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ライター：奈良　昌紀 通信事æ¥è€…ã®ãƒ‡ãƒ¼ã‚¿ã‚»ãƒ³ã‚¿ãƒ¼ã«ãŠã„ã¦ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãƒ»ã‚µãƒ¼ãƒãƒ¼é‹ç”¨ã‚’経験ã—ãŸå¾Œã€ãƒãƒƒãƒˆãƒ¯ãƒ³ã‚·ã‚¹ãƒ†ãƒ ズã«å…¥ç¤¾ã€‚帯域制御やWAN高速化製å“担当を経ã¦ã€2008å¹´ã‹ã‚‰ä»®æƒ³åŒ–関連製å“を担当。ç¾åœ¨ã¯ä¸»ã«ã‚¯ãƒ©ã‚¦ãƒ‰ã€ä»®æƒ³ã‚¤ãƒ³ãƒ•ãƒ©ã®ç®¡ç†ã€è‡ªå‹•åŒ–ã€ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ä»®æƒ³åŒ–を担当。 ã¯ã˜ã‚ã« ã“ã¡ã‚‰ã®Blog記事ã§ã¯ã‚³ãƒ³ãƒ†ãƒŠç’°å¢ƒã§GPUを活用ã™ã‚‹ãŸã‚ã®NVIDIA AI Enterprise + VMware vSphere® with VMware Tanzu® ã‚’ã”紹介ã—ã¾ã—ãŸã€‚今回ã¯2回ã®è¨˜äº‹ã«åˆ†ã‘ã¦ã€ã“ã®ç’°å¢ƒã‚’利用ã—ã¦è‡ªç„¶è¨€èªžå‡¦ç†ãƒ¢ãƒ‡ãƒ«ã§ã‚ã‚‹BERTã‚’ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ã—ã€é–‹ç™ºã—ãŸAIモデルをTriton Inference Serverã«ã‚ˆã‚ŠKubernetes上ã§ã‚³ãƒ³ãƒ†ãƒŠã¨ã—ã¦å®Ÿè¡Œã—ã€Kubernetesã®ã‚ªãƒ¼ãƒˆã‚¹ã‚±ãƒ¼ãƒ«æ©Ÿèƒ½ã«ã‚ˆã£ã¦ã‚¹ã‚±ãƒ¼ãƒ«ã‚¢ã‚¦ãƒˆãƒ»ã‚¹ã‚±ãƒ¼ãƒ«ã‚¤ãƒ³ã‚’実ç¾ã™ã‚‹æ–¹æ³•ã‚’ã”紹介ã—ã¾ã™ã€‚ G
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今回㯠Transformer ç³»ã®ãƒ¢ãƒ‡ãƒ«ã€å…·ä½“çš„ã«ã¯ BERT, T5, GPT ã®æŽ¨è«–を高速化ã—ã¦ã¿ã¾ã™ã€‚高速化手法ã¨ã—㦠FasterTransformer, Torch-TensorRT, AWS Neuron を用ã„ã€ç´ ã® transfomers ã«æ¯”ã¹ã€ã©ã®ç¨‹åº¦é€Ÿããªã‚‹ã‹ï¼ˆãªã‚‰ãªã„ã‹ï¼‰ã€åˆ©ç‚¹ãƒ»æ¬ 点を確èªã—ã¦ã¿ã¾ã—ょã†ã€‚ 1. ã¯ã˜ã‚㫠今回㯠Transformer ç³»ã®ãƒ¢ãƒ‡ãƒ«ã€å…·ä½“çš„ã«ã¯ BERT, T5, GPT ã®æŽ¨è«–を様々ãªæŠ€è¡“を使ã£ã¦é«˜é€ŸåŒ–ã—ã¦ã¿ã¾ã™ã€‚ 高速化ã®å…ƒãƒã‚¿ã¯ Hugging Face ã® transformers1 縛りã¨ã—ã¦ã€ç´ ã® transformers ã§æŽ¨è«–ã™ã‚‹å ´åˆã«æ¯”ã¹ã€ ã©ã®ç¨‹åº¦é€Ÿããªã‚‹ã‹ï¼ˆãªã‚‰ãªã„ã‹ï¼‰è¦‹ã¦ã¿ã¾ã—ょã†ã€‚ 推論を高速化ã™ã‚‹æŠ€è¡“ã¨ã—ã¦ã¯ FasterTransfomer2, Torch-TensorRT3, AWS Neuron(
How Nvidia’s CUDA Monopoly In Machine Learning Is Breaking - OpenAI Triton And PyTorch 2.0
The 1,000-foot summary is that the default software stack for machine learning models will no longer be Nvidia’s closed-source CUDA. The ball was in Nvidia’s court, and they let OpenAI and Meta take control of the software stack. That ecosystem built its own tools because of Nvidia’s failure with their proprietary tools, and now Nvidia’s moat will be permanently weakened. TensorFlow vs. PyTorch A
