MLServer
Lean and Mean Docker containers
MLServer | Lean and Mean Docker containers | |
---|---|---|
4 | 39 | |
590 | 18,314 | |
5.9% | 1.3% | |
9.5 | 9.0 | |
5 days ago | 2 days ago | |
Python | Go | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
MLServer
-
Multi-model serving options
You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.
-
Serving Python Machine Learning Models With Ease
Ever trained a new model and just wanted to use it through an API straight away? Sometimes you don't want to bother writing Flask code or containerizing your model and running it in Docker. If that sounds like you, you definitely want to check out MLServer. It's a python based inference server that recently went GA and what's really neat about it is that it's a highly-performant server designed for production environments too. That means that, by serving models locally, you are running in the exact same environment as they will be in when they get to production.
-
Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
Lean and Mean Docker containers
-
Optimize Your Containerized App with SlimToolkit
SlimToolkit empowers developers to create better, smaller, and more secure containers without sacrificing their existing workflows. Explore the project on GitHub or visit the official website to learn more.
-
Is updating software in Docker containers useful?
And if you want to make the container quickly secure without bloats, maybe give this a try https://github.com/slimtoolkit/slim
-
An Overview of Kubernetes Security Projects at KubeCon Europe 2023
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.
-
Tips for reducing Docker image size
What about https://github.com/slimtoolkit/slim?
-
package a poetry project in a docker container for production
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.
-
Standard container sizes
Anyone tried using https://github.com/docker-slim/docker-slim To minify an image?..
- DockerSlim - Optimize Your Containerized App Dev Experience. Better, Smaller, Faster, and More Secure Containers Doing Less! Minify Docker Images by up to 30x.
- A practical approach to structuring Golang applications
- How to optimize docker image size?
-
M1: Docker doesn't find shared x64 shared objects even though platform was specified
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.
What are some alternatives?
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
minideb - A small image based on Debian designed for use in containers
alibi - Algorithms for explaining machine learning models
Go random string generator - Flexible and customizable random string generator
alibi-detect - Algorithms for outlier, adversarial and drift detection
pipx - Install and Run Python Applications in Isolated Environments
Mattermost - Mattermost is an open source platform for secure collaboration across the entire software development lifecycle..
dive - A tool for exploring each layer in a docker image
engineering - Slim.AI - All Things Engineering
gophish - Open-Source Phishing Toolkit
zotero - Zotero is a free, easy-to-use tool to help you collect, organize, annotate, cite, and share your research sources.
simple-scrypt - A convenience library for generating, comparing and inspecting password hashes using the scrypt KDF in Go 🔑