What is FEDML Launch?
No servers? No problem! Tap into a massive GPU marketplace (multi-cloud and multi-provider), offering high availability and low cost, to effortlessly launch your intricate ML jobs with a one line command:
fedml launch your_job.yaml
where the Job YAML
your_job.yaml defines the computing resource requirement and your source code workspace (code path).
Launch swiftly pairs AI jobs with the most economical GPU resources, auto-provisions, and effortlessly runs the job, eliminating complex environment setup and management. It supports a range of compute-intensive jobs for generative AI and LLMs, such as large-scale training, serverless deployments, and vector DB searches. FEDML Launch also facilitates on-prem cluster management and deployment on private or hybrid clouds.
FEDML Launch works as following consecutive actions:
- define your ML job without code change in a declarative format (e.g., YAML) or reuse our pre-built job templates ->
- launch your ML job with just one-line CLI or one-click in GUI
- search for cheaper GPUs across a large number of GPU providers without price lock-in
- provision automatically for your GPU resources and the software environment setup tailored for your job
- manage cluster for concurrent jobs with job queue support
- orchestrate your ML job across multi-nodes/geo-distributed environments, it can be model deployment across GPU nodes, distributed training, or even federated learning across clouds.
- run and monitor your job with rich observability features so you can see the real-time billing, metrics, logs, system performances, as well as diagnose performance bottleneck by fine-grained profiling.
- share and earn - contribute your idle GPUs to the launch GPU marketplace and earn $.
The value proposition of FEDML Launch:
- Find the lower prices without cloud vendor lock-in, in any clouds
- The highest GPU availability, provision in all zones/regions/clouds, even individual GPU contributors from the community
- Define your scheduling strategies to save money or request resources in a higher priority
- User-friendly MLOps to save time on environment management (AI docker hub for developers)
- On-premises GPU cluster management
- Provide Machine Learning as a Service (MLaaS) with Launch: if you have GPU resources, valuable datasets, or even a foundation model and hope to provide cloud service for your own customers to use them as Inference API or Training As a Service, FEDML Launch would be the off-the-shelf enterprise solution for it. Please contact us to discuss the deployment.
- FEDML Launch is versatile in any AI jobs, including training, deployment, and federated learning. It can also be used for complex multi-step jobs such as serving AI agents, building a customized machine learning pipeline for model continual refinement.