• About
  • Get Started
  • Guides
  • ValidMind Library
    • ValidMind Library
    • Supported Models
    • QuickStart Notebook

    • TESTING
    • Run Tests & Test Suites
    • Test Descriptions
    • Test Sandbox (BETA)

    • CODE SAMPLES
    • All Code Samples · LLM · NLP · Time Series · Etc.
    • Download Code Samples · notebooks.zip
    • Try it on JupyterHub

    • REFERENCE
    • ValidMind Library Python API
  • Support
  • Training
  • Releases
  • Documentation
    • About ​ValidMind
    • Get Started
    • Guides
    • Support
    • Releases

    • Python Library
    • ValidMind Library

    • ValidMind Academy
    • Training Courses
  • Log In
    • Public Internet
    • ValidMind Platform · US1
    • ValidMind Platform · CA1

    • Private Link
    • Virtual Private ValidMind (VPV)

    • Which login should I use?
  1. Store model credentials in .env files

EU AI Act Compliance — Read our original regulation brief on how the EU AI Act aims to balance innovation with safety and accountability, setting standards for responsible AI use

  • ValidMind Library
  • Supported models

  • QuickStart
  • Quickstart for model documentation
  • Install and initialize ValidMind Library
  • Store model credentials in .env files

  • Model Development
  • 1 — Set up ValidMind Library
  • 2 — Start model development process
  • 3 — Integrate custom tests
  • 4 — Finalize testing & documentation

  • Model Validation
  • 1 — Set up ValidMind Library for validation
  • 2 — Start model validation process
  • 3 — Developing a challenger model
  • 4 — Finalize validation & reporting

  • Model Testing
  • Run tests & test suites
    • Add context to LLM-generated test descriptions
    • Configure dataset features
    • Document multiple results for the same test
    • Explore test suites
    • Explore tests
    • Dataset Column Filters when Running Tests
    • Load dataset predictions
    • Log metrics over time
    • Run individual documentation sections
    • Run documentation tests with custom configurations
    • Run tests with multiple datasets
    • Intro to Unit Metrics
    • Understand and utilize RawData in ValidMind tests
    • Introduction to ValidMind Dataset and Model Objects
    • Run Tests
      • Run dataset based tests
      • Run comparison tests
  • Test descriptions
    • Data Validation
      • ACFandPACFPlot
      • ADF
      • AutoAR
      • AutoMA
      • AutoStationarity
      • BivariateScatterPlots
      • BoxPierce
      • ChiSquaredFeaturesTable
      • ClassImbalance
      • DatasetDescription
      • DatasetSplit
      • DescriptiveStatistics
      • DickeyFullerGLS
      • Duplicates
      • EngleGrangerCoint
      • FeatureTargetCorrelationPlot
      • HighCardinality
      • HighPearsonCorrelation
      • IQROutliersBarPlot
      • IQROutliersTable
      • IsolationForestOutliers
      • JarqueBera
      • KPSS
      • LaggedCorrelationHeatmap
      • LJungBox
      • MissingValues
      • MissingValuesBarPlot
      • MutualInformation
      • PearsonCorrelationMatrix
      • PhillipsPerronArch
      • ProtectedClassesCombination
      • ProtectedClassesDescription
      • ProtectedClassesDisparity
      • ProtectedClassesThresholdOptimizer
      • RollingStatsPlot
      • RunsTest
      • ScatterPlot
      • ScoreBandDefaultRates
      • SeasonalDecompose
      • ShapiroWilk
      • Skewness
      • SpreadPlot
      • TabularCategoricalBarPlots
      • TabularDateTimeHistograms
      • TabularDescriptionTables
      • TabularNumericalHistograms
      • TargetRateBarPlots
      • TimeSeriesDescription
      • TimeSeriesDescriptiveStatistics
      • TimeSeriesFrequency
      • TimeSeriesHistogram
      • TimeSeriesLinePlot
      • TimeSeriesMissingValues
      • TimeSeriesOutliers
      • TooManyZeroValues
      • UniqueRows
      • WOEBinPlots
      • WOEBinTable
      • ZivotAndrewsArch
      • Nlp
        • CommonWords
        • Hashtags
        • LanguageDetection
        • Mentions
        • PolarityAndSubjectivity
        • Punctuations
        • Sentiment
        • StopWords
        • TextDescription
        • Toxicity
    • Model Validation
      • BertScore
      • BleuScore
      • ClusterSizeDistribution
      • ContextualRecall
      • FeaturesAUC
      • MeteorScore
      • ModelMetadata
      • ModelPredictionResiduals
      • RegardScore
      • RegressionResidualsPlot
      • RougeScore
      • TimeSeriesPredictionsPlot
      • TimeSeriesPredictionWithCI
      • TimeSeriesR2SquareBySegments
      • TokenDisparity
      • ToxicityScore
      • Embeddings
        • ClusterDistribution
        • CosineSimilarityComparison
        • CosineSimilarityDistribution
        • CosineSimilarityHeatmap
        • DescriptiveAnalytics
        • EmbeddingsVisualization2D
        • EuclideanDistanceComparison
        • EuclideanDistanceHeatmap
        • PCAComponentsPairwisePlots
        • StabilityAnalysisKeyword
        • StabilityAnalysisRandomNoise
        • StabilityAnalysisSynonyms
        • StabilityAnalysisTranslation
        • TSNEComponentsPairwisePlots
      • Ragas
        • AnswerCorrectness
        • AspectCritic
        • ContextEntityRecall
        • ContextPrecision
        • ContextPrecisionWithoutReference
        • ContextRecall
        • Faithfulness
        • NoiseSensitivity
        • ResponseRelevancy
        • SemanticSimilarity
      • Sklearn
        • AdjustedMutualInformation
        • AdjustedRandIndex
        • CalibrationCurve
        • ClassifierPerformance
        • ClassifierThresholdOptimization
        • ClusterCosineSimilarity
        • ClusterPerformanceMetrics
        • CompletenessScore
        • ConfusionMatrix
        • FeatureImportance
        • FowlkesMallowsScore
        • HomogeneityScore
        • HyperParametersTuning
        • KMeansClustersOptimization
        • MinimumAccuracy
        • MinimumF1Score
        • MinimumROCAUCScore
        • ModelParameters
        • ModelsPerformanceComparison
        • OverfitDiagnosis
        • PermutationFeatureImportance
        • PopulationStabilityIndex
        • PrecisionRecallCurve
        • RegressionErrors
        • RegressionErrorsComparison
        • RegressionPerformance
        • RegressionR2Square
        • RegressionR2SquareComparison
        • RobustnessDiagnosis
        • ROCCurve
        • ScoreProbabilityAlignment
        • SHAPGlobalImportance
        • SilhouettePlot
        • TrainingTestDegradation
        • VMeasure
        • WeakspotsDiagnosis
      • Statsmodels
        • AutoARIMA
        • CumulativePredictionProbabilities
        • DurbinWatsonTest
        • GINITable
        • KolmogorovSmirnov
        • Lilliefors
        • PredictionProbabilitiesHistogram
        • RegressionCoeffs
        • RegressionFeatureSignificance
        • RegressionModelForecastPlot
        • RegressionModelForecastPlotLevels
        • RegressionModelSensitivityPlot
        • RegressionModelSummary
        • RegressionPermutationFeatureImportance
        • ScorecardHistogram
    • Ongoing Monitoring
      • CalibrationCurveDrift
      • ClassDiscriminationDrift
      • ClassificationAccuracyDrift
      • ClassImbalanceDrift
      • ConfusionMatrixDrift
      • CumulativePredictionProbabilitiesDrift
      • FeatureDrift
      • PredictionAcrossEachFeature
      • PredictionCorrelation
      • PredictionProbabilitiesHistogramDrift
      • PredictionQuantilesAcrossFeatures
      • ROCCurveDrift
      • ScoreBandsDrift
      • ScorecardHistogramDrift
      • TargetPredictionDistributionPlot
    • Prompt Validation
      • Bias
      • Clarity
      • Conciseness
      • Delimitation
      • NegativeInstruction
      • Robustness
      • Specificity
  • Test sandbox beta

