Nvtabular

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, allowing
- 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](https://github.com/NVIDIA/NVTabular/issues/707)

Breaking Changes

- The API for NVTabular has been significantly refactored, and existing code targeting 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](https://nvidia-merlin.github.io/NVTabular/v0.4.0/examples/index.html)

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

Improvements

- Add MultiHot categorical support for both preprocessing and dataloading
- Add support for pretrained embeddings to the dataloaders
- Add a Recsys2020 competition example notebook
- Add ability to automatically map tensorflow feature columns to a NVTabular workflow
- Multi-Node support

Improvements

- Add Multi-GPU support using Dask-cuDF
- Add support for reading datasets from S3, GCS and HDFS
- Add 11 new operators: ColumnSimilarity, Dropna, Filter, FillMedian, HashBucket, JoinGroupBy, JoinExternal, LambdaOp, NormalizeMinMax, TargetEncoding and DifferenceLag
- Add HugeCTR integration and an example notebook showing an end to end workflow
- Signicantly faster dataloaders featuring a unified backend between TensorFlow and PyTorch

Improvements

- Switch to using the release version of cudf 0.14

Bug Fixes

- Fix PyTorch dataloader for compatibility with deep learning examples
- Fix FillMissing operator with constant fill
- Fix missing yaml dependency on conda install
- Fix get_emb_sz off-by-one error

- Initial public release of NVTabular