seldon-core
Lean and Mean Docker containers
seldon-core | Lean and Mean Docker containers | |
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14 | 38 | |
4,220 | 18,194 | |
1.2% | 0.7% | |
7.6 | 9.0 | |
5 days ago | 11 days ago | |
HTML | Go | |
GNU General Public License v3.0 or later | 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.
seldon-core
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seldon-core VS MLDrop - a user suggested alternative
2 projects | 20 Feb 2023
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. Seldon Core is a production grade open source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
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[D] BentoML's Compatibility with Seldon;
I am using BentoML to build the docker container for a BERT model, and then deploy that using Seldon on GKE. The model's REST API endpoint works fine. at terms of compatibility with Seldon, the metrics are being scraped by Prometheus and visualized on Grafana. The only Seldon component that doesn't appear to be working is the request logging, which I have working for other applications that were deployed on Seldon. I am using the elastic stack from here. From my understanding, request logging should still be compatible and the ⠀only lost functionality should be Seldon's model metadata. Any insight on how to get the centralized request logging working? No errors were shown; it's just that the logs aren't being captured and sent to ElasticSearch. Anyone have any success using BentoML with Seldon and not losing any of Seldon's features?
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Building a Responsible AI Solution - Principles into Practice
While tools in the model experimentation space normally include diagnostic charts on a model's performance, there are also specialised solutions that help ensure that the deployed model continues to perform as they are expected to. This includes the likes of seldon-core, why-labs and fiddler.ai.
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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)
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Has anyone implemented Seldon?
Also note our github repo has a link to our slack where you can ask active users: https://github.com/SeldonIO/seldon-core
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
If you want to serve your model at scale, with a bunch of production features you should have a look at the open-source framework Seldon Core. It does what you're asking for plus a bunch of other cool stuff like routing, logging and monitoring.
- Seldon Core : Open-source platform for rapidly deploying machine learning models on Kubernetes
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Looking for open-source model serving framework with dashboard for test data quality
Seldon ticks most of those boxes if you already have some experience with kubernetes. You can set up a/b tests, do payload logging to elastic and then do monitoring on top of that, and it has drift detection and model explainer modules too. Idk about great expectations integration, but you could probably do something with a custom transformer module as part of the inference graph.
Lean and Mean Docker containers
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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
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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.
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Tips for reducing Docker image size
What about https://github.com/slimtoolkit/slim?
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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.
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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?
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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.
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I deleted 78% of my Redis container and it still works
Maybe this would help in that regard: https://github.com/docker-slim/docker-slim
What are some alternatives?
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
minideb - A small image based on Debian designed for use in containers
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
Go random string generator - Flexible and customizable random string generator
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
pipx - Install and Run Python Applications in Isolated Environments
great_expectations - Always know what to expect from your data.
dive - A tool for exploring each layer in a docker image
alibi-detect - Algorithms for outlier, adversarial and drift detection
gophish - Open-Source Phishing Toolkit
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
simple-scrypt - A convenience library for generating, comparing and inspecting password hashes using the scrypt KDF in Go 🔑