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Documentation Index

Fetch the complete documentation index at: https://hubify.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

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RunPod Integration

RunPod is the primary GPU compute provider for Hubify Labs. This guide covers connecting your RunPod account, configuring pods, and optimizing for cost.

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Connecting RunPod

1

Create a RunPod account

Sign up at runpod.io and add billing information.
2

Generate an API key

Go to RunPod Settings > API Keys and create a key with full access.
3

Add to Hubify

hubify pod config --provider runpod --api-key "your-runpod-api-key"
4

Verify

hubify pod config --test

RunPod connection: OK
Available GPUs: H200, H100, A100, A40, RTX 4090
Account balance: $245.00

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Available GPU Types

GPUVRAMBest ForApprox. Cost/hr
H200141 GBLarge models, full-dataset anomaly detection$3.89
H10080 GBMCMC chains, training, most experiments$2.49
A10080 GBGeneral GPU compute$1.64
A4048 GBMedium workloads, figure generation$0.79
RTX 409024 GBSmall models, prototyping$0.44
Pricing varies by availability and region. Spot instances can be up to 80% cheaper.

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Pod Configuration

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Default Settings

# Set defaults for all new pods
hubify pod config --default-gpu h100
hubify pod config --default-region us-east
hubify pod config --idle-timeout 15m

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Docker Images

Hubify provides pre-built images with common scientific packages:
ImageContents
hubify/base:latestPython 3.11, CUDA 12, PyTorch 2.1
hubify/cosmo:latestBase + Cobaya, GetDist, Astropy, HEALPy
hubify/ml:latestBase + Transformers, Accelerate, Datasets
hubify/astro:latestBase + Astropy, Photutils, SEP, Source Extractor
hubify pod config --default-image hubify/cosmo:latest

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SSH Access

# Add your SSH key
hubify pod ssh-key add --file ~/.ssh/id_ed25519.pub

# SSH into a running pod
hubify pod ssh pod-abc123

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Performance Tips

  • Use DataLoader for GPU inference: num_workers=16, pin_memory=True, prefetch_factor=4 gives a 32x speedup over serial processing
  • Pre-stage large datasets on persistent storage so pods start instantly
  • Use spot instances for non-urgent experiments (set --spot flag)
  • Match GPU to workload: do not use an H200 for figure generation
# Run on a spot instance
hubify experiment run --name "overnight-chain" --pod h100 --spot

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Cost Management

# Set monthly budget
hubify pod budget --monthly 500

# Set per-experiment cap
hubify pod budget --per-experiment 50

# View current spend
hubify pod budget --show

# Alert at 80% of budget
hubify pod budget --alert-threshold 0.8

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Persistent Storage

Upload datasets to RunPod persistent storage so they survive pod restarts: 
# Upload a dataset
hubify pod storage upload ./planck_likelihood.tar.gz

# Mount in experiments
hubify experiment run --name "my-chain" --storage planck_likelihood.tar.gz

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Troubleshooting

The requested GPU type may be sold out. Try a different GPU or region:
hubify pod list --available
Upgrade to a GPU with more VRAM, or reduce batch size. H200 (141 GB) handles the largest workloads.
Spot instances can be reclaimed. Use checkpointing for long experiments:
hubify experiment resume EXP-051 --from-checkpoint latest