envd
CloudForest
envd | CloudForest | |
---|---|---|
31 | 4 | |
1,919 | 735 | |
0.5% | - | |
8.8 | 0.0 | |
4 days ago | about 2 years ago | |
Go | Go | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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.
envd
- Show HN: Dockerfile Alternative for AI/ML
- Show HN: Reproducible Development Environment for LLM
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20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
envd - A command-line tool that helps you create the container-based environment for AI/ML, from development to the production. Python is all you need to know to use this tool.
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Launching ModelZ Beta!
Contribute to open source projects: Modelz is built on top of envd, mosec, modelz-llm and many other open source projects. If you're interested in contributing to these projects, you can check out their GitHub repositories and start contributing.
- Kubernetes for Data Science with Kubeflow
- Show HN: Reproducible development environments using starlark, without Nix
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Kubeflow, Jupyter notebook online. Question to community [D]
Maybe you can have a look at https://github.com/tensorchord/envd. You can deploy it to kubernetes, and use vsvocde-remote-ssh or jupyter with the pod.
- Show HN: envd – development environment for AI/ML, based on Docker buildkit
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This Week In Python
envd – Development environment for AI/ML, based on buildkit
- GitHub - tensorchord/envd: 🏕️ Development environment for machine learning
CloudForest
-
Trinary Decision Trees for missing value handling
I implemented something like this in a [pre xgboost boosting framework](https://github.com/ryanbressler/CloudForest) ~10 years ago and it worked well.
It isn't even that much of a speed hit using the classical sorting CART implementation. However xgboost and ligthgbm use histogram based approximate sorting which might be harder to adapt in a performant way. And certainly the code will be a lot messier.
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Future of Golang
Personally, my Go-to ML tool for tabular data is here: https://github.com/ryanbressler/CloudForest
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[D] Best methods for imbalanced multi-class classification with high dimensional, sparse predictors
The best method i've seen for dealing with this bias is to create "artificial contrasts" by including possibly many permutated copies of each feature and then doing a statistical test of the random forest importance values for each feature vs its shuffled contrasts. This method is described here: https://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf and there is an implementation here: https://github.com/ryanbressler/CloudForest
What are some alternatives?
GoLearn - Machine Learning for Go
libsvm - libsvm go version
goga - Golang Genetic Algorithm
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
gobrain - Neural Networks written in go
go-pr - Pattern recognition package in Go lang.
go-galib - Genetic Algorithms library written in Go / golang
shield - Bayesian text classifier with flexible tokenizers and storage backends for Go
bayesian - Naive Bayesian Classification for Golang.