Welcome to NVTabular’s documentation!

• Introduction
  • Getting Started
    • Conda
  • Examples and Tutorials
  • Contributing
  • Learn More
• How it Works
  • Operations
  • A higher level of abstraction
  • Framework Interoperability
  • Multi-GPU Support
  • Multi-Node Support
  • MultiHot Encoding and Pre-existing Embeddings
  • CPU Support
  • Getting your data ready for NVTabular
    • Troubleshooting
• Examples
  • Criteo Example
    • Data Prep
    • Initializing the Memory Pool
    • Dataset and Dataset Schema
    • Preprocessing
    • Data Loading
    • Training
  • Rossmann Example
    • Overview
    • Preparing our dataset
    • Training a Network
    • Choose a Framework
    • TensorFlow
    • PyTorch
    • fast.ai
  • Multi-GPU Example
    • NVTabular + Dask Integration
    • Simple Multi-GPU Toy Example
    • Notes on Shuffling
  • HugeCTR Example
    • Data Prep
    • Dataset and Dataset Schema
    • Deploy a Distributed-Dask Cluster
    • Preprocessing
    • Training
  • Outbrain Example
    • Overview
    • Preparing Our Dataset
    • Initiate NVTabular Workflow
    • Feature Engineering
    • Training a TF W&D Model
  • RecSys2020 Example
    • Overview
    • Getting Started
    • Preparing our dataset
    • Splitting dataset into training and test
    • Feature Engineering
    • Training our model
• API Documentation
  • Workflow
  • Dataset
  • Operators
    • Categorify
    • Clip
    • ColumnSimilarity
    • DifferenceLag
    • Dropna
    • FillMissing
    • FillMedian
    • Filter
    • HashBucket
    • HashedCross
    • JoinExternal
    • JoinGroupby
    • LambdaOp
    • LogOp
    • Moments
    • Median
    • MinMax
    • Normalize
    • NormalizeMinMax
    • TargetEncoding
  • Torch Dataloader
  • Torch Layers
  • TensorFlow Dataloader
  • TensorFlow Layers

Indices and tables

• Index

• Module Index

• Search Page