v0.5.2

Bug Fixes

• Fix Movielens TF example running on 1080ti #792
• Fix Multihot output from get_embedding_sizes #808
• Fix accelerated training documentation #791

v0.5.1

Improvements

• Update dependencies to use cudf 0.19
• Removed conda from docker containers, leading to much smaller container sizes
• Added CUDA 11.2 support
• Added FastAI v2.3 support

Bug Fixes

• Fix NVTabular preprocessing with HugeCTR inference

v0.5.0

Improvements

• Adding Horovod integration to NVTabular's dataloaders, allowing you to use multiple GPU's to train TensorFlow and PyTorch models
• Adding a Groupby operation for use with session based recommender models
• Added ability to read and write datasets partitioned by a column
• Add example notebooks for using Triton Inference Server with NVTabular
• Restructure and simplify Criteo example notebooks
• Add support for PyTorch inference with Triton Inference Server

Bug Fixes

• Fix bug with preprocessing categorical columns with NVTabular not working with HugeCTR and Triton Inference Server #707

v0.4.0

Breaking Changes

• The API for NVTabular has been signficantly refactored, and existing code targetting the 0.3 API will need to be updated.
  Workflows are now represented as graphs of operations, and applied using a sklearn 'transformers' style api. Read more by
  checking out the examples

Improvements

• Triton integration support for NVTabular with TensorFlow and HugeCTR models
• Recommended cloud configuration and support for AWS and GCP
• Reorganized examples and documentation
• Unified Docker containers for Merlin components (NVTabular, HugeCTR and Triton)
• Dataset analysis and generation tools

v0.3.0

Don't pin conda python version to x.x (#460)

explicitly state that we work with python 3.7+