Edit on GitHub

Compare Experiments

DVC makes it easy to iterate on your project using Git commits with tags or Git branches. It provides a way to try different ideas, keep track of them, switch back and forth. To find the best performing experiment or track the progress, project metrics are supported in DVC (as described in one of the previous chapters).

Let's run evaluate for the latest bigrams experiment we created in previous chapters. It mostly takes just running the dvc repro:

$ git checkout master
$ dvc checkout
$ dvc repro evaluate.dvc

git checkout master and dvc checkout commands ensure that we have the latest experiment code and data respectively. And dvc repro, as we discussed in the Reproduce chapter, is a way to run all the necessary commands to build the model and measure its performance.

$ git commit -am "Evaluate bigrams model"
$ git tag -a "bigrams-experiment" -m "Bigrams experiment evaluation"

Now, we can use -T option of the dvc metrics show command to see the difference between the baseline and bigrams experiments:

$ dvc metrics show -T

baseline-experiment:
    auc.metric: 0.588426
bigrams-experiment:
    auc.metric: 0.602818

DVC provides built-in support to track and navigate JSON, TSV or CSV metric files if you want to track additional information. See dvc metrics to learn more.

🐛 Found an issue? Let us know! Or fix it:

Edit on GitHub

Have a question? Join our chat, we will help you:

Discord Chat