TensorFlow Recommenders Addons

TensorFlow Recommenders Addons(TFRA) are a collection of projects related to large-scale recommendation systems built upon TensorFlow by introducing the Dynamic Embedding Technology to TensorFlow that makes TensorFlow more suitable for training models of Search, Recommendations, and Advertising and makes building, evaluating, and serving sophisticated recommenders models easy. See approved TensorFlow RFC #313. Those contributions will be complementary to TensorFlow Core and TensorFlow Recommenders etc.

For Apple silicon(M1), please refer to Apple Silicon Support.

Main Features

• Make key-value data structure (dynamic embedding) trainable in TensorFlow
• Get better recommendation effect compared to static embedding mechanism with no hash conflicts
• Compatible with all native TensorFlow optimizers and initializers
• Compatible with native TensorFlow CheckPoint and SavedModel format
• Fully support train and inference recommenders models on GPUs
• Support TF serving and Triton Inference Server as inference framework
• Support variant Key-Value implements as dynamic embedding storage and easy to extend
  • cuckoohash_map (from Efficient Computing at Carnegie Mellon, on CPU)
  • nvhash (from NVIDIA, on GPU)
  • Redis
• Support half synchronous training based on Horovod
  • Synchronous training for dense weights
  • Asynchronous training for sparse weights

Subpackages

• tfra.dynamic_embedding, RFC
• tfra.embedding_variable, RFC

Contributors

TensorFlow Recommenders-Addons depends on public contributions, bug fixes, and documentation. This project exists thanks to all the people and organizations who contribute. [Contribute]

A special thanks to NVIDIA Merlin Team and NVIDIA China DevTech Team, who have provided GPU acceleration technology support and code contribution.

Tutorials & Demos

See tutorials and demo for end-to-end examples of each subpackages.

Installation

Stable Builds

TensorFlow Recommenders-Addons is available on PyPI for Linux, macOS. To install the latest version, run the following:

pip install tensorflow-recommenders-addons

By default, CPU version will be installed. To install GPU version, run the following:

pip install tensorflow-recommenders-addons-gpu

To use TensorFlow Recommenders-Addons:

import tensorflow as tf
import tensorflow_recommenders_addons as tfra

Compatibility with Tensorflow

TensorFlow C++ APIs are not stable and thus we can only guarantee compatibility with the version TensorFlow Recommenders-Addons(TFRA) was built against. It is possible TFRA will work with multiple versions of TensorFlow, but there is also a chance for segmentation faults or other problematic crashes. Warnings will be emitted if your TensorFlow version does not match what it was built against.

Additionally, TFRA custom ops registration does not have a stable ABI interface so it is required that users have a compatible installation of TensorFlow even if the versions match what we had built against. A simplification of this is that TensorFlow Recommenders-Addons custom ops will work with pip-installed TensorFlow but will have issues when TensorFlow is compiled differently. A typical example of this would be conda-installed TensorFlow. RFC #133 aims to fix this.

Compatibility Matrix

GPU is supported by version 0.2.0 and later.

Check nvidia-support-matrix for more details.

NOTICE

• The release packages have a strict version binding relationship with TensorFlow.
• Due to the significant changes in the Tensorflow API, we can only ensure version 0.2.0 compatibility with TF1.15.2 on CPU & GPU, but there are no official releases, you can only get it through compiling by the following:

PY_VERSION="3.7" \
TF_VERSION="1.15.2" \
TF_NEED_CUDA=1 \
sh .github/workflows/make_wheel_Linux_x86.sh

# .whl file will be created in ./wheelhouse/

• If you need to work with TensorFlow 1.14.x or older version, we suggest you give up, but maybe this doc can help you : Extract headers from TensorFlow compiling directory. At the same time, we find some OPs used by TRFA have better performance, so we highly recommend you update TensorFlow to 2.x.

Installing from Source

For all developers, we recommend you use the development docker containers which are all GPU enabled:

docker pull tfra/dev_container:latest-python3.8  # "3.7", "3.9" are all avaliable.
docker run --privileged --gpus all -it --rm -v $(pwd):$(pwd) tfra/dev_container:latest-3.8

CPU Only

You can also install from source. This requires the Bazel build system (version == 5.1.1). Please install a TensorFlow on your compiling machine, The compiler needs to know the version of Tensorflow and its headers according to the installed TensorFlow.

export TF_VERSION="2.8.3"  # "2.6.3" are well tested.
pip install tensorflow[-gpu]==$TF_VERSION

git clone https://github.com/tensorflow/recommenders-addons.git
cd recommenders-addons

# This script links project with TensorFlow dependency
python configure.py

bazel build --enable_runfiles build_pip_pkg
bazel-bin/build_pip_pkg artifacts

pip install artifacts/tensorflow_recommenders_addons-*.whl

GPU Support

Only TF_NEED_CUDA=1 is required and other environment variables are optional:

export TF_VERSION="2.8.3"  # "2.6.3" is well tested.
export PY_VERSION="3.8" 
export TF_NEED_CUDA=1
export TF_CUDA_VERSION=11.2
export TF_CUDNN_VERSION=8.1
export CUDA_TOOLKIT_PATH="/usr/local/cuda"
export CUDNN_INSTALL_PATH="/usr/lib/x86_64-linux-gnu"

python configure.py

And then build the pip package and install:

bazel build --enable_runfiles build_pip_pkg
bazel-bin/build_pip_pkg artifacts
pip install artifacts/tensorflow_recommenders_addons_gpu-*.whl

Apple Silicon Support

Requirements:

• macOS 12.0.0+
• Python 3.8 or 3.9
• tensorflow-macos 2.6.0
• bazel 4.1.0+

The natively supported TensorFlow is maintained by Apple. Please see the instruction Get started with tensorflow-metal to install the Tensorflow on apple silicon devices.

