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Jun 14, 2022
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Update backends versions and fix links to test data - cherry pick #928

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15 changes: 8 additions & 7 deletions README.md
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Expand Up @@ -86,13 +86,14 @@ make -C opt GPU=1
RedisAI currently supports PyTorch (libtorch), Tensorflow (libtensorflow), TensorFlow Lite, and ONNXRuntime as backends. This section shows the version map between RedisAI and supported backends. This extremely important since the serialization mechanism of one version might not match with another. For making sure your model will work with a given RedisAI version, check with the backend documentation about incompatible features between the version of your backend and the version RedisAI is built with.


| RedisAI | PyTorch | TensorFlow | TFLite | ONNXRuntime |
|:--------|:-------:|:----------:|:------:|:-------------:|
| 1.0.3 | 1.5.0 | 1.15.0 | 2.0.0 | 1.2.0 |
| 1.2.5 | 1.9.0 | 2.6.0 | 2.0.0 | 1.9.0 |
| master | 1.10.1 | 2.6.0 | 2.0.0 | 1.9.0 |

Note: Keras and TensorFlow 2.x are supported through graph freezing. See [this script](https://github.com/RedisAI/RedisAI/blob/master/tests/flow/test_data/tf2-minimal.py) to see how to export a frozen graph from Keras and TensorFlow 2.x.
| RedisAI | PyTorch | TensorFlow | TFLite | ONNXRuntime |
|:--------|:--------:|:----------:|:------:|:-----------:|
| 1.0.3 | 1.5.0 | 1.15.0 | 2.0.0 | 1.2.0 |
| 1.2.7 | 1.11.0 | 2.8.0 | 2.0.0 | 1.11.1 |
| master | 1.11.0 | 2.8.0 | 2.0.0 | 1.11.1 |

Note: Keras and TensorFlow 2.x are supported through graph freezing. See [this script](http://dev.cto.redis.s3.amazonaws.com/RedisAI/test_data/tf2-minimal.py
) to see how to export a frozen graph from Keras and TensorFlow 2.x.

## Loading the Module
To load the module upon starting the Redis server, simply use the `--loadmodule` command line switch, the `loadmodule` configuration directive or the [Redis `MODULE LOAD` command](https://redis.io/commands/module-load) with the path to module's library.
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4 changes: 2 additions & 2 deletions docs/intro.md
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Expand Up @@ -195,13 +195,13 @@ A **Model** is a Deep Learning or Machine Learning frozen graph that was generat

Models, like any other Redis and RedisAI data structures, are identified by keys. A Model's key is created using the [`AI.MODELSTORE` command](commands.md#aimodelstore) and requires the graph payload serialized as protobuf for input.

In our examples, we'll use one of the graphs that RedisAI uses in its tests, namely 'graph.pb', which can be downloaded from [here](https://github.com/RedisAI/RedisAI/raw/master/tests/flow/test_data/graph.pb). This graph was created using TensorFlow with [this script](https://github.com/RedisAI/RedisAI/blob/master/tests/flow/test_data/tf-minimal.py).
In our examples, we'll use one of the graphs that RedisAI uses in its tests, namely 'graph.pb', which can be downloaded from [here](http://dev.cto.redis.s3.amazonaws.com/RedisAI/test_data/graph.pb). This graph was created using TensorFlow with [this script](http://dev.cto.redis.s3.amazonaws.com/RedisAI/test_data/tf-minimal.py).

??? info "Downloading 'graph.pb'"
Use a web browser or the command line to download 'graph.pb':

```
wget https://github.com/RedisAI/RedisAI/raw/master/tests/flow/test_data/graph.pb
wget http://dev.cto.redis.s3.amazonaws.com/RedisAI/test_data/graph.pb
```

You can view the computation graph using [Netron](https://lutzroeder.github.io/netron/), which supports all frameworks supported by RedisAI.
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