# monolith

**Repository Path**: hehehelloworld/monolith

## Basic Information

- **Project Name**: monolith
- **Description**: ByteDance's Recommendation System
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2023-05-25
- **Last Updated**: 2024-06-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

 Monolith

## What is it?

[Monolith](https://arxiv.org/abs/2209.07663) 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.


## Discussion Group

### Join us at Discord

https://discord.gg/QYTDeKxGMX

## Quick start

### Build from source

Currently, we only support compilation on the Linux.

First, download bazel 3.1.0
```bash
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
```bash
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,
```bash
bazel run //monolith/native_training:demo --output_filter=IGNORE_LOGS
```

### Demo and tutorials

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