  • Notebooks
  • Code samples
    • Capital Markets
      • Quickstart for knockout option pricing model documentation
      • Quickstart for Heston option pricing model using QuantLib
    • Credit Risk
      • Document an application scorecard model
      • Document an application scorecard model
      • Document an application scorecard model
      • Document a credit risk model
      • Document an application scorecard model
    • Custom Tests
      • Implement custom tests
      • Integrate external test providers
    • Model Validation
      • Validate an application scorecard model
    • Nlp and Llm
      • Sentiment analysis of financial data using a large language model (LLM)
      • Summarization of financial data using a large language model (LLM)
      • Sentiment analysis of financial data using Hugging Face NLP models
      • Summarization of financial data using Hugging Face NLP models
      • Automate news summarization using LLMs
      • Prompt validation for large language models (LLMs)
      • RAG Model Benchmarking Demo
      • RAG Model Documentation Demo
    • Ongoing Monitoring
      • Ongoing Monitoring for Application Scorecard
      • Quickstart for ongoing monitoring of models with ValidMind
    • Regression
      • Document a California Housing Price Prediction regression model
    • Time Series
      • Document a time series forecasting model
      • Document a time series forecasting model

  • Reference
  • ValidMind Library Python API

On this page

  • Why is this recommended?
  • Prerequisites
  • Steps
  • What's next
  • Edit this page
  • Report an issue

Store model credentials in .env files

Published

May 12, 2025

Learn how to store model identifier credentials in a .env file instead of using inline credentials. This topic is relevant for model developers who want to follow best practices for security when running notebooks.

Why is this recommended?

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.

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

Prerequisites

1 Register models in the inventory

2 Manage permissions

Steps

  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. Locate 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. Locate the code snippet and click Copy snippet to clipboard.
  3. Paste the credentials into your .env file in the following format:

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

3 Working with the model inventory

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",
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
  1. Insert this code snippet above your model identifier credentials:

    %load_ext dotenv
    %dotenv dev.env
  2. Remove the inline credentials from vm.init().[^4]

    The updated code cell should now look like this:

    %load_ext dotenv
    %dotenv .env
    
    import validmind as vm
    
    vm.init(
    )

To enable monitoring when you are storing credentials in an .env file:4

%load_ext dotenv
%dotenv .env

import validmind as vm

vm.init(
   monitoring=True
)

4 Enable monitoring

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

What's next

  • Working with model documentation
  • ValidMind Library
Install and initialize ValidMind Library

© Copyright 2025 ValidMind Inc. All Rights Reserved.

  • Edit this page
  • Report an issue
Cookie Preferences
  • validmind.com

  • Privacy Policy

  • Terms of Use