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avatar Dean
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dvc-streamlit-example
mirror of https://github.com/sicara/dvc-streamlit-example.git

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   download_dataset:
    4. 

  4.     cmd: ( wget https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip
    5. 

  5.       -O cats_and_dogs_filtered.zip && unzip cats_and_dogs_filtered.zip -d ./data/raw
    6. 

  6.       && rm cats_and_dogs_filtered.zip ) &> logs/download_dataset.out
    7. 

  7.     outs:
    8. 

  8.     - path: ./data/raw
    9. 

  9.       md5: 174fe7fcb400140a007ac05c36b5bfd4.dir
    10. 

  10.       size: 68556316
    11. 

  11.       nfiles: 3001
    12. 

  12.     - path: logs/download_dataset.out
    13. 

  13.       md5: f211c659367eb1b32f30b385df5260de
    14. 

  14.       size: 325690
    15. 

  15.   split_dataset:
    16. 

  16.     cmd: python scripts/split_dataset.py &> logs/split_dataset.out
    17. 

  17.     deps:
    18. 

  18.     - path: ./data/raw
    19. 

  19.       md5: 174fe7fcb400140a007ac05c36b5bfd4.dir
    20. 

  20.       size: 68556316
    21. 

  21.       nfiles: 3001
    22. 

  22.     - path: scripts/split_dataset.py
    23. 

  23.       md5: e99a47b198dee35710b7c745b60e928c
    24. 

  24.       size: 1277
    25. 

  25.     params:
    26. 

  26.       params.yaml:
    27. 

  27.         data.dataset.val_test_split:
    28. 

  28.           val: 0.7
    29. 

  29.           test: 0.3
    30. 

  30.     outs:
    31. 

  31.     - path: ./data/dataset/dataset.csv
    32. 

  32.       md5: 2f6641598b7fc6c8d1dd031a6d595161
    33. 

  33.       size: 68108
    34. 

  34.     - path: ./data/dataset/test
    35. 

  35.       md5: 326353ae0eed20bee508f30419a0ec25.dir
    36. 

  36.       size: 6815914
    37. 

  37.       nfiles: 305
    38. 

  38.     - path: ./data/dataset/train
    39. 

  39.       md5: 0a1a83aa537b94b2b07eb1503701eca7.dir
    40. 

  40.       size: 45613376
    41. 

  41.       nfiles: 2000
    42. 

  42.     - path: ./data/dataset/val
    43. 

  43.       md5: d34d9a2762c720f7415020cd9ddabb1e.dir
    44. 

  44.       size: 16126886
    45. 

  45.       nfiles: 695
    46. 

  46.     - path: logs/split_dataset.out
    47. 

  47.       md5: 6fa4014992ad6a7d326be58d4a2563b4
    48. 

  48.       size: 2224
    49. 

  49.   train:
    50. 

  50.     cmd: python scripts/train.py &> logs/train.out
    51. 

  51.     deps:
    52. 

  52.     - path: ./data/dataset/train
    53. 

  53.       md5: 0a1a83aa537b94b2b07eb1503701eca7.dir
    54. 

  54.       size: 45613376
    55. 

  55.       nfiles: 2000
    56. 

  56.     - path: ./data/dataset/val
    57. 

  57.       md5: d34d9a2762c720f7415020cd9ddabb1e.dir
    58. 

  58.       size: 16126886
    59. 

  59.       nfiles: 695
    60. 

  60.     - path: scripts/train.py
    61. 

  61.       md5: a6edfb77adf3b631391890188d246d2c
    62. 

  62.       size: 3264
    63. 

  63.     params:
    64. 

  64.       params.yaml:
    65. 

  65.         model:
    66. 

  66.           backbone: tensorflow.keras.applications.ResNet50
    67. 

  67.           preprocess_input: tensorflow.keras.applications.resnet50.preprocess_input
    68. 

  68.         train:
    69. 

  69.           batch_size: 32
    70. 

  70.           img_size:
    71. 

  71.           - 160
    72. 

  72.           - 160
    73. 

  73.           learning_rate: 0.0001
    74. 

  74.           subdir: train
    75. 

  75.           epochs:
    76. 

  76.             frozen: 10
    77. 

  77.             unfrozen: 15
    78. 

  78.           fine_tune_at: 100
    79. 

  79.     outs:
    80. 

  80.     - path: ./data/train/best_weights.h5
    81. 

  81.       md5: d8adc1a3678ae740ac214f352d48a05e
    82. 

  82.       size: 172679472
    83. 

  83.     - path: ./data/train/model
    84. 

  84.       md5: f620686421a581af909b576489fe2abb.dir
    85. 

  85.       size: 177275013
    86. 

  86.       nfiles: 3
    87. 

  87.     - path: ./data/train/training_metrics
    88. 

  88.       md5: d74d74a16fe3c4a5424441503827213e.dir
    89. 

  89.       size: 3810
    90. 

  90.       nfiles: 4
    91. 

  91.     - path: logs/train.out
    92. 

  92.       md5: 37770d8d49a959d4343bbf82906848da
    93. 

  93.       size: 276679
    94. 

  94.   evaluate:
    95. 

  95.     cmd: python scripts/evaluate.py &> logs/evaluate.out
    96. 

  96.     deps:
    97. 

  97.     - path: ./data/dataset/test
    98. 

  98.       md5: 326353ae0eed20bee508f30419a0ec25.dir
    99. 

  99.       size: 6815914
    100. 

  100.       nfiles: 305
    101. 

  101.     - path: ./data/train/model
    102. 

  102.       md5: f620686421a581af909b576489fe2abb.dir
    103. 

  103.       size: 177275013
    104. 

  104.       nfiles: 3
    105. 

  105.     - path: scripts/evaluate.py
    106. 

  106.       md5: aada14c8861cb28746abb5aa78e9c4c3
    107. 

  107.       size: 1377
    108. 

  108.     outs:
    109. 

  109.     - path: ./data/evaluation/metrics.json
    110. 

  110.       md5: 5ba98eca1d691aa800be75ffd1aa7ea0
    111. 

  111.       size: 55
    112. 

  112.     - path: ./data/evaluation/predictions.csv
    113. 

  113.       md5: e140a05dde76c4c47af03c63103762b5
    114. 

  114.       size: 21512
    115. 

  115.     - path: logs/evaluate.out
    116. 

  116.       md5: ec5490a55b5a6b528dccfbd1b2eb81f4
    117. 

  117.       size: 4839
    118.
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