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bittensor-sn85-vibe-minerbittensor sn85 振动矿机

Agent Skill

bittensor-sn85-vibe-miner 用于辅助视频、动画、脚本化剪辑和多媒体生成流程,适合在 OpenClaw 中需要整理视频素材、生成脚本或维护合成项目时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:bittensor-sn85-vibe-miner(bittensor sn85 振动矿机)
来源仓库:https://github.com/maxquick/bittensor-sn85-vibe-miner
安装命令:
openclaw skills install bittensor-sn85-vibe-miner
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

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openclaw skills install bittensor-sn85-vibe-miner

简介

自动设置 GPU 加速的 Bittensor Subnet 85 视频升级和压缩挖矿程序,并进行存储、监控和性能优化。

SKILL.md

Bittensor SN85 VidAIo Miner Skill

Deploy and optimize video compression/upscaling miners on Bittensor Subnet 85 (VidAIo).

Overview

Subnet: 85 (VidAIo/Vibe) Task: Video compression (HEVC/AV1) and upscaling (SD→HD, HD→4K) Repo: https://github.com/Cazure8/vidaio-subnet GPU Required: RTX 3090+ (4090 recommended) Platform: Vast.ai recommended (cheap GPU rentals)

Architecture

SN85 runs dual miners (compression + upscaling) with separate backend services:

Validator → Miner (axon) → Backend Service (Flask) → ffmpeg/video2x

4 PM2 Processes:

  1. video-miner — Upscaling axon (receives SD2HD/HD24K tasks)
  2. video-miner-compress — Compression axon (receives HEVC/AV1 tasks)
  3. video-upscaler — Backend service (port 19000)
  4. video-compressor — Backend service (port 19001)

Prerequisites

# Bittensor wallet with at least 0.4τ for registration
btcli wallet create --wallet.name moltypython
btcli wallet new_hotkey --wallet.name moltypython --hotkey mining
btcli wallet new_hotkey --wallet.name moltypython --hotkey mining2

# Register both hotkeys
btcli subnet register --netuid 85 --wallet.name moltypython --wallet.hotkey mining
btcli subnet register --netuid 85 --wallet.name moltypython --wallet.hotkey mining2

Vast.ai Setup

1. Launch Instance

Requirements:

  • RTX 4090 (24GB VRAM)
  • 64GB+ disk
  • Ubuntu 24.04 (NVENC support verified)
  • At least 4 open TCP ports (SSH + 3 miner ports)

Template:

Image: nvidia/cuda:13.0.2-cudnn9-devel-ubuntu24.04
Disk: 64GB
GPU: RTX 4090

2. Critical Port Configuration

⚠️ Vast.ai occupies ports 8080, 11111, 18384 by default (Jupyter, Portal, Syncthing). Do NOT use these!

Recommended mapping:

Internal → External (Vast assigns)
19000    → 26565  (upscaler backend)
19001    → 26833  (compressor backend)
22       → XXXXX  (SSH)

Vast.ai routes external ports through Caddy reverse proxy. You MUST configure Caddy to forward without auth:

# Edit /etc/caddy/Caddyfile
sudo tee /etc/caddy/Caddyfile << 'EOF'
:8384 {
    reverse_proxy localhost:19000
}

:1111 {
    reverse_proxy localhost:19001
}
EOF

sudo systemctl reload caddy

Verify external access:

curl -I http://<VAST_PUBLIC_IP>:<EXTERNAL_PORT>
# Should return 404 from Bittensor axon, NOT 401 Unauthorized

Installation

# Clone repo
cd /root
git clone https://github.com/Cazure8/vidaio-subnet.git
cd vidaio-subnet

# Install dependencies
python3 -m venv venv
source venv/bin/activate
pip install -e .

# Copy wallets from local machine
scp -P <VAST_SSH_PORT> -r ~/.bittensor/wallets/moltypython root@<VAST_IP>:/root/.bittensor/wallets/

Install Optimized ffmpeg

System ffmpeg lacks NVENC. Use BtbN static build:

cd /tmp
wget https://github.com/BtbN/FFmpeg-Builds/releases/download/latest/ffmpeg-master-latest-linux64-gpl.tar.xz
tar xf ffmpeg-master-latest-linux64-gpl.tar.xz
sudo cp ffmpeg-master-latest-linux64-gpl/bin/* /usr/local/bin/
ffmpeg -version | grep libsvtav1  # Verify AV1 support

Install video2x for Upscaling

pip install video2x==6.3.1
# Downloads NCNN models automatically on first run

PM2 Startup (Critical!)

