lambda-labs-gpu-cloud

You can launch on-demand GPU instances for ML training by using API-based provisioning to allocate dedicated GPUs with SSH access, persistent storage, and pre-installed ML stacks without managing hardware.

Yes, you can use multi-node clusters for distributed training on Lambda Labs. The infrastructure supports 1-Click Clusters with pre-installed Lambda Stack software to rapidly scale large-scale training workloads.

Yes, the Lambda Labs GPU cloud supports persistent storage for checkpoints. It provides persistent filesystems specifically designed to retain data, model checkpoints, and outputs across instance lifecycles.

Yes, you need the lambda-cloud-client package version 1.0.0 or higher to automate GPU cloud provisioning. It enables API-based launching, monitoring, and scaling of ML workloads via the CLI.

The best way to manage SSH keys when provisioning GPU instances is through the API-based provisioning workflow, which handles SSH key management alongside region selection to secure access to your ML environments.

Limitations of using on-demand GPU cloud for inference workloads include reliance on available instance capacity and per-minute pricing, though it provides flexible regions and pre-installed software for rapid deployment.