# Install TensorFlow macOS dependencies
conda install -c apple tensorflow-deps==2.6.0

# Install base TensorFlow
python -m pip install tensorflow-macos==2.6.0

If you see any issue with installing tensorflow-macos, please contact the Apple Developer Forums: tensorflow-metal for help.

Install TFRA on Apple Silicon via PIP

python -m pip install tensorflow-recommenders-addons --no-deps

Install TFRA on Apple Silicon from Source

export TF_VERSION="2.6.0"  # Specify your Tensorflow version here, 2.8.0 is well tested.
export PY_VERSION="3.8"    # Specify your python version here, "3.9" is well tested.

# Building TFRA wheel
PY_VERSION=$PY_VERSION TF_VERSION=$TF_VERSION TF_NEED_CUDA="0" sh .github/workflows/make_wheel_macOS_arm64.sh

# Install the wheel
python -m pip install --no-deps ./artifacts/*.whl

Known Issues:

The Apple silicon version of TFRA doesn't support:

• Data type float16
• Synchronous training based on Horovod
• save_to_file_system
• load_from_file_system
• warm_start_util

save_to_file_system and load_from_file_system are not supported because TFIO is not supported on apple silicon devices. Horovod and warm_start_util are not supported because the natively supported tensorflow-macos doesn't support V1 Tensorflow networks.

These issues may be fixed in the future release.

Data Type Matrix for tfra.dynamic_embedding.Variable

To use GPU by tfra.dynamic_embedding.Variable

The tfra.dynamic_embedding.Variable will ignore the device placement mechanism of TensorFlow, you should specify the devices onto GPUs explicitly for it.

import tensorflow as tf
import tensorflow_recommenders_addons as tfra

de = tfra.dynamic_embedding.get_variable("VariableOnGpu",
                                         devices=["/job:ps/task:0/GPU:0", ],
                                         # ...
                                         )

Usage restrictions on GPU

• Only work on Nvidia GPU with cuda compute capability 6.0 or higher.
• Considering the size of the .whl file, currently dim only supports less than or equal to 200, if you need longer dim, please submit an issue.
• Only dynamic_embedding APIs and relative OPs support running on GPU.
• For GPU HashTables manage GPU memory independently, TensorFlow should be configured to allow GPU memory growth by the following:

sess_config.gpu_options.allow_growth = True

Inference

With TensorFlow Serving

Compatibility Matrix

Serving TFRA-enable models by custom ops in TensorFlow Serving.

## If enable GPU OPs
export SERVING_WITH_GPU=1 

## Specifiy the branch of TFRA
export TFRA_BRANCH="master" # The `master` and `r0.6` are available.

## Create workspace, modify the directory as you prefer to.
export TFRA_SERVING_WORKSPACE=~/tfra_serving_workspace/
mkdir -p $TFRA_SERVING_WORKSPACE && cd $TFRA_SERVING_WORKSPACE

## Clone the release branches of serving and TFRA according to `Compatibility Matrix`.
git clone -b r2.8 https://github.com/tensorflow/serving.git
git clone -b $TFRA_BRANCH https://github.com/tensorflow/recommenders-addons.git

## Run config shell script
cd $TFRA_SERVING_WORKSPACE/recommenders-addons/tools
bash config_tfserving.sh $TFRA_BRANCH $TFRA_SERVING_WORKSPACE/serving $SERVING_WITH_GPU

## Build serving with TFRA OPs.
cd $TFRA_SERVING_WORKSPACE/serving
./tools/run_in_docker.sh bazel build tensorflow_serving/model_servers:tensorflow_model_server

For more detail, please refer to the shell script ./tools/config_tfserving.sh.

NOTICE

• Distributed inference is only supported when using Redis as Key-Value storage.
• Reference documents: https://www.tensorflow.org/tfx/serving/custom_op

With Triton

When building the custom operations shared library it is important to use the same version of TensorFlow as is being used in Triton. You can find the TensorFlow version in the Triton Release Notes. A simple way to ensure you are using the correct version of TensorFlow is to use the NGC TensorFlow container corresponding to the Triton container. For example, if you are using the 23.05 version of Triton, use the 23.05 version of the TensorFlow container.

docker pull nvcr.io/nvidia/tritonserver:22.05-py3

export TFRA_BRANCH="master"
git clone -b $TFRA_BRANCH https://github.com/tensorflow/recommenders-addons.git
cd recommenders-addons

python configure.py
bazel build //tensorflow_recommenders_addons/dynamic_embedding/core:_cuckoo_hashtable_ops.so ##bazel 5.1.1 is well tested
mkdir /tmp/so
#you can also use the so file from pip install package file from "(PYTHONPATH)/site-packages/tensorflow_recommenders_addons/dynamic_embedding/core/_cuckoo_hashtable_ops.so"
cp bazel-bin/tensorflow_recommenders_addons/dynamic_embedding/core/_cuckoo_hashtable_ops.so /tmp/so

#tfra saved_model directory "/models/model_repository"
docker run --net=host -v /models/model_repository:/models nvcr.io/nvidia/tritonserver:22.05-py3 bash -c \
  "LD_PRELOAD=/tmp/so/_cuckoo_hashtable_ops.so:${LD_PRELOAD} tritonserver --model-repository=/models/ --backend-config=tensorflow,version=2 --strict-model-config=false"

NOTICE

• The above LD_LIBRARY_PATH and backend-config must be set Because the default backend is tf1.

Community

• SIG Recommenders mailing list: [email protected]

Acknowledgment

We are very grateful to the maintainers of tensorflow/addons for borrowing a lot of code from tensorflow/addons to build our workflow and documentation system. We also want to extend a thank you to the Google team members who have helped with CI setup and reviews!

License

Apache License 2.0