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Ananddd06
/
MachineLearningPipeline

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   preprocess:
    4. 

  4.     cmd: python src/preprocess.py
    5. 

  5.     deps:
    6. 

  6.     - path: data/raw/diabetes.csv
    7. 

  7.       hash: md5
    8. 

  8.       md5: f2906818eda8fcfc8f8416557ab1e6df
    9. 

  9.       size: 23873
    10. 

  10.     - path: src/preprocess.py
    11. 

  11.       hash: md5
    12. 

  12.       md5: 7cbd42bdf80d8c066ba916d6a4bd6312
    13. 

  13.       size: 788
    14. 

  14.     params:
    15. 

  15.       params.yaml:
    16. 

  16.         preprocess.input: data/raw/diabetes.csv
    17. 

  17.         preprocess.output: data/processed/diabetes.csv
    18. 

  18.     outs:
    19. 

  19.     - path: data/processed/diabetes.csv
    20. 

  20.       hash: md5
    21. 

  21.       md5: 1c644ff1dd47bea0375d260747f2e40a
    22. 

  22.       size: 23106
    23. 

  23.   train:
    24. 

  24.     cmd: python src/train.py
    25. 

  25.     deps:
    26. 

  26.     - path: data/raw/diabetes.csv
    27. 

  27.       hash: md5
    28. 

  28.       md5: f2906818eda8fcfc8f8416557ab1e6df
    29. 

  29.       size: 23873
    30. 

  30.     - path: src/train.py
    31. 

  31.       hash: md5
    32. 

  32.       md5: d4dea4a8c1b124f29739180ef544f460
    33. 

  33.       size: 4187
    34. 

  34.     params:
    35. 

  35.       params.yaml:
    36. 

  36.         train.data: data/processed/diabetes.csv
    37. 

  37.         train.max_depth:
    38. 

  38.         - 5
    39. 

  39.         - 10
    40. 

  40.         - None
    41. 

  41.         train.min_samples_leaf:
    42. 

  42.         - 1
    43. 

  43.         - 2
    44. 

  44.         train.min_samples_split:
    45. 

  45.         - 2
    46. 

  46.         - 5
    47. 

  47.         train.model: models/diabetes_model.pkl
    48. 

  48.         train.n_estimators:
    49. 

  49.         - 100
    50. 

  50.         - 200
    51. 

  51.         train.random_state: 42
    52. 

  52.     outs:
    53. 

  53.     - path: models/diabetes_model.pkl
    54. 

  54.       hash: md5
    55. 

  55.       md5: 0b623fd52b625913d5e179a901afc1f6
    56. 

  56.       size: 1441495
    57. 

  57.   evaluate:
    58. 

  58.     cmd: python src/evaluate.py
    59. 

  59.     deps:
    60. 

  60.     - path: data/processed/diabetes.csv
    61. 

  61.       hash: md5
    62. 

  62.       md5: 1c644ff1dd47bea0375d260747f2e40a
    63. 

  63.       size: 23106
    64. 

  64.     - path: models/diabetes_model.pkl
    65. 

  65.       hash: md5
    66. 

  66.       md5: 0b623fd52b625913d5e179a901afc1f6
    67. 

  67.       size: 1441495
    68. 

  68.     - path: src/evaluate.py
    69. 

  69.       hash: md5
    70. 

  70.       md5: 381b14b7a62a46dcb068343819b4cec1
    71. 

  71.       size: 1190
    72.
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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!

MachineLearningPipeline is now integrated with AWS S3!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
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!

MachineLearningPipeline is now integrated with Google Cloud 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!

MachineLearningPipeline is now integrated with Azure Cloud Storage!

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

Bucket name cannot be the same as the repository name. Please change one of them.

Access key (If needed)

Congratulations!

MachineLearningPipeline is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes