Quickstart¶
Get started with Verta in 5 minutes.
1. Setup the Verta Server¶
You have a few options here:
- If you are on Verta Core or Verta Enterprise, you do not need to set up a Verta Server; please reach out to Verta Support for instructions for your server information.
- You are only looking to run open-source ModelDB, head over to the ModelDB repo for installation instructions.
2. Setup the Verta Client¶
The Verta client supports Python 2.7 & 3.5–3.8!
# via pip python -m pip install verta
Python 2.7 & 3.6–3.8 is supported through conda, as well!
# via conda conda install verta -c conda-forge
Following Python best practices, we recommended creating a virtual environment using venv or conda.
3. Obtain your Verta Credentials¶
On Verta Enterprise, log into the Verta Web App (e.g., https://app.verta.ai) and visit the Profile page to find your developer key.
If using ModelDB open-source, you will not require any special credentials
4. Integrate the Verta package into your workflow¶
- Create a Verta client object to connect to the Verta server.
from verta import Client client = Client(HOST, VERTA_EMAIL, VERTA_DEV_KEY)If using open-source ModelDB, leave `VERTA_EMAIL` and `VERTA_DEV_KEY` blank and set `HOST` to `localhost:3000`
- Version your models
proj = client.set_project("Fraud Detection") expt = client.set_experiment("Recurrent Neural Net")run = client.set_experiment_run("Two-Layer Dropout LSTM") run.log_hyperparameter("num_layers", 2) run.log_hyperparameter("hidden_size", 512) run.log_hyperparameter("dropout", 0.5)
- Associate metadata with your models
run.log_metric("accuracy", 0.95) run.log_tags(["experiment1"])
5. Check out your models!¶
Now that you have versioned a few models, you can interact with them in a variety of ways:
- Build dashboards on the Verta Web App based on the models
- Merge, branch, and manage all changes to your models
- Share your models and reports with your organization or publicly
- Deploy versioned models via Verta Deployment and Monitoring