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0.5.1 Final Release tasks (#787)
* Update changelog * Update conda environments/recipes to cudf 0.19 * Update examples notebooks and docker compose files to reference the 0.5.1 docker containers * Remove 'source activate merlin' from example notebooks/ READMES
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‎CHANGELOG.md‎

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# NVTabular v0.5.1 (4 May 2021)
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## Improvements
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* Update dependencies to use cudf 0.19
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* Removed conda from docker containers, leading to much smaller container sizes
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* Added CUDA 11.2 support
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* Added FastAI v2.3 support
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## Bug Fixes
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* Fix NVTabular preprocessing with HugeCTR inference
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# NVTabular v0.5.0 (13 April 2021)
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## Improvements

‎conda/environments/nvtabular_dev_cuda10.1.yml‎

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channels:
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- rapidsai
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- nvidia
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- rapidsai-nightly
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- fastai
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- pytorch
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- conda-forge
@@ -10,15 +11,15 @@ dependencies:
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- clang=8.0.1
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- clang-tools=8.0.1
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- cupy>=7.2.0,<9.0.0a
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- cudf>=0.18.*
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- dask-cudf>=0.18.*
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- dask-cuda>=0.18.*
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- rmm>=0.18.*
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- cudf>=0.19.*
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- dask-cudf>=0.19.*
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- dask-cuda>=0.19.*
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- rmm>=0.19.*
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- cmake>=3.12
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- cmake_setuptools>=0.1.3
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- python>=3.7
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- numba>=0.49.0,!=0.51.0
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- pandas>=1.0,<1.2.0dev0
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- numba>=0.49.0
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- pandas>=1.0,<=1.2.4
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- pyarrow=1.0.1
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- fastavro>=0.22.9
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- notebook>=0.5.0
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- black
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- isort
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- pre_commit
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- dask>=2.22.0
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- distributed>=2.22.0
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- dask==2021.4.0
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- distributed>=2.22.0,<=2021.4.0
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- streamz
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- dlpack
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- arrow-cpp=1.0.1

‎conda/environments/nvtabular_dev_cuda10.2.yml‎

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channels:
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- rapidsai
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- nvidia
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- rapidsai-nightly
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- fastai
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- pytorch
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- conda-forge
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- clang=8.0.1
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- clang-tools=8.0.1
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- cupy>=7.2.0,<9.0.0a
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- cudf>=0.18.*
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- dask-cudf>=0.18.*
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- dask-cuda>=0.18.*
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- rmm>=0.18.*
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- cudf>=0.19.*
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- dask-cudf>=0.19.*
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- dask-cuda>=0.19.*
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- rmm>=0.19.*
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- cmake>=3.12
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- cmake_setuptools>=0.1.3
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- python>=3.7
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- numba>=0.49.0,!=0.51.0
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- pandas>=1.0,<1.2.0dev0
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- numba>=0.49.0
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- pandas>=1.0,<=1.2.4
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- pyarrow=1.0.1
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- fastavro>=0.22.9
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- notebook>=0.5.0
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- black
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- isort
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- pre_commit
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- dask>=2.22.0
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- distributed>=2.22.0
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- dask==2021.4.0
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- distributed>=2.22.0,<=2021.4.0
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- streamz
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- dlpack
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- arrow-cpp=1.0.1

‎conda/environments/nvtabular_dev_cuda11.0.yml‎

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channels:
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- rapidsai
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- nvidia
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- rapidsai-nightly
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- fastai
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- pytorch
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- conda-forge
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- clang=8.0.1
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- clang-tools=8.0.1
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- cupy>=7.2.0,<9.0.0a
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- cudf>=0.18.*
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- dask-cudf>=0.18.*
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- dask-cuda>=0.18.*
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- rmm>=0.18.*
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- cudf>=0.19.*
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- dask-cudf>=0.19.*
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- dask-cuda>=0.19.*
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- rmm>=0.19.*
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- cmake>=3.12
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- cmake_setuptools>=0.1.3
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- python>=3.7
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- numba>=0.49.0,!=0.51.0
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- pandas>=1.0,<1.2.0dev0
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- numba>=0.49.0
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- pandas>=1.0,<=1.2.4
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- pyarrow=1.0.1
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- fastavro>=0.22.9
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- notebook>=0.5.0
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- black
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- isort
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- pre_commit
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- dask>=2.22.0
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- distributed>=2.22.0
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- dask==2021.4.0
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- distributed>=2.22.0,<=2021.4.0
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- streamz
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- dlpack
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- arrow-cpp=1.0.1

