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HyperTrain (Distributed Training Engine)

HyperTrain is a fully managed, Kubernetes-based distributed training engine built to tackle the sheer compute and orchestration challenges of enterprise-grade AI. We've abstracted away the infrastructure headaches—no more wrestling with complex resource scheduling, distributed framework compatibility, or environment dependencies.

With HyperTrain, you get an isolated, cost-effective, and out-of-the-box environment that seamlessly bridges your code with massive compute capacity. Whether you are fine-tuning Foundation Models or training from scratch, you can spin up distributed AI training jobs with a single click and zero operational overhead.

Use HyperTrain to orchestrate massive distributed clusters, track real-time GPU metrics, debug inside live containers, and ship production-ready models like a pro. Bring your data and code, and let Alaya NeW Cloud handle the heavy lifting!

🚀 Key Features​

  • Seamless Framework Integration: Out-of-the-box support for industry-standard distributed frameworks, including PyTorch, DeepSpeed, MPI, and TensorFlow.
  • Granular Observability: Monitor real-time compute capacity usage (GPU VRAM, CPU, Memory). Access live logs, events, and raw YAML files at the Pod level.
  • Interactive Debugging: Need to troubleshoot? SSH directly into the live container terminal or map exposed web ports right from your browser.
  • Cost-Aware Job Lifecycle: Leverage a true pay-as-you-go model. Pause running tasks to immediately stop billing and tear down Pods, then resume right where you left off.
  • Workflow Automation (Templates): Save your complex infrastructure configurations as reusable Templates to spin up future training runs in seconds.
  • Resilient Workloads: Built-in auto-retry mechanisms and configurable timeouts ensure your training jobs recover gracefully from unexpected failures.