Introduction to Deep Learning on HDInsight with Intel Deep Learning framework: BigDL (R) Intel

   

Presenters

• Denny Lee, Principal Program Manager, CosmosDB
• Tom Drabas, Data Scientist, WDG

In close cooperation with Intel

• Sergey Ermolin, Power/Performance Optimization
• Ding Ding, Software Engineer
• Jiao Wang, Software Engineer
• Jason Dai, Senior Principle Engineer and CTO, Big Data Technologies
• Yiheng, Wang, Software Engineer
• Xianyan Jia, Software Engineer

Special thanks to

• Felix Cheung, Principal Software Engineer
• Xiaoyong Zhu, Program Manager
• Alejandro Guerrero Gonzalez, Senior Software Engineer

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Setting up the environment

1. Clone the Github repository

The folders in this repo:

1. data folder - contains a set of 4 files that can be downloaded from http://yann.lecun.com/exdb/mnist/:
   1. train-images-idx3-ubyte - set of training images in a binary format with a specific schema (we'll get to that)
   2. train-labels-idx1-ubyte - corresponding set of training labels
   3. t10k-images-idx3-ubyte - set of testing (validation) images
   4. t10k-labels-idx1-ubyte - corresponding set of testing (validation) labels
2. jars folder - contains two compiled jars for the BigDL:
   1. bigdl-0.2.0-SNAPSHOT-spark-2.0-jar-with-dependencies.jar - BigDL compiled for Spark 2.0
   2. bigdl-0.2.0-SNAPSHOT-spark-2.1-jar-with-dependencies.jar - BigDL compiled for Spark 2.1
3. notebook folder - contains the notebook for the workshop

2. Upload BigDL jar

Grab the jar from the jars folder appropriate for your version of Spark.

1. Go to Azure Dashboard and click on your cluster. Scroll down to the Storage accounts
2. Click on the default storage account
3. Go to Blobs
4. Select the default container
5. Upload the jar appropriate for your version of Spark to the root of the folder
6. Check if uploaded successfully

3. Upload the data

Similarly to uploading the BigDL upload the data from the data folder. Upload the data into the /tmp folder in your default storage.