Data Science And Data Engineering In HDP 3.0

Saumitra Buragohain, et al, show off some of the things added to the Hortonworks Data Platform for data scientists and data engineers:

We leverage the power of HDP 3.0 from efficient storage (erasure coding), GPU pooling to containerized TensorFlow and Zeppelin to enable this use case. We will the save the details for a different blog (please see the video)- to summarize, as we trained the car on a track, we collected about 30K images with corresponding steering angle data. The training data was stored in a HDP 3.0 cluster and the TensorFlow model was trained using 6 GPU cards and then the model was deployed back on the car. The deep learning use case highlights the combined power of HDP 3.0.

Click through for more additions and demos.

Related Posts

Working With The Databricks API Via Powershell

Gerhard Brueckl has a Powershell module for interacting with Databricks, either Azure or AWS: As most of our deployments use PowerShell I wrote some cmdlets to easily work with the Databricks API in my scripts. These included managing clusters (create, start, stop, …), deploying content/notebooks, adding secrets, executing jobs/notebooks, etc. After some time I ended […]

Read More

Kafka Connect Converters And Serialization

Robin Moffatt goes into great detail on Apache Kafka Connect converters and serialization techniques: Kafka Connect is modular in nature, providing a very powerful way of handling integration requirements. Some key components include: Connectors – the JAR files that define how to integrate with the data store itself Converters – handling serialization and deserialization of […]

Read More

Categories

October 2018
MTWTFSS
« Sep Nov »
1234567
891011121314
15161718192021
22232425262728
293031