inferencedb
mosec
| inferencedb | mosec | |
|---|---|---|
| 9 | 11 | |
| 77 | 709 | |
| - | 1.4% | |
| 0.0 | 8.5 | |
| almost 2 years ago | 9 days ago | |
| Python | Python | |
| 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.
inferencedb
- [P] InferenceDB - Makes it easy to store predictions of real-time ML models in S3
- InferenceDB: Stream inferences of real-time ML models to S3 using Kafka
- InferenceDB: Stream inferences of real-time ML models in production to any data lake 🚀
- InferenceDB – Stream ML inferences to S3 or any data lake with CRDs
- InferenceDB – Stream predictions from KServe to S3 or any data lakes
- InferenceDB – Stream predictions of real-time ML models to data lakes
mosec
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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.
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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.
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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?
kfserving - Standardized Serverless ML Inference Platform on Kubernetes [Moved to: https://github.com/kserve/kserve]
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!
GPflow - Gaussian processes in TensorFlow
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.
text-generation-inference - Large Language Model Text Generation Inference
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
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.
inference-benchmark - Benchmark for machine learning model online serving (LLM, embedding, Stable-Diffusion, Whisper)
gunicorn - gunicorn 'Green Unicorn' is a WSGI HTTP Server for UNIX, fast clients and sleepy applications.
envd - 🏕️ Reproducible development environment
whylogs - An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