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

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Intern Program

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The deadline date for proposal applications is Friday 19th March 2021.

Discuss your ideas with the project mentor and send your final proposal to Lorraine Chapman.

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

Focus on various of storage type, datasets and HPCC cluster parameters.

Thor
Roxie

More information coming soon.

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

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.

Completion of this project involves:

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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:

Coming soon

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Xiaoming Wang
Contact Details

Backup Mentor: Godson Fortil
Contact Details

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Skills needed

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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	HPCC Systems website