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jinensetpal
/
CheXNet
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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   modeling:
    4. 

  4.     cmd: python modeling/src/train.py
    5. 

  5.     deps:
    6. 

  6.     - path: data_labeling/data
    7. 

  7.       md5: d5e800f8272228036af78a7b20797ea2.dir
    8. 

  8.       size: 45077186807
    9. 

  9.       nfiles: 112128
    10. 

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

  11.       md5: 95f0a08bc679fb2ed2ce6e4beb45df69
    12. 

  12.       size: 459
    13. 

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

  14.       md5: d53c3a89f312016c8c4d6eadcc3bfbbd
    15. 

  15.       size: 3520
    16. 

  16.     - path: modeling/src/const.yaml
    17. 

  17.       md5: d3a0bfb133f0a1dbd0ec9f54e9fc6b99
    18. 

  18.       size: 930
    19. 

  19.     - path: modeling/src/train.py
    20. 

  20.       md5: 480c5f83e9b720220ceabc5a4e7bf556
    21. 

  21.       size: 3075
    22. 

  22.     outs:
    23. 

  23.     - path: evaluating/eval/metrics/metrics.csv
    24. 

  24.       md5: 79188bef445b50e706054627aed761f7
    25. 

  25.       size: 127
    26. 

  26.     - path: evaluating/eval/params/params.yml
    27. 

  27.       md5: 70f2ed35c06d14dc5966111a38728cab
    28. 

  28.       size: 33
    29. 

  29.     - path: prod_model
    30. 

  30.       md5: d4e1b3e57ca3bf7b18567f84853286a2.dir
    31. 

  31.       size: 35791202
    32. 

  32.       nfiles: 4
    33. 

  33.   data-processing:
    34. 

  34.     cmd: python3.10 -m data_processing.src.data_processing
    35. 

  35.     deps:
    36. 

  36.     - path: data_labeling/data
    37. 

  37.       md5: d5e800f8272228036af78a7b20797ea2.dir
    38. 

  38.       size: 45077186807
    39. 

  39.       nfiles: 112128
    40. 

  40.     - path: data_processing/src/const.yaml
    41. 

  41.       md5: 99bfdecaf747637acda48ab403c3ef9e
    42. 

  42.       size: 177
    43. 

  43.     - path: data_processing/src/data_processing.py
    44. 

  44.       md5: 0f90ab3018fe200b7759238ad9dc79f0
    45. 

  45.       size: 5066
    46. 

  46.     outs:
    47. 

  47.     - path: data_processing/samples
    48. 

  48.       md5: 585378b7d88b5ff66c4017a1f23e51d5.dir
    49. 

  49.       size: 622701
    50. 

  50.       nfiles: 16
    51. 

  51.   evaluating:
    52. 

  52.     cmd: python3.10 -m evaluating.src.heatmap
    53. 

  53.     deps:
    54. 

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

  55.       md5: 9dc549ea1b8b750163dd22fc762c8ef4
    56. 

  56.       size: 104
    57. 

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

  58.       md5: e3dcf50f4c54069d70fce6fa9b807667
    59. 

  59.       size: 3333
    60. 

  60.     - path: prod_model
    61. 

  61.       md5: c92e53557b0c248ac113b1da63f5a2c4.dir
    62. 

  62.       size: 92658306
    63. 

  63.       nfiles: 4
    64. 

  64.     outs:
    65. 

  65.     - path: evaluating/heatmap_eval
    66. 

  66.       md5: 8f3e300fcb85d546f5bfe41b79879299.dir
    67. 

  67.       size: 2600192
    68. 

  68.       nfiles: 16
    69.
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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!

CheXNet 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!

CheXNet 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!

CheXNet 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!

CheXNet is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes