Baseten provides a platform and open-source tooling (Truss) to package, deploy, and scale machine learning models as production-ready APIs.
Baseten is a platform designed to streamline the deployment of machine learning models into production. It enables users to take a trained ML model and transform it into a fully-deployed, scalable API. The core of its developer tooling is Truss, an open-source model packaging tool that containerizes model code and configuration for deployment, testing, and scaling on Baseten. The platform supports a typical model development workflow, including initializing projects, defining model logic and dependencies, deploying with live-reloading for iteration, and promoting stable versions to production. Truss handles the creation of standardized containers, eliminating the need for local Docker installations and providing a consistent way to configure and serve models. It supports various ML stacks, including frameworks like PyTorch and Hugging Face transformers, inference engines like TensorRT-LLM, and any pip or apt installable packages. When developing on Baseten, users define a Model class for loading and inference logic, a configuration file for the runtime environment and dependencies, and optional extra assets like model weights. These components form a Truss, which is a portable, reproducible package for deployment, versioning, and scaling. Baseten offers both stable, autoscaled 'published deployments' for live traffic and 'development deployments' with live-reloading for rapid iteration and testing.
Integrations: PyTorch, Hugging Face Transformers, Diffusers, TensorRT-LLM, SGLang, vLLM
Platforms: Web
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