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.
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.
