Trainy

Trainy is a GPU infrastructure platform designed for AI teams that need to run large-scale machine learning workloads without the complexity of managing cloud networking, scheduling, and fault recovery. Teams submit jobs via simple YAML files and Trainy handles multi-node networking, priority queuing, health monitoring, and automatic failure recovery. It supports both on-demand GPU access and reserved dedicated clusters, enabling a hybrid approach that minimizes idle GPU time and infrastructure costs.

• Simple YAML Job Submission: Write a config file specifying nodes, GPU types, and priority, then deploy with a single CLI command — no code changes needed.
• Multi-Node Training Support: Scale AI workloads across thousands of GPUs with high-bandwidth networking (3.2 TB/s Infiniband) configured automatically.
• Cross-Cloud Compatibility: Deploy to any cloud provider with the same YAML file and switch providers without changing your workflow.
• Multi-Framework Support: Run PyTorch, HuggingFace, JAX, Ray, and any Python-based ML framework without modification.
• Preemptive Priority Queue: High-priority jobs automatically pause lower-priority ones and resume them on completion, keeping GPUs busy 24/7.
• Health Monitoring & Fault Detection: Continuous GPU health checks, automated failure recovery, and direct cloud provider escalation prevent costly downtime.
• Resource Management Dashboard: Real-time visibility into GPU utilization, costs, and cluster performance to make informed infrastructure decisions.
• On-Demand Pricing: Pay only when training runs — zero cost for idle GPUs — with no annual contract lock-in required.
• Reserved Clusters: Dedicated GPU allocation with enterprise SLA, advanced monitoring, and cluster utilization insights for teams with predictable workloads.
• Fast Setup: Go from zero to a functional multi-node training setup with high-bandwidth networking in under 20 minutes.