Mining Guide

Prerequisites

GPU: NVIDIA (CUDA), AMD (ROCm), Apple Silicon (MPS), or Intel (DirectML)
Python: 3.10+
RAM: 8GB minimum, 16GB recommended
Storage: 10GB for models and cached data
Internet: Required for downloading data from MAST/SDSS/ZTF

Quick Start

1. Install Dependencies

git clone https://github.com/thekillsquad007/voidmap.git
cd voidmap/miner
pip install -r requirements.txt

Multi-backend support — requirements.txt installs the right PyTorch for your hardware:

# NVIDIA (default)
pip install -r requirements.txt

# AMD (ROCm 6.2)
pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.2
pip install onnxruntime onnx

# Apple Silicon (MPS)
pip install -r requirements.txt  # MPS included in stock PyTorch

# Intel/AMD (DirectML via ONNX)
pip install -r requirements.txt
pip install onnxruntime-directml

# CPU only
pip install -r requirements.txt  # works on CPU as fallback

2. Detect Your GPU

python voidmap-miner.py --detect

Output:

  PyTorch: 2.1.0
  GPU 0: NVIDIA GeForce RTX 3080 (10.0 GB)
  Backend: ONNX (CUDA)
  ONNX providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
  lightkurve: OK
  astropy: OK
  Device: cuda
  Results: /home/user/.voidmap/results

The miner auto-selects the best backend in this priority order:

PyTorch CUDA (NVIDIA) — fastest
PyTorch ROCm (AMD)
ONNX DirectML (Intel/AMD on Windows)
ONNX CPU (anywhere, always works)
PyTorch CPU (fallback)

3. Start Mining

# Solo mode: mine and save results locally (no on-chain submission)
python voidmap-miner.py --task exoplanet --rounds 10

# Submit mode: mine and submit directly to MiningPool on-chain
python voidmap-miner.py --task exoplanet --rounds 10 --submit \
  --rpc https://mainnet.base.org \
  --pk 0xYOUR_PRIVATE_KEY

# Run continuously until interrupted
python voidmap-miner.py --task exoplanet --submit --rpc https://mainnet.base.org --pk 0x...

# Mine all three task types in rotation
python voidmap-miner.py --task all --submit --rpc https://mainnet.base.org --pk 0x...

4. View Your Results

python voidmap-miner.py --list-results

Output:

  Recent Results (5 total):

  exoplanet_TOI-732_1717440000.json
    Target: TOI-732 | Prediction: PLANET (87.3%)
    Quality: 87/100 | Backend: onnx-cuda | Source: MAST TESS SPOC 2-min cadence
    Submitted: tx 0xabc...def (block 12345)

  galaxy_NGC_4565_1717439000.json
    Galaxy: NGC 4565 | Class: Spiral (92.1%)
    Quality: 92/100 | Backend: pytorch-rocm | Source: SDSS DR18 cutout

Mining Modes

Solo Mining (Default)

Mine and save results locally without on-chain submission:

python voidmap-miner.py --task exoplanet --rounds 100

No blockchain transaction fees
No wallet needed
Results stored at ~/.voidmap/results/
Useful for testing, development, or contributing to research without claiming rewards

Solo + Submit (Mainnet)

Mine AND submit each valid result directly to the MiningPool contract:

python voidmap-miner.py --task exoplanet --submit \
  --rpc https://mainnet.base.org \
  --pk $DEPLOYER_PK

Costs ~0.00001 ETH per submission (Base gas is cheap)
Earns VOID rewards based on quality + halving + elastic multiplier
12-second cooldown enforced on-chain between submissions per miner
Min 2-second compute duration enforced on-chain (anti-ASIC)
Quality must be ≥ 50 to be accepted

Pool Mining (Stratum)

Connect to a mining pool for more consistent rewards:

python stratum_miner.py --pool ws://pool.voidmap.org:3333 --user YOUR_ADDRESS

Work distributed by pool
Shares validated locally
Rewards split among pool members
2% pool fee

Task Details

Exoplanet Transit Detection

python voidmap-miner.py --task exoplanet --rounds 10

What happens:

Downloads real TESS light curve from MAST (NASA)
Preprocesses (quality filter, normalize, phase-fold)
Runs AstroNetCNN model
Outputs: PLANET / FALSE_POSITIVE / NO_SIGNAL

Known targets (pre-verified):

TOI-732 (2 planets)
TOI-1452 (1 planet)
TOI-700 (4 planets)
TOI-1259, TOI-1444

# List known targets
python voidmap-miner.py --list-targets

# Mine specific target
python voidmap-miner.py --task exoplanet --target-idx 0

Galaxy Morphology Classification

python voidmap-miner.py --task galaxy --rounds 10

What happens:

Downloads galaxy image from SDSS DR18 (NSF)
Resizes to 224x224 RGB
Runs ConvNeXT classifier
Outputs: Spiral / Elliptical / Irregular / Merger / Unknown

Anomaly Detection

python voidmap-miner.py --task anomaly --rounds 10

What happens:

Downloads ZTF alerts from Fink broker (NSF)
Extracts features
Runs autoencoder
Outputs: anomaly scores + flags

Mining All Tasks

python voidmap-miner.py --task all --rounds 30

Rotates through all 3 task types. Each task processed in turn.

