Alibaba AI Acceleration solution include the Cloud-Native AI Suite layer, AI Job Scheduling layer, AI Data Acceleration layer, AI Computing Acceleration Engine layer, Resource Scheduling layer, and Computing Resource layer. Each layer offers specific acceleration solutions based on AI application characteristics in various scenarios.
Cloud-Native AI Suite: Simplify operations with underlying services and components, monitor GPU resource utilization, and collect and analyze logs of AI jobs. You can use command lines to submit AI jobs and check the schedule of model training.
AI Job Scheduling: Schedule AI jobs to accelerate models training, increase GPU utilization, and save costs.
AIACC: Leverage deep learning frameworks such as Tensoflow, PyTorch, MXNet, and Caffe to categorize images and achieve image recognition, Click-Through Rate prediction, Neuro-Linguistic Programming, speech recognition, face recognition, etc.
Data Acceleration: Adopt the architecture with separated storage and computing resources with data stored in different devices from on-premises and model training executed on Alibaba Cloud. Data are stored in small files and warmed up before model training to improve transmission and training efficiency.
Resource Scheduling: Increase GPU utilization and save costs by sharing GPU resources with multiple AI jobs and running single AI jobs on multiple GPUs for distributed model training.
Computing Resources-GPU: Manage and schedule GPU memory and computing power as containers with cGPU to accelerate AI training and inference.