Why Alibaba Cloud
Alibaba Cloud provides a full-stack solution for generative AI development, which includes a collection of compute-optimized ECS instances with high-performance GPUs, a powerful AI computing platform, a one-stop AI development platform, and acceleration features for AI model training and inference. This solution helps you build and optimize foundation models (FMs) based on your business needs, so you can create new and intelligent customer experiences, and drive business transformation with innovations in generative AI.
Powered by Compute-Optimized GPUs
Train and run large-scale FMs containing billions of parameters with high performance, scalability, and cost effectiveness on Alibaba Cloud ECS instances and NVIDIA GPUs
A Diverse Selection of Pre-Built FMs
Choose FMs for your business from a wide range of pre-built models in PAI-EAS, including Alibaba Cloud's Tongyi Qianwen (Qwen), Stable Diffusion, Llama 2, and more from Hugging Face
Streamlined FM Integration
Integrate and deploy FMs in easy steps with Machine Learning Platform for AI and ECS, and tap into the capabilities and services of Alibaba Cloud to speed up innovation
Model Training / Inference Optimization
Apply end-to-end performance optimization to accelerate dataset processing, model training, and model inference, and enhance it with a GPU-based AI acceleration solution
Alibaba Cloud provides two solutions for you to speed up generative AI development. You can leverage Lingjun to complete compute-intensive tasks of large-scale FMs, or choose our unified hardware/software acceleration to customize your AI acceleration strategy.Learn More About FM Training and Inference
1. GPUs for Model Training and Inference
Model Training: gn7 series of ECS instances power large-scale training tasks with high-performance NVIDIA GPUs
Model Inference: gn6 series of ECS instances provide a cost-effective choice for model inference tasks
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2. AI Acceleration
3. Platform for AI
PAI provides an end-to-end optimization solution to including PAI-iTAG for data labeling, PAI-DSW for model building, PAI-DLC for model training, and PAI-EAS for model inference and deployment.
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A Wide Selection of Open-Source FMs
Tongyi Qianwen (Qwen)
Alibaba Cloud has released two open-source Tongyi Qianwen models: Qwen, the large language model (including Qwen-7B and Qwen-14B), and Qwen-Chat, the chat model (including Qwen-7B-Chat and Qwen-14B-Chat). Qwen-14B and Qwen-7B outperform other baseline models of a similar size on a series of benchmark datasets (including MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc.) that evaluate the models' capabilities on natural language understanding, mathematical problem solving, coding, etc. Please refer to OpenCompass Large Language Model Leaderboard or download Qwen Technical Report for details.
You can use PAI-EAS to deploy web UI applications based on the open source Qwen models, and use the web UI and API operations to perform model inference.
Llama is an open-source LLM. Compared to its predecessor, Llama 1, Llama 2 has more training data and a longer context length, making it more versatile and accurate in generating natural language at scale. You can run Llama 2 on PAI-EAS in WebUI with just a few clicks or use API with Python Wrapper code to set up Llama 2 service.
Stable Diffusion is an open-source and popular cross-modal generation model that leverages advanced deep learning algorithms and techniques to generate visually compelling images based on text descriptions. You can deploy the Stable Diffusion models and create a text-to-image generation service with a few clicks in PAI-EAS.
ChatGLM is an open-source LLM based on the General Language Model (GLM) architecture. ChatGLM-6B is trained on 1 trillion tokens and uses supervised fine-tuning (SFT), self-feedback system, and human feedback reinforcement learning (RLHF) to better align with human preferences. You can quickly deploy the ChatGLM application in PAI-EAS.
Alibaba Cloud’s vector data management apparatus includes vector database AnalyticDB for PostgreSQL and vector search engines, which are designed to store, manage, and retrieve vector embedding data as high-dimensional data effectively and efficiently. This makes it accurate and fast to query or retrieve data based on their vector distance or similarity, simplifying the AI training and prediction process.Download Whitepaper
You can easily deploy generative AI applications on Compute Nest to provide various types of generative AI services, without concerns about the underlying architecture.
For more generative AI applications, stay tuned for Wanxiang from DAMO Academy.
Image Generation Based on Text and Color Depth
Image Generation Based on Text and Masked Image
Image Generation Based on Text and Color Palette
Image Generation Based on Image and Palette
Image Generation Based on Color Intensity and Palette
Region-Specific Image Editing
Image Embedding Based on Sketch and Depth (Text in Image)
Image Embedding Based on Sketch and Depth (No Text in Image)
Clothes Virtual Try-On Based on Human Image
Clothes Virtual Try-On Based on Pose, Clothes Image, and Text
Alibaba Cloud Keeps Driving Innovation in Generative AI