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Expand Up @@ -2,9 +2,9 @@

## 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](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 an advanced recommendation system:
* collisionless embedding tables guarantee unique representation for different ID features
* real-time training captures the latest hotspots and helps users to discover new interests rapidly

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

Expand All @@ -19,7 +19,7 @@ https://discord.gg/QYTDeKxGMX

### Build from source

Currently, we only support compilation on the Linux.
Currently, we only support compilation on Linux.

First, download bazel 3.1.0
```bash
Expand All @@ -29,7 +29,7 @@ wget https://github.com/bazelbuild/bazel/releases/download/3.1.0/bazel-3.1.0-ins
rm bazel-3.1.0-installer-linux-x86_64.sh
```

Then, prepare a python environment
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
Expand All @@ -43,4 +43,4 @@ 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).
There is a tutorial in [markdown/demo](markdown/demo) on how to run distributed async training, and a few guides on how to use the `MonolithModel` API [here](markdown).