> ## Documentation Index > Fetch the complete documentation index at: https://runcrate.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Batch Operations with AI Agents > Use MCP tools to deploy multiple GPU instances, run commands across a fleet, and check status in bulk — all through conversation. export const RuncrateStyles = () => { if (typeof document !== 'undefined' && !document.getElementById('runcrate-overrides')) { const s = document.createElement('style'); s.id = 'runcrate-overrides'; s.textContent = ` /* Match Runcrate's rounding scale (--radius: 0.75rem) */ .rounded-sm { border-radius: 0.5rem !important; } /* 8px */ .rounded-md { border-radius: 0.625rem !important; } /* 10px */ .rounded-lg { border-radius: 0.75rem !important; } /* 12px */ .rounded-l-sm { border-top-left-radius: 0.5rem !important; border-bottom-left-radius: 0.5rem !important; } .rounded-r-sm { border-top-right-radius: 0.5rem !important; border-bottom-right-radius: 0.5rem !important; } .rounded-l-md { border-top-left-radius: 0.625rem !important; border-bottom-left-radius: 0.625rem !important; } .rounded-r-md { border-top-right-radius: 0.625rem !important; border-bottom-right-radius: 0.625rem !important; } .rounded-l-lg { border-top-left-radius: 0.75rem !important; border-bottom-left-radius: 0.75rem !important; } .rounded-r-lg { border-top-right-radius: 0.75rem !important; border-bottom-right-radius: 0.75rem !important; } /* Cards: never pure white in light mode */ .card { background-color: #fcfcfc !important; border-radius: 0.75rem !important; } html.dark .card { background-color: #141414 !important; } /* Docs hero box */ .rc-hero { background-color: #fcfcfc; border: 1px solid #e0e0e0; } html.dark .rc-hero { background-color: #141414; border-color: #242424; } html.dark .rc-hero h1 { color: #f5f5f5; } /* Runcrate scrollbar — thin, transparent track, hide-until-hover thumb */ ::-webkit-scrollbar { width: 6px; height: 6px; background-color: transparent; } ::-webkit-scrollbar-track { background-color: transparent; } ::-webkit-scrollbar-thumb { background-color: rgba(155, 155, 155, 0.5); border-radius: 10px; transition: opacity 0.3s ease; opacity: 0; } ::-webkit-scrollbar-thumb:hover { background-color: rgba(155, 155, 155, 0.7); } *:hover::-webkit-scrollbar-thumb, *:focus::-webkit-scrollbar-thumb, *:active::-webkit-scrollbar-thumb { opacity: 1; } * { scrollbar-width: thin; scrollbar-color: rgba(155, 155, 155, 0.5) transparent; } `; document.head.appendChild(s); } return null; }; Deploy a fleet of instances, run the same command on all of them, and monitor everything at once. Your AI agent handles the coordination. *** ## "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: > > | Instance | GPU | Status | Cost | > | -------- | -------- | ------- | --------- | > | sweep-1 | RTX 4090 | running | \$0.35/hr | > | sweep-2 | RTX 4090 | running | \$0.35/hr | > | sweep-3 | RTX 4090 | running | \$0.35/hr | > | sweep-4 | RTX 4090 | running | \$0.35/hr | > > Fleet burn rate: **\$1.40/hr**. All 4 are ready for commands. *** ## "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: > > | Instance | Learning Rate | Status | > | -------- | ------------- | ---------------- | > | sweep-1 | 1e-4 | training started | > | sweep-2 | 5e-5 | training started | > | sweep-3 | 1e-5 | training started | > | sweep-4 | 5e-6 | training started | > > Check back in \~30 minutes for results, or ask me to monitor progress. *** ## "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` > | Instance | GPU Util | Last Log Line | Status | > | -------- | -------- | ------------------------------------- | ------- | > | sweep-1 | 0% | `Training complete. Best loss: 0.312` | done | > | sweep-2 | 0% | `Training complete. Best loss: 0.287` | done | > | sweep-3 | 94% | `Epoch 4/5 Step 800/1000 Loss: 0.301` | running | > | sweep-4 | 91% | `Epoch 3/5 Step 620/1000 Loss: 0.445` | running | > > **sweep-2** (lr=5e-5) has the best loss so far at 0.287. > Terminating finished instances to save money. 3. **`file_download`** on sweep-1 and sweep-2 — downloads results 4. **`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). *** ## Tools used in this workflow | Tool | Purpose | | ------------------------------------- | ----------------------------------------- | | `list_gpu_types` | Check availability before bulk deployment | | `create_instance` / `instance_status` | Deploy fleet and wait for readiness | | `file_upload` / `ssh_execute` | Distribute code and run commands | | `file_download` | Retrieve results from completed runs | | `delete_instance` | Tear down finished instances |