Store model credentials in .env files

Learn how to store model identifier credentials in an .env file instead of using inline credentials, allowing you to follow best practices for security when running Jupyter Notebooks.

NoneWhy is it recommended to store credentials in an .env file?

Storing credentials in a .env file is considered a best practice for security. Embedding credentials directly within the code makes them more susceptible to accidental exposure when sharing code or collaborating on models.

Keeping model credentials in a separate file also allows for precise access control and ensures that sensitive credentials are not publicly accessible.

Prerequisites

• You are logged in to ValidMind.
• The model is registered in the model inventory.¹
• You are the model owner, model validator, or another model stakeholder with sufficient permissions to perform the tasks in this guide.²

¹ Register models in the inventory

² Manage model stakeholders

Before you follow the steps on this page, we recommend that you first complete our introductory guide on how to Install and initialize the ValidMind Library.

1. Store credentials in an .env file

a. Get your code snippet

In the ValidMind Platform, retrieve the code snippet for your model:

1. In the left sidebar, click Inventory.

2. Select a model by clicking on it or find your model by applying a filter or searching for it.³

3. In the left sidebar that appears for your model, click Getting Started.

4. Select the document you want to automatically upload test results to.⁴

5. Click Copy snippet to clipboard.

³ Working with the model inventory

⁴ Working with model documents

b. Create an .env file

1. Create a new file in the same folder as your notebook and name it .env.

   This is a hidden file, so you may need to change your settings to view it.

2. Use the code snippet from your clipboard to build the credentials in your .env file in the following format:⁵

   VM_API_HOST=<api_host>
   VM_API_KEY=<api_key>
   VM_API_SECRET=<api_secret>
   VM_API_MODEL=<model>

⁵ For example, if your credentials look like this:

import validmind as vm

vm.init(
api_host = "https://api.prod.validmind.ai/api/v1/tracking",
api_key = "key123",
api_secret = "secret456",
document = "document-key",
model = "model789"
)

Then your .env file should look like this:

VM_API_HOST=https://api.prod.validmind.ai/api/v1/tracking
VM_API_KEY=key123
VM_API_SECRET=secret456
VM_API_MODEL=model789

To define the document to connect the ValidMind Library to, or to enable monitoring, include these in your vm.init() snippet within your Jupyter Notebook.⁶

⁶ Use credentials in your Jupyter Notebook

2. Use credentials in your Jupyter Notebook

1. After installing the ValidMind Library within your Jupyter Notebook,⁷ insert this code snippet in a code cell above your model identifier credentials:

   %load_ext dotenv
   %dotenv dev.env

2. Remove the inline credentials from vm.init() after importing the library:

   Without ongoing monitoring

   Example updated code cell:

   %load_ext dotenv
   %dotenv .env
   
   import validmind as vm
   
   vm.init(
      document="document-key",
   )

   With ongoing monitoring

   Example updated code cell including ongoing monitoring:⁸

   %load_ext dotenv
   %dotenv .env
   
   import validmind as vm
   
   vm.init(
      document="monitoring",
      monitoring=True
   )

   NoneA template must already be applied to your selected document to populate test results in the ValidMind Platform.

   Selecting a document to connect to requires ValidMind Library version >=2.12.0.⁹ Attempting to initialize the library with a document that does not have a template applied will result in an error.

   ⁹ Install and initialize the ValidMind Library

3. Run the cell.

   Instead of using inline credentials, this code cell will now load your model credentials from a .env file.

⁷ Install and initialize the ValidMind Library

⁸ Enable monitoring

What's next