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 make TensorFlow more suitable for trainning models of Search, Recommendations and Advertising. These projects are contributed and maintained by the community. Those contributions will be complementary to TensorFlow Core and TensorFlow Recommenders etc.
See approved TensorFlow RFC #313.
TensorFlow has open-sourced TensorFlow Recommenders (github.com/tensorflow/recommenders), an open-source TensorFlow package that makes building, evaluating, and serving sophisticated recommender models easy.
Further, this repo is maintained by TF SIG Recommenders (recommenders@tensorflow.org) for community contributions. SIG Recommenders can contributes more addons as complementary to TensorFlow Recommenders, or any helpful libraries related to recommendation systems using TensorFlow. The contribution areas can be broad and don't limit to the topic listed below:
- Training with scale: How to train from super large sparse features? How to deal with dynamic embedding and key-value parameters?
- Serving with efficiency: Given recommendation models are usually pretty large, how to serve super large models easily, and how to serve efficiently?
- Modeling with SoTA techniques: online learning, multi-target learning, deal with quality inconsistent among online and offline, model understandability, GNN etc.
- End-to-end pipeline: how to train continuously, e.g. integrate with platforms like TFX.
- Vendor specific extensions and platform integrations: for example, runtime specific frameworks (e.g. NVIDIA Merlin, …), and integrations with Cloud services (e.g. GCP, AWS, Azure…)
See docs/tutorials/ for end-to-end examples of each subpackages.
We adopt proxy maintainership as in TensorFlow Recommenders-Addons:
Projects and subpackages are compartmentalized and each is maintained by those with expertise and vested interest in that component.
Subpackage maintainership will only be granted after substantial contribution has been made in order to limit the number of users with write permission. Contributions can come in the form of issue closings, bug fixes, documentation, new code, or optimizing existing code. Submodule maintainership can be granted with a lower barrier for entry as this will not include write permissions to the repo.
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 tfraTensorFlow 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.
GPU is supported from version 0.2.0
| TFRA | TensorFlow | Compiler | CUDA | CUDNN | Compute Capability |
|---|---|---|---|---|---|
| 0.3.0 | 2.5.1 | GCC 7.3.1 | 11.2 | 8.1 | 6.0, 6.1, 7.0, 7.5, 8.0, 8.6 |
| 0.2.0 | 2.4.1 | GCC 7.3.1 | 11.0 | 8.0 | 6.0, 6.1, 7.0, 7.5, 8.0 |
| 0.2.0 | 1.15.2 | GCC 7.3.1 | 10.0 | 7.6 | 6.0, 6.1, 7.0, 7.5 |
| 0.1.0 | 2.4.1 | GCC 7.3.1 | - | - | - |
Check nvidia-support-matrix for more details.
NOTICE
- The release packages have 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="3.6" \
TF_VERSION="1.15.2" \
TF_NEED_CUDA=1 \
sh .github/workflows/make_wheel_Linux.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 dirctory. At the same time, we find some OPs used by TRFA have better performance, so we highly recommend you update TensorFlow to 2.x.
You can also install from source. This requires the Bazel build system (version == 3.7.2). 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=["2.5.1", "2.4.1", "1.15.2"]
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
Only TF_NEED_CUDA=1 is required and other environment variables are optional:
PY_VERSION="3.7" \
TF_NEED_CUDA=1 \
TF_CUDA_VERSION=11.2 \
TF_CUDNN_VERSION=8.1 \
CUDA_TOOLKIT_PATH="/usr/local/cuda" \
CUDNN_INSTALL_PATH="/usr/lib/x86_64-linux-gnu" \
python configure.pyAnd then build the pip package and install:
bazel build --enable_runfiles build_pip_pkg
TF_NEED_CUDA=1 bazel-bin/build_pip_pkg artifacts
pip install artifacts/tensorflow_recommenders_addons_gpu-*.whl| Values \ Keys | int64 | int32 | string |
|---|---|---|---|
| float | CPU, GPU | CPU | CPU |
| half | CPU, GPU | - | CPU |
| int32 | CPU, GPU | CPU | CPU |
| int8 | CPU, GPU | - | CPU |
| int64 | CPU | - | CPU |
| double | CPU, CPU | CPU | CPU |
| bool | - | - | CPU |
| string | CPU | - | - |
The tfra.dynamic_embedding.Variable will ignore the device placement mechanism of TensorFlow,
you should specify the devices onto GPUs explictly 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
dimonly supports less than or equal to 200, if you need longerdim, please submit an issue. - Only
dynamic_embeddingAPIs 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| TFRA | TensorFlow | Serving | Compiler | CUDA | CUDNN | Compute Capability |
|---|---|---|---|---|---|---|
| 0.3.0 | 2.5.1 | 2.5.2 | GCC 7.3.1 | 11.2 | 8.1 | 6.0, 6.1, 7.0, 7.5, 8.0, 8.6 |
| 0.2.0 | 2.4.1 | 2.4.0 | GCC 7.3.1 | 11.0 | 8.0 | 6.0, 6.1, 7.0, 7.5, 8.0 |
| 0.2.0 | 1.15.2 | 1.15.0 | GCC 7.3.1 | 10.0 | 7.6 | 6.0, 6.1, 7.0, 7.5 |
| 0.1.0 | 2.4.1 | 2.4.0 | GCC 7.3.1 | - | - | - |
NOTICE：Reference documents: https://www.tensorflow.org/tfx/serving/custom_op
When compiling, set the environment variable:
export FOR_TF_SERVING = "1"
Tensorflow Serving modification(model_servers/BUILD):
SUPPORTED_TENSORFLOW_OPS = if_v2([]) + if_not_v2([
"@org_tensorflow//tensorflow/contrib:contrib_kernels",
"@org_tensorflow//tensorflow/contrib:contrib_ops_op_lib",
]) + [
"@org_tensorflow_text//tensorflow_text:ops_lib",
"//tensorflow_recommenders_addons/dynamic_embedding/core:_cuckoo_hashtable_ops.so",
"//tensorflow_recommenders_addons/dynamic_embedding/core:_math_ops.so",
]
TensorFlow Recommenders-Addons is a community-led open source project. As such, the project depends on public contributions, bug fixes, and documentation. This project adheres to TensorFlow's Code of Conduct.
Please follow up the contributing guide for more details.
- SIG Recommenders mailing list: recommenders@tensorflow.org
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.
Apache License 2.0