TensorFlow Recommenders Addons are a collection of projects related to large-scale recommendation systems built upon TensorFlow. They 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?
• 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…)

• RFC: Dynamic Embedding
• RFC: Embedding Variable

• tfra.dynamic_embedding
• tfra.embedding_variable

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

To ensure you have a version of TensorFlow that is compatible with TensorFlow Recommenders-Addons, you can specify the tensorflow extra requirement during install:

pip install tensorflow-recommenders-addons[tensorflow]

Similar extras exist for the tensorflow-gpu and tensorflow-cpu packages.

To use TensorFlow Recommenders-Addons:

import tensorflow as tf
import tensorflow_recommenders_addons as tfra

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 when loading a custom op if your TensorFlow version does not match what it was built against.

Additionally, 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.

NOTICE：The release packages have strict version binding relationship with TensorFlow. If you need to work with other versions of TensorFlow, we recommend you installing from source.

You can also install from source. This requires the Bazel build system (version >= 1.0.0).

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

# This script links project with TensorFlow dependency
python3 ./configure.py

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

pip install artifacts/tensorflow_recommenders_addons-*.whl

Reference documents: https://www.tensorflow.org/tfx/serving/custom_op

TFRA modification(tensorflow_recommenders_addons.bzl):

deps = deps + [
        # "@local_config_tf//:libtensorflow_framework",
        "@local_config_tf//:tf_header_lib",
    ]

native.cc_library(
        name = name,
        srcs = srcs,
        copts = copts,
        alwayslink = 1,
        features = select({
            "//tensorflow_recommenders_addons:windows": ["windows_export_all_symbols"],
            "//conditions:default": [],
        }),
        deps = deps,
        **kwargs
    )

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 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