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KerasTuner integration guide

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neptune.ai 2.x documentation

neptune-ai/neptune-client

Home
Home
- Changelog
- Introduction
- Neptune explained
  Neptune explained
Getting started
Getting started
- Quickstart
- Installation
  Installation
- Create a project
- Set API token and project
- Neptune tutorial
- Onboarding guide for teams
- Best practices
- Advanced configuration
  Advanced configuration
Using Neptune
Using Neptune
- Add Neptune to your code
- What you can log
  What you can log
- Runs
  Runs
  - Create a run
  - Resume a run
  - What is logged automatically
  - Groups
  - Customize
    Customize
    
    Name
    
    Identifier
    
    Color
    
    Description and other info
    
    Tags
    
    Set base namespace
    
    Set x values for series
  - Manage
    Manage
    
    Add field to existing runs
    
    Stop or abort remotely
    
    Overwrite data
    
    Copy runs between projects
    
    Back up runs
    
    Delete data
  - Advanced logging
    Advanced logging
    
    Log from multiple scripts
    
    Pass run object between functions
    
    Log to multiple runs at once
    
    Log synchronously with wait() and sync()
    
    Log series of data points to single field
    
    Offline logging
  - Experiments
  - Charts
  - Side-by-side
  - Images
  - Monitoring
  - Source code
  - Parallel coordinates
  - Artifacts
  - Dashboards
- Model metadata
  Model metadata
  - Tutorial: Track models e2e
  - Migrate model registry to runs
  - How-tos
    How-tos
    
    Log model metadata
    
    Query model metadata
    
    Manage model stage
    
    Access production-ready models
- Project metadata
- Reports
- Reproducibility
  Reproducibility
- Query & download
  Query & download
- Use case tutorials
  Use case tutorials
  - Basics: Track and organize experiments
  - Track HPO jobs
  - Track distributed jobs
  - Track sequential pipelines
  - Monitor training live
  - Visualize cross-validation results
  - Data versioning
    Data versioning
    
    Version datasets in runs
    
    Compare datasets between runs
    
    Share data versions on project level
Integrations
Integrations
- Airflow
  Airflow
  - Integration guide
  - API reference
- Altair
- Amazon SageMaker
  Amazon SageMaker
- Amazon Web Services
- argparse
- Arize
- Azure ML
- Bokeh
- CatBoost
- Colab
- DALEX
- Deepnote
- Detectron2
  Detectron2
  - Integration guide
  - API reference
- Docker
- Evidently
- fastai
  fastai
  - Integration guide
  - API reference
- GitHub Actions
- Great Expectations
- 🤗 Transformers
  🤗 Transformers
  - Integration guide
  - API reference
- Kedro
  Kedro
  - Integration guide
  - API reference
- Keras
  Keras
  - Integration guide
  - API reference
- Leaflet (Folium)
- LightGBM
  LightGBM
  - Integration guide
  - API reference
- Lightning
  Lightning
  - Integration guide
  - API reference
- Matplotlib
- MLflow
  MLflow
  - Integration guide
  - API reference
- MosaicML Composer
  MosaicML Composer
  - Integration guide
  - API reference
- Optuna
  Optuna
  - Integration guide
  - API reference
- pandas
- Plotly
- Prophet
  Prophet
  - Integration guide
  - API reference
- Python Logger
- PyTorch
  PyTorch
  - Integration guide
  - API reference
- PyTorch Ignite
  PyTorch Ignite
  - Integration guide
  - API reference
- PyTorch Lightning
- Sacred
  Sacred
  - Integration guide
  - API reference
- scikit-learn
  scikit-learn
  - Integration guide
  - API reference
- Seaborn
- skorch
  skorch
  - Integration guide
  - API reference
- TensorBoard
  TensorBoard
  - Integration guide
  - API reference
- TensorFlow
- Weights & Biases
- XGBoost
  XGBoost
  - Integration guide
  - API reference
- ZenML
- Community integrations
- Demo: Using multiple integrations together
- Deprecated integrations
  Deprecated integrations
  - Catalyst
    Catalyst
    
    Integration guide
    
    API reference
  - JupyterLab and Jupyter Notebook
    JupyterLab and Jupyter Notebook
    
    Extension overview
    
    Uploading checkpoints
    
    Viewing and comparing notebooks
    
    Uploading through CLI
  - KerasTuner
  - R
    R
    
    Overview
    
    Installation and usage
  - scikit-optimize
API reference
API reference
- Run
- management
- Index
- Field types
- Environment variables
- Modes
- sys namespace
- All packages and classes
  All packages and classes
- CLI
  CLI
- Errors and messages
  Errors and messages
Management
Management
- Invite people
- User roles
- Service accounts
- Reset API token
- SSO
- Manage subscription
- Delete data
- Projects
  Projects
Self-hosted
Self-hosted
- Upgrading to 2.5
- Default installation
- Custom installation
  Custom installation
- Administration
  Administration
  - Access admin console
  - Adding users
- Compatibility with Python packages
- Troubleshooting
- Installing older versions
  Installing older versions
  - 2.4
    2.4
    
    Upgrading
    
    Prerequisites
    
    Default installation
    
    Custom installation
    
    Helm chart reference
    
    Connect to Neptune from your code
  - 2.3
    2.3
    
    Upgrading
    
    Prerequisites
    
    Configuration
    
    Expose Neptune
    
    Load balancer configuration
    
    Install Neptune
    
    Connect to Neptune
- FAQ
Help
Help

KerasTuner integration#

Legacy integration

This integration is not supported in the new Neptune client.

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