From d2351edf3b2a43e3455a723e4044c48b4c114b35 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 8 Apr 2020 17:14:42 +0300
Subject: [PATCH 01/45] WIP

---
 docs/commands.md      | 487 ++++++++++++++++++++++++++----------------
 docs/requirements.txt |   3 +
 mkdocs.yml            |  14 +-
 3 files changed, 319 insertions(+), 185 deletions(-)
 create mode 100644 docs/requirements.txt

diff --git a/docs/commands.md b/docs/commands.md
index 6b421eb51..ff58e72d4 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -1,305 +1,432 @@
 # RedisAI Commands
+RedisAI is a Redis module, and as such it implements several data types and the respective commands to use them.
 
-## AI.TENSORSET
+All of RedisAI's commands are begin with the `AI.` prefix. The following sections describe these commands.
 
-Set a tensor.
+**Syntax Conventions**
 
-Stores a tensor of defined type with shape given by shape1..shapeN.
+The following conventions are used for describing the RedisAI Redis API:
 
-```sql
-AI.TENSORSET tensor_key data_type shape1 shape2 ... [BLOB data | VALUES val1 val2 ...]
+* `COMMAND`: a command or an argument name
+* `<mandatory>`: a mandatory argument
+* `[optional]`: an optional argument
+* `"`: a literal double quote character
+* `|`: an exclusive logical or operator
+* `...`: more of the same as before
+
+## AI.TENSORSET
+The **`AI.TENSORSET`** command stores a tensor as the value of a key.
+
+**Redis API**
+
+```
+AI.TENSORSET <key> <type>
+   <shape> [shape ...] [BLOB <data> | VALUES <val> [val ...]]
 ```
 
-* tensor_key - Key for storing the tensor
-* data_type - Numeric data type of tensor elements, one of FLOAT, DOUBLE, INT8, INT16, INT32, INT64, UINT8, UINT16
-* shape - Shape of the tensor, that is how many elements for each axis
+_Arguments_
 
-Optional args:
+* **key**: the tensor's key name
+* **type**: the tensor's data type can be one of: 'FLOAT', 'DOUBLE', 'INT8', 'INT16', 'INT32', 'INT64', 'UINT8' or 'UINT16'
+* **shape**: one or more dimensions, or the number of elements per axis, for the tensor
+* **BLOB**: indicates that data is in binary format and is provided via the subsequent `data` argument
+* **VALUES**: indicates that data is numeric and is provided by one or more subsequent `val` arguments
 
-* BLOB data - provide tensor content as a binary buffer
-* VALUES val1 val2 - provide tensor content as individual values
+_Return_
 
-> If no BLOB or VALUES are specified, the tensor is allocated but not initialized to any value.
+A simple 'OK' string or an error.
 
-### TENSORSET Example
+**Examples**
 
-> Set a 2x2 tensor at `foo`
-> 1 2
-> 3 4
+Given the following: $\begin{equation*} A = \begin{bmatrix} 1 & 2 \\ 3 & 4 \\ \end{bmatrix} \end{equation*}$
+
+This will set the key 'mytensor' to the 2x2 RedisAI tensor:
 
-```sql
-AI.TENSORSET foo FLOAT 2 2 VALUES 1 2 3 4
 ```
+redis> AI.TENSORSET mytensor FLOAT 2 2 VALUES 1 2 3 4
+OK
+```
+
+!!! note "Uninitialized Tensor Values"
+    As both `BLOB` and `VALUES` are optional arguments, it is possible to use the `AI.TENSORSET` to create an uninitialized tensor.
+    TBD: why is that useful?
 
-!!! warning "Overhead of `AI.TENSORSET` with the optional arg VALUES"
-        
-    It is possible to set a tensor by specifying each individual value (VALUES ... ) or the entire tensor content as a binary buffer (BLOB ...). You should always try to use the `BLOB` option since it removes the overhead of parsing each individual value and does not require serialization/deserialization of the tensor, thus reducing the overall command latency an improving the maximum attainable performance of the model server.
----
+!!! important "Using `BLOB` is preferable to `VALUES`"
+    While it is possible to set the tensor using binary data or numerical values, it is recommended that you use the `BLOB` option. It requires less resources and performs better compared to specifying the values discretely.
 
 ## AI.TENSORGET
+The **`AI.TENSORGET`** command returns a tensor stored as key's value.
 
-Get a tensor.
+**Redis API**
 
-```sql
-AI.TENSORGET tensor_key [BLOB | VALUES | META]
+```
+AI.TENSORGET <key> <format>
 ```
 
-* tensor_key - Key for the tensor
-* BLOB - Return tensor content as a binary buffer
-* VALUES - Return tensor content as a list of values
-* META - Only return tensor meta data (datat type and shape)
+_Arguments_
 
-### TENSORGET Example
+* **key**: the tensor's key name
+* **format**: the reply's format can be on of the following:
+    * **BLOB**: returns the binary representation of the tensor's data
+    * **VALUES**: returns the numerical representation of the tensor's data
+    * **META**: returns the tensor's meta data exclusively
 
-Get binary data for tensor at `foo`. Meta data is also returned.
+_Return_
+
+Array containing the tensor's data.
+
+The returned array consists of the following elements:
+
+1. The tensor's data type as a String
+1. The tensor's shape as an Array consisting of an item per dimension
+1. The tensor's data when called with the `BLOB` or `VALUES` arguments
+
+The reply's third element type, that is the tensor's data, depends on the given argument:
+
+* **BLOB**: the tensor's binary data as a String
+* **VALUES**: the tensor's values an Array
+* **META**: when used no data is returned
+
+**Examples**
+
+Given a tensor value stored at the 'mytensor' key:
 
-```sql
-AI.TENSORGET foo BLOB
+```
+redis> AI.TENSORSET mytensor FLOAT 2 2 VALUES 1 2 3 4
+OK
+```
+
+The following shows how the tensor's binary data is read:
+
+```
+redis> AI.TENSORGET mytensor BLOB
+1) FLOAT
+2) 1) (integer) 2
+   2) (integer) 2
+3) "\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"
 ```
 
-!!! warning "Overhead of `AI.TENSORGET` with the optional arg VALUES"
-        
-    It is possible to receive a tensor as a list of each individual value (VALUES ... ) or the entire tensor content as a binary buffer (BLOB ...). You should always try to use the `BLOB` option since it removes the overhead of replying each individual value and does not require serialization/deserialization of the tensor, thus reducing the overall command latency an improving the maximum attainable performance of the model server.
----
+!!! important "Using `BLOB` is preferable to `VALUES`"
+    While it is possible to get the tensor as binary data or numerical values, it is recommended that you use the `BLOB` option. It requires less resources and performs better compared to returning the values discretely.
 
 ## AI.MODELSET
+The **`AI.MODELSET`** commands stores a model as the value of a key.
 
-Set a model.
+**Redis API**
 
-```sql
-AI.MODELSET model_key backend device [INPUTS name1 name2 ... OUTPUTS name1 name2 ...] model_blob
+```
+AI.MODELSET <key> <backend> <device>
+    [INPUTS <name> ...] [OUTPUTS name ...] <model>
 ```
 
-* model_key - Key for storing the model
-* backend - The backend corresponding to the model being set. Allowed values: `TF`, `TORCH`, `ONNX`.
-* device - Device where the model is loaded and where the computation will run. Allowed values: `CPU`, `GPU`.
-* INPUTS name1 name2 ... - Name of the nodes in the provided graph corresponding to inputs [`TF` backend only]
-* OUTPUTS name1 name2 ... - Name of the nodes in the provided graph corresponding to outputs [`TF` backend only]
-* model_blob - Binary buffer containing the model protobuf saved from a supported backend
+_Arguments_
 
-### MODELSET Example
+* **key**: the model's key name
+* **backend**: the backend for the model can be one of:
+    * **TF**: a TensorFlow backend
+    * **TORCH**: a PyTorch backend
+    * **ONNX**: a ONNX backend
+* **device**: the device that will execute the model can be of:
+    * **CPU**: a CPU device
+    * **GPU**: a GPU device
+* **INPUTS**: one or more names of the model's input nodes (applicable only for TensorFlow models)
+* **OUTPUTS**: one or more names of the model's output nodes (applicable only for TensorFlow models)
+* **model**: the Protobuf-serialized model
 
-```sql
-AI.MODELSET resnet18 TORCH GPU < foo.pt
-```
+_Return_
 
-```sql
-AI.MODELSET resnet18 TF CPU INPUTS in1 OUTPUTS linear4 < foo.pb
-```
+A simple 'OK' string or an error.
 
-```sql
-AI.MODELSET mnist_net ONNX CPU < mnist.onnx
+**Examples**
+
+This example shows to set a model 'mymodel' key using the contents of a local file with [`redis-cli`](https://redis.io/topics/cli). Refer to the [Clients Page](clients.md) for additional client choice that are native to your programming language:
+
+```
+$ cat mobilenet_v2_1.4_224_frozen.pb | redis-cli -x AI.MODELSET mymodel TF CPU INPUTS input OUTPUTS MobilenetV2/Predictions/Reshape_1
+OK
 ```
 
 ## AI.MODELGET
+The **`AI.MODELGET`** command returns a model stored as a key's value.
 
-Get a model.
+**Redis API**
 
-```sql
-AI.MODELGET model_key
 ```
+AI.MODELGET <key>
+```
+
+_Arguments
+
+* **key**: the model's key name
 
-* model_key - Key for the model
+_Return_
 
-The command returns the model as serialized by the backend, that is a string containing a protobuf.
+A Bulk String that is the Protobuf-serialized representation of the model by the backend.
 
+**Examples**
+
+Assuming that your model is stored under the 'mymodel' key, you can use the following read and save it to local file 'model.ext' with [`redis-cli`](https://redis.io/topics/cli):
+
+```
+$ redis-cli --raw AI.MODELGET mymodel > model.ext
+```
 
 ## AI.MODELDEL
+The **`AI.MODELDEL`** deletes a model stored as a key's value.
 
-Removes a model at a specified key.
+**Redis API**
 
-```sql
-AI.MODELDEL model_key
+```
+AI.MODELDEL <key>
 ```
 
-* model_key - Key for the model
+_Arguments_
 
-Currently, the command is fully equivalent to calling `DEL` on `model_key`.
+* **key**: the model's key name
 
+_Return_
 
-## AI.MODELRUN
+A simple 'OK' string or an error.
 
-Run a model.
+**Examples**
 
-```sql
-AI.MODELRUN model_key INPUTS input_key1 ... OUTPUTS output_key1 ...
+Assuming that your model is stored under the 'mymodel' key, you can delete it like this:
+
+```
+redis> AI.MODELDEL mymodel
+OK
 ```
 
-* model_key - Key for the model
-* INPUTS input_key1 ... - Keys for tensors to use as inputs
-* OUTPUTS output_key2 ... - Keys for storing output tensors
+!!! note "The `AI.MODELDEL` vis a vis the `DEL` command"
+    The `AI.MODELDEL` is equivalent to the [Redis `DEL` command](https://redis.io/commands/del) and should be used in its stead. This ensures compatibility with all deployment options (i.e., stand-alone vs. cluster, OSS vs. Enterprise).
 
-The request is queued and evaded asynchronously from a separate thread. The client blocks until the computation finishes.
+## AI.MODELRUN
+The **`AI.MODELRUN`** command runs a model stored as a key's value using its specified backend and device. It accepts one or more input tensors and store output tensors.
 
-If needed, input tensors are copied to the device specified in `AI.MODELSET` before execution.
+The run request is put in a queue and is executed asynchronously by a worker thread. The client that had issued the run request is blocked until the model's run is completed. When needed, tensors' data is automatically copied to the device prior to execution.
 
-### MODELRUN Example
+!!! warning "Intermediate memory overhead"
+    The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by `maxmemory` configuration settings of Redis.
+
+**Redis API**
 
-```sql
-AI.MODELRUN resnet18 INPUTS image12 OUTPUTS label12
 ```
+AI.MODELRUN <key> INPUTS <input> [input ...] OUTPUTS <output> [output ...]
+```
+
+_Arguments_
+
+* **key**: the model's key name
+* **INPUTS**: denotes the beginning of the input tensors keys' list, followed by one or more key names
+* **OUTPUTS**: denotes the beginning of the output tensors keys' list, followed by one or more key names
+
+_Return_
 
-!!! warning "Intermediate tensors memory overhead when issuing `AI.MODELRUN` and `AI.SCRIPTRUN`"
-        
-    The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory settings on Redis.
----
+A simple 'OK' string or an error.
 
