This example DVC project demonstrates the different ways to employ Checkpoint Experiments with DVC.

This scenario uses DVCLive to generate checkpoints for iterative model training. The model is a simple convolutional neural network (CNN) classifier trained on the MNIST data of handwritten digits to predict the digit (0-9) in each image.

To try it out for yourself:

1. Fork the repository and clone to your local workstation.
2. Install the prerequisites in requirements.txt (if you are using pip, run pip install -r requirements.txt).

Start training the model with dvc exp run. It will train for 10 epochs (you can use Ctrl-C to cancel at any time and still recover the results of the completed epochs), each of which will generate a checkpoint.

Dvclive will track performance at each checkpoint. Open logs.html in your web browser during training to track performance over time (you will need to refresh after each epoch completes to see updates). Metrics will also be logged to .tsv files in the logs directory.

Once the training script completes, you can view the results of the experiment with:

$ dvc exp show
┏━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━┳━━━━━━━━┓
┃ Experiment    ┃ Created  ┃ step ┃    acc ┃
┡━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━╇━━━━━━━━┩
│ workspace     │ -        │    9 │ 0.4997 │
│ live          │ 03:43 PM │    - │        │
│ │ ╓ exp-34e55 │ 03:45 PM │    9 │ 0.4997 │
│ │ ╟ 2fe819e   │ 03:45 PM │    8 │ 0.4394 │
│ │ ╟ 3da85f8   │ 03:45 PM │    7 │ 0.4329 │
│ │ ╟ 4f64a8e   │ 03:44 PM │    6 │ 0.4686 │
│ │ ╟ b9bee58   │ 03:44 PM │    5 │ 0.2973 │
│ │ ╟ e2c5e8f   │ 03:44 PM │    4 │ 0.4004 │
│ │ ╟ c202f62   │ 03:44 PM │    3 │ 0.1468 │
│ │ ╟ eb0ecc4   │ 03:43 PM │    2 │  0.188 │
│ │ ╟ 28b170f   │ 03:43 PM │    1 │ 0.0904 │
│ ├─╨ 9c705fc   │ 03:43 PM │    0 │ 0.0894 │
└───────────────┴──────────┴──────┴────────┘

You can manage it like any other DVC experiments, including:

• Run dvc exp run again to continue training from the last checkpoint.
• Run dvc exp apply [checkpoint_id] to revert to any of the prior checkpoints (which will update the model.pt output file and metrics to that point).
• Run dvc exp run --reset to drop all the existing checkpoints and start from scratch.

Using dvclive to add checkpoints to a DVC project requires a few additional lines of code.

In your training script, use dvclive.log() to log metrics and dvclive.next_step() to make a checkpoint with those metrics. See the train.py script for an example:

    # Iterate over training epochs.
    for i in range(1, EPOCHS+1):
        train(model, x_train, y_train)
        torch.save(model.state_dict(), "model.pt")
        # Evaluate and checkpoint.
        metrics = evaluate(model, x_test, y_test)
        for metric, value in metrics.items():
            dvclive.log(metric, value)
        dvclive.next_step()

Then, in dvc.yaml, add the checkpoint: true option to your model output and a live section to your stage output. See dvc.yaml for an example:

stages:
  train:
    cmd: python train.py
    deps:
    - train.py
    outs:
    - model.pt:
        checkpoint: true
    live:
      logs:
        summary: true
        html: true

If you do not already have a dvc.yaml stage, you can use dvc stage add to create it:

$ dvc stage add -n train -d train.py -c model.pt --live logs python train.py

That's it! For users already familiar with logging metrics in DVC, note that you no longer need a metrics section in dvc.yaml since dvclive is already logging metrics.