Multi-Backend Support

The miner auto-selects the best ML backend for your hardware.

Backend Selection Priority

ModelBackend.auto()
  → PyTorchBackend(cuda)        # NVIDIA GPU
  → PyTorchBackend(rocm)        # AMD GPU
  → ONNXBackend(providers=['DmlExecutionProvider'])  # Intel/AMD on Windows
  → ONNXBackend(providers=['CUDAExecutionProvider']) # NVIDIA via ONNX
  → ONNXBackend(providers=['CPUExecutionProvider'])   # any CPU
  → PyTorchBackend(cpu)         # PyTorch CPU fallback

Backend Comparison

Backend	Speed	Hardware	Notes
`pytorch-cuda`	★★★★★	NVIDIA	Fastest, requires CUDA toolkit
`pytorch-rocm`	★★★★	AMD	Requires ROCm 6.2
`onnx-cuda`	★★★★	NVIDIA	ONNX runtime, no CUDA toolkit needed
`onnx-dml`	★★★	Intel/AMD	DirectML on Windows
`onnx-cpu`	★★	any CPU	Slowest but always works
`pytorch-cpu`	★★	any CPU	Last resort fallback

ONNX Model

The pre-trained model is exported to ONNX (miner/astronet_cnn.onnx, 15MB) for cross-platform compatibility. Max inference difference vs PyTorch: 0.00000191 (negligible).

Manual Backend Override

# Force a specific backend
python voidmap-miner.py --task exoplanet --backend onnx-cpu

Available backends: pytorch-cuda, pytorch-rocm, pytorch-mps, pytorch-cpu, onnx-cuda, onnx-rocm, onnx-dml, onnx-cpu.

HiveOS / Docker Deployment

Option A: Docker (Recommended)

HiveOS has Docker support built in. One command and you're mining.

NVIDIA GPUs:

# Pull the image (after release)
docker pull thekillsquad007/voidmap-miner:latest

# Run with your wallet
docker run -d \
  --name voidmap-miner \
  --restart unless-stopped \
  --gpus all \
  -e VOIDMAP_PK=0xYOUR_PRIVATE_KEY \
  -e VOIDMAP_RPC=https://mainnet.base.org \
  -e VOIDMAP_TASK=all \
  thekillsquad007/voidmap-miner:latest

AMD GPUs:

docker run -d \
  --name voidmap-miner \
  --restart unless-stopped \
  --device /dev/kfd \
  --device /dev/dri \
  --security-opt seccomp=unconfined \
  -e VOIDMAP_PK=0xYOUR_PRIVATE_KEY \
  -e VOIDMAP_RPC=https://mainnet.base.org \
  -e VOIDMAP_TASK=all \
  thekillsquad007/voidmap-miner:latest

Docker Compose:

# NVIDIA
docker compose up -d voidmap-miner

# AMD
docker compose --profile amd up -d voidmap-miner-amd

Option B: Native Install

# SSH into your HiveOS rig
ssh hive@YOUR_RIG_IP

# Run the installer
curl -fsSL https://raw.githubusercontent.com/thekillsquad007/voidmap/main/install_hiveos.sh | sudo bash

# Configure
export VOIDMAP_PK=0xYOUR_PRIVATE_KEY
export VOIDMAP_RPC=https://mainnet.base.org
export VOIDMAP_TASK=all

# Start
sudo systemctl start voidmap-miner
sudo systemctl status voidmap-miner

# Use the `voidmap` wrapper
voidmap --detect
voidmap --task exoplanet --rounds 10

The installer:

Auto-detects GPU vendor (NVIDIA / AMD / Intel)
Installs ROCm PyTorch for AMD GPUs
Installs ONNX Runtime
Creates a voidmap wrapper at /usr/local/bin/voidmap
Installs systemd service voidmap-miner.service
Configures auto-start on boot

Configuration

Environment Variables

# Wallet
export VOIDMAP_PK=0xYOUR_PRIVATE_KEY  # for --submit mode

# RPC endpoint
export VOIDMAP_RPC=https://mainnet.base.org

# Results directory
export VOIDMAP_RESULTS_DIR=~/.voidmap/results

# Model cache
export VOIDMAP_MODEL_DIR=~/.voidmap/models

# IPFS (optional, for storing results off-chain)
export PINATA_API_KEY=your_key
export PINATA_API_SECRET=your_secret

# Mining config
export VOIDMAP_TASK=exoplanet  # exoplanet | galaxy | anomaly | all
export VOIDMAP_COOLDOWN=12    # seconds between submissions (min 12 enforced on-chain)

