Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
Resources  
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Explore Pricing
Product  
Data Engine LLMs Platform Enterprise
Connect to Dagshub Discord channel
Register
Login

jinensetpal
/
CheXNet
forked from nirbarazida/CheXNet

Watch 1
Issues 0 Pull Requests 0 Discussions 0

dvc.lock 1.9 KB
Permalink History Raw

You have to be logged in to leave a comment. Sign In
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68 
  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   modeling:
    4. 

  4.     cmd: python -m modeling.src.train
    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: 99bfdecaf747637acda48ab403c3ef9e
    12. 

  12.       size: 177
    13. 

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

  14.       md5: 0f90ab3018fe200b7759238ad9dc79f0
    15. 

  15.       size: 5066
    16. 

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

  17.       md5: 4ccbd89e9e3153d6247a501f84de0619
    18. 

  18.       size: 409
    19. 

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

  20.       md5: 20da8ccc2dfcbae7c582feea3e073ab2
    21. 

  21.       size: 4854
    22. 

  22.     outs:
    23. 

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

  24.       md5: 8fa3a7edc0956820319d799a5cda75e5
    25. 

  25.       size: 706950
    26. 

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

  27.       md5: 4a17ea49b51b1d00343acceeed887b8e
    28. 

  28.       size: 172
    29. 

  29.     - path: prod_model
    30. 

  30.       md5: 7496e862cca128fd26b05094ef9994a3.dir
    31. 

  31.       size: 92658302
    32. 

  32.       nfiles: 4
    33. 

  33.   data-processing:
    34. 

  34.     cmd: python -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: facc246ea32365227811f72e8f97edcc
    42. 

  42.       size: 425
    43. 

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

  44.       md5: bfe29ea6e1c93db38971cdb7e0d6c440
    45. 

  45.       size: 5529
    46. 

  46.     outs:
    47. 

  47.     - path: data_processing/samples
    48. 

  48.       md5: 51338cbaa9267e80d9fceba110835f2b.dir
    49. 

  49.       size: 602932
    50. 

  50.       nfiles: 16
    51. 

  51.   evaluating:
    52. 

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

  53.     deps:
    54. 

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

  55.       md5: 287849bf05c905039ed42aae0b1be077
    56. 

  56.       size: 155
    57. 

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

  58.       md5: 0de71c1901441b6dc998755b4c7d6d89
    59. 

  59.       size: 4464
    60. 

  60.     - path: prod_model
    61. 

  61.       md5: 7496e862cca128fd26b05094ef9994a3.dir
    62. 

  62.       size: 92658302
    63. 

  63.       nfiles: 4
    64. 

  64.     outs:
    65. 

  65.     - path: evaluating/heatmap_eval
    66. 

  66.       md5: 892a4c96c627473d17fe088107005340.dir
    67. 

  67.       size: 1577463
    68. 

  68.       nfiles: 16
    69.
Prev
Next
Tip!

Press p or to see the previous file or, n or to see the next file

Comments

Write Preview
Loading...
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