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This project was completed by a student accepted on to the 2021 HPCC Systems Intern Program.

Student work experience opportunities also exist for students who want to suggest their own project idea. Project suggestions must be relevant to HPCC Systems and of benefit to our open source community.  

Find out about the HPCC Systems Summer Internship Program.

Project Description

This will is a continuing work from last year "Process robotics data with HPCC Systems". The main focus will be on HPCC System cluster on Kubernetes, particularly on Microsoft Azure. The project will adopt existing General Neural Network (GNN) model to local and Azure Kubernetes cluster. Some related code and environment may also need be updated for example, latest ROS, Ubuntu 20.04 and potential new TensorFlow release, etc. The student also will help to identify any necessary change or add-on in HPCC-Platform to support Machine Learning on HPCC System Cloud in both local and public cloud such as Azure, AWS and Google Cloud.

If you are interested in this project, please contact Contact Details

Completion of this project involves:

  • Learning HPCC System ML GNN
  • Learning previous Robotics GNN code
  • Collect or get train data (images) 
  • Load the image data to the cloud
  • Train the model 

By the mid term review we would expect you to have:

  • Load image data to the cloud 
  • Written initial ECL code
  • Trained a model with HPCC Systems GNN with some initial result
Mentor

Mentor: David De Hilster <David.Dehilster@lexisnexisrisk.com>

Backup Mentor: Fortil, Godson  <Godson.Fortil@lexisnexisrisk.com>

Skills needed
  • Docker and Kubernetes
  • Git, CMake
  • Build ROS package
  • HPCC Systems Platform and ECL
  • Python, Unix Bash,
  • Machine Learning, Neural Networks, particularly Convolution Neural Networks (CNN)
  • Kera, TensorFlow
  • Ability to build and test the HPCC system (guidance will be provided).
  • Ability to write test code. Knowledge of ECL is not a requirement since it should be possible to re-use existing code with minimal changes for this purpose. Links are provided below to our ECL training documentation and online courses should you wish to become familiar with the ECL  language.
Deliverables

Midterm

  • Load image data to the cloud 
  • Written initial ECL code
  • Trained a model with HPCC Systems GNN with some initial result

End of project

  • Tuning hyperparameters to improve model training.
  • Documentation
  • A complete github project
Other resources
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