Tagged articles

inference

104 articles · Page 2 of 2

Dec 14, 2020 · Artificial Intelligence

LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models

This article introduces LightSeq, an open‑source, GPU‑accelerated inference engine that dramatically speeds up Transformer‑based models such as BERT and GPT by up to 14× over TensorFlow, supports multiple decoding strategies, integrates seamlessly with major deep‑learning frameworks, and provides detailed performance benchmarks and technical optimizations.

GPULightSeqNLP

0 likes · 15 min read

LightSeq: High‑Performance Open‑Source Inference Engine for Transformers, GPT and Other NLP Models

JD Tech Talk

Nov 16, 2020 · Artificial Intelligence

Practical Guide to Deploying Federated Learning: Architecture, Deployment, Training, and Inference

This article provides a comprehensive overview of federated learning engineering, covering deployment via Docker containers, the design of training and inference frameworks, key services such as communication, training, model management, and registration, and practical considerations for scaling and reliability in production environments.

AIDockerarchitecture

0 likes · 11 min read

Practical Guide to Deploying Federated Learning: Architecture, Deployment, Training, and Inference

JD Tech Talk

Nov 13, 2020 · Artificial Intelligence

Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference

This article provides a comprehensive engineering overview of federated learning, covering its core distributed‑learning concept, Docker‑based deployment, detailed training‑service architecture with validation, scheduling, metadata, and model‑management components, as well as a complete inference framework and workflow for production use.

AI EngineeringDockerdistributed systems

0 likes · 12 min read

Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference

Didi Tech

Jul 5, 2019 · Artificial Intelligence

How Didi’s Jianshu Machine Learning Platform Boosts AI Development Efficiency

An in‑depth look at Didi’s Jianshu Machine Learning Platform reveals its end‑to‑end AI workflow—from experiment environments and batch training to high‑availability online serving—highlighting resource‑efficient Kubernetes scheduling, Docker‑based reproducible environments, a custom parameter server, and the IFX inference engine that together accelerate development, training, and deployment.

AIPlatformDockerKubernetes

0 likes · 11 min read

How Didi’s Jianshu Machine Learning Platform Boosts AI Development Efficiency