‎conda/environments/nvtabular_dev_cuda11.2.yml‎

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name: nvtabular_dev_11.2
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channels:
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- rapidsai
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- nvidia
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- rapidsai-nightly
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- fastai
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- pytorch
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- conda-forge
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- defaults
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dependencies:
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- clang=8.0.1
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- clang-tools=8.0.1
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- cupy>=7.2.0,<9.0.0a
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- cudf>=0.19.*
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- dask-cudf>=0.19.*
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- dask-cuda>=0.19.*
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- rmm>=0.19.*
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- cmake>=3.12
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- cmake_setuptools>=0.1.3
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- python>=3.7
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- numba>=0.49.0,!=0.51.0
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- pandas>=1.0,<1.3.0dev0
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- pyarrow=1.0.1
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- fastavro>=0.22.9
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- notebook>=0.5.0
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- nvtx>=0.2.1
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- cython>=0.29,<0.30
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- fsspec>=0.6.0
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- scikit-learn
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- pytest
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- sphinx
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- sphinx_rtd_theme
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- sphinxcontrib-websupport
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- nbsphinx
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- numpydoc
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- ipython
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- recommonmark
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- pandoc=<2.0.0
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- cudatoolkit=11.2
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- pip
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- partd
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- flake8
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- black
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- isort
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- pre_commit
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- dask==2021.4.0
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- distributed>=2.22.0,<=2021.4.0
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- streamz
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- dlpack
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- arrow-cpp=1.0.1
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- boost-cpp
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- double-conversion
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- rapidjson
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- flatbuffers
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- hypothesis
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- pytorch
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- fastai
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- pip:
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- tensorflow-gpu
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- graphviz
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- sphinx-markdown-tables
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- git+https://github.com/dask/dask.git
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- git+https://github.com/dask/distributed.git
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- git+https://github.com/python-streamz/streamz.git

‎conda/recipes/meta.yaml‎

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build:
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- python x.x
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- setuptools
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- cudf>=0.18.0
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- cudf>=0.19.0
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run:
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- python>=3.7.0
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- cudf>=0.18.0
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- dask-cudf>=0.18.0
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- cudf>=0.19.0
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- dask-cudf>=0.19.0
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- cupy>=7.2.0,<9.0.0a
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- dask>=2.22.0
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- distributed>=2.22.0
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- dask==2021.4.0
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- distributed>=2.22.0,<=2021.4.0
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- PyYAML>=5.3
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- nvtx>=0.2.1
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‎docs/source/training/index.rst‎

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.. toctree::
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:maxdepth: 2
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Tensorflow <tensorflow>
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TensorFlow <tensorflow>
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PyTorch <pytorch>
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HugeCTR <hugectr>

‎examples/README.md‎

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```
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2. Activate the ```merlin``` conda environment by running the following command:
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```
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root@2efa5b50b909: source activate merlin
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```
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You should receive the following response, which indicates that the environment has been activated:
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```
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(merlin)root@2efa5b50b909:
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```
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3. Install jupyter-lab with `conda` or `pip` by running the following command:
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2. Install jupyter-lab with `conda` or `pip` by running the following command:
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```
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pip install jupyterlab
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```
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For more information, see [Installation Guide](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html).
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4. Start the jupyter-lab server by running the following command:
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3. Start the jupyter-lab server by running the following command:
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```
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jupyter-lab --allow-root --ip='0.0.0.0' --NotebookApp.token='<password>'
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```
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5. Open any browser to access the jupyter-lab server using <MachineIP>:8888.
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4. Open any browser to access the jupyter-lab server using <MachineIP>:8888.
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6. Once in the server, navigate to the ```/nvtabular/``` directory and try out the examples.
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5. Once in the server, navigate to the ```/nvtabular/``` directory and try out the examples.

‎examples/getting-started-movielens/04a-Triton-Inference-with-TF.ipynb‎

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"metadata": {},
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"source": [
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"```\n",
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"docker run -it --gpus device=0 -p 8000:8000 -p 8001:8001 -p 8002:8002 -v ${PWD}:/model/ nvcr.io/nvidia/merlin/merlin-inference:0.5\n",
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"```\n",
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"\n",
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"Once the container launches, activate the `merlin` environment:\n",
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"```\n",
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"source activate merlin\n",
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"```"
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"docker run -it --gpus device=0 -p 8000:8000 -p 8001:8001 -p 8002:8002 -v ${PWD}:/model/ nvcr.io/nvidia/merlin/merlin-inference:0.5.1\n",
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"```\n"
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},
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{

‎examples/getting-started-movielens/inference-HugeCTR/README.md‎

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```
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Activate the merlin conda environment by running the following command:
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```
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root@2efa5b50b909: source activate merlin
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```
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You should receive the following response, indicating that the environment has been activated:
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```
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(merlin)root@2efa5b50b909:
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```
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1) Start the jupyter-lab server by running the following command. In case the container does not have `JupyterLab`, you can easily [install](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html) it either using conda or pip.
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```
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jupyter-lab --allow-root --ip='0.0.0.0' --NotebookApp.token='<password>'
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root@02d56ff0738f:/opt/tritonserver#
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```
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```
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root@02d56ff0738f:/opt/tritonserver# source activate merlin
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```
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3) Your saved model should be in the `/model` directory. Navigate to the `model` working directory inside the triton server container to check the saved models:
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```
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cd /model

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