l5kit VS Lean and Mean Docker containers

Level 5 | Self-driving Research | Palo Alto, US & London, UK & Tokyo, Japan | Full-Time + Interns | Onsite (Hybrid - 2 days in p/w - currently closed)

Level 5 at Woven Planet develops real-time car automation solutions via applied Machine Learning and Computer Vision (SFM, SLAM, 3D Perception etc). The system is adding self-driving capabilities to vehicles with the end goal of providing autonomy features to all cars from the largest car company in the world, Toyota. For more information please read through https://www.self-driving-cars.org

The platform at Level 5 is written in Python & C++. We are an AWS environment with additional use of NumPy, PyTorch, GRPC, Kafka, Kubernetes, Terraform, SageMaker, Spark, Postgres & others. We work in a OneBox cloud environment with continuous deployment and are firm believers in the benefits of open source (https://github.com/woven-planet/l5kit). We apply multiple flavours of ML to petabytes of data; such as Deep Learning, Transformers, Neural Networks & Reinforcement Learning. If you are interested in applying Machine Learning (ML) to real world data - look no further. https://www.self-driving-cars.org/datasets

If you like the idea of working on some of the most challenging problems in applied computer science. We are looking for talent across Data, Computer Vision, Machine Learning, Infrastructure, Research - and of course Software Engineering. Please find our jobs at https://boards.greenhouse.io/l5

And if you want to make the container quickly secure without bloats, maybe give this a try https://github.com/slimtoolkit/slim

Slim.ai presents the data in a more user friendly way than many of the other tools in this post. On top of its open source SlimToolkit for identifying the contents of an image, Slim.ai uses Trivy for vulnerability scanning.

What about https://github.com/slimtoolkit/slim?

A last practice that I do not use at all and which may interest you is to use slim toolkit to keep only the useful elements in your final image.

Anyone tried using https://github.com/docker-slim/docker-slim To minify an image?..

Distroless images are better left for people with serious need for lightweight images and good Linux knowledge because they require lot of planning with the build so that they stay light and work. If you need lighter images but docker isn't your main tool and you can't afford to take hours and hours of practicing different build strategies you can check docker-slim (https://dockersl.im/). With this tool you can easily size down the images.

Maybe this would help in that regard: https://github.com/docker-slim/docker-slim