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
$ 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
$ 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
metric files if you want to track additional information. See
dvc metrics to