+**Examples**
 
+Assuming that running the model that's stored at 'mymodel' with the tensor 'mytensor' as input outputs two tensors - 'classes' and 'predictions', the following command does that:
+
+```
+redis> AI.MODELRUN mymodel INPUTS mytensor OUTPUTS classes predictions
+OK
+```
 
 ## AI.SCRIPTSET
+The **`AI.SCRIPTSET`** command stores a [TorchScript](https://pytorch.org/docs/stable/jit.html) as the value of a key.
 
-Set a script.
+**Redis API**
 
-```sql
-AI.SCRIPTSET script_key device script_source
 ```
+AI.SCRIPTSET <key> <device> "<script>"
+```
+
+_Arguments_
 
-* script_key - Key for storing the script
-* device - The device where the script will execute
-* script_source - A string containing [TorchScript](https://pytorch.org/docs/stable/jit.html) source code
+* **key**: the script's key name
+* **device**: the device that will execute the model can be of:
+    * **CPU**: a CPU device
+    * **GPU**: a GPU device
+* **script**: the script's source code
 
-### SCRIPTSET Example
+_Return_
 
-Given addtwo.txt as:
+A simple 'OK' string or an error.
+
+**Examples**
+
+Given the following contents of the file 'addtwo.py':
 
 ```python
 def addtwo(a, b):
     return a + b
 ```
 
-```sql
-AI.SCRIPTSET addscript GPU < addtwo.txt
+It can be stored as a RedisAI script using the CPU device with [`redis-cli`](https://redis.io/topics/rediscli) as follows:
+
+```
+$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU
+OK
 ```
 
 ## AI.SCRIPTGET
+The **`AI.SCRIPTGET`** command returns the [TorchScript](https://pytorch.org/docs/stable/jit.html) stored as a key's value.
 
-Get a script.
+**Redis API**
 
-```sql
-AI.SCRIPTGET script_key
 ```
+AI.SCRIPTGET <key>
+```
+
+_Arguments_
+
+* **key**: the script's key name
+
+_Return_
 
-* script_key - key for the script
+Array consisting of two items:
 
+1. The script's device as a String
+2. The script's source code as a String
+
+**Examples**
+
+The following shows how to read the script stored at the 'myscript' key:
+
+```
+redis> AI.SCRIPTGET myscript
+1) CPU
+2) def addtwo(a, b):
+    return a + b
+```
 
 ## AI.SCRIPTDEL
+The **`AI.SCRIPTDEL`** deletes a script stored as a key's value.
 
-Removes a script at a specified key.
+**Redis API**
 
-```sql
-AI.SCRIPTDEL script_key
+```
+AI.SCRIPTDEL <key>
 ```
 
-* script_key - key for the script
+_Arguments_
 
-Currently, the command is fully equivalent to calling `DEL` on `script_key`.
+* **key**: the script's key name
 
+_Return_
 
-### SCRIPTGET Example
+A simple 'OK' string or an error.
+
+**Examples**
 
-```sql
-AI.SCRIPTGET addscript
 ```
+redis> AI.SCRIPTDEL myscript
+OK
+```
+
+!!! note "The `AI.SCRIPTDEL` vis a vis the `DEL` command"
+    The `AI.SCRIPTDEL` is equivalent to the [Redis `DEL` command](https://redis.io/commands/del) and should be used in its stead. This ensures compatibility with all deployment options (i.e., stand-alone vs. cluster, OSS vs. Enterprise).
+
 
 ## AI.SCRIPTRUN
+The **`AI.SCRIPTRUN`** command runs a script stored as a key's value on its specified device. It accepts one or more input tensors and store output tensors.
 
-Run a script.
+**Redis API**
 
-```sql
-AI.SCRIPTRUN script_key fn_name INPUTS input_key1 ... OUTPUTS output_key1 ...
+```
+AI.SCRIPTRUN <key> <function> INPUTS <input> [input ...] OUTPUTS <output> [output ...]
 ```
 
-* tensor_key - Key for the script
-* fn_name - Name of the function to execute
-* INPUTS input_key1 ... - Keys for tensors to use as inputs
-* OUTPUTS output_key1 ... - Keys for storing output tensors
+_Arguments_
 
-If needed, input tensors are copied to the device specified in `AI.SCRIPTSET` before execution.
+* **key**: the script's key name
+* **function**: the name of the function to run
+* **INPUTS**: denotes the beginning of the input tensors keys' list, followed by one or more key names
+* **OUTPUTS**: denotes the beginning of the output tensors keys' list, followed by one or more key names
 
-### SCRIPTRUN Example
+_Return_
+
+A simple 'OK' string or an error.
+
+**Examples**
+
+The following is an example of running the previously-created 'myscript' on two input tensors:
 
-```sql
-AI.SCRIPTRUN addscript addtwo INPUTS a b OUTPUTS c
+```
+redis> AI.TENSORSET mytensor1 FLOAT 1 VALUES 40
+OK
+redis> AI.TENSORSET mytensor2 FLOAT 1 VALUES 2
+OK
+redis> AI.SCRIPTRUN myscript addtwo INPUTS mytensor1 mytensor2 OUTPUTS result
+OK
+redis> AI.TENSORGET result VALUES
+1) FLOAT
+2) 1) (integer) 1
+3) 1) "42"
 ```
 
-!!! warning "Intermediate tensors memory overhead when issuing `AI.MODELRUN` and `AI.SCRIPTRUN`"
-        
-    The execution of models will generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by maxmemory settings on Redis.
----
+!!! warning "Intermediate memory overhead"
+    The execution of scripts may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by `maxmemory` configuration settings of Redis.
 
 ## AI.INFO
+The **`AI.INFO`** command returns information about the execution a model or a script.
 
-Return information about runs of a `MODEL` or a `SCRIPT`.
-
-At each `MODELRUN` or `SCRIPTRUN`, RedisAI will collect statistcs specific for each `MODEL` or `SCRIPT`,
-specific for the node (hence nodes in a cluster will have to be queried individually for their info).
-The following information is collected:
+Runtime information is collected each time that [`AI.MODELRUN`](#aimodelrun) or [`AI.SCRIPTRUN`]|(#aiscriptrun) is called. The information is stored locally by the executing RedisAI engine, so when deployed in a cluster each shard stores its own runtime information.
 
-- `KEY`: the key being run
-- `TYPE`: either `MODEL` or `SCRIPT`
-- `BACKEND`: the type of backend (always `TORCH` for `SCRIPT`)
-- `DEVICE`: the device where the run has been executed
-- `DURATION`: cumulative duration in microseconds
-- `SAMPLES`: cumulative number of samples obtained from the 0-th (batch) dimension (for `MODEL` only)
-- `CALLS`: number of calls
-- `ERRORS`: number of errors generated after the run has been submitted (i.e. excluding errors generated during parsing of the command)
+**Redis API**
 
-```sql
-AI.INFO <model_or_script_key>
+```
+AI.INFO <key> [RESETSTAT]
 ```
 
-Statistcs are accumulated until the same command with an extra `RESETSTAT` argument is called. This resets the statistics relative to the model or script.
+_Arguments_
 
-```sql
-AI.INFO <model_or_script_key> RESETSTAT
-```
+* **key**: the key name of a model or script
+* **RESETSTAT**: resets all statistics associated with the key
 
-The command can be called on a key until that key is removed using `MODELDEL` or `SCRIPTDEL`.
+_Return_
 
-### AI.INFO Example
+An array with alternating entries that represent the following key-value pairs:
 
-```sql
-AI.INFO amodel
-
->  1) KEY
->  2) "amodel"
->  3) TYPE
->  4) MODEL
->  5) BACKEND
->  6) TORCH
->  7) DEVICE
->  8) CPU
->  9) DURATION
-> 10) (integer) 6511
-> 11) SAMPLES
-> 12) (integer) 2
-> 13) CALLS
-> 14) (integer) 1
-> 15) ERRORS
-> 16) (integer) 0
+* **KEY**: a String of the name of the key storing the model or script value
+* **TYPE**: a String of the type of value (i.e. 'MODEL' or 'SCRIPT')
+* **BACKEND**: a String of the type of backend (always 'TORCH' for 'SCRIPT' value type)
+* **DEVICE**: a String of the device where execution took place
+* **DURATION**: the cumulative duration of executions in microseconds
+* **SAMPLES**: the cumulative number of samples obtained from the 0th (batch) dimension (only applicable for RedisAI models)
+* **CALLS**: the total number of executions
+* **ERRORS**: the total number of errors generated by executions (excluding any errors generated during parsing commands)
+
+When called with the `RESETSTAT` argument, the command returns a simple 'OK' string.
+
+**Examples**
+
+The following example obtains the previously-run 'myscript' script's runtime statistics:
+
+```
+redis> AI.INFO myscript
+ 1) KEY
+ 2) "myscript"
+ 3) TYPE
+ 4) SCRIPT
+ 5) BACKEND
+ 6) TORCH
+ 7) DEVICE
+ 8) CPU
+ 9) DURATION
+10) (integer) 11391
+11) SAMPLES
+12) (integer) -1
+13) CALLS
+14) (integer) 1
+15) ERRORS
+16) (integer) 0
 ```
 
+The runtime statistics for that script can be reset like so:
+
 ```sql
-AI.INFO amodel RESETSTAT
-
-> OK
-
-AI.INFO amodel
-
->  1) KEY
->  2) "amodel"
->  3) TYPE
->  4) MODEL
->  5) BACKEND
->  6) TORCH
->  7) DEVICE
->  8) CPU
->  9) DURATION
-> 10) (integer) 0
-> 11) SAMPLES
-> 12) (integer) 0
-> 13) CALLS
-> 14) (integer) 0
-> 15) ERRORS
-> 16) (integer) 0
+redis> AI.INFO myscript RESETSTAT
+OK
 ```
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 000000000..73568352f
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,3 @@
+mkdocs>=1.0.4
+mkdocs-material>=4.6.3
+-e git+https://github.com/RedisLabs/mkdocs-versions-menu.git#egg=mkdocs-versions-menu
diff --git a/mkdocs.yml b/mkdocs.yml
index bb9ccc5ae..2dc5fef1e 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -23,14 +23,18 @@ theme:
   feature:
     tabs: false
 
-pages:
-    - 'Quickstart': index.md
-    - 'Commands': commands.md
-    - 'Backend': dataandbackend.md
-    - 'Configuration': configuring.md
+extra_javascript:
+  - 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
+
+nav:
+  - 'Quickstart': index.md
+  - 'Commands': commands.md
+  - 'Backend': dataandbackend.md
+  - 'Configuration': configuring.md
 
 markdown_extensions:
   - admonition
+  - pymdownx.arithmatex
   - codehilite:
       guess_lang: False
   - toc:

From 82b05282b795e0ea2dde0ef0a38ce9c8c7481588 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Thu, 9 Apr 2020 15:35:35 +0300
Subject: [PATCH 02/45] WIP

---
 docs/commands.md | 48 ++++++++++++++++--------------------------------
 1 file changed, 16 insertions(+), 32 deletions(-)

diff --git a/docs/commands.md b/docs/commands.md
index 47cea96bb..aeb636921 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -130,17 +130,9 @@ _Arguments_
 * **device**: the device that will execute the model can be of:
     * **CPU**: a CPU device
     * **GPU**: a GPU device
-* **TAG**: an optional string for tagging the model, such as a version number or any arbitrary identifier
-* **BATCHSIZE**: batch incoming requests from multiple clients if they hit the same model and if input tensors have the same
-                shape. Upon MODELRUN, the request queue is visited, input tensors from compatible requests are concatenated
-                along the 0-th (batch) dimension, up until BATCHSIZE is exceeded. The model is then run for the entire batch,
-                results are unpacked back among the individual requests and the respective clients are unblocked.
-                If the batch size of the inputs to the first request in the queue exceeds BATCHSIZE, the request is served
-                in any case. Default is 0 (no batching).
-* **MINBATCHSIZE**: Do not execute a MODELRUN until the batch size has reached MINBATCHSIZE. This is primarily used to force
-                   batching during testing, but it can also be used under normal operation. In this case, note that requests
-                   for which MINBATCHSIZE is not reached will hang indefinitely.
-                   Default is 0 (no minimum batch size).
+* **TAG**: an optional string for tagging the model such as a version number or any arbitrary identifier
+* **BATCHSIZE**: when provided with an `n` that is greater than 0, the engine will batch incoming requests from multiple clients that use the model with input tensors of the same shape. When `AI.MODELRUN` is called the requests queue is visited and input tensors from compatible requests are concatenated along the 0th (batch) dimension until `n` is exceeded. The model is then run for the entire batch and the results are unpacked back to the individual requests unblocking their respective clients. If the batch size of the inputs to of first request in the queue exceeds `BATCHSIZE`, the request is served immediately (default value: 0).
+* **MINBATCHSIZE**: when provided with an `m` that is greater than 0, the engine will postpone calls to `AI.MODELRUN` until the batch's size had reached `m`. This is primarily used to force batching during testing, but it can also be used under normal operation. In this case, note that requests for which `m` is not reached will hang indefinitely (defai;t value: 0).
 * **INPUTS**: one or more names of the model's input nodes (applicable only for TensorFlow models)
 * **OUTPUTS**: one or more names of the model's output nodes (applicable only for TensorFlow models)
 * **model**: the Protobuf-serialized model
@@ -154,22 +146,10 @@ A simple 'OK' string or an error.
 This example shows to set a model 'mymodel' key using the contents of a local file with [`redis-cli`](https://redis.io/topics/cli). Refer to the [Clients Page](clients.md) for additional client choice that are native to your programming language:
 