⚠️ MUST set PYTHONPATH or imports fail!

cd /root/vidaio-subnet

# Upscaler miner (UID 165 in our case)
PYTHONPATH=/root/vidaio-subnet pm2 start venv/bin/python --name video-miner --interpreter none \
  -- neurons/miner.py --netuid 85 --subtensor.network finney \
  --wallet.name moltypython --wallet.hotkey mining \
  --axon.port 19000 --axon.external_port 26565 --logging.debug

# Compressor miner (UID 78 in our case)
PYTHONPATH=/root/vidaio-subnet pm2 start venv/bin/python --name video-miner-compress --interpreter none \
  -- neurons/miner_compress.py --netuid 85 --subtensor.network finney \
  --wallet.name moltypython --wallet.hotkey mining2 \
  --axon.port 19001 --axon.external_port 26833 --logging.debug

# Upscaler backend
PYTHONPATH=/root/vidaio-subnet pm2 start venv/bin/python --name video-upscaler --interpreter none \
  -- services/upscaling/server.py

# Compressor backend
PYTHONPATH=/root/vidaio-subnet pm2 start venv/bin/python --name video-compressor --interpreter none \
  -- services/compress/server.py

pm2 save
pm2 startup  # Auto-start on reboot

Verify ports match registration:

pm2 logs video-miner --lines 50 --nostream | grep "AxonInfo.*26565"
pm2 logs video-miner-compress --lines 50 --nostream | grep "AxonInfo.*26833"
# If wrong port appears, kill and restart with correct --axon.external_port

Optimizations (Production-Ready)

1. Upscaler Speed Fix

Problem: Default config tries 4x upscaling on 4K input → GPU OOM/truncation.

Fix: Dynamic scaling based on input resolution.

Edit /root/vidaio-subnet/services/upscaling/server.py:

# Around line 60, replace hardcoded scale=4 with:
input_width = int(probe['streams'][0]['width'])
if input_width >= 3840:  # 4K input
    scale = 2  # 4K → 8K
elif input_width >= 1920:  # HD input
    scale = 2  # HD → 4K
else:  # SD input
    scale = 4  # SD → HD

Restart: pm2 restart video-upscaler

2. Compressor Speed Optimization

Problem: AV1 encoding takes 90-155s (validator timeout is ~60s).

Fixes:

A. Use fastest AV1 preset

Edit /root/vidaio-subnet/services/compress/utils/encoder_configs.py:

# Line ~40, change preset for all scene types:
"preset": "12",  # Was 10, now 12 (fastest)

B. Add encoding timeout

Edit /root/vidaio-subnet/services/compress/utils/encode_video.py:

# Around line 80, add timeout to subprocess.run():
result = subprocess.run(
    cmd,
    capture_output=True,
    text=True,
    timeout=45  # Kill if exceeds 45s
)

C. Skip VMAF validation (trust lookup table)

Edit /root/vidaio-subnet/services/compress/compression_optimized.py:

# Line ~120, set:
skip_vmaf = True  # Saves 5-10s per task

Restart: pm2 restart video-compressor

3. Network Timeout Fix

Problem: Large video downloads timeout with default 5s httpx connect timeout.

