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Deploy a fleet of instances, run the same command on all of them, and monitor everything at once. Your AI agent handles the coordination.

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”Deploy 4 RTX 4090 instances for a hyperparameter sweep.”

The agent provisions the fleet:
  1. list_gpu_types — confirms RTX 4090 availability and pricing
  2. create_instance — creates sweep-1 with RTX 4090
  3. create_instance — creates sweep-2 with RTX 4090
  4. create_instance — creates sweep-3 with RTX 4090
  5. create_instance — creates sweep-4 with RTX 4090
  6. instance_status — polls all four until running
Deployed 4 instances: Fleet burn rate: $1.40/hr. All 4 are ready for commands.

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”Install dependencies on all of them and start training with different learning rates.”

The agent runs commands across the fleet:
  1. ssh_execute on each instance — pip install torch transformers datasets
  2. file_upload on each instance — uploads train.py
  3. ssh_execute on each instance — starts training with different args:
    • sweep-1: python train.py --lr 1e-4
    • sweep-2: python train.py --lr 5e-5
    • sweep-3: python train.py --lr 1e-5
    • sweep-4: python train.py --lr 5e-6
All 4 instances are running training with different learning rates: Check back in ~30 minutes for results, or ask me to monitor progress.

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”Check training status on all instances and tear down any that are done.”

The agent checks each instance and cleans up:
  1. ssh_execute on each — tail -5 /root/train.log
  2. ssh_execute on each — nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader
sweep-2 (lr=5e-5) has the best loss so far at 0.287. Terminating finished instances to save money.
  1. file_download on sweep-1 and sweep-2 — downloads results
  2. delete_instance — terminates sweep-1 and sweep-2
Downloaded results from sweep-1 and sweep-2. Terminated both. Fleet cost reduced to $0.70/hr (2 remaining).

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Tools used in this workflow