Mosec
High-performance ML model serving: Rust web layer, Python API, dynamic batching, and CPU/GPU pipelines.
About
Mosec is a high-performance, open-source model serving framework for building ML model-enabled backends and microservices. It bridges the gap between a trained model and an efficient online API: the web layer and task coordination are built in Rust for speed and efficient CPU utilization via async I/O, while the user-facing interface is pure Python and model-framework agnostic, so serving code mirrors offline testing code.
Mosec aggregates requests from different users for batched inference (dynamic batching), spawns multiple processes for pipelined stages to handle mixed CPU/GPU/IO workloads, and is cloud-friendly with model warmup, graceful shutdown, and Prometheus monitoring metrics for Kubernetes deployments. It installs with pip install mosec on Linux or macOS with Python 3.7 or newer.
What it can do
- Rust-based web layer with async I/O for high throughput
- Python API that reuses offline model code unchanged
- Dynamic batching across concurrent requests
- Multi-stage pipelined workers for CPU/GPU/IO workloads
- Prometheus metrics, model warmup, and graceful shutdown
Setup
pip install mosec (Linux/macOS, Python 3.7+); define a Worker class wrapping your model and run the service.
Prerequisites
A trained machine learning model to serve; Python 3.7+ on Linux or macOS.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 94; Trending score 68/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.