lambda-labs-gpu-cloud

You can launch GPU instances for ML training and inference by using the skill to provision single- or multi-GPU environments through the console flow, authenticating via API key, and connecting via SSH to start running experiments.

To keep datasets and checkpoints across instance restarts on a GPU cloud, you mount persistent filesystems to your instances. This ensures your models, data, and outputs remain durable and do not reset when machines change.

Yes, you can run distributed ML training on 16 to 512 GPUs using Lambda Labs by deploying 1-Click Slurm clusters, which come pre-installed with a distributed ML stack for large-scale jobs.

Yes, you need an API key to automate GPU instance provisioning and termination. The skill uses the lambda-cloud-client and API automation patterns to authenticate, select GPU regions, and manage the instance lifecycle.

Yes, this skill supports mounting persistent filesystems for durable outputs during batch inference. You can stage data once via persistent storage and save inference results to ensure they persist across instance restarts.

The best way to automate workflows for repeatable ML experiments on Lambda Labs is by using the provided API and client patterns to script instance launching, SSH access, and persistent storage mounting for checkpoints and outputs.