docs - moving docker to be first to tryout (#97)

* moving docker to be first to tryout

* adding client libs

Author: K-Jo

README.md

@@ -10,6 +10,41 @@ Expect changes in the API and internals.
 ## Cloning
 If you want to run examples, make sure you have [git-lfs](https://git-lfs.github.com) installed when you clone.
 
+## Quickstart
+
+1. [Docker](#docker)
+2. [Build](#building)
+
+## Docker
+
+To quickly tryout RedisAI, launch an instance using docker:
+
+```sh
+docker run -p 6379:6379 -it --rm redisai/redisai
+```
+
+### Give it a try
+
+On the client, load the model
+```sh
+redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < examples/models/graph.pb
+```
+
+Then create the input tensors, run the computation graph and get the output tensor (see `load_model.sh`). Note the signatures:
+* `AI.TENSORSET tensor_key data_type dim1..dimN [BLOB data | VALUES val1..valN]`
+* `AI.MODELRUN graph_key INPUTS input_key1 ... OUTPUTS output_key1 ...`
+```sh
+redis-cli
+> AI.TENSORSET bar FLOAT 2 VALUES 2 3
+> AI.TENSORSET baz FLOAT 2 VALUES 2 3
+> AI.MODELRUN foo INPUTS bar baz OUTPUTS jez
+> AI.TENSORGET jez VALUES
+1) FLOAT
+2) 1) (integer) 2
+3) 1) "4"
+   2) "9"
+```
+
 ## Building
 This will checkout and build and download the libraries for the backends
 (TensorFlow and PyTorch) for your platform.
@@ -30,15 +65,7 @@ make
 cd ..
 ```
 
-## Docker
-
-To quickly tryout RedisAI, launch an instance using docker:
-
-```sh
-docker run -p 6379:6379 -it --rm redisai/redisai
-```
-
-## Running the server
+### Running the server
 
 You will need a redis-server version 4.0.9 or greater. This should be
 available in most recent distributions:
@@ -54,25 +81,15 @@ To start redis with the RedisAI module loaded:
 redis-server --loadmodule build/redisai.so
 ```
 
-On the client, load the model
-```
-redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < graph.pb
-```
+## Client libraries
+
+Some languages have client libraries that provide support for RedisAI's commands:
+
+| Project | Language | License | Author | URL |
+| ------- | -------- | ------- | ------ | --- |
+| JRedisAI | Java | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/JRedisAI) |
+| redisai-py | Python | BSD-2 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-py) |
 
-Then create the input tensors, run the computation graph and get the output tensor (see `load_model.sh`). Note the signatures: 
-* `AI.TENSORSET tensor_key data_type dim1..dimN [BLOB data | VALUES val1..valN]`
-* `AI.MODELRUN graph_key INPUTS input_key1 ... OUTPUTS output_key1 ...`
-```
-redis-cli
-> AI.TENSORSET bar FLOAT 2 VALUES 2 3
-> AI.TENSORSET baz FLOAT 2 VALUES 2 3
-> AI.MODELRUN foo INPUTS bar baz OUTPUTS jez
-> AI.TENSORGET jez VALUES
-1) FLOAT
-2) 1) (integer) 2
-3) 1) "4"
-   2) "9"
-```
 
 ## Backend Dependancy
 

docs/index.md

@@ -6,16 +6,37 @@ RedisAI is a Redis module for serving tensors and executing deep learning models
 
 1. [Docker](#docker)
 2. [Build](#building)
-3. [Start](#start)
 
 ## Docker
 
-To quickly tryout RedisAI, launch an instance using Docker:
+To quickly tryout RedisAI, launch an instance using docker:
 
 ```sh
 docker run -p 6379:6379 -it --rm redisai/redisai
 ```
 
+### Give it a try
+
+On the client, load the model
+```sh
+redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < examples/models/graph.pb
+```
+
+Then create the input tensors, run the computation graph and get the output tensor (see `load_model.sh`). Note the signatures:
+* `AI.TENSORSET tensor_key data_type dim1..dimN [BLOB data | VALUES val1..valN]`
+* `AI.MODELRUN graph_key INPUTS input_key1 ... OUTPUTS output_key1 ...`
+```sh
+redis-cli
+> AI.TENSORSET bar FLOAT 2 VALUES 2 3
+> AI.TENSORSET baz FLOAT 2 VALUES 2 3
+> AI.MODELRUN foo INPUTS bar baz OUTPUTS jez
+> AI.TENSORGET jez VALUES
+1) FLOAT
+2) 1) (integer) 2
+3) 1) "4"
+   2) "9"
+```
+
 ## Building
 This will checkout and build and download the libraries for the backends
 (TensorFlow and PyTorch) for your platform.
@@ -51,25 +72,13 @@ To start redis with the RedisAI module loaded, you need to make sure the depende
 LD_LIBRARY_PATH=<PATH_TO>/deps/install/lib redis-server --loadmodule build/redisai.so
 ```
 
-If you want to run examples, make sure you have [git-lfs](https://git-lfs.github.com) installed when you clone.
-On the client, load the model
-```
-./deps/redis/src/redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < graph.pb
-```
+## Client libraries
 
-Then create the input tensors, run the computation graph and get the output tensor (see `load_model.sh`). Note the signatures: 
-* `AI.TENSORSET tensor_key data_type dim1..dimN [BLOB data | VALUES val1..valN]`
-* `AI.MODELRUN graph_key INPUTS input_key1 ... OUTPUTS output_key1 ...`
-```
-redis-cli
-> AI.TENSORSET bar FLOAT 2 VALUES 2 3
-> AI.TENSORSET baz FLOAT 2 VALUES 2 3
-> AI.MODELRUN foo INPUTS bar baz OUTPUTS jez
-> AI.TENSORGET jez VALUES
-1) FLOAT
-2) 1) (integer) 2
-3) 1) "4"
-   2) "9"
-```
+Some languages have client libraries that provide support for RedisAI's commands:
+
+| Project | Language | License | Author | URL |
+| ------- | -------- | ------- | ------ | --- |
+| JRedisAI | Java | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/JRedisAI) |
+| redisai-py | Python | BSD-2 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-py) |
 
 Full documentation of the api can be found [here](commands.md).