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Deep Learning 101: Using Apache MXNet in Apache Zeppelin Notebooks

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Deep Learning 101: Using Apache MXNet in Apache Zeppelin Notebooks

Learn how to run deep learning models in Apache Zeppelin notebooks using Apache MXNet.

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Apache Zeppelin Notebook:

As you can see, we can format this data as a table using Apache Zeppelin display technology.

Use this print statement:

print("%table top1pct\ttop1\top2\ttop2pct\ttop3pct\ttop3\ttop4pct\ttop4\ttop5pct\ttop5\timagefilename\truntime\tuuid\n" + top1pct + "\t" + top1 + "\t" +  top2pct + "\t" + top2 + "\t" +  top3pct + "\t" + top3  + "\t" + top4pct + "\t" + top4 + "\t" +  top5pct + "\t" + top5  + "\t" + filename  + "\t" + str(round(end - start)) + "\t" + uniqueid + "\n" )      

We use the PySpark interpreter to run this Python script, but there's no Spark in here just yet.

This data also gets loaded into Apache Hive via Apache NiFi, as shown here:

Deep Learning Models

You will need to download the following pre-built Inception models and reference them on your server:

  • synset.txt

  • Inception-BN-0000.params

  • Inception-BN-symbol.json

See here for more details.

curl --header 'Host: data.mxnet.io' --header 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.11; rv:45.0) Gecko/20100101 Firefox/45.0' --header 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8' --header 'Accept-Language: en-US,en;q=0.5' --header 'Referer: http://data.mxnet.io/models/imagenet/' --header 'Connection: keep-alive' 'http://data.mxnet.io/models/imagenet/inception-bn.tar.gz' -o 'inception-bn.tar.gz' -L

curl http://data.mxnet.io/models/imagenet/synset.txt

Here's a similar model.

The source code can be found here and here.

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apache mxnet ,deep learning ,python ,apache zeppelin ,opencv ,ai ,tutorial

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