NVIDIA/triton-inference-server
10,756 estrellas·1,794 forks·Python·BSD-3-Clause·8 vistasdocs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html↗
Triton Inference Server
Triton Inference Server is a high-performance AI model inference server and multi-framework model runtime designed for deploying machine learning models across cloud, data center, and embedded edge infrastructure. It serves as an execution engine that allows for the concurrent running of models from various frameworks to optimize hardware utilization.
The project features a dynamic batching inference engine that groups individual requests into larger batches to increase total processing throughput. It also provides a model ensemble pipeline, which enables the chaining of multiple models together to create complex data processing and inference sequences.
The server covers broader capabilities including model lifecycle management through a central storage repository, performance monitoring for hardware utilization and latency, and the ability to integrate in-process via native APIs. It supports routing requests through standard web protocols and utilizes shared memory for efficient data exchange.
Features
- Model Inference Servers - Acts as a dedicated server application hosting machine learning models to provide scalable, network-accessible inference services.
- Model Serving APIs - Provides a high-performance infrastructure for exposing deep learning models as network-accessible inference services.
- Batch Inference Engines - Groups individual requests into larger batches dynamically to increase total processing throughput.
- Chaining Pipelines - Enables the creation of complex workflows by chaining multiple models via ensembles or scripting.
- High Throughput Inference - Maximizes hardware utilization by combining dynamic batching and concurrent model execution for high-volume throughput.
- Model Deployment Pipelines - Provides standardized toolchains for deploying and serving AI models across cloud and edge infrastructure.
- Backend Runtimes - Provides pluggable runtime backends to execute models from various frameworks using hardware-optimized kernels.
- Model Inference Runtimes - Provides an execution layer supporting multiple AI frameworks to run models concurrently and optimize hardware use.
- Model Serving - Deploys trained machine learning models to production environments to provide scalable inference endpoints.
- Inference Batching - Implements dynamic batching to group individual inference requests, maximizing hardware throughput.
- Model Pipeline Orchestration - Provides ensemble pipelines that chain multiple models together into complex data processing sequences.
- In-Process Agent Execution - Allows the inference engine to be run directly within the host application process using native APIs.
- Inference Pipeline Orchestrators - Provides a framework for executing multi-stage machine learning inference pipelines using model ensembles.
- Edge AI Model Deployment - Optimizes and deploys machine learning models to run efficiently on embedded edge devices and data centers.
- Lifecycle Management - Controls active memory usage by dynamically loading and unloading models from a central repository.
- Inference Pipelines - Implements sequential chaining of models where the output of one serves as the input to the next.
- Model Lifecycle Management - Automatically loads or unloads models from a central storage repository based on configuration changes.
- Shared Memory Data Exchange - Utilizes shared memory regions for zero-copy transfer of large tensors between clients and the server.
- In-Process Integration - Exposes a native C-API allowing applications to embed the inference engine directly to eliminate network overhead.
- Execution Models - Executes multiple models or model instances concurrently to maximize hardware resource utilization.
- Concurrent Inference Instances - Manages multiple concurrent model instances in memory to process requests in parallel across GPUs and CPUs.
- Inference Performance Monitoring - Provides integrated telemetry for tracking GPU utilization, request throughput, and response latency.
- Infrastructure and Deployment - Optimized cloud inferencing solution for GPUs.
- Gestión de infraestructura - Solución de inferencia en la nube optimizada para cargas de trabajo de GPU.
