alibi-detect
Lean and Mean Docker containers
alibi-detect | Lean and Mean Docker containers | |
---|---|---|
9 | 38 | |
2,085 | 18,238 | |
1.6% | 0.9% | |
7.6 | 9.0 | |
12 days ago | about 17 hours ago | |
Python | 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.
alibi-detect
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Numerous tools exist for detecting anomalies in time series data, but Alibi Detect stood out to me, particularly for its capabilities and its compatibility with both TensorFlow and PyTorch backends.
- Looking for recommendations to monitor / detect data drifts over time
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[D] Distributions to represent an Image Dataset
That is, to see whether a test image belongs in the distribution of the training images and to provide a routine for special cases. After a bit of reading Ive found that this is related to the field of drift detection in which I tried out alibi-detect . Whereby the training images are trained by an autoencoder and any subsequent drift will be flagged by the AE.
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[D] Which statistical test would you use to detect drift in a dataset of images?
Wasserstein distance is not very suitable for drift detection on most problems given that the sample complexity (and estimation error) scales with O(n^(-1/d)) with n the number of instances (100k-10m in your case) and d the feature dimension (192 in your case). More interesting will be to use for instance a detector based on the maximum mean discrepancy (MMD) with estimation error of O(n^(-1/2)). Notice the absence of the feature dimension here. You can find scalable implementations in Alibi Detect (disclosure: I am a contributor): MMD docs, image example. We just added the KeOps backend for the MMD detector to scale and speed up the drift detector further, so if you install from master, you can leverage this backend and easily scale the detector to 1mn instances on e.g. 1 RTX2080Ti GPU. Check this example for more info.
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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/
- What Machine Learning model monitoring tools can you recommend?
- Ask HN: Who is hiring? (December 2021)
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[D] How do you deal with covariate shift and concept drift in production?
I work in this area and also contribute to outlier/drift detection library https://github.com/SeldonIO/alibi-detect. To tackle this type of problem, I would strongly encourage following a more principled, fundamentally (statistically) sound approach. So for instance measuring metrics such as the KL-divergence (or many other f-divergences) will not be that informative since it has a lot of undesirable properties for the problem at hand (in order to be informative requires already overlapping distributions P and Q, it is asymmetric, not a real distance metric, will not scale well with data dimensionality etc). So you should probably look at Integral Probability Metrics (IPMs) such as the Maximum Mean Discrepancy (MMD) instead which have much nicer behaviour to monitor drift. I highly recommend the Interpretable Comparison of Distributions and Models NeurIPS workshop talks for more in-depth background.
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[D] Is this a reasonable assumption in machine learning?
All of the above functionality and more can be easily used under a simple API in https://github.com/SeldonIO/alibi-detect.
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?
pytorch-widedeep - A flexible package for multimodal-deep-learning to combine tabular data with text and images using Wide and Deep models in Pytorch
minideb - A small image based on Debian designed for use in containers
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Go random string generator - Flexible and customizable random string generator
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
pipx - Install and Run Python Applications in Isolated Environments
seldon-core - An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models
dive - A tool for exploring each layer in a docker image
river - 🌊 Online machine learning in Python
gophish - Open-Source Phishing Toolkit
Anomaly_Detection_Tuto - Anomaly detection tutorial on univariate time series with an auto-encoder
simple-scrypt - A convenience library for generating, comparing and inspecting password hashes using the scrypt KDF in Go 🔑