 ```
-$ cat mobilenet_v2_1.4_224_frozen.pb | redis-cli -x AI.MODELSET mymodel TF CPU INPUTS input OUTPUTS MobilenetV2/Predictions/Reshape_1
+$ cat resnet50.pb | redis-cli -x AI.MODELSET mymodel TF CPU TAG imagenet:5.0 INPUTS images OUTPUTS output
 OK
 ```
 
-```sql
-AI.MODELSET mnist_net ONNX CPU TAG mnist:lenet:v0.1 < mnist.onnx
-```
-
-```sql
-AI.MODELSET mnist_net ONNX CPU BATCHSIZE 10 < mnist.onnx
-```
-
-```sql
-AI.MODELSET resnet18 TF CPU BATCHSIZE 10 MINBATCHSIZE 6 INPUTS in1 OUTPUTS linear4 < foo.pb
-```
-
 ## AI.MODELGET
 The **`AI.MODELGET`** command returns a model's meta data and blob stored as a key's value.
 
@@ -182,21 +162,24 @@ AI.MODELGET <key> [META | BLOB]
 _Arguments
 
 * **key**: the model's key name
-* META - Only return information on backend, device and tag
-* BLOB - Return information on backend, device and tag, as well as a binary blob containing the serialized model
+* **META**: will only return the model's meta information (default)
+* **BLOB**: will return the model's meta information and a blob containing the serialized model
 
 _Return_
 
-A Bulk String that is the Protobuf-serialized representation of the model by the backend.
+An array of alternating key-value pairs as follows:
 
-The command returns a list of key-value strings, namely `BACKEND backend DEVICE device TAG tag [BLOB blob]`.
+1. **BACKEND**: the backend used by the model as a String
+1. **DEVICE**: the device used to execute the model as a String
+1. **TAG**: the model's tag as a String
+1. **BLOB**: a blob containing the serialized model (when called with the `BLOB` argument) as a String
 
 **Examples**
 
 Assuming that your model is stored under the 'mymodel' key, you can use the following read and save it to local file 'model.ext' with [`redis-cli`](https://redis.io/topics/cli):
 
 ```
-$ redis-cli --raw AI.MODELGET mymodel > model.ext
+$ redis-cli --raw AI.MODELGET mymodel BLOB > model.ext
 ```
 
 ## AI.MODELDEL
@@ -345,11 +328,12 @@ _Arguments_
 
 _Return_
 
-Array consisting of two items:
+An array with alternating entries that represent the following key-value pairs:
 !!!!The command returns a list of key-value strings, namely `DEVICE device TAG tag [SOURCE source]`.
 
-1. The script's device as a String
-2. The script's source code as a String
+1. **DEVICE**: the script's device as a String
+1. **TAG**: the scripts's tag as a String
+1. **SOURCE**: the script's source code as a String
 
 **Examples**
 

From 5c7e849c4d38a249d429c549db3e950d09ce9ff3 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Thu, 9 Apr 2020 15:51:08 +0300
Subject: [PATCH 03/45] WIP

---
 docs/commands.md | 92 ++++++++++++++++++++++++++++++------------------
 1 file changed, 58 insertions(+), 34 deletions(-)

diff --git a/docs/commands.md b/docs/commands.md
index aeb636921..2242d9f80 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -176,7 +176,18 @@ An array of alternating key-value pairs as follows:
 
 **Examples**
 
-Assuming that your model is stored under the 'mymodel' key, you can use the following read and save it to local file 'model.ext' with [`redis-cli`](https://redis.io/topics/cli):
+Assuming that your model is stored under the 'mymodel' key, you can obtain its metadata with:
+
+```
+redis> 1) BACKEND
+2) TF
+3) DEVICE
+4) CPU
+5) TAG
+6) imagenet:5.0
+```
+
+You can also save it to the local file 'model.ext' with [`redis-cli`](https://redis.io/topics/cli) like so:
 
 ```
 $ redis-cli --raw AI.MODELGET mymodel BLOB > model.ext
@@ -245,27 +256,34 @@ OK
 ```
 
 ## AI._MODELLIST
+The **AI._MODELLIST** command returns all the models in the database.
 
-NOTE: `_MODELLIST` is EXPERIMENTAL and might be removed in future versions.
+!!! warning "Experimental API"
+    `AI._MODELLIST` is an EXPERIMENTAL command that may be removed in future versions.
 
-List all models. Returns a list of (key, tag) pairs.
+**Redis API**
 
-```sql
+```
 AI._MODELLIST
 ```
 
-### _MODELLIST Example
+_Arguments_
+
+None.
 
-```sql
-AI.MODELSET model1 TORCH GPU TAG resnet18:v1 < foo.pt
-AI.MODELSET model2 TORCH GPU TAG resnet18:v2 < bar.pt
+_Return_
 
-AI._MODELLIST
+An array with an entry per model. Each entry is a an array with two entries:
 
->  1) 1) "model1"
->  1) 2) "resnet18:v1"
->  2) 1) "model2"
->  2) 2) "resnet18:v2"
+1. The model's key name as a String
+1. The model's tag as a String
+
+**Examples**
+
+```
+redis> > AI._MODELLIST
+1) 1) "mymodel"
+   2) imagenet:5.0
 ```
 
 ## AI.SCRIPTSET
@@ -281,7 +299,7 @@ _Arguments_
 
 
 * **key**: the script's key name
-* TAG tag - Optional string tagging the script, such as a version number or other identifier
+* **TAG**: an optional string for tagging the script such as a version number or any arbitrary identifier
 * **device**: the device that will execute the model can be of:
     * **CPU**: a CPU device
     * **GPU**: a GPU device
@@ -303,14 +321,10 @@ def addtwo(a, b):
 It can be stored as a RedisAI script using the CPU device with [`redis-cli`](https://redis.io/topics/rediscli) as follows:
 
 ```
-$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU
+$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU TAG myscript:v0.1
 OK
 ```
 
-```sql
-AI.SCRIPTSET addscript GPU TAG myscript:v0.1 < addtwo.txt
-```
-
 ## AI.SCRIPTGET
 The **`AI.SCRIPTGET`** command returns the [TorchScript](https://pytorch.org/docs/stable/jit.html) stored as a key's value.
 
@@ -341,8 +355,12 @@ The following shows how to read the script stored at the 'myscript' key:
 
 ```
 redis> AI.SCRIPTGET myscript
-1) CPU
-2) def addtwo(a, b):
+1) DEVICE
+2) CPU
+3) TAG
+4) "myscript:v0.1"
+5) SOURCE
+6) def addtwo(a, b):
     return a + b
 ```
 
@@ -415,29 +433,35 @@ redis> AI.TENSORGET result VALUES
     The execution of scripts may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by `maxmemory` configuration settings of Redis.
 
 ## AI._SCRIPTLIST
+The **AI._SCRIPTLIST** command returns all the scripts in the database.
 
-NOTE: `_SCRIPTLIST` is EXPERIMENTAL and might be removed in future versions.
+!!! warning "Experimental API"
+    `AI._MODELLIST` is an EXPERIMENTAL command that may be removed in future versions.
 
-List all scripts. Returns a list of (key, tag) pairs.
+**Redis API**
 
-```sql
+```
 AI._SCRIPTLIST
 ```
 
-### _SCRIPTLIST Example
+_Arguments_
 
-```sql
-AI.SCRIPTSET script1 GPU TAG addtwo:v0.1 < addtwo.txt
-AI.SCRIPTSET script2 GPU TAG addtwo:v0.2 < addtwo.txt
+None.
 
-AI._SCRIPTLIST
+_Return_
+
+An array with an entry per script. Each entry is a an array with two entries:
+
+1. The script's key name as a String
+1. The script's tag as a String
 
->  1) 1) "script1"
->  1) 2) "addtwo:v0.1"
->  2) 1) "script2"
->  2) 2) "addtwo:v0.2"
+**Examples**
+
+```
+redis> > AI._SCRIPTLIST
+1) 1) "myscript"
+   2) "myscript:v0.1"
 ```
----
 
 ## AI.INFO
 The **`AI.INFO`** command returns information about the execution a model or a script.

From 21f651f8e7992f8087fea5371c8c52c22787affc Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Sun, 19 Apr 2020 01:41:08 +0300
Subject: [PATCH 04/45] WIP

---
 docs/clients.md        |  11 ++
 docs/commands.md       |  55 +++++++-
 docs/configuration.md  | 126 +++++++++++++++++
 docs/configuring.md    | 134 ------------------
 docs/dataandbackend.md |   3 -
 docs/examples.md       |   1 +
 docs/index.md          | 108 +++------------
 docs/intro.md          | 303 +++++++++++++++++++++++++++++++++++++++++
 docs/quickstart.md     |  65 +++++++++
 mkdocs.yml             |  14 +-
 10 files changed, 585 insertions(+), 235 deletions(-)
 create mode 100644 docs/clients.md
 create mode 100644 docs/configuration.md
 delete mode 100644 docs/configuring.md
 delete mode 100644 docs/dataandbackend.md
 create mode 100644 docs/examples.md
 create mode 100644 docs/intro.md
 create mode 100644 docs/quickstart.md

diff --git a/docs/clients.md b/docs/clients.md
new file mode 100644
index 000000000..fd7d10e44
--- /dev/null
+++ b/docs/clients.md
@@ -0,0 +1,11 @@
+# RedisAI Clients
+
+Some languages already have client libraries that provide support for RedisAI's commands. The following table lists the known ones:
+
+| Project | Language | License | Author | URL |
+| ------- | -------- | ------- | ------ | --- |
+| JRedisAI | Java | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/JRedisAI) |
+| redisai-py | Python | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-py) |
+| redisai-go | Go | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-go) |
+
+The full documentation for RedisAI's API can be found at the [Commands page](commands.md).
diff --git a/docs/commands.md b/docs/commands.md
index 2242d9f80..590cd4333 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -27,7 +27,7 @@ AI.TENSORSET <key> <type>
 _Arguments_
 
 * **key**: the tensor's key name
-* **type**: the tensor's data type can be one of: 'FLOAT', 'DOUBLE', 'INT8', 'INT16', 'INT32', 'INT64', 'UINT8' or 'UINT16'
+* **type**: the tensor's data type can be one of: `FLOAT`, `DOUBLE`, `INT8`, `INT16`, `INT32`, `INT64`, `UINT8` or `UINT16`
 * **shape**: one or more dimensions, or the number of elements per axis, for the tensor
 * **BLOB**: indicates that data is in binary format and is provided via the subsequent `data` argument
 * **VALUES**: indicates that data is numeric and is provided by one or more subsequent `val` arguments
@@ -321,7 +321,7 @@ def addtwo(a, b):
 It can be stored as a RedisAI script using the CPU device with [`redis-cli`](https://redis.io/topics/rediscli) as follows:
 
