ByteDance's Recommendation System

Monolith

Monolith is a deep learning framework for large scale recommendation modeling. It introduces two important features which are crucial for advanced recommendation system:

• collisionless embedding tables guarantees unique represeantion for different id features
• real time training captures the latest hotspots and help users to discover new intersts rapidly

Monolith is built on the top of TensorFlow and supports batch/real-time training and serving.

https://discord.gg/QYTDeKxGMX

Currently, we only support compilation on the Linux.

First, download bazel 3.1.0

wget https://github.com/bazelbuild/bazel/releases/download/3.1.0/bazel-3.1.0-installer-linux-x86_64.sh && \
  chmod +x bazel-3.1.0-installer-linux-x86_64.sh && \
  ./bazel-3.1.0-installer-linux-x86_64.sh && \
  rm bazel-3.1.0-installer-linux-x86_64.sh

Then, prepare a python environment

pip install -U --user pip numpy wheel packaging requests opt_einsum
pip install -U --user keras_preprocessing --no-deps

Finally, you can build any target in the monolith. For example,

bazel run //monolith/native_training:demo --output_filter=IGNORE_LOGS

There are a tutorial in markdown/demo on how to run distributed async training, and few guides on how to use the MonolithModel API here.