Monolith is a state-of-the-art deep learning framework specifically designed for large-scale recommendation modeling. Built on top of TensorFlow, it introduces two groundbreaking features essential for modern recommendation systems:
Collisionless Embedding Tables: Ensures unique representation for different ID features, eliminating the risk of feature collision and improving model accuracy.
Real-Time Training: Captures the latest trends and user interests dynamically, enabling rapid discovery of new preferences and hotspots.
Monolith supports both batch and real-time training and serving, making it a versatile tool for recommendation systems. Currently, it is optimized for Linux environments. To get started, you'll need to install Bazel 3.1.0 and set up a Python environment with specific dependencies like NumPy, Wheel, and Keras Preprocessing.
Example build command:
bash
bazel run //monolith/native_training:demo --output_filter=IGNORE_LOGS
The framework also includes comprehensive tutorials and guides, such as running distributed async training and utilizing the MonolithModel API, making it accessible for both beginners and advanced users.