 ```
-$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript CPU TAG myscript:v0.1
+$ cat addtwo.py | redis-cli -x AI.SCRIPTSET myscript addtwo CPU TAG myscript:v0.1
 OK
 ```
 
@@ -466,7 +466,7 @@ redis> > AI._SCRIPTLIST
 ## AI.INFO
 The **`AI.INFO`** command returns information about the execution a model or a script.
 
-Runtime information is collected each time that [`AI.MODELRUN`](#aimodelrun) or [`AI.SCRIPTRUN`]|(#aiscriptrun) is called. The information is stored locally by the executing RedisAI engine, so when deployed in a cluster each shard stores its own runtime information.
+Runtime information is collected each time that [`AI.MODELRUN`](#aimodelrun) or [`AI.SCRIPTRUN`](#aiscriptrun) is called. The information is stored locally by the executing RedisAI engine, so when deployed in a cluster each shard stores its own runtime information.
 
 **Redis API**
 
@@ -520,7 +520,54 @@ redis> AI.INFO myscript
 
 The runtime statistics for that script can be reset like so:
 
-```sql
+```
 redis> AI.INFO myscript RESETSTAT
 OK
 ```
+
+## AI.CONFIG
+The **AI.CONFIG** command sets the value of configuration directives at run-time, and allows loading DL/ML backends dynamically.
+
+!!! info "Loading DL/ML Backends at Bootstrap"
+    Instead of loading your backends dynamically, you can have RedisAI load them during bootstrap. See the [Configuration page](configuration.md) for more information.
+
+**Redis API**
+```
+AI.CONFIG <BACKENDSPATH <path>> | <LOADBACKEND <backend> <path>>
+```
+
+_Arguments_
+
+* **BACKENDSPATH**: Specifies the default base backends path to `path`. The backends path is used when dynamically loading a backend (default: '{module_path}/backends', where `module_path` is the module's path).
+* **LOADBACKEND**: Loads the DL/ML backend specified by the `backend` identifier from `path`. If `path` is relative, it is resolved by prefixing the `BACKENDSPATH` to it. If `path` is absolute then it is used as is. The `backend` can be one of:
+    * **TF**: the TensorFlow backend
+    * **TFLITE**: The TensorFlow Lite backend
+    * **TORCH**: The PyTorch backend
+    * **ONNX**: ONNXRuntime backend
+
+_Return_
+
+A simple 'OK' string or an error.
+
+**Examples**
+
+The following sets the default backends path to '/usr/lib/redis/modules/redisai/backends':
+
+```
+redis> AI.CONFIG BACKENDSPATH /usr/lib/redis/modules/redisai/backends
+OK
+```
+
+This loads the PyTorch backend with a path relative to `BACKENDSPATH`:
+
+```
+redis> AI.CONFIG LOADBACKEND TORCH redisai_torch/redisai_torch.so
+OK
+```
+
+This loads the PyTorch backend with a full path:
+
+```
+redis> AI.CONFIG LOADBACKEND TORCH /usr/lib/redis/modules/redisai/backends/redisai_torch/redisai_torch.so
+OK
+```
diff --git a/docs/configuration.md b/docs/configuration.md
new file mode 100644
index 000000000..1c1ddacc3
--- /dev/null
+++ b/docs/configuration.md
@@ -0,0 +1,126 @@
+# RedisAI Configuration
+RedisAI provides configuration options that control its operation. These options can be set when the module is bootstrapped, that is loaded to Redis, and in some cases also during runtime.
+
+The following sections describe the configuration options the means for setting them.
+
+## Setting Configuration Options
+
+**Bootstrap Configuration**
+
+Configuration options can be set when the module is loaded. The options are passed as a list of option names and their respective values. Configuration is supported both when using the `loadmodule` configuration directive as well as via the [Redis `MODULE LOAD` command](https://redis.io/commands/module-load).
+
+!!! example "Example: Setting configuration options"
+    For setting the module's configuration options from the command line use:
+
+    ```
+    redis-server --loadmodule /usr/lib/redis/modules/redisai.so <opt1> <val1> ...
+    ```
+
+    For setting the module's configuration options in with .conf file use the following format:
+
+    ```
+    loadmodule /usr/lib/redis/modules/redisai.so <opt1> <val1> ...
+    ```
+
+    For setting the module's configuration with the [`MODULE LOAD`](https://redis.io/commands/module-load) command use:
+
+    ```
+    redis> loadmodule /usr/lib/redis/modules/redisai.so <opt1> <val1> ...
+    ```
+
+**Runtime Configuration**
+
+Some configuration options may be set at runtime via the [`AI.CONFIG` command](commands.md#aiconfig).
+
+Refer to each option's description for its runtime configurability.
+
+## Backend
+By default RedisAI doesn't load any of its backend libraries when it is initialized. Backend libraries are then loaded lazily when models that require them are loaded.
+
+The following backend configuration options make it possible to have RedisAI preemptively load one or more backend libraries.
+
+### BACKENDSPATH
+The **BACKENDSPATH** configuration option sets the default base path that RedisAI will use for dynamically loading backend libraries.
+
+_Expected Value_
+
+A String that is an absolute path.
+
+_Default Value_
+
+This option is initialized with the module's path suffixed by '/backends'.
+
+For example, if the module is loaded from '/usr/lib/redis/modules/redisai.so', this option's value will be set by default to "/usr/lib/redis/modules/backends".
+
+_Runtime Configurability_
+
+Supported.
+
+**Examples**
+
+To set the default backends path to '/usr/lib/backends' when loading the module from command line use the following:
+
+```sh
+redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
+               BACKENDSPATH /usr/lib/backends
+```
+
+### TF, TFLITE, TORCH and ONNX
+The **TF**, **TFLITE**, **TORCH** and **ONNX** configuration options load the TensorFlow, TensorFlow Lite, PyTorch and ONNXRuntime backend libraries, respectively.
+
+Each of the options requires a path to the library's binary. If the provided path is a relative path, it is resolved by prefixing the `BACKENDSPATH` to it. If the provided path is an absolute path then it is used as is.
+
+_Expected Value_
+
+A String that is a path.
+
+_Default Value_
+
+Each of the options has its own default values as follows:
+
+* **TF**: `"<BACKENDSPATH>/redisai_tensorflow/redisai_tensorflow.so"`
+* **TFLITE**: `"<BACKENDSPATH>/redisai_tflite/redisai_tflite.so"`
+* **TORCH**: `"<BACKENDSPATH>/redisai_torch/redisai_torch.so"`
+* **ONNX**: `"<BACKENDSPATH>/redisai_onnxruntime/redisai_onnxruntime.so"`
+
+_Runtime Configurability_
+
+Supported.
+
+**Examples**
+
+This loads the PyTorch backend at boot time located at '/usr/lib/backends/torch_custom/torch_xyz.so' using a relative path and `BACKENDSPATH`:
+
+```
+redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
+               BACKENDSPATH /usr/lib/backends \
+               TORCH torch_custom/torch_xyz.so
+```
+
+## Device
+
+### THREADS_PER_QUEUE
+The **THREADS_PER_QUEUE** configuration option controls the number of worker threads allocated to each device's job queue. Multiple threads can be used for executing different independent operations in parallel.
+
+This option significantly improves the performance of simple, low-effort computation-wise models since there is spare computation cycle available from modern CPUs and hardware accelerators (GPUs, TPUs, ...).
+
+_Expected Value_
+
+An Integer greater than zero.
+
+_Default Value_
+
+1
+
+_Runtime Configurability_
+
+Not supported.
+
+**Examples**
+
+To set the number of threads per queue to 4 when loading the module from command line use the following:
+
+```
+redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
+               THREADS_PER_QUEUE 4
+```
diff --git a/docs/configuring.md b/docs/configuring.md
deleted file mode 100644
index 0672a9072..000000000
--- a/docs/configuring.md
+++ /dev/null
@@ -1,134 +0,0 @@
-# Configuration
-
-RedisAI supports both run-time configuration options and others that should be specified when loading the module. 
-
-## Configuration Options During Loading
-
-In general, passing configuration options is done by appending arguments after the `--loadmodule` argument in the command line, `loadmodule` configuration directive in a Redis config file, or the `MODULE LOAD` command. 
-
-The module dynamic library `redisai.so` can be located in any path, provided that we specify the full path or a path relative to where the `redis-server` command is issued. The additional arguments are options passed to the module. Currently the supported options are:
-
-- `BACKENDSPATH`: specify the default backends path used when loading a dynamic backend library.
-- `TORCH`: specify the location of the PyTorch backend library, and dynamically load it. The location can be given in two ways, absolute or relative to the `<BACKENDSPATH>`. Using this option replaces the need for loading the PyTorch backend on runtime.
-- `TF`: specify the location of the TensorFlow backend library, and dynamically load it. The location can be given in two ways, absolute or relative to the `<BACKENDSPATH>`. Using this option replaces the need for loading the TensorFlow backend on runtime.
-- `TFLITE`: specify the location of the TensorFlow Lite backend library, and dynamically load it. The location can be given in two ways, absolute or relative to the `<BACKENDSPATH>`. Using this option replaces the need for loading the TensorFlow Lite backend on runtime.
-- `ONNX`: specify the location of the ONNXRuntime backend library, and dynamically load it. The location can be given in two ways, absolute or relative to the `<BACKENDSPATH>`. Using this option replaces the need for loading the ONNXRuntime backend on runtime.
-- `THREADS_PER_QUEUE`: specify the fixed number of worker threads up front per device. This option is described in detail at [THREADS_PER_QUEUE](##THREADS_PER_QUEUE) section and can be only set when loading the module.
-
-
-### Configuration Examples
-
-In redis.conf:
-
-```
-loadmodule redisai.so OPT1 OPT2
-```
-
-From redis-cli:
-
-```
-127.0.0.6379> MODULE load redisai.so OPT1 OPT2
-```
-
-From command line using relative path:
-
-```
-$ redis-server --loadmodule ./redisai.so OPT1 OPT2
-```
-
-From command line using full path:
-
-```
-$ redis-server --loadmodule /usr/lib/redis/modules/redisai.so OPT1 OPT2
-```
-
-
-### THREADS_PER_QUEUE
-
-```
-THREADS_PER_QUEUE {number}
-```
-Enable configuring the main thread to create a fixed number of worker threads up front per device. This controls the maximum number of threads to be used for parallel execution of independent different operations. 
-
-This option can significantly improve the model run performance for simple models (models that require low computation effort), since there is usually room for extra computation on modern CPU's and hardware accelerators (GPUs, TPUs, etc.).
-
-#### THREADS_PER_QUEUE Default
-
-By default only one worker thread is used per device. 
-
-#### THREADS_PER_QUEUE Example
-
-```
-$ redis-server --loadmodule ./redisai.so THREADS_PER_QUEUE 4
-```
-
----
-
-
-## Setting Configuration Options In Run-Time
-
-### AI.CONFIG BACKENDSPATH
-
-Specify the default backends path to use when dynamically loading a backend. 
-
-```sql
-AI.CONFIG BACKENDSPATH <default_location_of_backend_libraries>
-```
-
-#### AI.CONFIG BACKENDSPATH Example
-
-
-```sql
-AI.CONFIG BACKENDSPATH /usr/lib/redis/modules/redisai/backends
-```
-
-### AI.CONFIG LOADBACKEND
-
-Load a DL/ML backend.
-
-```sql
-AI.CONFIG LOADBACKEND <backend_identifier> <location_of_backend_library>
-```
-
-RedisAI currently supports PyTorch (libtorch), Tensorflow (libtensorflow), TensorFlow Lite, and ONNXRuntime as backends. 
-
-Allowed backend identifiers are:
--  `TF` (TensorFlow)
--  `TFLITE` (TensorFlow Lite)
--  `TORCH` (PyTorch)
--  `ONNX` (ONNXRuntime)
-
-
-
-By default, RedisAI starts with the ability to set and get tensor data, but setting and running models and scritps requires a computing backend to be loaded, which can be done during loading, as [explained above](##-Configuration-Options-During-Loading), or at or run-time using the `AI.CONFIG` commmand.
-
-This command allows to dynamically load a backend by specifying the backend identifier and the path to the backend library. Currently, once loaded, a backend cannot be unloaded, and there can be at most one backend per identifier loaded.
-
-
-If you don't specify a backend on load time, RedisAI will look into the default location lazily, when a model of a given backend is loaded.
-
-The default location relative to the `<BACKENDSPATH>` directory. If unspecified, by default RedisAI will look for:
-- ONNXRuntime dynamic library at: `<BACKENDSPATH>/redisai_onnxruntime/redisai_onnxruntime.so`
-- TensorFlow dynamic library at: `<BACKENDSPATH>/redisai_tensorflow/redisai_tensorflow.so`
-- TensorFlow Lite dynamic library at: `<BACKENDSPATH>/redisai_tflite/redisai_tflite.so`
-- PyTorch dynamic library at: `<BACKENDSPATH>/redisai_torch/redisai_torch.so`
-
-Any library dependency will be resolved automatically, and the mentioned directories are portable on all platforms.
-
-If relative, it is relative to `<BACKENDSPATH>`.
-
-
-#### AI.CONFIG LOADBACKEND Examples
-
- Load the TORCH backend, relative to `BACKENDSPATH`
-
-```sql
-AI.CONFIG LOADBACKEND TORCH redisai_torch/redisai_torch.so
-```
-
- Load the TORCH backend, specifying full path
-
-
-```sql
-AI.CONFIG LOADBACKEND TORCH /usr/lib/redis/modules/redisai/backends/redisai_torch/redisai_torch.so
-```
diff --git a/docs/dataandbackend.md b/docs/dataandbackend.md
deleted file mode 100644
index 5579c438a..000000000
--- a/docs/dataandbackend.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# RedisAI Data Structures and Backends
-
-![Throughput](images/RedisAI_data_structures_and_backends.png "RedisAI Data Structures and Backends")
diff --git a/docs/examples.md b/docs/examples.md
new file mode 100644
index 000000000..2fd9f9570
--- /dev/null
+++ b/docs/examples.md
@@ -0,0 +1 @@
+TBD
\ No newline at end of file
diff --git a/docs/index.md b/docs/index.md
index 103f0eb9e..29aefc713 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -1,99 +1,27 @@
-<img src="images/logo.svg" alt="logo" width="200"/>
-
-# RedisAI
+# <img src="images/logo.svg" alt="logo" style="width: 2em; vertical-align: middle;"/> RedisAI
 [![Mailing List](https://img.shields.io/badge/Mailing%20List-RedisAI-blue)](https://groups.google.com/forum/#!forum/redisai)
 [![Gitter](https://badges.gitter.im/RedisLabs/RedisAI.svg)](https://gitter.im/RedisLabs/RedisAI?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
 
