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72 lines
2.1 KiB
Bash
72 lines
2.1 KiB
Bash
#!/bin/bash
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# DGX Spark - Qwen3-Coder-Next-FP8 起動スクリプト
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# https://docs.techswan.online/tech/dgx-spark-qwen3-coder-next/
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set -e
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CONTAINER_NAME="qwen3-coder-next-fp8"
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IMAGE="nvcr.io/nvidia/vllm:25.11-py3"
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MODEL="Qwen/Qwen3-Coder-Next-FP8"
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PORT="${PORT:-8000}"
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-32768}"
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-32}"
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GPU_MEM_UTIL="${GPU_MEM_UTIL:-0.85}"
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echo "=== DGX Spark - Qwen3-Coder-Next-FP8 起動スクリプト ==="
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echo ""
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# 既存コンテナ確認
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if docker ps -a --format '{{.Names}}' | grep -q "^${CONTAINER_NAME}$"; then
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echo "⚠️ 既存コンテナを停止・削除..."
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docker stop "$CONTAINER_NAME" 2>/dev/null || true
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docker rm "$CONTAINER_NAME" 2>/dev/null || true
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fi
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# イメージ確認・取得
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if ! docker images --format '{{.Repository}}:{{.Tag}}' | grep -q "^${IMAGE}$"; then
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echo "📦 NGC vLLMイメージを取得中..."
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docker pull "$IMAGE"
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fi
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echo ""
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echo "🚀 コンテナ起動..."
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echo " Model: $MODEL"
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echo " Port: $PORT"
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echo " Max Context: $MAX_MODEL_LEN"
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echo " GPU Memory: ${GPU_MEM_UTIL}%"
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echo ""
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docker run -d --name "$CONTAINER_NAME" \
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--gpus all \
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-p "${PORT}:8000" \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--ipc=host \
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"$IMAGE" \
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vllm serve "$MODEL" \
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--max-model-len "$MAX_MODEL_LEN" \
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--max-num-seqs "$MAX_NUM_SEQS" \
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--gpu-memory-utilization "$GPU_MEM_UTIL" \
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--trust-remote-code
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echo ""
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echo "✅ コンテナ起動しました！"
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echo ""
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echo "📋 ログ確認:"
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echo " docker logs -f $CONTAINER_NAME"
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echo ""
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echo "🔍 起動確認（Application startup complete が出るまで待つ）:"
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echo " 初回起動は15-20分かかります（モデルダウンロード + ロード）"
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echo ""
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echo "🧪 テスト:"
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echo " curl http://localhost:${PORT}/health"
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echo ""
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echo "💬 チャット:"
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cat << 'CURL_EXAMPLE'
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "Qwen/Qwen3-Coder-Next-FP8",
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"messages": [{"role": "user", "content": "Hello!"}]
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}'
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CURL_EXAMPLE
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