docker-compose.yml 1.5 KB

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  1. version: "3.8"
  2. services:
  3. # vLLM - Qwen3 0.6B
  4. vllm-0.6b:
  5. image: swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/vllm/vllm-openai:latest
  6. container_name: vllm-qwen3-0.6b
  7. ports:
  8. - "8100:8000"
  9. command: >
  10. --model Qwen/Qwen3-Embedding-0.6B
  11. --dtype float16
  12. --port 8000
  13. volumes:
  14. - ./models:/root/.cache/huggingface
  15. deploy:
  16. resources:
  17. reservations:
  18. devices:
  19. - driver: nvidia
  20. count: all
  21. capabilities: [ gpu ]
  22. # # vLLM - Qwen3 4B
  23. # vllm-4b:
  24. # image: vllm/vllm-openai:latest
  25. # container_name: vllm-qwen3-4b
  26. # ports:
  27. # - "8200:8000"
  28. # command: >
  29. # --model Qwen/Qwen3-Embedding-4B
  30. # --dtype float16
  31. # --api-port 8000
  32. # volumes:
  33. # - ./models:/root/.cache/huggingface
  34. #
  35. # # vLLM - Qwen3 8B
  36. # vllm-8b:
  37. # image: vllm/vllm-openai:latest
  38. # container_name: vllm-qwen3-8b
  39. # ports:
  40. # - "8300:8000"
  41. # command: >
  42. # --model Qwen/Qwen3-Embedding-8B
  43. # --dtype float16
  44. # --api-port 8000
  45. # volumes:
  46. # - ./models:/root/.cache/huggingface
  47. # Milvus 向量数据库
  48. milvus:
  49. image: swr.cn-north-4.myhuaweicloud.com/ddn-k8s/docker.io/milvusdb/milvus:v2.4.5
  50. # image: milvusdb/milvus:v2.4.4-standalone
  51. container_name: milvus
  52. ports:
  53. - "19530:19530"
  54. - "9091:9091"
  55. environment:
  56. - ETCD_USE_EMBED=true
  57. - MINIO_USE_EMBED=true
  58. - PULSAR_USE_EMBED=true
  59. volumes:
  60. - ./milvus_data:/var/lib/milvus
  61. command: ["milvus", "run", "standalone"]