Quickstart

Get started with Verta in 5 minutes.

1. Obtain your Verta Credentials

• Sign-up on the Verta instance setup for your organization.

• Log into the Verta Web App and visit the Profile page to find your developer key.

Note that your developer key is unique to you. As with a password, don't share it with others!

2. Setup the Verta Client

The Verta client supports Python 3.7–3.10!

# via pip
python -m pip install verta

The Verta client is installable 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. Integrate the Verta package into your workflow

1. Create a Verta client object to connect to the Verta server.

from verta import Client
client = Client(HOST, email=VERTA_EMAIL, dev_key=VERTA_DEV_KEY)

2. 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)

3. 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