Deployment Teaching Environment
Teachers can quickly deploy shared GPUs environment with teaching tools for students.
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AI Education solution with multi-tenancy and hierarchical architecture. Professors and Teaching assistants can set up separate project for each student. Teachers and students could concentrate on AI model network development instead of wasting time on system and GPU resource management.
Learn more about AI Education Cloud
Teachers can quickly deploy shared GPUs environment with teaching tools for students.
Teachers can monitor resource usage at any time during class, and can manage resources flexibly in needed.
Teachers can easily recycle resources after class, and can allocate resources for students to do homework or projects.
Quickly activate the service and operate directly with the Jupyter interface, which greatly reduces the tedious system operation process!
A single GPU can be allocated to multiple students, making efficient use of precious GPU resources!
There are multiple built-in AI development frameworks, and the teaching software installation package can be customized to best meet the needs of the course.
Customize resource quotas and group member management permissions according to different course and research needs.
It can integrate campus account and storage services to quickly start exclusive solutions!
Provide all kinds of documents and education training, and provide up to 7x24 hours of local professional services!
Project Research, Teaching Environment Construction
Use independent computing resources
Research assignments are used as needed independent or shared computing resources.
Use CPU/GPU shared resources
