# CARLS: Cross-platform Asynchronous Representation Learning System

## Components Overview

*   TensorFlow API: Python functions that are implemented by C++ ops.
*   KnowledgeBank Service (KBS): a gRPC server that implements embedding
    lookup/update.
*   KnowledgeBank Manager: client side C++ hub that talks to KBS.
*   Storage System: underlying storage for Knowledge Bank, e.g.,
    InProtoKnowledgeBank for in-memory storage.

![](g3doc/images/knowledge_bank_server.png)

# CARLS: Cross-platform Asynchronous Representation Learning System

## Components Overview

*   TensorFlow API: Python functions that are implemented by C++ ops.
*   KnowledgeBank Service (KBS): a gRPC server that implements embedding
    lookup/update.
*   KnowledgeBank Manager: client side C++ hub that talks to KBS.
*   Storage System: underlying storage for Knowledge Bank, e.g.,
    InProtoKnowledgeBank for in-memory storage.

![](g3doc/images/knowledge_bank_server.png)

## An End-to-End Example

Below are the intructions to run the example under
[examples/graph_keras_mlp_cora.py](examples/graph_keras_mlp_cora.py) by building
from source.

### Prerequisite

Follow the instructions from
[tensorflow.org](https://www.tensorflow.org/install/source#install_bazel) to
install Bazel.

### Step One: Download the Neural Structured Leaning source code.

```sh
$ git clone https://github.com/tensorflow/neural-structured-learning.git
$ cd neural-structured-learning
```

### Step Two: Download the data to /tmp/cora.

```sh
$ bash neural_structured_learning/examples/preprocess/cora/prep_data.sh
```

### Step Three: Run the example

```sh
$ bazel run research/carls/examples:graph_keras_mlp_cora -- \
/tmp/cora/train_merged_examples.tfr /tmp/cora/test_examples.tfr \
--alsologtostderr --output_dir=/tmp/carls
```

Add instructions to run the cora example using CARLS.
Add instructions to run the cora example using CARLS.

PiperOrigin-RevId: 361487926
