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puneethp
/
dvc_session

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  1. split:
    2. 

  2.   cmd: python src/train_test_split.py -i data/iris.csv -o data/split/
    3. 

  3.   deps:
    4. 

  4.   - path: data/iris.csv
    5. 

  5.     md5: 7fafe97ac39bd89f1718ff44e5ff667e
    6. 

  6.     size: 7856
    7. 

  7.   - path: src/train_test_split.py
    8. 

  8.     md5: 50f96bee5d5ae0312e0e3edc2e6c3547
    9. 

  9.     size: 1057
    10. 

  10.   outs:
    11. 

  11.   - path: data/split
    12. 

  12.     md5: cc7cd1229f7bd51dfa622f78551d30e5.dir
    13. 

  13.     size: 2812
    14. 

  14.     nfiles: 2
    15. 

  15. featurize:
    16. 

  16.   cmd: python src/feature_engineering.py -i data/split/ -f data/features/ -o models/pca/
    17. 

  17.   deps:
    18. 

  18.   - path: data/split
    19. 

  19.     md5: cc7cd1229f7bd51dfa622f78551d30e5.dir
    20. 

  20.     size: 2812
    21. 

  21.     nfiles: 2
    22. 

  22.   - path: src/feature_engineering.py
    23. 

  23.     md5: 9bb228c9c91e237bb6a7b5a9f4ff1ad3
    24. 

  24.     size: 1527
    25. 

  25.   params:
    26. 

  26.     params.yaml:
    27. 

  27.       pca:
    28. 

  28.         n_components: 2
    29. 

  29.   outs:
    30. 

  30.   - path: data/features
    31. 

  31.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    32. 

  32.     size: 6201
    33. 

  33.     nfiles: 2
    34. 

  34.   - path: models/pca/metrics.yaml
    35. 

  35.     md5: be80dd50f895ef995068f03a19515e23
    36. 

  36.     size: 82
    37. 

  37.   - path: models/pca/model.gz
    38. 

  38.     md5: 9c9616bac20afb0b93315965e62781eb
    39. 

  39.     size: 693
    40. 

  40. train_logistic:
    41. 

  41.   cmd: python src/logistic_regression.py -i data/features/ -o models/logistic/
    42. 

  42.   deps:
    43. 

  43.   - path: data/features
    44. 

  44.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    45. 

  45.     size: 6201
    46. 

  46.     nfiles: 2
    47. 

  47.   - path: src/logistic_regression.py
    48. 

  48.     md5: e3de2b80eaff0bfa5add17ff69d0ce3d
    49. 

  49.     size: 941
    50. 

  50.   params:
    51. 

  51.     params.yaml:
    52. 

  52.       logistic:
    53. 

  53.         penalty: l2
    54. 

  54.         n_jobs: 4
    55. 

  55.         fit_intercept: false
    56. 

  56.   outs:
    57. 

  57.   - path: models/logistic/metrics.yaml
    58. 

  58.     md5: 8ccdb2a0ae1ba07f6852a701b54136c2
    59. 

  59.     size: 88
    60. 

  60.   - path: models/logistic/model.gz
    61. 

  61.     md5: 5561f86323166fd1f31fc576cbcfdbfa
    62. 

  62.     size: 582
    63. 

  63. train_svc:
    64. 

  64.   cmd: python src/linear_svc.py -i data/features/ -o models/svc/
    65. 

  65.   deps:
    66. 

  66.   - path: data/features
    67. 

  67.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    68. 

  68.     size: 6201
    69. 

  69.     nfiles: 2
    70. 

  70.   - path: src/linear_svc.py
    71. 

  71.     md5: e9c0f614cdfe95ca4e134e70c5a6d290
    72. 

  72.     size: 895
    73. 

  73.   params:
    74. 

  74.     params.yaml:
    75. 

  75.       svc:
    76. 

  76.         penalty: l2
    77. 

  77.   outs:
    78. 

  78.   - path: models/svc/metrics.yaml
    79. 

  79.     md5: b7dab754fad4e4a22b94f6e10a4066e6
    80. 

  80.     size: 90
    81. 

  81.   - path: models/svc/model.gz
    82. 

  82.     md5: f0a6fb032acc0c4b0db2ae65a1caa917
    83. 

  83.     size: 612
    84. 

  84. train_forrest:
    85. 

  85.   cmd: python src/random_forrest.py -i data/features/ -o models/r_forrest/
    86. 

  86.   deps:
    87. 

  87.   - path: data/features
    88. 

  88.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    89. 

  89.     size: 6201
    90. 

  90.     nfiles: 2
    91. 

  91.   - path: src/random_forrest.py
    92. 

  92.     md5: e0c09051086e9b0a4001ba5a8becb681
    93. 

  93.     size: 1129
    94. 

  94.   params:
    95. 

  95.     params.yaml:
    96. 

  96.       forrest:
    97. 

  97.         n_estimators: 10
    98. 

  98.         max_samples: 30
    99. 

  99.         n_jobs: 4
    100. 

  100.   outs:
    101. 

  101.   - path: models/r_forrest/metrics.yaml
    102. 

  102.     md5: b39f20fd69885b2442756a66e0f6ab74
    103. 

  103.     size: 91
    104. 

  104.   - path: models/r_forrest/model.gz
    105. 

  105.     md5: 65974680086b35dcf3dba7957d588e7b
    106. 

  106.     size: 3259
    107. 

  107. train_ensemble:
    108. 

  108.   cmd: python src/ensemble.py -i data/features/ -m models/ -o models/ensemble/
    109. 

  109.   deps:
    110. 

  110.   - path: data/features
    111. 

  111.     md5: ae8b2ac6927d2d20a0f6463223bb7c18.dir
    112. 

  112.     size: 6201
    113. 

  113.     nfiles: 2
    114. 

  114.   - path: models/logistic/model.gz
    115. 

  115.     md5: 5561f86323166fd1f31fc576cbcfdbfa
    116. 

  116.     size: 582
    117. 

  117.   - path: models/r_forrest/model.gz
    118. 

  118.     md5: 65974680086b35dcf3dba7957d588e7b
    119. 

  119.     size: 3259
    120. 

  120.   - path: models/svc/model.gz
    121. 

  121.     md5: f0a6fb032acc0c4b0db2ae65a1caa917
    122. 

  122.     size: 612
    123. 

  123.   - path: src/ensemble.py
    124. 

  124.     md5: 32546bb8ccedce60c04fbb34e3a65e1a
    125. 

  125.     size: 1287
    126. 

  126.   params:
    127. 

  127.     params.yaml:
    128. 

  128.       ensemble:
    129. 

  129.         voting: hard
    130. 

  130.   outs:
    131. 

  131.   - path: models/ensemble/metrics.yaml
    132. 

  132.     md5: b7dab754fad4e4a22b94f6e10a4066e6
    133. 

  133.     size: 90
    134. 

  134.   - path: models/ensemble/model.gz
    135. 

  135.     md5: 1925a43d76f1bbd0bd9be43d57db54fc
    136. 

  136.     size: 6804
    137.
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Integrate Google Cloud Storage
Use Google Storage
Select bucket
Upload key
Finish

Use Google Cloud Storage!

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

Specify your Google Storage bucket

Service Account Key

Congratulations!

dvc_session is now integrated with Google Cloud Storage!

Delete Storage Key

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Yes
Integrate AWS S3
Use S3 remote
Select bucket
Access key
Finish

Use AWS S3 as storage!

Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

dvc_session is now integrated with AWS S3!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
Finish

Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

dvc_session is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

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!

dvc_session is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes