lambda-labs-gpu-cloud

Provision and manage on-demand GPU cloud instances for ML workloads.

Updated Jun 25, 2026

Search Tags:#machine learning #cloud #infrastructure #gpu #training #lambda labs

Ready to run

npx skills add https://github.com/Rheasilvia/hermes-desktop --skill lambda-labs-gpu-cloud-rheasilvia

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lambda-labs-gpu-cloud
Source: https://github.com/Rheasilvia/hermes-desktop/tree/main/optional-skills/mlops/lambda-labs
Command: npx skills add https://github.com/Rheasilvia/hermes-desktop --skill lambda-labs-gpu-cloud-rheasilvia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires lambda-cloud-client, and includes references (resource) components.

What problem does it solve?

This skill solves the challenge of provisioning and managing high-performance GPU infrastructure for machine learning tasks, eliminating the complexity of manual cloud configuration and hardware setup.

Core Features & Use Cases

On-Demand GPU Provisioning: Quickly launch instances with various NVIDIA GPUs including H100, A100, and B200 for training or inference.
Distributed Training Support: Orchestrate multi-node Slurm clusters for large-scale model training and fine-tuning.
Persistent Storage Management: Utilize attached filesystems to maintain datasets and model checkpoints across instance lifecycles.

Quick Start

Use the lambda-labs-gpu-cloud skill to launch a new 8x H100 instance in the us-west-1 region with my-ssh-key attached.

Frequently Asked Questions about lambda-labs-gpu-cloud

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I provision on-demand GPU cloud instances for ML training?▼

To provision on-demand GPU cloud instances for ML training, use the skill to launch single-node or distributed multi-node environments on Lambda Labs infrastructure with NVIDIA GPUs like H100 and A100. It manages the instance lifecycle via API-based calls.

Can I orchestrate multi-node Slurm clusters for distributed training in the cloud?▼

Yes, you can orchestrate multi-node Slurm clusters for distributed training in the cloud. The skill facilitates deploying large-scale model training and fine-tuning environments across multiple nodes on Lambda Labs infrastructure.

Does lambda-labs-gpu-cloud support persistent storage for model checkpoints?▼

Lambda-labs-gpu-cloud supports persistent storage for model checkpoints by utilizing attached filesystems. This maintains datasets and model checkpoints across instance lifecycles even after terminating GPU instances.

What types of NVIDIA GPUs can I launch for machine learning workloads?▼

You can launch instances with various NVIDIA GPUs for machine learning workloads, specifically H100, A100, and B200 configurations. These are provisioned on-demand for high-performance training or inference tasks.

Do I need the lambda-cloud-client dependency to manage instance lifecycles?▼

Yes, you need the lambda-cloud-client dependency to manage instance lifecycles. It provides the programmatic API integration required to provision, configure, and control GPU cloud instances on Lambda Labs.