-RedisAI is a Redis module for serving tensors and executing deep learning models.
-
-## 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 a backend (TF, TORCH or ONNX), and set the model
-```sh
-redis-cli AI.CONFIG LOADBACKEND TF install/backends/redisai_tensorflow/redisai_tensorflow.so
-redis-cli -x AI.MODELSET foo TF CPU INPUTS a b OUTPUTS c < test/test_data/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, PyTorch, ONNXRuntime) for your platform.  Note that this requires CUDA to be installed.
-```sh
-bash get_deps.sh
-```
-Alternatively, run the following to only fetch the CPU-only backends.
-```sh
-bash get_deps.sh cpu
-```
+RedisAI is a Redis module for executing Deep Learning/Machine Learning models and managing their data. Its purpose is being a "workhorse" for model serving, by providing out-of-the-box support for popular DL/ML frameworks and unparalleled performance. RedisAI both simplifies the deployment and serving of graphs by leveraging on Redis' production-proven infrastructure, as well as maximizes computation throughput by adhering to the principle of data locality.
 
-After the dependencies are downloaded, build the module itself. Note that
-CMake 3.0 or higher is required.
+## Where Next?
+  * The [Introduction](intro.md) is the recommended starting point
+  * The [Quickstart](quickstart.md) page provides information about building, installing and running RedisAI
+  * The [Commands](commands.md) page is a reference of RedisAI's API
+  * The [Clients](clients.md) page lists RedisAI clients by programming language
+  * The [Configuration](configuration.md) page explains how to configure RedisAI
 
-```sh
-mkdir build
-cd build
-cmake -DDEPS_PATH=../deps/install ..
-make && make install
-cd ..
-```
+## Quick Links
+  * [Source code repository](https://github.com/RedisAI/RedisAI)
+  * [Releases](https://github.com/RedisAI/RedisAI/releases)
+  * [Docker image](https://hub.docker.com/r/redisai/redisai/)
 
-### Running the server
+## Contact Us
+If you have questions, want to provide feedback or perhaps report an issue or [contribute some code](contrib.md), here's where we're listening to you:
 
-You must have a redis-server version 4.0.9 or greater, available in most recent distributions:
-
-```sh
-redis-server --version
-Redis server v=4.0.9 sha=00000000:0 malloc=libc bits=64 build=c49f4faf7c3c647a
-```
-
-To start Redis with the RedisAI module loaded:
-
-```
-redis-server --loadmodule install/redisai.so
-```
-
-## 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-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-py) |
-| redisai-go | Go | BSD-3 | [RedisLabs](https://redislabs.com/) | [Github](https://github.com/RedisAI/redisai-go) |
-
-Full documentation of the api can be found [here](commands.md).
-
-## Mailing List / Forum
-
-Got questions? Feel free to ask at the [RedisAI mailing list](https://groups.google.com/forum/#!forum/redisai).
+  * [Mailing list](https://groups.google.com/forum/#!forum/redisai)
+  * [Gitter chatroom](https://gitter.im/RedisLabs/RedisAI)
+  * [Repository](https://github.com/RedisAI/RedisAI/issues)
 