# Pool settings (if using pool mining)
export VOIDMAP_POOL_PORT=3333

Command Line Options

python voidmap-miner.py --help

Options:
  --task {exoplanet,galaxy,anomaly,all}  Mining task
  --rounds N                            Number of rounds (default: infinite)
  --batch N                             Batch size
  --target-idx N                        Specific target index
  --backend {pytorch-cuda,pytorch-rocm,pytorch-cpu,onnx-cuda,onnx-dml,onnx-cpu}
                                         Force specific ML backend
  --submit                              Submit results on-chain to MiningPool
  --rpc URL                             Base RPC endpoint
  --pk KEY                              Wallet private key (for --submit)
  --detect                              Show hardware + backend info
  --list-tasks                          Show available tasks
  --list-results                        Show past results
  --list-targets                        Show known exoplanet targets

Output Format

Results are saved as JSON at ~/.voidmap/results/:

{
  "task": "exoplanet_transit",
  "target": "TOI-732",
  "tic_id": "TIC 307210830",
  "prediction": "PLANET",
  "confidence": 0.873,
  "probabilities": {
    "planet": 0.873,
    "false_positive": 0.092,
    "no_signal": 0.035
  },
  "quality_score": 87,
  "model": "AstroNetCNN (sarojpatil16/exoplanet-transit-detector)",
  "backend": "onnx-cuda",
  "data_source": "MAST TESS SPOC 2-min cadence",
  "input_hash": "0xabc123...",
  "output_hash": "0xdef456...",
  "model_hash": "0x789abc...",
  "samples": 2048,
  "duration_ms": 4521,
  "gpu": "NVIDIA GeForce RTX 3080",
  "ipfs_cid": "QmXxx...",
  "timestamp": 1717440000,
  "tx_hash": "0x123abc...def"  // only if --submit
}

Anti-ASIC Checks

The miner automatically enforces the on-chain anti-ASIC rules:

Check	Threshold	Failure Mode
Quality floor	≥ 50	Reject submission
Compute duration	≥ 2s	Reject submission (reverted on-chain)
Submission cooldown	≥ 12s	Skip submission
Hardware signature	GPU detected	Mark as suspicious
Data integrity	Hash matches	Reject submission

If --submit mode detects that your work would be rejected on-chain, it skips the transaction to save gas.

Troubleshooting

"No TESS data found for TIC X"

Target may not have TESS observations
Try a different target index
Check MAST portal for available data

"Download failed"

Check internet connection
MAST may be temporarily unavailable
Try again in a few minutes

"Quality too low"

Model confidence was below 50
This can happen with noisy data
The share will be rejected by the pool

"CUDA out of memory"

Reduce batch size: --batch 16
Close other GPU applications
Use a GPU with more VRAM

"Backend not found" / "No GPU detected"

The miner auto-falls back to ONNX-CPU. To force a specific backend:

python voidmap-miner.py --backend onnx-cpu --task exoplanet

"Submit reverted: Compute too fast"

Your result was rejected for being under 2 seconds. The miner should have caught this before sending. If it didn't, file an issue.

AMD GPU not detected in Docker

Ensure you've added --device /dev/kfd --device /dev/dri --security-opt seccomp=unconfined flags. Also check that ROCm is installed on the host (HiveOS supports ROCm out of the box for many cards).

Submitting on testnet

# Testnet (Base Sepolia)
python voidmap-miner.py --task exoplanet --submit \
  --rpc https://sepolia.base.org \
  --pk $DEPLOYER_PK

Hardware Performance Guide

Approximate submissions per hour (exoplanet task, single GPU):

GPU	VRAM	Backend	Submissions/hr
RTX 4090	24GB	pytorch-cuda	~300
RTX 3080	10GB	pytorch-cuda	~200
RTX 3060	12GB	pytorch-cuda	~150
RX 7900 XTX	24GB	pytorch-rocm	~250
RX 6800 XT	16GB	pytorch-rocm	~180
Apple M2 Max	32GB	pytorch-mps	~100
Intel Arc A770	16GB	onnx-dml	~120
CPU (32 cores)	n/a	pytorch-cpu	~10

Numbers assume 12s cooldown per submission. Higher quality = more VOID per submission.

Next Steps

Join a pool for more consistent rewards
Read about anti-ASIC measures
Query results via API
Read about mainnet features (halving, elastic mint, challenge/slash, timelock)
Deploy contracts to earn on-chain

Mining Guide

Mining Guide

Prerequisites

Quick Start

1. Install Dependencies

2. Detect Your GPU

3. Start Mining

4. View Your Results

Mining Modes

Solo Mining (Default)

Solo + Submit (Mainnet)

Pool Mining (Stratum)

Task Details

Exoplanet Transit Detection

Galaxy Morphology Classification

Anomaly Detection

Mining All Tasks

Multi-Backend Support

Backend Selection Priority

Backend Comparison

ONNX Model

Manual Backend Override

HiveOS / Docker Deployment

Option A: Docker (Recommended)

Option B: Native Install

Configuration

Environment Variables

Command Line Options

Output Format

Anti-ASIC Checks

Troubleshooting

"No TESS data found for TIC X"

"Download failed"

"Quality too low"

"CUDA out of memory"

"Backend not found" / "No GPU detected"

"Submit reverted: Compute too fast"

AMD GPU not detected in Docker

Submitting on testnet

Hardware Performance Guide

Next Steps

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