Welcome to NVTabular’s documentation!

• Introduction
  • Benefits
  • Performance
  • Installation
    • Installing NVTabular Using Conda
    • Installing NVTabular with Docker
  • Notebook Examples and Tutorials
  • Feedback and Support
• Core Features
  • TensorFlow and PyTorch Interoperability
  • HugeCTR Interoperability
  • Multi-GPU Support
  • Multi-Node Support
  • Multi-Hot Encoding and Pre-Existing Embeddings
  • Shuffling Datasets
  • Cloud Integration
• Accelerated Training
  • TensorFlow
  • PyTorch
  • HugeCTR
• Examples
  • Getting Started with Movielens
    • Download and Convert
    • ETL with NVTabular
    • Training with TensorFlow
    • Training with PyTorch
  • Advanced Ops with Outbrain
    • Download and Convert
    • ETL with NVTabular
    • Training with TensorFlow
  • Scaling to Large Datasets with Criteo
    • Download and Convert
    • ETL with NVTabular
    • Training with TensorFlow
    • Training with HugeCTR
    • Training with FastAI
    • Triton Inference with TensorFlow
    • Triton Inference with HugeCTR
  • Winning Solution of the RecSys2020 Competition
    • Overview
    • Getting Started
    • Preparing our dataset
    • Splitting dataset into training and test
    • Feature Engineering
    • Training our model
  • Tabular Data with Rossmann
    • Download and Convert
    • ETL with NVTabular
    • Training with TensorFlow
    • Training with PyTorch
    • Training with FastAI
  • Multi-GPU Example
    • NVTabular + Dask Integration
    • Simple Multi-GPU Toy Example
    • Notes on Shuffling
• API Documentation
  • Workflow
  • Dataset
  • Operators
    • Bucketize
    • Categorify
    • Clip
    • ColumnSimilarity
    • DifferenceLag
    • Dropna
    • FillMissing
    • FillMedian
    • Filter
    • Groupby
    • HashBucket
    • HashedCross
    • JoinExternal
    • JoinGroupby
    • LambdaOp
    • ListSlice
    • LogOp
    • Normalize
    • NormalizeMinMax
    • Rename
    • TargetEncoding
    • Operator
    • StatOperator
  • ColumnGroup
  • Torch Dataloader
  • Torch Layers
  • TensorFlow Dataloader
  • TensorFlow Layers
• Additional Resources
  • Architecture
    • Operations
    • A Higher Level of Abstraction
  • Cloud Integration
    • Amazon Web Services
    • Google Cloud Platform
  • Troubleshooting
    • Checking the Schema of the Parquet File
    • Setting the Row Group Size for the Parquet Files
  • Github Repo

Indices and tables

• Index

• Module Index

• Search Page