 ## License
-
-Redis Source Available License Agreement - see [LICENSE](https://raw.githubusercontent.com/RedisAI/RedisAI/master/LICENSE)
+RedisAI is licensed under the [Redis Source Available License Agreement](https://github.com/RedisAI/RedisAI/blob/master/LICENSE).
diff --git a/docs/intro.md b/docs/intro.md
new file mode 100644
index 000000000..5b0af6c41
--- /dev/null
+++ b/docs/intro.md
@@ -0,0 +1,303 @@
+# Introduction to RedisAI
+
+## What is RedisAI?
+RedisAI is a Redis module for executing Deep Learning/Machine Learning models and managing their data. Its purpose is being a "workhorse" for model serving, by providing out-of-the-box support for popular DL/ML frameworks and unparalleled performance. RedisAI both simplifies the deployment and serving of graphs by leveraging on Redis' production-proven infrastructure, as well as maximizes computation throughput by adhering to the principle of data locality.
+
+This introduction is intended to present the core concepts it uses and the functionality it provides.
+
+!!! important "Prerequisites"
+    Before diving into RedisAI please make sure that you are familiar with the basic concepts of machine learning and Redis.
+
+In broad strokes, RedisAI's looks as follows:
+
+![RedisAI Data Structures and Backends](images/RedisAI_data_structures_and_backends.png "RedisAI Data Structures and Backends")
+TBD: diagram needs to be simplified and updated to include TensorFlow Lite.
+
+### What is Redis?
+**Redis** is an ...
+
+!!! quote "https://redis.io"
+    ... open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
+
+!!! tip "Redis is also..."
+    An acronym for **RE**mote **DI**ctionary **S**erver that's pronounced "red" like the color, then "iss".
+
+??? info "Further reference"
+    More about Redis:
+
+      * [Home page](https://redis.io)
+      * [Interactive tutorial](http://try.redis.io)
+      * [Repository](https://github.com/antirez/redis)
+
+Basically, Redis is many things, but one of the things it is best known for is being a _Key-Value_ server. That means that the data in Redis is represented as keys - unique string identifiers - and their respective values.
+
+For example, this shows the basic use of the native [Redis String data structure](https://redis.io/topics/data-types-intro#redis-strings) using the [`SET`](https://redis.io/commands/set) and [`GET`](https://redis.io/commands/get) commands:
+
+```
+redis> SET mykey somevalue
+OK
+redis> GET mykey
+"somevalue"
+```
+
+### What is a Redis Module?
+A **Redis module** is a shared library that can be loaded by the Redis server during runtime. Modules are useful for extending Redis with new data structures and logic. A Redis module leverages on the server's infrastructure. RedisAI is implemented as module, which allows it direct access to the data managed by the server.
+
+??? info "Further reference"
+    You can learn more about Redis modules at:
+
+      * [Redis Modules API](https://redis.io/documentation#redis-modules-api)
+      * [Modules published at redis.io](https://redis.io/modules)
+
+### Why RedisAI?
+RedisAI bundles together best-of-breed technologies for delivering stable and performant graph serving. Every DL/ML framework offers its own backend for executing the graphs developed by it, and the common practice for serving these is building a simple server.
+
+RedisAI aims to be that server, saving you from the need of installing the backend you're using and developing a server for it. By itself that does not justify RedisAI's existence so there's more to it. Because RedisAI is implemented as a Redis module it automatically benefits from the server's capabilities: be it Redis' native data types, its robust eco-system of clients, high-availability, persistence, clustering and Enterprise support.
+
+Because Redis is an in-memory data structure server RedisAI uses it for storing all of its data. The main data type supported by RedisAI is the Tensor that is the standard representation of data in the DL/ML domain. Because tensors are stored memory space of the Redis server they are readily accessible to any of RedisAI's backend libraries at minimal latency.
+
+The locality of data, that is tensor data in adjacency to DL/ML models backends, allows RedisAI to provide optimal performance when serving models. It also makes it a perfect choice for deploying DL/ML models in production and allowing them to be used by any application.
+
+Furthermore, RedisAI is also an optimal testbed for models as it allows the parallel execution of multiple graphs and, in future versions, assessing their respective performance in real-time.
+
+#### Data Structures
+RedisAI provides the following data structures:
+
+* **Tensor**: represents an n-dimensional array of values
+* **Model**: represents a frozen graph by one of the supported DL/ML framework backends
+* **Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
+
+#### DL/ML Backends
+RedisAI supports the following DL/ML identifiers and respective backend libraries:
+
+* **TF**: the TensorFlow backend
+* **TFLITE**: The TensorFlow Lite backend
+* **TORCH**: The PyTorch backend
+* **ONNX**: ONNXRuntime backend
+
+Backend libraries are dynamically loaded as needed, but can also be loaded during booting or at runtime.
+
+??? info "Further reference"
+    Refer to these pages for more information on loading backends:
+
+    * [`AI.CONFIG` command](commands.md#aiconfig)
+    * [Backend configuration](configuration.md#backend)
+
+## Getting Started
+The easiest way to get a standalone Redis server with RedisAI bootstrapped locally is to use the official RedisAI Docker container image:
+
+```
+docker run -d --name redisai -p 6379:6379 redisai/redisai:latest
+```
+
+??? info "Further reference"
+    For more information on installing RedisAI refer to the [Quickstart page](quickstart.md).
+
+Once the server is up, you can connect to it using any [Redis client](https://redis.io/clients). Better yet, some languages already have client implementations for RedisAI - the list can be found at the [Clients page](clients.md). RedisAI clients wrap the core API and simplify the interaction with the module.
+
+We'll begin by using the official [`redis-cli`](https://redis.io/topics/rediscli) Redis client. If you have it locally installed feel free to use that, but it is also available from the container:
+
+```
+docker exec -it redisai redis-cli
+```
+
+## Using RedisAI Tensors
+A **Tensor** is an n-dimensional array and is the standard vehicle for DL/ML data. RedisAI adds to Redis a Tensor data structure that implements the tensor type. Like any datum in Redis, RedisAI's Tensors are identified by key names.
+
+Creating new RedisAI tensors is done with the [`AI.TENSORSET` command](commands.md#aitensorset). For example, consider the tensor: $\begin{equation*} tA = \begin{bmatrix} 2 \\ 3 \end{bmatrix} \end{equation*}$.
+
+We can create the RedisAI Tensor with the key name 'tA' with the following command:
+
+```
+AI.TENSORSET tA FLOAT 2 VALUES 2 3
+```
+
+Copy the command to your cli and hit the `<ENTER>` on your keyboard to execute it. It should looks as follows:
+
+!!! example "Example: setting a tensor"
+    ```
+    $ docker exec -it redisgears redis-cli
+    127.0.0.1:6379> AI.TENSORSET tA FLOAT 2 VALUES 2 3
+    OK
+    ```
+
+The reply 'OK' means that the operation was successful. We've called the `AI.TENSORSET` command to set the key named 'tA' with the tensor's data, but the name could have been any string value. The `FLOAT` argument specifies the type of values that the tensor stores, and in this case a double-precision floating point. After the type argument comes the tensor's shape as a list of its dimensions, or just a single dimension of 2.
+
+The `VALUES` argument tells RedisAI that the tensor's data will be given as a sequence of numeric values, and in this case the numbers 2 and 3. This is useful for development purposes and creating small tensors, however for practical purposes the `AI.TENSORSET` command also supports importing data in binary format.
+
+The Redis key 'tA' now stores a RedisAI Tensor. We can verify that using standard Redis commands such as [`EXISTS`](https://redis.io/commands/exists) and [`TYPE`](https://redis.io/commands/type):
+
+!!! example "Example: tensor key existence and type"
+
+    ```
+    127.0.0.1:6379> EXISTS tA
+    (integer) 1
+    127.0.0.1:6379> TYPE tA
+    AI_TENSOR
+    ```
+
+Using `AI.TENSORSET` with the same key name, as long as it already stores a RedisAI Tensor, will overwrite the existing data with the new. To delete a RedisAI tensor, use the [Redis `DEL` command](https://redis.io/commands/del).
+
+RedisAI Tensors are used as inputs and outputs in the execution of models and scripts. For reading the data from a RedisAI Tensor value there is the [`AI.TENSORGET` command](commands.md#aitensorget):
+
+!!! example "Example: getting a tensor key"
+
+    ```
+    127.0.0.1:6379> AI.TENSORGET tA VALUES
+    1) INT8
+    2) 1) (integer) 2
+    3) 1) (integer) 2
+       1) (integer) 3
+    ```
+
+## Loading Models
+A **Model** is a Deep Learning or Machine Learning frozen graph that was generated by some framework. The RedisAI Model data structure represents a DL/ML model that is stored in the database and can be run.
+
+Models, like any other Redis and RedisAI data structures, are identified by keys. A Model's key is created using the [`AI.MODELSET` command](commands.md#aimodelset) 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/test/test_data/graph.pb).
+
+??? info "Downloading 'graph.pb'"
+    Use a web browser or the command line to download 'graph.pb':
+
+    ```
+    wget https://github.com/RedisAI/RedisAI/raw/master/test/test_data/graph.pb
+    ```
+
+redis-cli doesn't provide a way to read files' contents, so to load the model with it we'll use the command line and output pipes:
+
+```
+cat graph.pb | docker exec -i redisai redis-cli -x \
+               AI.MODELSET mymodel TF CPU INPUTS a b OUTPUTS c
+```
+
+??? example "Example: loading a model from command line"
+    ```
+    $ cat graph.pb | docker exec -i redisai redis-cli -x \
+               AI.MODELSET mymodel TF CPU INPUTS a b OUTPUTS c
+    OK
+    ```
+
+!!! important "Use a client in your language of choice"
+    For practical purposes you are encouraged to use a programmatic Redis or RedisAI client in the language of your choice for interacting with RedisAI. Refer to the following pages for further information:
+
+    * [Redis clients page](https://redis.io/clients)
+    * [RedisAI clients page](clients.md)
+
+Like most commands, `AI.MODELSET`'s first argument is a key's name, which is 'mymodel' in the example. The next two arguments are the model's DL/ML backend and device it will be executed on. 'graph.pb' in the example is a TensorFlow graph and is denoted by `TF` argument. The model will be executed on the CPU as instructed by the `CPU` argument.
+
+TensorFlow models also require declaring the names of their inputs and outputs. The inputs for 'graph.pb' are called 'a' and 'b', whereas its single output is called 'c'. These names are provided as additional arguments after the 'INPUTS' and 'OUTPUTS' arguments, respectively.
+
+## Running Models
+Once a RedisAI Model key has been set with `AI.MODELSET` it can be run with any Tensor keys from the database as its input. The model's output, after it was executed, is stored in RedisAI Tensors as well.
+
+The model stored at 'mymodel' expects two input tensors so we'll use the previously-create 'tA' and create another input tensor, $\begin{equation*} tB = \begin{bmatrix} 3 \\ 5 \end{bmatrix} \end{equation*}$, with the following command:
+
+```
+AI.TENSORSET tB FLOAT 2 VALUES 3 5
+```
+
+The model can now be run with the [`AI.MODELRUN` command](commands.md#aimodelrun) as follows:
+
+```
+AI.MODELRUN mymodel INPUTS tA tB OUTPUTS tResult
+```
+
+!!! example "Example: running a model"
+
+    ```
+    127.0.0.1:6379> AI.TENSORSET tA FLOAT 2 VALUES 2 3
+    OK
+    127.0.0.1:6379> AI.TENSORSET tB FLOAT 2 VALUES 3 5
+    OK
+    127.0.0.1:6379> AI.MODELRUN mymodel INPUTS tA tB OUTPUTS tModel
+    OK
+    ```
+
+The first argument to `AI.MODELRUN` is the name of the key at which the RedisAI Model is stored. The names of RedisAI Tensor keys that follow the `INPUTS` argument are used as input for the model. Similarly, following the `OUTPUT` argument are the key names of RedisAI Tensors that the model outputs.
+
+The inputs for the example are the tensors stored under the 'tA' and 'tB' keys. Once the model's run had finished, a new RedisAI Tensor key called 'tResult' is created and stores the model's output.
+
+!!! example "Example: fetching the model's output"
+
+    ```
+    127.0.0.1:6379> AI.TENSORGET tModel VALUES
+    1) FLOAT
+    2) 1) (integer) 2
+    3) 1) "6"
+       2) "15"
+    ```
+
+The model we've imported from 'graph.pb' takes two input tensors as input and outputs a tensor that is the product of multiplying them. In the case of the example above it looks like this:
+
+$$
+\begin
+{equation*} tModel = tA \times tB =
+\begin{bmatrix}
+2 \\ 3
+\end{bmatrix}
+\times
+\begin{bmatrix}
+3 \\ 5
+\end{bmatrix}
+ =
+\begin{bmatrix}
+2 \times 3  \\ 3 \times 5
+\end{bmatrix}
+ =
+\begin{bmatrix}
+6  \\ 15
+\end{bmatrix}
+\end
+{equation*}
+$$
+
+## Model Management
+The [`AI.MODELGET` command](commands.md#aimodelget) can be used for retrieving information about a model and its serialized blob. The [`AI.INFO` command](commands.md#aiinfo) shows runtime statistics about the model's runs. Lastly, RedisAI Model keys can be deleted with the [`AI.MODELDEL` command](commands.md#aimodeldel).
+
+## Scripting
+RedisAI makes it possible to run [TorchScript](https://pytorch.org/docs/stable/jit.html) with the PyTorch backend. Scripts are useful for performing pre- and post-processing operations on tensors.
+
+The RedisAI Script data structure is managed via a set of dedicated commands, similarly to the models. A RedisAI Script key is:
+
+* Created with the [`AI.SCRIPTSET` command](commands.md#aiscriptset)
+* Run with the [`AI.SCRIPTRUN` command](commands.md#aiscriptrun)
+* Deleted with the [`AI.SCRIPTSEL` command](commands.md#aiscriptdel)
+
+We can create a RedisAI Script that performs the same computation as the 'graph.pb' model. The script can look like this:
+
+```py
+def multiply(a, b):
+    TBD
+```
+
+Assuming that the script is stored in the 'myscript.py' file it can be uploaded via command line and the `AI.SCRIPTSET` command as follows:
+
+```
+cat myscript.py | docker exec -i redisai redis-cli -x AI.SCRIPTSET myscript CPU
+```
+
+This will store the PyTorch Script from 'myscript.py' under the 'myscript' key and will associate it with the CPU device for execution. Once loaded, the script can be run with the following:
+
+```
+AI.SCRIPTRUN myscript multiply INPUTS tA tB OUTPUTS tScript
+```
+
+!!! example "Example: running a script"
+
+    ```
+    127.0.0.1:6379> AI.SCRIPTRUN myscript multiply INPUTS tA tB OUTPUTS tScript
+    OK
+    127.0.0.1:6379> AI.TENSORGET tScript VALUES
+    1) FLOAT
+    2) 1) (integer) 2
+    3) 1) "6"
+       2) "15"
+    ```
+
+## Where Next?
+This covers the basics of RedisAI's data structures and capabilities. For more information refer to:
+
+* The [Commands page](commands.md) provides a reference of the RedisAI API
+* The [Clients page](clients.md) lists RdisAI clients in several programming languages
+* The [Examples page](examples.md) showcases several examples that can be extended
diff --git a/docs/quickstart.md b/docs/quickstart.md
new file mode 100644
index 000000000..b754662fa
--- /dev/null
+++ b/docs/quickstart.md
@@ -0,0 +1,65 @@
+# RedisAI Quickstart
+RedisAI is a Redis module. To run it you'll need a Redis server (v4.0.9 or greater), the module's shared library and its dependencies.
+
+The following sections describe how to get started with RedisAI.
+
+## Docker
+The quickest way to try RedisAI is by launching its official Docker container image:
+
+```
+docker run -p 6379:6379 redisai/redisai:latest
+```
+
+## Building and Running
+You can compile and build the module from its source code.
+
+### Prerequisites
+* CUDA needs to be installed for GPU support.
+* CMake 3.0 or higher needs to be installed.
+* Redis v4.0.9 or greater.
+
+### Get the Source Code
+You can obtain the module's source code by cloning the project's repository using git like so:
+
+```sh
+git clone https://github.com/RedisAI/RedisAI
+```
+
+Switch to the project's directory with:
+
+```sh
+cd RedisAI
+```
+
+### Building the Dependencies
+Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
+
+```sh
+bash get_deps.sh
+```
+
+Alternatively, you can run the following to fetch the CPU-only backends.
+
+```sh
+bash get_deps.sh cpu
+```
+
+### Building the Module
+Once the dependencies have been built, you can build the RedisAI module with:
+
+```sh
+mkdir build
+cd build
+cmake -DDEPS_PATH=../deps/install ..
+make && make install
+cd ..
+```
+
+### Loading the Module
+To load the module on the same server is was compiled on 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.
+
+For example, to load the module from the project's path with a server command line switch use the following:
+
+```sh
+redis-server --loadmodule ./install/redisai.so
+```
diff --git a/mkdocs.yml b/mkdocs.yml
index 2dc5fef1e..13dd218fa 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -1,4 +1,4 @@
-site_name: RedisAI - A Redis module for serving Deep learning and Machine learning models.
+site_name: RedisAI - A Server for Maching and Deep Learning Models
 site_url: http://redisai.io
 repo_url: https://github.com/RedisAI/RedisAI
 repo_name: RedisAI/RedisAI
@@ -27,14 +27,20 @@ extra_javascript:
   - 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.0/MathJax.js?config=TeX-MML-AM_CHTML'
 
 nav:
-  - 'Quickstart': index.md
+  - 'Home': index.md
+  - 'Introduction': intro.md
+  - 'Quickstart': quickstart.md
   - 'Commands': commands.md
-  - 'Backend': dataandbackend.md
-  - 'Configuration': configuring.md
+  - 'Clients': clients.md
+  - 'Configuration': configuration.md
+  - 'Performance': performance.md
+  - 'Examples': examples.md
 
 markdown_extensions:
   - admonition
   - pymdownx.arithmatex
+  - pymdownx.details
+  - pymdownx.superfences
   - codehilite:
       guess_lang: False
   - toc:

From 1b521e717c2ce578b3d063f688b947b8e61cead8 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Sun, 19 Apr 2020 17:20:45 +0300
Subject: [PATCH 05/45] Adds preview

---
 .github/workflows/deploy-docs.yaml | 32 ++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 .github/workflows/deploy-docs.yaml

diff --git a/.github/workflows/deploy-docs.yaml b/.github/workflows/deploy-docs.yaml
new file mode 100644
index 000000000..6ccc80a18
--- /dev/null
+++ b/.github/workflows/deploy-docs.yaml
@@ -0,0 +1,32 @@
+name: Upload draft website to S3
+
+on:
+  push:
+    branches:
+    - docs-ih
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@master
+    - name: Set up Python 3.x
+      uses: actions/setup-python@v1
+      with:
+        python-version: '3.x'
+    - name: Display Python version
+      run: python -c "import sys; print(sys.version)"
+    - name: Install docs dependencies
+      run: pip install -r docs/requirements.txt
+    - name: Build docs
+      run: mkdocs build
+    - uses: jakejarvis/s3-sync-action@master
+      with:
+        args: --acl public-read --follow-symlinks --delete
+      env:
+        AWS_S3_BUCKET: ${{ secrets.DOCS_AWS_S3_BUCKET }}
+        AWS_ACCESS_KEY_ID: ${{ secrets.DOCS_AWS_ACCESS_KEY_ID }}
+        AWS_SECRET_ACCESS_KEY: ${{ secrets.DOCS_AWS_SECRET_ACCESS_KEY }}
+        AWS_REGION: 'us-east-2'
+        SOURCE_DIR: 'site'
\ No newline at end of file

From 91b2987419f34a24be7e1650f729fb90716db73e Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 19 Apr 2020 19:13:50 +0100
Subject: [PATCH 06/45] [add] updated performance section with diagrams and
 some rephrasing

---
 docs/images/flask_modelserver.png   | Bin 0 -> 759974 bytes
 docs/images/redisai_modelserver.png | Bin 0 -> 364955 bytes
 docs/images/tfs_modelserver.png     | Bin 0 -> 729943 bytes
 docs/performance.md                 |  37 +++++++++++++++++++++++-----
 4 files changed, 31 insertions(+), 6 deletions(-)
 create mode 100644 docs/images/flask_modelserver.png
 create mode 100644 docs/images/redisai_modelserver.png
 create mode 100644 docs/images/tfs_modelserver.png

diff --git a/docs/images/flask_modelserver.png b/docs/images/flask_modelserver.png
new file mode 100644
index 0000000000000000000000000000000000000000..4b1ca1933e8a78e5c6117907003f31d50415d9e9
GIT binary patch
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z-SB&Nqm+U)bHZBe&-Lh#8P?5L;%bVJjNZ;@NcUc7<pw?|4QL@SGtk_AOceA00DzQI
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diff --git a/docs/performance.md b/docs/performance.md
index 5e3b62c55..89034a90a 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -4,7 +4,7 @@ To get an early sense of what RedisAI is capable of, you can test it with [`redi
 any other Redis command. However, in order to have more control over the tests, we'll use a 
 a tool written in Go called [_AIBench_](https://github.com/RedisAI/aibench). 
 
