chore(compose): remediate vulnerable service images
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+8
-7
@@ -45,7 +45,7 @@ services:
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command:
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- |
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set -e
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for m in medium small; do
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for m in medium small base; do
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if [ -f /app/models/ggml-$$m.bin ]; then
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echo "Model ggml-$$m.bin already present, skipping download."
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else
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@@ -55,9 +55,10 @@ services:
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done
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# Primary whisper.cpp server: NVIDIA RTX 5070 Ti via CUDA (Blackwell sm_120).
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# Uses ggml-small.bin (~850 MiB VRAM) — fits alongside gemma 3 12b which runs
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# with `--parallel 1` (frees ~900 MiB of VRAM). Benchmarked at ~150 ms per
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# short clip, ~93x faster than the CPU server below with identical WER.
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# Uses ggml-base.bin to keep the service alive while llama-server owns most of
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# the laptop GPU VRAM. The previous ggml-small.bin profile needed ~465 MiB
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# contiguous CUDA memory and restarted when only ~560 MiB fragmented VRAM was
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# free. CPU whisper-server below remains the higher-accuracy fallback.
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#
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# The official `ghcr.io/ggml-org/whisper.cpp:main-cuda` ships kernels only
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# for sm_75/80/86/90 and fails to init CUDA on Blackwell. We build a custom
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@@ -79,7 +80,7 @@ services:
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entrypoint: ["whisper-server"]
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command:
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- --model
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- /app/models/ggml-small.bin
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- /app/models/ggml-base.bin
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- --host
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- 0.0.0.0
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- --port
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@@ -224,7 +225,7 @@ services:
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# Optional local dependency: liteLLM proxy for unified LLM API.
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# Start with: docker compose --profile api up -d litellm
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litellm:
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image: litellm/litellm:v1.82.3-stable.patch.2
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image: litellm/litellm:v1.83.7-stable
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container_name: litellm
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restart: unless-stopped
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profiles: ["api"]
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@@ -309,7 +310,7 @@ services:
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# Dedicated local n8n instance for agent-oriented workflows.
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# Start with: docker compose --profile automation up -d n8n-agent
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n8n-agent:
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image: docker.n8n.io/n8nio/n8n:2.11.3
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image: docker.n8n.io/n8nio/n8n:2.22.1
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container_name: n8n-agent
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restart: unless-stopped
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profiles: ["automation"]
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