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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
- Tutorials
  Tutorials
  - Query & download metadata
    Query & download metadata
    
    Query the API
    
    NQL syntax
    
    Find the ID
    
    Download from the app
    
    Download artifact metadata
  - Reproducibility
    Reproducibility
    
    Reproduce runs
    
    Restart run from checkpoint
    
    Re-run failed training
  - Data versioning
    Data versioning
    
    Version datasets in runs
    
    Compare datasets between runs
    
    Share data versions on project level
  - Track and organize experiments
  - Monitor training live
  - Visualize cross-validation results
  - Track HPO jobs
  - Track distributed jobs
  - Track sequential pipelines
  - Log to existing run or model object
- Runs
  Runs
  - Create a run
  - Resume a run
  - What is logged automatically
  - Groups
  - Customize
    Customize
    
    Name
    
    Identifier
    
    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 scripts
    
    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
- Models
  Models
  - Register a model
  - Create a model version
  - Query model metadata
  - Manage model version stage
  - Neptune runs vs models
  - Examples
    Examples
    
    Track models e2e
    
    Access production-ready models
    
    Associate model version with run
- Project metadata
- Reports
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
- neptune
- management
- utils
- Run
- Model
- ModelVersion
- Project
- Index
- Types
- Environment variables
- Modes
- sys namespace
- CLI
  CLI
- Errors and messages
  Errors and messages
Management
Management
- Invite people
- SSO
- User roles
- Service accounts
- Reset API token
- Projects
  Projects
- Manage subscription
- Delete data
Self-hosted
Self-hosted
- Default installation
- Custom installation
  Custom installation
- Installing older versions
  Installing older versions
  - 2.3
    2.3
    
    Prerequisites
    
    Configuration
    
    Expose Neptune
    
    Load balancer configuration
    
    Install Neptune
    
    Connect to Neptune
- Upgrading
- Administration
  Administration
  - Access admin console
  - Adding users
- Connect to Neptune from your code
- Compatibility with Python packages
- Troubleshooting
- FAQ
Help
Help

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