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use DVC(Data Version Control) to manage a DL model training experiment
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deng a27d0a24d9 use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
.dvc init 2023-12-28 22:06:25 +08:00
dvclive use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
env use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
utils use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
.dvcignore init 2023-12-28 22:06:25 +08:00
.gitignore use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
README.md use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
dvc.lock use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
dvc.yaml use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
evaluate.py use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
params.yaml use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
prepare.py use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00
train.py use resnet50 to train a cifar10 classifier 2023-12-30 00:03:36 +08:00

README.md

Abstract

Attempt to use DVC, a data versioning tool, to track image classification model training with PyTorch, including data, trained model file, and used parameters. The data will be recorded and pushed to my private DVC remote via webdav🎁

Requirements

  • MacOS 13.3

Dirs

  • env
    • pt.yaml
      • conda env yaml to run this repo
  • utils
    • house pre-built functions

Files

  • prepare.py
    • prepare materials for model training
  • train.py
    • try to train a small neural network
  • evaluate.py
    • evaluate trained model with some metrics
tags: DVC