-This page is intended to provide clarity on how to obtain the benchmark numbers, and links to the most recent results. We encourage developers, data scientists, and architects to run these benchmarks for themselves on their particular hardware ,datasets, and Model Servers and pull request this documentation with links for the actual numbers.
+This page is intended to provide clarity on how to obtain the benchmark numbers, and links to the most recent results. We encourage developers, data scientists, and architects to run these benchmarks for themselves on their particular hardware,datasets, and Model Servers and pull request this documentation with links for the actual numbers.
 
 
 
@@ -30,10 +30,35 @@ Following the previously described, the predictive model to be developed is a ne
 ### Understanding the benchmark phases
 
 Benchmarking inference performance has four phases: 
-- **Model setup**: This step is specific for each DL solution being tested (see the benchmark GitHub repo for detailed deployment info for each supported DL solution).
-- **Transaction data parsing and reference data generation**: To keep benchmarking results from being affected by generating data on the fly, the proposed benchmark suite can generate the data required for the inference benchmarks first, and we can then use it as input to the benchmarking and reference data-loading phases. All inference benchmarks use the same dataset, together with random deterministic reference data if using the same random seed.
-- **Reference data loading**: The reference data that defines and describes the financial transactions already resides on a datastore common to all benchmarks. We use Redis as the primary datastore for the inference benchmarks.
-- **Inference query execution**: This phase looks at the achievable inference throughput of each Model Serving solution and performs a full percentile inference latency analysis. The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md#aibench-supplemental-guide-dl-rest-api-and-redis). They all share the same flow of requests, as described below.
+
+ - **Model setup**: This step is specific for each DL solution being tested (see the benchmark GitHub repo for detailed deployment info for each supported DL solution).
+
+ - **Transaction data parsing and reference data generation**: To keep benchmarking results from being affected by generating data on the fly, the proposed benchmark suite can generate the data required for the inference benchmarks first, and we can then use it as input to the benchmarking and reference data-loading phases. All inference benchmarks use the same dataset, together with random deterministic reference data if using the same random seed.
+
+ - **Reference data loading**: The reference data that defines and describes the financial transactions already resides on a datastore common to all benchmarks. We use Redis as the primary datastore for the inference benchmarks.
+
+ - **Inference query execution**: This phase looks at the achievable inference throughput of each Model Serving solution and performs a full percentile inference latency analysis. Model serving with and without reference data is supported across all solution to enable a deeper insight on the overall weight of the reference data and the extra network hop to fetch it.
+ 
+
+### Inference query execution diagrams per Model Serving Solution
+
+The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md#aibench-supplemental-guide-dl-rest-api-and-redis).
+  
+Bellow you can find a simplified version of the diagrams, including both tests cases ( with and without reference data).
+
+
+#### RedisAI
+
+![RedisAI Model Serving Solution Diagram](images/redisai_modelserver.png "RedisAI Model Serving Solution Diagram")
+
+
+#### TFServing + Redis
+
+![TFServing + Redis Model Serving Solution Diagram](images/tfs_modelserver.png "TFServing + Redis Model Serving Solution Diagram")
+
+#### Rest API + Redis
+
+![Rest API + Redis Model Serving Solution Diagram](images/flask_modelserver.png "Rest API + Redis Model Serving Solution Diagram")
 
 
 ### Model Serving optimizations
@@ -46,7 +71,7 @@ Some Model Servers have components that are lazily initialized, which can increa
 
 Specifically for TensorFlow serving we’ve achieved a better configuration that enabled for higher inference throughput by setting the inter_op_parallelism_threads option to 4 (enabling us to parallelize operations that have sub-operations that are inherently independent up to a degree of 4 concurrent sub-operations). We improved TensorFlow serving intra_op_parallelism_threads results by allowing the tool to adjust with the default behaviour. 
 
-For RedisAI, we maximized inference throughput by configuring the number of shards on Redis Enterprise to 4 and the number of threads per shard to 2.
+For RedisAI, we maximized inference throughput by limiting the inter-op and intra-op parallelism to 1 per shard, meaning that each added redis standalone instance/shard has an associated backend thread, in a shared-nothing architecture - a strategy that allows to maximize the hardware usage, and maintain a linear scalability both on vertical and horizontal flavours.
 
 For the standard web API Model Server, setting Gunicorn worker processes number to 24 for handling concurrent requests resulted in the highest inference throughput.
 

From 69f94f9ee29a51069562e0ededd4413e67ca1a1b Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 19 Apr 2020 22:38:04 +0100
Subject: [PATCH 07/45] [add] added configuration info regarding
 INTRA_OP_PARALLELISM and INTER_OP_PARALLELISM

---
 docs/configuration.md | 49 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/docs/configuration.md b/docs/configuration.md
index 1c1ddacc3..27c379fae 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -65,6 +65,55 @@ redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
                BACKENDSPATH /usr/lib/backends
 ```
 
+
+### INTER_OP_PARALLELISM
+The **INTER_OP_PARALLELISM** configuration option sets the number of threads used for parallelism between independent operations, by backend libraries. By default, 0 means RedisAI will not enforce a configuration and use the default configuration for each backend library.
+
+_Expected Value_
+
+An Integer greater or equal than zero.
+
+_Default Value_
+
+0
+
+_Runtime Configurability_
+
+Not supported.
+
+**Examples**
+
+To set the number of threads used for parallelism between independent operations to 1, when loading the module from command line use the following:
+
+```sh
+redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
+               INTER_OP_PARALLELISM 1
+```
+
+### INTRA_OP_PARALLELISM
+The **INTRA_OP_PARALLELISM** configuration option sets the number of threads used within an individual operation, by backend libraries. By default, 0 means RedisAI will not enforce a configuration and use the default configuration for each backend library.
+
+_Expected Value_
+
+An Integer greater or equal than zero.
+
+_Default Value_
+
+0
+
+_Runtime Configurability_
+
+Not supported.
+
+**Examples**
+
+To set the number of threads used within an individual operation to 1, when loading the module from command line use the following:
+
+```sh
+redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
+               INTRA_OP_PARALLELISM 1
+```
+
 ### TF, TFLITE, TORCH and ONNX
 The **TF**, **TFLITE**, **TORCH** and **ONNX** configuration options load the TensorFlow, TensorFlow Lite, PyTorch and ONNXRuntime backend libraries, respectively.
 

From d99306cd6f595bc0cc9394002505d487960a4ede Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 19 Apr 2020 23:38:18 +0100
Subject: [PATCH 08/45] [add] added AI.DAGRUN documentation

---
 docs/commands.md | 52 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)

diff --git a/docs/commands.md b/docs/commands.md
index 590cd4333..69cabb2b1 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -463,6 +463,58 @@ redis> > AI._SCRIPTLIST
    2) "myscript:v0.1"
 ```
 
+## AI.DAGRUN
+The **`AI.DAGRUN`** command specifies a direct acyclic graph of operations ( for now we only allow chaining ) to run within RedisAI. 
+It accepts one or more operations, split by the pipe-forward operator (`|>`). 
+
+
+By default, the DAG execution context is local, meaning that loading and persisting tensors should be done explicitly. The user should specify which key tensors to load from keyspace using the `LOAD` keyword, and which command outputs to persist to the keyspace using the `PERSIST` keyspace.
+
+When `PERSIST` is not present, object savings are done locally and kept only during the context of the DAG meaning that no output keys are open.
+
+As an example, if `command 1` sets a tensor, it can be referenced by any further command on the chaining. 
+
+
+**Redis API**
+
+```
+AI.DAGRUN [LOAD <number keys> <key 1> ... <key n>]
+          [PERSIST <number keys> <key 1> ... <key n>] |>
+          [command 1] |>  [command 2] |> [command n]
+```
+
+_Arguments_
+
+* **LOAD**: an optional argument, that denotes the beginning of the input tensors keys' list, followed by the number of keys, and one or more key names
+* **PERSIST**: an optional argument, that denotes the beginning of the output tensors keys' list, followed by the number of keys, and one or more key names
+* **|> command**: the chaining operator, that denotes the beginning of a RedisAI command, followed by one of the supported RedisAI commands ( for now `AI.TENSORGET`, `AI.TENSORGET`, and `AI.MODELRUN` ). Command spliting is done by the presence of another `|>`.
+
+_Return_
+
+An array with an entry reply per command. Each entry format respects the specified command reply.
+
+**Examples**
+
+Assuming that running the model that's stored at 'mymodel', we define a temporary tensor 'mytensor' and use it as input, and persist only one of the two outputs - discarding 'classes' and persisting 'predictions'. In the same command return the tensor value of 'predictions'.  The following command does that:
+
+
+```
+redis> AI.DAGRUN PERSIST 1 predictions |>
+          AI.TENSORSET mytensor FLOAT 1 2 VALUES 5 10 |>
+          AI.MODELRUN mymodel INPUTS mytensor OUTPUTS classes predictions |>
+          AI.TENSORGET predictions VALUES
+1) OK
+2) OK
+3) 1) FLOAT
+   2) 1) (integer) 2
+      2) (integer) 2
+   3) "\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@"
+```
+
+!!! warning "Intermediate memory overhead"
+    The execution of models and scripts within the DAG may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by `maxmemory` configuration settings of Redis.
+
+
 ## AI.INFO
 The **`AI.INFO`** command returns information about the execution a model or a script.
 