Triton Inference Server 2022 å¹´ 12 月 - 2023 å¹´ 2 月ã®ãƒªãƒªãƒ¼ã‚¹æ¦‚è¦
Reading Time: 2 minutes 2022 å¹´ 12 月ã‹ã‚‰ 2023 å¹´ 2 月ã«ã‹ã‘ã¦ãƒªãƒªãƒ¼ã‚¹ã•ã‚ŒãŸ Triton Inference Server ã®å„機能ãªã©ã«ã¤ã„ã¦ã€æ¦‚è¦ã‚’ãŠå±Šã‘ã—ã¾ã™ã€‚「Triton Inference Server ã£ã¦ä½•?ã€ã¨ã„ã†æ–¹ã¯ã€ä»¥ä¸‹ã®è¨˜äº‹ãªã©ã‚’ã”確èªãã ã•ã„。 GPU ã«æŽ¨è«–ã‚’: Triton Inference Server ã§ã‹ã‚“ãŸã‚“デプãƒã‚¤ NVIDIA Triton Inference Server を使用ã—ãŸã‚¨ãƒƒã‚¸ã§ã® AI モデルã®å±•é–‹ã®ç°¡ç´ 化 What’s New 今回ã®æœŸé–“ä¸ãƒªãƒªãƒ¼ã‚¹ã•ã‚ŒãŸãƒªãƒªãƒ¼ã‚¹ãƒŽãƒ¼ãƒˆã®æœ¬ä½“ã¯ã€ãã‚Œãžã‚Œä»¥ä¸‹ã®é€šã‚Šã§ã™ã€‚ 2.29.0 (NGC 22.12) https://github.com/triton-inference-server/server/releases/tag/v2.29.0 2.3
Amazon SageMaker 㧠NVIDIA Triton Inference Server を使用ã—ã¦ãƒ¢ãƒ‡ãƒ«ã‚µãƒ¼ãƒã®ãƒã‚¤ãƒ‘ースケールパフォーマンスを実ç¾ã™ã‚‹ | Amazon Web Services
Amazon Web Services ブãƒã‚° Amazon SageMaker 㧠NVIDIA Triton Inference Server を使用ã—ã¦ãƒ¢ãƒ‡ãƒ«ã‚µãƒ¼ãƒã®ãƒã‚¤ãƒ‘ースケールパフォーマンスを実ç¾ã™ã‚‹ 機械å¦ç¿’ (ML) アプリケーションã¯ãƒ‡ãƒ—ãƒã‚¤ãŒè¤‡é›‘ã§ã€å¤šãã®å ´åˆã€1 ã¤ã®æŽ¨è«–リクエストを処ç†ã™ã‚‹ãŸã‚ã«è¤‡æ•°ã® ML モデルãŒå¿…è¦ã§ã™ã€‚典型的ãªãƒªã‚¯ã‚¨ã‚¹ãƒˆã¯ã€å‰å‡¦ç†ã€ãƒ‡ãƒ¼ã‚¿å¤‰æ›ã€ãƒ¢ãƒ‡ãƒ«é¸æŠžãƒã‚¸ãƒƒã‚¯ã€ãƒ¢ãƒ‡ãƒ«é›†ç´„ã€å¾Œå‡¦ç†ãªã©ã®è¤‡æ•°ãƒ¢ãƒ‡ãƒ«ã«æ¸¡ã‚‹å ´åˆãŒã‚ã‚Šã¾ã™ã€‚ã“ã‚Œã«ã‚ˆã‚Šã€ã‚·ãƒªã‚¢ãƒ«æŽ¨è«–パイプラインã€ã‚¢ãƒ³ã‚µãƒ³ãƒ–ル (scatter gather)ã€ãƒ“ジãƒã‚¹ãƒã‚¸ãƒƒã‚¯ãƒ¯ãƒ¼ã‚¯ãƒ•ãƒãƒ¼ãªã©ã®ä¸€èˆ¬çš„ãªè¨è¨ˆãƒ‘ターンãŒé€²åŒ–ã—ã€ãƒªã‚¯ã‚¨ã‚¹ãƒˆã®ãƒ¯ãƒ¼ã‚¯ãƒ•ãƒãƒ¼å…¨ä½“ãŒæœ‰å‘éžå·¡å›žã‚°ãƒ©ãƒ• (DAG) ã¨ã—ã¦å®Ÿç¾ã•ã‚Œã‚‹ã«è‡³ã‚Šã¾ã—ãŸã€‚ã—ã‹ã—ãªãŒã‚‰ã€ãƒ¯ãƒ¼ã‚¯ãƒ•ãƒãƒ¼ãŒã‚ˆã‚Šè¤‡é›‘ã«ãªã‚‹ã«ã¤ã‚Œã¦ã€ã“れらã®ã‚¢ãƒ—リケーションã®å…¨ä½“çš„ãªãƒ¬ã‚¹
MLOps年末åçœä¼š: Triton Inference Server を深層å¦ç¿’モデル推論基盤ã¨ã—ã¦å°Žå…¥ã—ãŸã®ã§æŒ¯ã‚Šè¿”ã‚‹ | CyberAgent Developers Blog
