ydata-profiling
k3s
ydata-profiling | k3s | |
---|---|---|
43 | 292 | |
12,053 | 26,483 | |
0.9% | 1.2% | |
8.5 | 9.6 | |
10 days ago | 5 days ago | |
Python | Go | |
MIT License | 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.
ydata-profiling
- FLaNK 25 December 2023
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First 15 Open Source Advent projects
6. Ydata-synthetic and Ydata-profiling by YData | Github | tutorial
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Coding Wonderland: Contribute to YData Profiling and YData Synthetic in this Advent of Code
Send us your North ⭐️: "On the first day of Christmas, my true contributor gave to me..." a star in my GitHub tree! 🎵 If you love these projects too, star ydata-profiling or ydata-synthetic and let your friends know why you love it so much!
- Data exploration is not dead
- Explore your data in a single line of code
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Which preprocessing steps to improve the performance of a naive bayes classifier
My suggestion start with the EDA - there are a lot of packages that automate that for you already. My usual go-to: https://github.com/ydataai/ydata-profiling.
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Simulating sales data
If you're not sure about the behaviour of your data (i.e., if the original data has properties like seasonality), you can use ydata-profiling to profile your data first.
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I recorded a Data Science Project using Python and uploaded it on Youtube
Super cool! For EDA, you could give ydata-profiling a spin sometime and speed up the process!
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Ydata-Profiling and Dask
Hey guys,
We've been recently at the Dask Demo Day and we're hoping to launch a new feature on ydata-profiling, with the support for Dask dataframes!
We're looking for Dask Wizards to start collaborating on this feature, so if you're interested, please join us to define the roadmap of the project and start making it real
Current GitHub branch is here: https://github.com/ydataai/ydata-profiling/tree/feat/dask
Dedicated dask channel here: https://discord.gg/EHDBuSSDuy
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🧠 ydata-profiling + Dask!
We're looking for Dask Wizards 🧙🏻♂️ to start collaborating on this branch, so if you're interested, please join us to define the roadmap of the project and start making it real 🚀
k3s
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Ask HN: Are there any open source forks of nomad smd consul?
Opinionated meaning it picks, install, patches your CNI/Ingress/Load Balancer/DNS Server/Metrics Server/Monitoring Setup.
k3s is probably most well known as it ships with bunch of preinstall software: https://github.com/k3s-io/k3s so you can just start throwing yaml files at cluster and handling workloads. It's what I use for my homelab.
Paid things I've heard of include OpenStack and SideroLabs. Haven't used personally by SRE coworkers say good things about them.
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Linux fu: getting started with systemd
For self-hosting I've found https://k3s.io to be really good from the SUSE people. Works on basically any Linux distro and makes self-hosting k8s not miserable.
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Nix is a better Docker image builder than Docker's image builder
Yes it’s going to depend on which k8s distribution you’re using. We have work in-progress for k3s to natively support nix-snapshotter: https://github.com/k3s-io/k3s/pull/9319
For other distributions, nix-snapshotter works with official containerd releases so it’s just a matter of toml configuration and a systemd unit for nix-snapshotter.
We run Kubernetes outside of NixOS, but yes the NixOS modules provided by the nix-snapshotter certainly make it simple.
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15 Options To Build A Kubernetes Playground (with Pros and Cons)
K3S: is a lightweight distribution of Kubernetes that is designed for resource-constrained environments. It is an excellent option for running Kubernetes on a virtual machine or cloud server.
- FLaNK 25 December 2023
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K3s Traefik Ingress - configured for your homelab!
I recently purchased a used Lenovo M900 Think Centre (i7 with 32GB RAM) from eBay to expand my mini-homelab, which was just a single Synology DS218+ plugged into my ISP's router (yuck!). Since I've been spending a big chunk of time at work playing around with Kubernetes, I figured that I'd put my skills to the test and run a k3s node on the new server. While I was familiar with k3s before starting this project, I'd never actually run it before, opting for tools like kind (and minikube before that) to run small test clusters for my local development work.
- Best way to deploy K8s to single VPS for dev environment
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Single docker compose stack on multiple hosts. But how?
Kubernetes - k3s distribution
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Building a no-code Helm UI with Windmill - Part 1
I’ve created a local cluster with K3S and installing Windmill could not be simpler with just one chart to configure, which already has sane defaults to get started. For this demo we will also configure workers to passthrough environment variables to our scripts so that they have access to the Kubernetes API server for later.
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Highly scalable Minecraft cluster
You should be familiar with Kubernetes and have set up a Kubernetes cluster. I recommend k3s.
What are some alternatives?
dtale - Visualizer for pandas data structures
k0s - k0s - The Zero Friction Kubernetes
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
kubespray - Deploy a Production Ready Kubernetes Cluster
dataframe-go - DataFrames for Go: For statistics, machine-learning, and data manipulation/exploration
Nomad - Nomad is an easy-to-use, flexible, and performant workload orchestrator that can deploy a mix of microservice, batch, containerized, and non-containerized applications. Nomad is easy to operate and scale and has native Consul and Vault integrations.
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡
microk8s - MicroK8s is a small, fast, single-package Kubernetes for datacenters and the edge.
get-started-with-JAX - The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
Docker Compose - Define and run multi-container applications with Docker
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
k9s - 🐶 Kubernetes CLI To Manage Your Clusters In Style!