RunPod Training Manager

RunPod Training Manager

Run Unsloth training on RunPod GPU instances.

Prerequisites

1. RunPod API Key: echo $RUNPOD_API_KEY (get at runpod.io/​console/​user/​settings)
2. RunPod SDK: pip install runpod
3. Training notebook/​script: From funsloth-train

Workflow

1. Select GPU

RunPod typically has better prices than HF Jobs.

2. Choose Deployment

• Pod (Recommended): Persistent, SSH access, network storage
• Serverless: Pay per second, complex setup (better for inference)

3. Configure Network Volume (Recommended)

import runpod
volume = runpod.create_network_volume(name="funsloth-training", size_gb=50, region="US")

Allows: resume training, download checkpoints, share between pods.

4. Launch Pod

Use the official Unsloth Docker image for a pre-configured environment:

import runpod

pod = runpod.create_pod(
    name="funsloth-training",
    image_name="unsloth/​unsloth",  # Official image, supports all GPUs incl. Blackwell
    gpu_type_id="{gpu_type}",
    volume_in_gb=50,
    network_volume_id="{volume_id}",
    env={
        "HF_TOKEN": "{token}",
        "WANDB_API_KEY": "{key}",
        "JUPYTER_PASSWORD": "unsloth",
    },
    ports="8888/​http,22/​tcp",
)

The Unsloth image includes Jupyter Lab (port 8888) and example notebooks in /​workspace/​unsloth-notebooks/.

5. Upload and Run

# SSH into pod
ssh root@{pod_ip}

# Upload script
scp train.py root@{pod_ip}:/​workspace/

# Run training (use tmux for persistence)
tmux new -s training
cd /​workspace && python train.py
# Ctrl+B, D to detach

6. Monitor

# SSH monitoring
tail -f /​workspace/​training.log
nvidia-smi -l 1

# Dashboard
https://runpod.io/​console/​pods/​{pod_id}

7. Retrieve Checkpoints

# Save to network volume
cp -r /​workspace/​outputs /​runpod-volume/

# Download via SCP
scp -r root@{pod_ip}:/​workspace/​outputs ./

# Or push to HF Hub from pod

8. Stop Pod

runpod.stop_pod(pod_id)    # Can resume later
runpod.terminate_pod(pod_id)  # Deletes pod, keeps volume

9. Handoff

Offer funsloth-upload for Hub upload with model card.

Best Practices

1. Always use network volumes - pod storage is ephemeral
2. Use spot instances for lower costs (risk of preemption)
3. Set up SSH keys before creating pods
4. Stop pods when not training - charges per minute
5. Save checkpoints frequently with save_steps

Error Handling

Bundled Resources

• scripts/​train_sft.py - Training script template
• scripts/​estimate_cost.py - Cost estimation
• references/​PLATFORM_COMPARISON.md - RunPod vs alternatives
• references/​TROUBLESHOOTING.md - Common issues