Feature Request: LambdaLabs Automatic Runner Allocation

Lambda Labs is gaining popularity in the machine learning community due to its **competitive pricing**, availability of the **latest GPU models**, and overall **ease of use**. Integrating Lambda Labs cloud API support into CML (Continuous Machine Learning) would provide users with an additional cost-effective cloud option for running ML workloads in CI/CD pipelines.

## Motivation

- **Cost Efficiency:** Lambda Labs offers lower pricing compared to major cloud providers, making it an attractive option for ML engineers and researchers.
- **GPU Availability:** Easy access to NVIDIA GH200, H100, A100, etc., which may require additional effort, such as quota requests or meeting specific terms, on other cloud providers.
- **Simplicity:** Pre-configured ML environment simplify the workflow without the need for complex setup and configuration.

## Challenges

Lambda Labs provides a minimalistic and straightforward API compared to major cloud providers. While this makes integration relatively simple, there are some limitations to consider:

- **No Custom AMIs:** Unlike AWS or GCP, Lambda Labs does not allow using custom machine images. It only provides an Ubuntu 22.04 with their so-called "Lambda Stack" preconfigured.
- **No Cloud-Init Support:** There is no option to pass cloud-init metadata for instance customization at launch.

Since Lambda Labs lacks support for custom AMIs and cloud-init, the typical approach to setting up an instance involves manual instance configuration via SSH after the instance creation.

## Additional Context

- Lambda Labs official website: https://lambdalabs.com
- Lambda Cloud API documentation: https://docs.lambdalabs.com/public-cloud/cloud-api/
- FAQ: https://lambdalabs.com/service/gpu-cloud/faqs
- Lambda Stack: https://lambdalabs.com/lambda-stack-deep-learning-software