DEDUPEDcost. Cloud GPU jobs bill by the hour on your own cloud account; shut down resources when the job ends.
Who it is forA researcher with a laptop who needs a real GPU for a few hours of training.
The problemMoving a training job to the cloud usually means rewriting scripts for each provider.
The outcomeA remote training job started from one command, with the provider chosen by a flag.
What you need first
- Python with uv or pip, or Docker
- A GPU for real training runs (install the gpu extra)
- Optional: cloud accounts for remote jobs with oumi launch
Steps
- Install Oumi and set up credentials for your cloud provider
- Launch on GCP with
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml - Target another cloud with a flag, for example
--resources.cloud lambda - Or run locally in Docker with
docker pull ghcr.io/oumi-ai/oumi:latest - Scale up to a larger model recipe once the small job works
Prompt for your agent
Using oumi-ai/oumi, launch the SmolLM quickstart job on <cloud> with oumi launch up, monitor it until it finishes, and tell me how long it ran. Do not start any job larger than the quickstart without asking me.
Caveat. You need working credentials for each cloud; the README links to the launch docs for setup.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for oumi-ai/oumi and "Run a fine-tuning job on a cloud GPU"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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