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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: 12c2799ba405d46875d5db1f166c3ac0
    59. 

  59.       size: 1819
    60. 

  60.     params:
    61. 

  61.       params.yaml:
    62. 

  62.         training.lr: 0.01
    63. 

  63.         training.m_depth: 4
    64. 

  64.         training.min_leaf: 5
    65. 

  65.         training.min_split: 6
    66. 

  66.         training.n_est: 500
    67. 

  67.     outs:
    68. 

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

  69.       md5: 5517f8f6bc714beccfaf809129f732eb
    70. 

  70.       size: 922670
    71. 

  71.   evaluate:
    72. 

  72.     cmd: python3 src/evaluate.py
    73. 

  73.     deps:
    74. 

  74.     - path: data/gbrt_model.pkl
    75. 

  75.       md5: 5517f8f6bc714beccfaf809129f732eb
    76. 

  76.       size: 922670
    77. 

  77.     - path: data/processed_data/x_test.npy
    78. 

  78.       md5: 2d112d60d739d98a1c38f2ecaa3a575c
    79. 

  79.       size: 23424
    80. 

  80.     - path: data/processed_data/y_test.npy
    81. 

  81.       md5: c9df630695a5479df6e10a053d80f1b8
    82. 

  82.       size: 3040
    83. 

  83.     - path: data/scaling_model.pkl
    84. 

  84.       md5: c9dee1bc1761b14c472e6b107ba8eba1
    85. 

  85.       size: 728
    86. 

  86.     - path: src/evaluate.py
    87. 

  87.       md5: c50b2345b32b4b94abb3cfee69096f93
    88. 

  88.       size: 938
    89. 

  89.     outs:
    90. 

  90.     - path: data/prediction.csv
    91. 

  91.       md5: 5deeb6cfcf0f62a1b88332d0891c434b
    92. 

  92.       size: 13227
    93. 

  93.     - path: scores.json
    94. 

  94.       md5: e173ec2c7e4f97a5be2ec678da76357e
    95. 

  95.       size: 63
    96.
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Use any S3 compatible storage!

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gradient-boosting is now integrated with your S3 compatible storage!

Delete Storage Key

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Integrate Azure Cloud Storage
Use Azure Storage
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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

Are you sure you want to delete this access key?

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