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GPU acceleration is now on by default and verified end-to-end on the Blackwell RTX 5050 (sm_120): - Ollama offloads 100% to GPU (log: library=CUDA compute=12.0, BLACKWELL_NATIVE_FP4=1). compose passes GPU via CDI (devices: nvidia.com/gpu=all) to both ollama and javis. - Whisper STT on GPU: faster-whisper>=1.1.0 + nvidia-cublas/cudnn cu12, LD_LIBRARY_PATH baked into the image. Verified float16 transcribe on sm_120; bridge auto-falls back to CPU when no GPU is present. - Model: default chat model -> qwen3:8b (best 8GB-VRAM tool-calling, ~5GB Q4). Embed stays nomic-embed-text. - README documents the host one-time setup (nvidia-container-toolkit + `nvidia-ctk cdi generate`) and GPU on/off. Verified: image builds; GPU visible in both containers via compose; ollama ps = 100% GPU; faster-whisper cuda OK + CPU fallback OK; bridge /health 200.
35 lines
1.0 KiB
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35 lines
1.0 KiB
Plaintext
# Slim dependency set for the containerized brain bridge.
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# Excludes the upstream desktop GUI / dictation / packaging / alternate-TTS
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# stack (PyQt6, pyinstaller, sounddevice, webrtcvad, pynput, pygame,
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# chatterbox-tts/torch, mlx) which are unused in the Discord+VNC deployment.
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# --- Brain runtime (imported when the reply engine loads) ---
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python-dotenv==1.0.1
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# >=1.1.0 pulls a ctranslate2 with Blackwell (sm_120) CUDA kernels.
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faster-whisper>=1.1.0
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mcp==1.13.1
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numpy<2.0.0
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rapidfuzz==3.6.1
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requests==2.32.3
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# --- CUDA libraries for GPU-accelerated Whisper (RTX 5050 / sm_120) ---
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# ctranslate2 dlopens these at transcribe time; LD_LIBRARY_PATH is set in the
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# Dockerfile to point at them. Verified working on Blackwell sm_120.
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nvidia-cublas-cu12
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nvidia-cudnn-cu12
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# --- Bridge HTTP service ---
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flask>=3.0.0
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# --- Text-to-speech (Piper) ---
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piper-tts>=1.3.0
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# --- Built-in tools (lazily imported; needed for full functionality) ---
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beautifulsoup4>=4.12.0
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lxml>=4.9.0
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html2text>=2020.1.16
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geoip2==4.8.0
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Pillow==10.4.0
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pytesseract==0.3.13
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faiss-cpu>=1.7.4
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