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Toyota Research Institute is embracing DevOps automation to reduce tactical, manual IT operations and to make it faster and easier for their researchers to test ideas. TRI is a division of Toyota focused on transforming the human condition including making driving safer, improving accessibility of transportation and improving quality of life through robotics.

DevOps plays a key part of that role, introducing not just automation, but consistency, governance and optimization. To help TRI achieve their goals, they turned to deep learning on Amazon Web Services (AWS).

The engineering team's goal was to implement an Infrastructure as Code environment with automation to reduce errors, reduce manual errors and support the advanced research team. Specifically to achieve:

  • Reproducibility
  • Auditability
  • Scalability 
  • Reasonability

Using Amazon EC2 P3 instances, TRI is seeing a 4X faster time-to-train than the P2 instances they had used previously, reducing their training time from days to hours. 

The solution also includes:

  • Terraform
  • Jenkins
  • Ansible
  • AWS Lambda
  • AWS Auto Scaling
  • Amazon S3
  • Amazon RDS
  • Amazon ES

 

 

 

Toyota Research AWS Case Study

 

 

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AWS Machine Learning Case study

How TRI is using DevOps automation to drive its research and engineering

 

Flux7 CEO interviews the technical lead for Infrastructure Engineering at TRI, to talk about DevOps automation and cloud-based deep learning. Featured in DevOps.com 

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AWS Case Study Deep Learning

Toyota Research Institute accelerates safe automated driving with deep learning

 

TRI needed an IT platform that can handle large amounts of data, has the required processing power to train machine learning models quickly, and can scale to meet their requirements. Using AWS, they gained the ability to spin up compute and storage resources on demand and couple them with higher-level management and orchestration services.  Featured in Wired

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