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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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GPU Compute

Hubify Labs integrates directly with GPU cloud providers to give you on-demand access to high-end compute. Currently powered by RunPod, with Modal serverless functions coming soon.

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Pod Management

1

Provision

Specify GPU type and duration. The system finds the cheapest available pod matching your requirements.
2

Initialize

Your lab’s environment is set up automatically: Python packages, data mounts, SSH keys, and monitoring agents.
3

Execute

Run experiments. Logs stream in real time. Intermediate results checkpoint to persistent storage.
4

Monitor

Track GPU utilization, memory, and cost in real time from Captain View or CLI.
5

Teardown

Pods shut down automatically when experiments complete. Results are synced before teardown.

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GPU Options

GPUVRAMUse CaseApprox. Cost
NVIDIA H200141 GBLarge MCMC, multi-survey sweeps, foundation models$4-6/hr
NVIDIA H10080 GBTraining, medium MCMC, anomaly detection$2-4/hr
NVIDIA A10080 GBGeneral GPU compute, inference$1-2/hr
NVIDIA A4048 GBLight GPU tasks, development$0.50-1/hr

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Cost Controls

Set a monthly budget cap per lab: 
# Set budget cap
hubify pod budget --monthly 500

# View current spend
hubify pod cost --month current

# Get cost forecast
hubify pod cost --forecast
When you approach the budget limit:
  1. New experiments queue instead of launching
  2. You receive a notification
  3. The orchestrator suggests cost-saving alternatives (smaller GPU, CPU-only preprocessing)

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Auto-Optimization

The system picks the cheapest option for each experiment: 
Experiment needs ~2 hours on H100 ($2/hr) = $4
Same experiment runs ~45 min on H200 ($5/hr) = $3.75
→ System picks H200 (cheaper overall despite higher hourly rate)
Override with explicit pod selection when needed.

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Persistent Storage

Each lab gets persistent storage:
  • Survives pod teardowns
  • Pre-stage large datasets for instant access
  • Experiment outputs sync automatically
  • Configurable retention policies
# List persistent storage
hubify pod storage list

# Upload data to persistent storage
hubify pod storage upload ./large_dataset.fits

# Download results
hubify pod storage download /results/chain_samples.txt

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SSH Access

Every running pod is accessible via SSH: 
# Auto-connect to a pod
hubify pod ssh pod-abc123

# Get connection details
hubify pod info pod-abc123
# → SSH: root@205.196.19.52 -p 11452

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Idle Detection

An idle GPU is wasted money. Hubify monitors utilization and takes action when pods sit idle.
When a pod finishes its experiment and nothing is queued:
  1. Alert sent to you and the orchestrator
  2. System suggests next experiments that could use the pod
  3. If auto-schedule is enabled, the next experiment deploys automatically
  4. If nothing is queued for 15 minutes, the pod tears down

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DataLoader Best Practices

For production GPU inference, always use optimized DataLoaders: 
from torch.utils.data import DataLoader

loader = DataLoader(
    dataset,
    batch_size=64,
    num_workers=16,       # Parallel data loading
    pin_memory=True,      # Fast GPU transfer
    prefetch_factor=4,    # Prefetch batches
    persistent_workers=True
)
This pattern provides a 32x speedup over serial processing.

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CLI Reference

hubify pod list              # List all pods
hubify pod create --gpu h100 # Launch a pod
hubify pod status <id>       # Check pod status
hubify pod ssh <id>          # SSH into a pod
hubify pod stop <id>         # Terminate a pod
hubify pod cost              # View cost summary
hubify pod budget            # Manage budget