MLOps年末åçœä¼š: Triton Inference Server を深層å¦ç¿’モデル推論基盤ã¨ã—ã¦å°Žå…¥ã—ãŸã®ã§æŒ¯ã‚Šè¿”ã‚‹ ã“ã®è¨˜äº‹ã¯ CyberAgent Developers Advent Calendar 2022 ã®5日目ã®è¨˜äº‹ã§ã™ã€‚ AI事æ¥æœ¬éƒ¨ã§ã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã‚¨ãƒ³ã‚¸ãƒ‹ã‚¢ï¼ˆæ©Ÿæ¢°å¦ç¿’ & MLOpsé ˜åŸŸï¼‰ã‚’ã—ã¦ã„ã‚‹ yu-s (GitHub: @tuxedocat) ã§ã™1。ç¾åœ¨ã¯ 極予測LP ã¨ã„ã†ã€åºƒå‘Šãƒ©ãƒ³ãƒ‡ã‚£ãƒ³ã‚°ãƒšãƒ¼ã‚¸ã®åˆ¶ä½œã‚’AIã«ã‚ˆã‚Šåˆ·æ–°ã™ã‚‹ã¨ã„ã†ç›®æ¨™ã®ãƒ—ãƒãƒ€ã‚¯ãƒˆã«é–¢ã‚ã£ã¦ã„ã¾ã™ã€‚ ã“ã®è¨˜äº‹ã§ã¯æœ¬ãƒ—ãƒãƒ€ã‚¯ãƒˆã®MLOpsã®å–り組ã¿ã®ã†ã¡ã€ç‰¹ã«æ·±å±¤å¦ç¿’モデルをデプãƒã‚¤ã—ã¦é‹ç”¨ã™ã‚‹åŸºç›¤ã«ã¤ã„ã¦ã®æŠ€è¡“é¸å®šã‚’振り返ã£ã¦ã¿ã¾ã™ã€‚ タイトルã«ã‚ã‚‹ã¨ãŠã‚Š Triton Inference Server ã¨ã„ã†OSSã®æŽ¨è«–基盤を導入ã—ã¾ã—ãŸã€‚ å‰ç½®ã: プãƒãƒ€ã‚¯ãƒˆã‚„ãƒãƒ¼ãƒ ãªã©ã®ç’°å¢ƒã¨çµŒç·¯ 本記
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Optimize a PyTorch model - Container Service for Kubernetes - Alibaba Cloud Documentation Center
server/docs/customization_guide/compose.md at main · triton-inference-server/server
GitHub - triton-inference-server/pytriton: PyTriton is a Flask/FastAPI-like interface that simplifies Triton's deployment in Python environments.
Deploy Your Local GPT Server With Triton
fastertransformer_backend/docs/gpt_guide.md at main · triton-inference-server/fastertransformer_backend
server/docs/getting_started/quickstart.md at main · triton-inference-server/server
Surpassing NVIDIA FasterTransformer’s Inference Performance by 50%, Open Source Project Powers into the Future of Large Models Industrialization
GPU ã«æŽ¨è«–ã‚’: Triton Inference Server ã§ã‹ã‚“ãŸã‚“デプãƒã‚¤
GitHub - triton-inference-server/fastertransformer_backend
Solving AI Inference Challenges with NVIDIA Triton | NVIDIA Technical Blog
GitHub - NVIDIA/FasterTransformer: Transformer related optimization, including BERT, GPT
Deploying the Nvidia Triton Inference Server on Amazon ECS
Triton Inference Server
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