riton-inference-server/backend: Common source, scripts and utilities for creating Triton backends.

Common source, scripts and utilities for creating Triton backends.

Triton Inference Server Backend A Triton backend is the implementation that executes a model. A backend can be a wrapper around a deep-learning framework, like PyTorch, TensorFlow, TensorRT or ONNX Runtime. Or a backend can be custom C/C++ logic performing any operation (for example, image pre-processing). This repo contains documentation on Triton backends and also source, scripts and utilities for creating Triton backends. You do not need to use anything provided in this repo to create a Triton backend but you will likely find its contents useful. Frequently Asked Questions Full documentatio

related reading

• tutorials/Conceptual_Guide/Part_4-inference_acceleration/README.md at main · triton-inference-server/tutorials · GitHubgithub.com
• tutorials/HuggingFace at main · triton-inference-server/tutorials · GitHubgithub.com
• server/docs/user_guide/model_configuration.md at main · triton-inference-server/server · GitHubgithub.com
• tutorials/Conceptual_Guide/Part_1-model_deployment at main · triton-inference-server/tutorials · GitHubgithub.com
• GitHub - triton-lang/triton: Development repository for the Triton language and compiler · GitHubgithub.com
• Accelerated Inference for Large Transformer Models Using NVIDIA Triton Inference Server | NVIDIA Technical Blogdeveloper.nvidia.com
• ⭐️ Fast LLM Inference From Scratchandrewkchan.dev
• GitHub - huggingface/nanotron: Minimalistic large language model 3D-parallelism training · GitHubgithub.com
• GitHub - rasbt/LLMs-from-scratch: Implement a ChatGPT-like LLM in PyTorch from scratch, step by stepgithub.com
• API Reference — TensorRT LLMnvidia.github.io
• GitHub - wafer-ai/gpu-perf-engineering-resources: A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.github.com
• GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.github.com