#NVIDIA GPU

Explore an open-source project offering detailed guidance on LLM training and fine-tuning with NVIDIA GPUs and Ascend NPUs. The resources cover parameter-efficient methods like LoRA and QLoRA and introduce distributed training techniques. Access practical examples using frameworks like HuggingFace PEFT, DeepSpeed, and Megatron-LM to enhance large language models. Understand distributed AI framework complexities and learn effective LLM deployment strategies.

This open-source course provides a thorough examination of artificial intelligence and deep learning systems, based on comprehensive industry insights. Key topics include AI hardware, coding, inference systems, and essential framework technologies. Targeted at advanced undergraduate and graduate students as well as AI professionals, it offers understanding of AI's complete stack and lifecycle. The course includes text materials, video content on major platforms, and open-access presentations on GitHub, supporting community-based AI exploration and study.

dstack offers an efficient and user-friendly alternative to Kubernetes and Slurm, focusing on AI container management for both cloud-based and on-premise infrastructures. It supports NVIDIA, AMD GPUs, and Google Cloud TPUs, facilitating simplified deployment and training of AI models. With recent enhancements for AMD GPUs, AWS EFA, and a new control plane UI, dstack excels in providing scalable and secure AI environments, making it a preferred choice for AI operations.

FastDeploy enables efficient AI model deployment on Cloud, Mobile, and Edge platforms. It optimizes over 160 models, including text, vision, speech, and cross-modal applications. Key features include image classification, object detection, OCR, face detection, and NLP, addressing developer requirements across multiple scenarios and platforms. With recent updates for YOLOv8 and serving deployment visualization, it facilitates broad compatibility and straightforward integration into diverse environments.