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ML Experiments

Here we use three repositories

  • Dataset for training
  • A base model for transfer learning of fine-tuning
  • Experiment output. Versioned by the timestamp of an experiment.

In this use case, we use get and put commands to simplify the commands for programmatic use cases.

  1. Clone the training code
    git clone
    cd my-ml-project
  2. Download the dataset and the base model
    avc get -o dataset/ s3://mybucket/datasets/flowers-classification@v0.1.0
    avc get -o base/ s3://mybucket/models/my-base-model@v0.3.0
  3. Train and output your training result (trained model, experiment log, hyperparams, etc) to artifacts/ folder
    python ./
  4. Upload the artifacts
    avc put artifacts/ s3://mybucket/experiments/project1@202220303-100504