What problem does it solve? Provisioning dedicated GPU infrastructure for ML training and inference is complex and expensive. This Skill guides you through launching, configuring, and managing Lambda Labs GPU instances and clusters so you can run training jobs without managing hardware. ## Core Features & Use Cases - Instance Lifecycle Management: Launch, monitor, and terminate GPU instances (B200, H100, A100, A10, and more) via the console, Python API, or curl-based CLI. - Distributed Training Setup: Configure multi-GPU and multi-node training with PyTorch DDP, FSDP, DeepSpeed, and Slurm-based 1-Click Clusters with InfiniBand. - Persistent Storage & Cost Control: Attach persistent filesystems for checkpoints and datasets, and apply cost-optimization strategies like right-sizing GPUs and checkpointing. - Use Case: You need to fine-tune a 7B LLM. Launch an 8x A100 instance with an attached filesystem, SSH in, run distributed training with torchrun, and save checkpoints that survive instance termination. ## Quick Start Ask the agent to launch a single H100 GPU instance on Lambda Labs in the us-west-1 region using your configured API key and SSH key.