Edit /root/vidaio-subnet/vidaio_subnet_core/utilities/file_handler.py:

# Line ~30, replace httpx.AsyncClient() with:
async with httpx.AsyncClient(
    timeout=httpx.Timeout(30.0, read=120.0, write=30.0, pool=None)
) as client:

Restart: pm2 restart video-miner video-miner-compress

4. DNS Fix (Docker resolver issue)

echo -e "nameserver 8.8.8.8\
nameserver 1.1.1.1" | sudo tee /etc/resolv.conf

Monitoring

Check Registration Status

btcli wallet overview --wallet.name moltypython --wallet.hotkey mining --subtensor.network finney
btcli wallet overview --wallet.name moltypython --wallet.hotkey mining2 --subtensor.network finney

Look for:

  • UID assigned (e.g., 165, 78)
  • INCENTIVE > 0 (means earning)
  • EMISSION > 0 (τ per day)

Check Task Activity

# Upscaler
pm2 logs video-miner --lines 50 | grep "Receiving"

# Compressor
pm2 logs video-miner-compress --lines 50 | grep "Receiving"

Healthy output:

✅✅✅ Receiving SD2HD Request from validator: 5EUq... with uid: 1
🛜🛜🛜 Receiving CompressionRequest from validator: 5EUq... with uid: 1 | VMAF: 89.0 | Codec: hevc

Backend Response Times

# Check upscaler timing
pm2 logs video-upscaler --lines 100 | grep "Completed\|took"

# Check compressor timing
pm2 logs video-compressor --lines 100 | grep "Completed\|took"

Target: <50s per task (validator timeout ~60s)

Troubleshooting

1. Zero Incentive Despite Tasks

Symptoms: Receiving validator requests but INCENTIVE = 0.00

Causes:

  • Tasks timing out (>60s)
  • Output quality below threshold (VMAF, resolution)
  • Tasks failing silently (check backend logs)

Debug:

pm2 logs video-compressor --lines 200 | grep -i "error\|timeout\|failed"
pm2 logs video-upscaler --lines 200 | grep -i "error\|timeout\|failed"

2. "ModuleNotFoundError: No module named 'services'"

Cause: Missing PYTHONPATH

Fix: Kill all PM2 processes and restart with PYTHONPATH set (see PM2 Startup section)

3. Port Already in Use

Cause: Stale axon binding after restart

Fix:

pm2 kill  # Nuclear option
# Wait 10 seconds
# Restart all processes with PYTHONPATH

4. Validators Not Connecting (401 Unauthorized)

Cause: Caddy reverse proxy blocking with HTTP Basic Auth

Fix: Edit Caddyfile to remove auth (see Port Configuration section above)

5. "UnknownSynapseError" in Logs

Normal! Validators probe all UIDs with various synapse types. Ignore unless frequent.

Expected Performance

Upscaler (video2x + NVENC):

  • SD→HD: 20-35s
  • HD→4K: 30-50s

Compressor:

  • HEVC: 15-30s
  • AV1: 25-45s (with preset 12)

Both should complete within 60s validator deadline.

Costs

Vast.ai RTX 4090: ~$0.30-0.50/hour (~$220-360/month) Registration: 0.19τ per hotkey (0.38τ total) SN85 emissions: Variable (depends on competition/performance)

Maintenance

Daily Checks

ssh -p <PORT> root@<VAST_IP> "pm2 list && uptime"

Weekly

  • Check wallet balance growth
  • Review PM2 restart counts (high = instability)
  • Update vidaio-subnet repo if new commits

If Deregistered

  • Check balance (need 0.19τ to re-register)
  • Review logs for errors before last known task
  • Re-register: btcli subnet register --netuid 85 --wallet.name moltypython --wallet.hotkey <HOTKEY>

References

  • VidAIo Subnet: https://github.com/Cazure8/vidaio-subnet
  • Bittensor Docs: https://docs.bittensor.com
  • Vast.ai: https://vast.ai
  • VibeMiner (Ridges collab): https://github.com/maxquick/VibeMiner

Version History

  • 2.1.0 (Mar 15, 2026): Added compression optimizations, network timeout fix, verified 3+ day stable deployment
  • 2.0.0 (Mar 7, 2026): NVENC optimizations, dynamic upscaling, Caddy auth fix
  • 1.0.0 (Feb 13, 2026): Initial deployment

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