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avatar nirbarazida
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CheXNet

Watch 17
Issues 16 Pull Requests 5 Discussions 8

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   data-processing:
    4. 

  4.     cmd: python -m data_processing.src.data_processing
    5. 

  5.     deps:
    6. 

  6.     - path: data_labeling/data
    7. 

  7.       md5: 3e2ca4b761fd9bb698605613f1be9f1e.dir
    8. 

  8.       size: 45077187044
    9. 

  9.       nfiles: 112128
    10. 

  10.     - path: data_processing/src/const.yaml
    11. 

  11.       md5: 99bfdecaf747637acda48ab403c3ef9e
    12. 

  12.       size: 177
    13. 

  13.     - path: data_processing/src/data_processing.py
    14. 

  14.       md5: f345bcf47a884b3ea4e3d8932c07bcef
    15. 

  15.       size: 6091
    16. 

  16.     outs:
    17. 

  17.     - path: data_processing/samples
    18. 

  18.       md5: 6f1991c456efb9d94a6286fb45d742f7.dir
    19. 

  19.       size: 587922
    20. 

  20.       nfiles: 16
    21. 

  21.   modeling:
    22. 

  22.     cmd: python -m modeling.src.train
    23. 

  23.     deps:
    24. 

  24.     - path: data_labeling/data
    25. 

  25.       md5: 3e2ca4b761fd9bb698605613f1be9f1e.dir
    26. 

  26.       size: 45077187044
    27. 

  27.       nfiles: 112128
    28. 

  28.     - path: data_processing/src/const.yaml
    29. 

  29.       md5: 99bfdecaf747637acda48ab403c3ef9e
    30. 

  30.       size: 177
    31. 

  31.     - path: data_processing/src/data_processing.py
    32. 

  32.       md5: f345bcf47a884b3ea4e3d8932c07bcef
    33. 

  33.       size: 6091
    34. 

  34.     - path: modeling/src/const.yaml
    35. 

  35.       md5: de7cec160783642a601e0642505aad5a
    36. 

  36.       size: 426
    37. 

  37.     - path: modeling/src/train.py
    38. 

  38.       md5: bf1feb313191a9b2c51d46c8d5f2cfaf
    39. 

  39.       size: 5280
    40. 

  40.     outs:
    41. 

  41.     - path: evaluating/eval/metrics.csv
    42. 

  42.       md5: f0c89a07561ca8aea8ab3f4764b648e7
    43. 

  43.       size: 26
    44. 

  44.     - path: evaluating/eval/params.yml
    45. 

  45.       md5: 8a80554c91d9fca8acb82f023de02f11
    46. 

  46.       size: 3
    47. 

  47.     - path: prod_model
    48. 

  48.       md5: 0d41e6c5dd0997eb93898601365d9e46.dir
    49. 

  49.       size: 92824740
    50. 

  50.       nfiles: 4
    51. 

  51.   evaluating:
    52. 

  52.     cmd: python -m evaluating.src.heatmap
    53. 

  53.     deps:
    54. 

  54.     - path: evaluating/src/const.yaml
    55. 

  55.       md5: 7ece4ae7fe07370b9ef16bfdfa0298e0
    56. 

  56.       size: 103
    57. 

  57.     - path: evaluating/src/heatmap.py
    58. 

  58.       md5: 4e833755fee2f42a340a858ca288248d
    59. 

  59.       size: 5879
    60. 

  60.     - path: prod_model
    61. 

  61.       md5: 0d41e6c5dd0997eb93898601365d9e46.dir
    62. 

  62.       size: 92824740
    63. 

  63.       nfiles: 4
    64. 

  64.     outs:
    65. 

  65.     - path: evaluating/heatmap_eval
    66. 

  66.       md5: d6ab91b20da02e687278e869504008f2.dir
    67. 

  67.       size: 2110733
    68. 

  68.       nfiles: 16
    69.
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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!

CheXNet is now integrated with Google Cloud Storage!

Delete Storage Key

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Integrate AWS S3
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Access key
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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!

CheXNet is now integrated with AWS S3!

Delete Storage Key

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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!

CheXNet 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!

CheXNet is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes