envd
mosec
| envd | mosec | |
|---|---|---|
| 31 | 11 | |
| 1,919 | 707 | |
| 0.9% | 0.9% | |
| 8.8 | 8.5 | |
| 5 days ago | 2 days ago | |
| Go | Python | |
| 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.
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
mosec
-
20x Faster as the Beginning: Introducing pgvecto.rs extension written in Rust
Mosec - A high-performance serving framework for ML models, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine. Simple and faster alternative to NVIDIA Triton.
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[D] Handling Concurrent Request for ML Model API
- Yes C++ would be better, but you can try mosec. It has a Python interface and helps you handle all the difficult things about Python multiprocessing. The web service part is implemented in Rust thus it's fast enough for machine learning services.
-
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.
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Deploying a model with an API in docker
You could first create the image with the framework you like (e.g. bentoml or https://github.com/mosecorg/mosec for light weight).
- PostgresML is 8-40x faster than Python HTTP microservices
- Python Machine Learning Service Can Run Way More Faster
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[D] Open Source ML Organisations to contribute to?
If you're interested in machine learning model serving, can check mosec: https://github.com/mosecorg/mosec
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Why not multiprocessing
During the development of a machine learning serving project Mosec, I used a lot of multiprocessing to make it more efficient. I want to share some experiences and some researches related to Python multiprocessing.
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[P] Mosec: deploy your machine learning model in an easy and efficient way
That's a good example. I have met the same situation before. I have created a discussion in GitHub to track the DAG progress.
- Mosec: deploy your machine learning model in an easy and efficient way
What are some alternatives?
GoLearn - Machine Learning for Go
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!
goga - Golang Genetic Algorithm
GPflow - Gaussian processes in TensorFlow
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)
mlrun - MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.
go-pr - Pattern recognition package in Go lang.
text-generation-inference - Large Language Model Text Generation Inference
go-galib - Genetic Algorithms library written in Go / golang
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
bayesian - Naive Bayesian Classification for Golang.
postgresml - The GPU-powered AI application database. Get your app to market faster using the simplicity of SQL and the latest NLP, ML + LLM models.