TRI Drives Rapid Innovation with DevOps Automation

Toyota Research Institute Drives Rapid Innovation with DevOps Automation

Case Study

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 the accessibility of transportation and improving quality of life through robotics.

DevOps plays a key part in 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 (IaC) 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

DevOps.com Article:

How TRI is using DevOps Automation to Drive its Research and Engineering

TRI is working to build a future with a focus on reducing vehicle collisions, injuries, and fatalities. Working tirelessly behind the scenes is TRI’s Infrastructure Engineering team, responsible for designing, deploying and maintaining the infrastructure that makes this possible. Flux7 CEO Aater Suleman interviews the technical lead for Infrastructure Engineering at TRI, to talk about DevOps automation and cloud-based deep learning.

Read the Devops.com Article

Wired Article:

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.

Read the Wired Article

Toyota Research Institute: On-Demand Self-Service Portal for Data Scientists to Process Data Sets

Arthur Mandel from Flux7 and David Fluck from Toyota Research Institute explain how they leveraged the power of AWS P3 GPU instances and Service Catalog to create an on-demand self-service portal for their data scientists to process data sets quickly and securely. Find out how to use Service Catalog products and trigger them on-demand to create P3 compute clusters to process machine learning data sets.