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MLOps
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gradient-boosting
mirror of https://dagshub.com/skshreyas714/gradient-boosting

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   data-split:
    4. 

  4.     cmd: python3 src/data_split.py
    5. 

  5.     deps:
    6. 

  6.     - path: data/raw_data/clean_data.csv
    7. 

  7.       md5: b20c8cf632976544627b454eec4569cd
    8. 

  8.       size: 333768
    9. 

  9.     - path: src/data_split.py
    10. 

  10.       md5: d5346a51294d0b8a6f043cb38a746501
    11. 

  11.       size: 959
    12. 

  12.     params:
    13. 

  13.       params.yaml:
    14. 

  14.         data-split.seed: 42
    15. 

  15.         data-split.split: 0.2
    16. 

  16.     outs:
    17. 

  17.     - path: data/processed_data/x_test.npy
    18. 

  18.       md5: 2d112d60d739d98a1c38f2ecaa3a575c
    19. 

  19.       size: 23424
    20. 

  20.     - path: data/processed_data/x_train.npy
    21. 

  21.       md5: 084fe9b3d0ddd59d73acf1c26fad3055
    22. 

  22.       size: 93120
    23. 

  23.     - path: data/processed_data/y_test.npy
    24. 

  24.       md5: c9df630695a5479df6e10a053d80f1b8
    25. 

  25.       size: 3040
    26. 

  26.     - path: data/processed_data/y_train.npy
    27. 

  27.       md5: 75253d33076ed9bc5211e9ec8b6f22d7
    28. 

  28.       size: 11752
    29. 

  29.   normalize:
    30. 

  30.     cmd: python3 src/normalize.py
    31. 

  31.     deps:
    32. 

  32.     - path: data/processed_data/x_test.npy
    33. 

  33.       md5: 2d112d60d739d98a1c38f2ecaa3a575c
    34. 

  34.       size: 23424
    35. 

  35.     - path: data/processed_data/x_train.npy
    36. 

  36.       md5: 084fe9b3d0ddd59d73acf1c26fad3055
    37. 

  37.       size: 93120
    38. 

  38.     - path: src/normalize.py
    39. 

  39.       md5: 66d3708d2a1e129a808b8a3aca92425e
    40. 

  40.       size: 592
    41. 

  41.     outs:
    42. 

  42.     - path: data/scaling_model.pkl
    43. 

  43.       md5: c9dee1bc1761b14c472e6b107ba8eba1
    44. 

  44.       size: 728
    45. 

  45.   training:
    46. 

  46.     cmd: python3 src/training.py
    47. 

  47.     deps:
    48. 

  48.     - path: data/processed_data/x_train.npy
    49. 

  49.       md5: 084fe9b3d0ddd59d73acf1c26fad3055
    50. 

  50.       size: 93120
    51. 

  51.     - path: data/processed_data/y_train.npy
    52. 

  52.       md5: 75253d33076ed9bc5211e9ec8b6f22d7
    53. 

  53.       size: 11752
    54. 

  54.     - path: data/scaling_model.pkl
    55. 

  55.       md5: c9dee1bc1761b14c472e6b107ba8eba1
    56. 

  56.       size: 728
    57. 

  57.     - path: src/training.py
    58. 

  58.       md5: 8282267d5b654e23a4fbe2403bf034e6
    59. 

  59.       size: 1818
    60. 

  60.     params:
    61. 

  61.       params.yaml:
    62. 

  62.         training.lr: 0.1
    63. 

  63.         training.m_depth: 3
    64. 

  64.         training.min_leaf: 1
    65. 

  65.         training.min_split: 2
    66. 

  66.         training.n_est: 250
    67. 

  67.     outs:
    68. 

  68.     - path: data/gbrt_model.pkl
    69. 

  69.       md5: ed3b755d460e9422ceae5c3422d6b643
    70. 

  70.       size: 315767
    71.
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Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

gradient-boosting is now integrated with Azure Cloud Storage!

Delete Storage Key

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