
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AVX (Advanced Vector Extensions) is a set of CPU instructions for accelerating numerical computations. See [Wikipedia](https://en.wikipedia.org/wiki/Advanced_Vector_Extensions) for more details.
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Note that AVX only applies to nd4j-native (CPU) backend for x86 devices, not GPUs and not ARM/PPC devices.
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Why AVX matters: performance. You want to use the version of ND4J compiled with the highest level of AVX supported by your system.
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AVX support for different CPUs - summary:
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* Most modern x86 CPUs: AVX2 is supported
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* Some high-end server CPUs: AVX512 may be supported
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* Old CPUs (pre 2012) and low power x86 (Atom, Celeron): No AVX support (usually)
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Note that CPUs supporting later versions of AVX include all earlier versions also. This means it's possible run a generic x86 or AVX2 binary on a system supporting AVX512.
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However it is not possible to run binaries built for later versions (such as avx512) on a CPU that doesn't have support for those instructions.
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Note on current snapshots (and in future releases, after 1.0.0-beta5) you may get a warning as follows, if AVX is not configured optimally:
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```
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o.n.l.c.n.CpuNDArrayFactory - *********************************** CPU Feature Check Warning ***********************************
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o.n.l.c.n.CpuNDArrayFactory - Warning: Initializing ND4J with Generic x86 binary on a CPU with AVX/AVX2 support
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o.n.l.c.n.CpuNDArrayFactory - Using ND4J with AVX/AVX2 will improve performance. See deeplearning4j.org/cpu for more details
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o.n.l.c.n.CpuNDArrayFactory - Or set environment variable ND4J_IGNORE_AVX=true to suppress this warning
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o.n.l.c.n.CpuNDArrayFactory - ************************************************************************************************
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```
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### Configuring AVX in ND4J/DL4J
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As noted earlier, for best performance you should use the version of ND4J that matches your CPU's supported AVX level.
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ND4J defaults configuration (when just including the nd4j-native or nd4j-native-platform dependencies without maven classifier configuration):
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* 1.0.0-beta5 and earlier: "generic x86" (no AVX) is the default for nd4j/nd4j-platform dependencies
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* Current snapshots and later versions of ND4J: AVX2 is the default
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To configure AVX2 and AVX512, you need to specify a classifier for the appropriate architecture.
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The following binaries (nd4j-native classifiers) are provided for x86 architectures:
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* Generic x86 (no AVX): `linux-x86_64`, `windows-x86_64`, `macosx-x86_64`
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* AVX2: `linux-x86_64-avx2`, `windows-x86_64-avx2`, `macosx-x86_64-avx2`
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* AVX512: `linux-x86_64-avx512`
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**Example: Configuring AVX2 on Windows (Maven pom.xml)**
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```
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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<classifier>windows-x86_64-avx2</classifier>
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</dependency>
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```
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**Example: Configuring AVX512 on Linux (Maven pom.xml)**
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```
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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</dependency>
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<dependency>
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<groupId>org.nd4j</groupId>
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<artifactId>nd4j-native</artifactId>
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<version>${nd4j.version}</version>
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<classifier>linux-x86_64-avx512</classifier>
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</dependency>
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```
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Note that you need *both* nd4j-native dependencies - with and without the classifier.
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