JupyterHub with PyTorch and CUDA®
JupyterHub with PyTorch and CUDA® is your all-in-one product for multi-user Jupyter environments, deep learning and GPU acceleration. Deploy on Kubernetes for seamless scalability and resource management.
Flexibility
Customizable environments for various data science and ML needs.
Scalability
From small teams to large organizations, grow as your needs evolve.
Performance
Leverage GPU acceleration for compute-intensive tasks.
Collaboration
Foster teamwork with shared notebooks and resources.
Simplicity
Deploy easily in Nebius AI infrastructure.
Security
Robust authentication and user management.
Why choose JupyterHub with PyTorch and CUDA
Where you definitely need JupiterHub
Machine learning
- Data preprocessing and exploration for ML projects
- Prototyping and experimenting with ML algorithms
- Training and fine-tuning deep learning models using Python
- Developing and testing AI applications in a scalable environment
- Running distributed ML workloads across multiple nodes
- Accelerated computing tasks utilizing NVIDIA GPUs
Research, science and analitics
- Collaborative research in academic and scientific environments
- Teaching programming or data science courses
- Sharing and reproducing research results in computational sciences
- Interactive coding and visualization for data analysis
Ready to transform your data science workflow with one click?
Experience the advantages of collaborative, GPU-accelerated data science and machine learning.