dvc.yaml

params:
- results/train/params.yaml
metrics:
- results/train/metrics.json
- results/evaluate/metrics.json
plots:
- results/train/plots/metrics:
    x: step
- results/evaluate/plots/images
artifacts:
  pool-segmentation:
    path: models/model.pkl
    type: model
    desc: This is a Computer Vision (CV) model that's segmenting out swimming pools
      from satellite images.
    labels:
    - cv
    - segmentation
    - satellite-images
    - shufflenet_v2_x2_0
stages:
  data_split:
    cmd: python src/data_split.py
    deps:
    - data/pool_data
    - src/data_split.py
    params:
    - base
    - data_split
    outs:
    - data/test_data
    - data/train_data
  train:
    cmd: python src/train.py
    deps:
    - data/train_data
    - src/train.py
    params:
    - base
    - train
    outs:
    - models/model.pkl
    - models/model.pth
    - results/train
  evaluate:
    cmd: python src/evaluate.py
    deps:
    - data/test_data
    - models/model.pkl
    - src/evaluate.py
    params:
    - base
    - evaluate
    outs:
    - results/evaluate
  sagemaker:
    cmd: cp models/model.pth sagemaker/code/model.pth && cd sagemaker && tar -cpzf
      model.tar.gz code/ && cd .. && mv sagemaker/model.tar.gz .  && rm sagemaker/code/model.pth
    deps:
    - models/model.pth
    outs:
    - model.tar.gz