lambda-labs-gpu-cloud

To provision on-demand GPU cloud instances for ML training, you can launch instances directly from the Lambda console and connect via SSH to start your workloads. This provides dedicated GPU access for immediate training and inference tasks.

Yes, persistent filesystems are supported to maintain data persistence across instance restarts. You can attach optional filesystems to your GPU cloud instances to ensure your training data and model states remain intact.

Yes, it supports high-performance multi-node clusters designed for large-scale training and inference tasks. You can utilize 1-Click clusters to quickly deploy scalable multi-node environments for your ML workloads.

SSH key management is handled natively to provide simple SSH access to your dedicated GPU instances. You configure your keys during the API-based provisioning process to securely connect to your environment.

The best way to scale ML infrastructure for multi-node training is using 1-Click clusters. This feature provisions high-performance multi-node clusters optimized for large-scale training and inference workloads via an API-based workflow.