From 6042cf26eaec3d06de2a3d1c7c47e94dafef9ab3 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 01:40:42 +0100
Subject: [PATCH 09/45] [wip] kicked off examples page

---
 docs/commands.md                              |   2 +-
 docs/examples.md                              |  73 +++++++++-
 docs/requirements.txt                         |   1 +
 .../tensorflow_resnet50-checkpoint.ipynb      | 131 ++++++++++++++++++
 docs/snippets/notebooks/data/cat.jpg          |   3 +
 .../notebooks/data/cat_classified.jpg         |   3 +
 .../notebooks/data/imagenet_classes.json      |   1 +
 .../models/tensorflow/imagenet/resnet50.pb    |   3 +
 .../tensorflow/mobilenet/mobilenet_224.pb     |   3 +
 .../models/tensorflow/tinyyolo/tinyyolo.pb    |   3 +
 .../tensorflow/tinyyolo/yolo_boxes_script.py  |  89 ++++++++++++
 .../notebooks/tensorflow_resnet50.output      |   5 +
 .../snippets/notebooks/tensorflow_resnet50.py |  56 ++++++++
 mkdocs.yml                                    |   4 +
 14 files changed, 375 insertions(+), 2 deletions(-)
 create mode 100644 docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb
 create mode 100644 docs/snippets/notebooks/data/cat.jpg
 create mode 100644 docs/snippets/notebooks/data/cat_classified.jpg
 create mode 100644 docs/snippets/notebooks/data/imagenet_classes.json
 create mode 100644 docs/snippets/notebooks/models/tensorflow/imagenet/resnet50.pb
 create mode 100644 docs/snippets/notebooks/models/tensorflow/mobilenet/mobilenet_224.pb
 create mode 100644 docs/snippets/notebooks/models/tensorflow/tinyyolo/tinyyolo.pb
 create mode 100644 docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
 create mode 100644 docs/snippets/notebooks/tensorflow_resnet50.output
 create mode 100644 docs/snippets/notebooks/tensorflow_resnet50.py

diff --git a/docs/commands.md b/docs/commands.md
index 69cabb2b1..ab3833494 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -464,7 +464,7 @@ redis> > AI._SCRIPTLIST
 ```
 
 ## AI.DAGRUN
-The **`AI.DAGRUN`** command specifies a direct acyclic graph of operations ( for now we only allow chaining ) to run within RedisAI. 
+The **`AI.DAGRUN`** command specifies a direct acyclic graph of operations to run within RedisAI. 
 It accepts one or more operations, split by the pipe-forward operator (`|>`). 
 
 
diff --git a/docs/examples.md b/docs/examples.md
index 2fd9f9570..a104d4c7c 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -1 +1,72 @@
-TBD
\ No newline at end of file
+# RedisAI Examples
+The following sections consist of various sample projects and python notebook examples showing the uses for RedisAI.
+
+To contribute your example (and get the credit for it), click the "Edit this page" button at the top to submit a Pull Request.
+
+## Sample projects
+This is a list of RedisAI sample projects that can be used as-is or as an inspiration source.
+
+| Example | Description | Author | License | URL |
+| --- | --- | --- | --- | --- |
+| ChatBotDemo | An example of using RedisAI and a Web App for Conversational AI (i.e. chatbot) | [RedisLabs](https://redislabs.com/) | Apache-2.0 | [git](https://github.com/RedisAI/ChatBotDemo) |
+| AnimalRecognitionDemo | An example of using Redis Streams, RedisGears and RedisAI for Realtime Video Analytics (i.e. filtering cats) | [RedisLabs](https://redislabs.com/) | BSD-3-Clause | [git](https://github.com/RedisGears/AnimalRecognitionDemo) |
+| EdgeRealtimeVideoAnalytics | An example of using Redis Streams, RedisGears, RedisAI and RedisTimeSeries for Realtime Video Analytics (i.e. counting people) | [RedisLabs](https://redislabs.com/) | Apache-2.0 | [git](https://github.com/RedisGears/EdgeRealtimeVideoAnalytics) |
+
+## Python notebook examples
+
+### Visual Recognition Models
+
+
+#### Resnet50
+
+Here is an example of Python code that classifies ImageNet classes with ResNet50 using a trained tensorflow model.
+
+**Author: [RedisLabs](https://redislabs.com/)**
+
+**Python notebook example**
+
+```python
+{{ include('notebooks/tensorflow_resnet50.py') }}
+```
+**Sample output**
+
+```bash
+{{ include('notebooks/tensorflow_resnet50.output') }}
+```
+![](./snippets/notebooks/data/cat_classified.jpg )
+
+
+#### YOLO - You only look once
+
+Here is an example of Python code that classifies images with object detection by YOLO algorithm with bounding boxes using a trained tensorflow model.
+
+**Author: [RedisLabs](https://redislabs.com/)**
+
+**Python notebook example**
+
+```python
+TBD
+```
+**Sample output**
+
+```bash
+TBD
+```
+
+### Natural Language Classification
+
+
+Here is an example of Python code that classifies text using a trained Natural Language Classifier model.
+
+**Author: [RedisLabs](https://redislabs.com/)**
+
+**Python notebook example**
+
+```python
+TBD
+```
+**Sample output**
+
+```bash
+TBD
+```
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 73568352f..b8faa0652 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,3 +1,4 @@
 mkdocs>=1.0.4
 mkdocs-material>=4.6.3
 -e git+https://github.com/RedisLabs/mkdocs-versions-menu.git#egg=mkdocs-versions-menu
+-e git+https://github.com/RedisLabs/mkdocs-include.git#egg=mkdocs-include
diff --git a/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb b/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb
new file mode 100644
index 000000000..c82a78074
--- /dev/null
+++ b/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'PIL.Image' has no attribute 'op'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-0a2a397ad722>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# model, image, and image classes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mtf_model_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'./models/tensorflow/imagenet/resnet50.pb'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mimg_jpg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/cat.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mimage_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./data/imagenet_classes.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'PIL.Image' has no attribute 'op'"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --upgrade \"redisai>=0.5.0\"\n",
+    "!pip install --upgrade \"ml2rt\"\n",
+    "!pip install --upgrade \"scikit-image\"\n",
+    "!pip install --upgrade \"numpy\"\n",
+    "!pip install --upgrade \"matplotlib\"\n",
+    "!pip install --upgrade \"pillow>=7.0.0\"\n",
+    "\n",
+    "from PIL import Image\n",
+    "import redisai\n",
+    "import json\n",
+    "import redisai as rai\n",
+    "from ml2rt import load_model\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# model, image, and image classes\n",
+    "tf_model_path = './models/tensorflow/imagenet/resnet50.pb'\n",
+    "img_jpg = Image.op/('./data/cat.jpg')\n",
+    "image_classes = json.load(op/(\"./data/imagenet_classes.json\"))\n",
+    "\n",
+    "# normalize\n",
+    "raw_image = np.array(img_jpg).astype(np.float32)\n",
+    "img = np.expand_dims(raw_image, axis=0)\n",
+    "img /= 256.0\n",
+    "\n",
+    "# load the model from disk\n",
+    "tf_model = load_model(tf_model_path)\n",
+    "\n",
+    "# connect to RedisAI\n",
+    "con = redisai.client.Client(host=\"localhost\", port=6379)\n",
+    "\n",
+    "# set the model ( only once )\n",
+    "con.modelset(\n",
+    "    'resnet', rai.Backend.tf, device,\n",
+    "    inputs=['images'], outputs=['output'], data=tf_model)\n",
+    "\n",
+    "# set the tensor \n",
+    "tensorset_result = con.tensorset('image', tensor=img)\n",
+    "print(tensorset_result)\n",
+    "\n",
+    "# run the model\n",
+    "modelrun_result = con.modelrun('resnet', 'image', 'out')\n",
+    "print(modelrun_result)\n",
+    "\n",
+    "# get results\n",
+    "output_tensor = con.tensorget('out', as_numpy=True)\n",
+    "\n",
+    "# Get the indices of maximum element in numpy array\n",
+    "result = np.where(output_tensor == np.amax(output_tensor))\n",
+    "\n",
+    "print(\"maximum result {:.2f} belongs to image class at position {} which is {}\".format(output_tensor.max(),result[1][0],image_classes[str(result[1][0])]))\n",
+    "# display the image and add image class that it belongs to\n",
+    "plt.figure()\n",
+    "plt.imshow(raw_image)\n",
+    "# plt.title(image_classes[str(result[1][0)])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/docs/snippets/notebooks/data/cat.jpg b/docs/snippets/notebooks/data/cat.jpg
new file mode 100644
index 000000000..674c321b4
--- /dev/null
+++ b/docs/snippets/notebooks/data/cat.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6705f3b1bada344ec2ab7bc33d626ba64eb7f50e1b6bab7e09240e493e91875d
+size 52647
diff --git a/docs/snippets/notebooks/data/cat_classified.jpg b/docs/snippets/notebooks/data/cat_classified.jpg
new file mode 100644
index 000000000..d6f5124fd
--- /dev/null
+++ b/docs/snippets/notebooks/data/cat_classified.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:736790963619339be55eea9ae4986751e744d8e9670a14b3aaf5d95d41fcc056
+size 26775
diff --git a/docs/snippets/notebooks/data/imagenet_classes.json b/docs/snippets/notebooks/data/imagenet_classes.json
new file mode 100644
index 000000000..c4ea65927
--- /dev/null
+++ b/docs/snippets/notebooks/data/imagenet_classes.json
@@ -0,0 +1 @@
+{"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby catamount", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "\"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\"", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "\"academic gown, academic robe, judges robe\"", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "\"carpenters kit, tool kit\"", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "\"jack-o-lantern\"", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "\"loupe, jewelers loupe\"", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "\"pajama, pyjama, pjs, jammies\"", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "\"plane, carpenters plane, woodworking plane\"", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "\"plunger, plumbers helper\"", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "\"potters wheel\"", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "\"spider web, spiders web\"", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue", "1000": "None of the above"}
\ No newline at end of file
diff --git a/docs/snippets/notebooks/models/tensorflow/imagenet/resnet50.pb b/docs/snippets/notebooks/models/tensorflow/imagenet/resnet50.pb
new file mode 100644
index 000000000..b4dfd356e
--- /dev/null
+++ b/docs/snippets/notebooks/models/tensorflow/imagenet/resnet50.pb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7018654e139bbbc15518e204af9c08e84a520247f42e2d81fe547538ab865742
+size 102621616
diff --git a/docs/snippets/notebooks/models/tensorflow/mobilenet/mobilenet_224.pb b/docs/snippets/notebooks/models/tensorflow/mobilenet/mobilenet_224.pb
new file mode 100644
index 000000000..41e3481fd
--- /dev/null
+++ b/docs/snippets/notebooks/models/tensorflow/mobilenet/mobilenet_224.pb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:111479258f3841c93d0a7a377c976c24e8281077818991931429d2277dd88590
+size 24508794
diff --git a/docs/snippets/notebooks/models/tensorflow/tinyyolo/tinyyolo.pb b/docs/snippets/notebooks/models/tensorflow/tinyyolo/tinyyolo.pb
new file mode 100644
index 000000000..cea7a0eac
--- /dev/null
+++ b/docs/snippets/notebooks/models/tensorflow/tinyyolo/tinyyolo.pb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5a48ca053cf228a10023f11f92274efdaac7a0f991d7f15066add62523612137
+size 63481382
diff --git a/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py b/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
new file mode 100644
index 000000000..423c28bde
--- /dev/null
+++ b/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
@@ -0,0 +1,89 @@
+# This will be loaded as script hence no imports
+# import torch
+
+
+def nms(boxes):
+    # assuming only one image in one batch
+    boxes = boxes.squeeze()
+    nms_thresh = 0.45
+    conf_thresh = 0.2
+    no_of_valid_elems = (boxes[:, 4] > conf_thresh).nonzero().numel()
+    boxes_confs_inv = 1 - boxes[:, 4]
+    _, sort_ids = torch.sort(boxes_confs_inv)
+    x1 = boxes[:, 0]
+    y1 = boxes[:, 1]
+    x2 = boxes[:, 2]
+    y2 = boxes[:, 3]
+    area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    for index in range(no_of_valid_elems):
+        i = sort_ids[index]
+        new_ind = index + 1
+        if float(boxes[i, 4]) > conf_thresh:
+            xx1 = torch.max(x1[i], x1[sort_ids[new_ind:]])
+            yy1 = torch.max(y1[i], y1[sort_ids[new_ind:]])
+            xx2 = torch.min(x2[i], x2[sort_ids[new_ind:]])
+            yy2 = torch.min(y2[i], y2[sort_ids[new_ind:]])
+            w = torch.max(torch.zeros(1, device=boxes.device), xx2 - xx1 + 1)
+            h = torch.max(torch.zeros(1, device=boxes.device), yy2 - yy1 + 1)
+            overlap = (w * h) / area[sort_ids[new_ind:]]
+            higher_nms_ind = (overlap > nms_thresh).nonzero()
+            boxes[sort_ids[new_ind:][higher_nms_ind]] = torch.zeros(7, device=boxes.device)
+    return boxes.unsqueeze(0)
+
+
+def get_region_boxes(output):
+    conf_thresh = 0.2
+    num_classes = 20
+    num_anchors = 5
+    anchor_step = 2
+    anchors_ = [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]
+
+    anchors = torch.empty(num_anchors * 2, device=output.device)
+    for i in range(num_anchors * 2):
+        anchors[i] = anchors_[i]
+
+    batch = output.size(0)
+    # assert(output.size(1) == (5+num_classes)*num_anchors)
+    h = output.size(2)
+    w = output.size(3)
+
+    output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w)
+
+    grid_x = torch.linspace(0, w-1, w, device=output.device).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
+    grid_y = torch.linspace(0, h-1, h, device=output.device).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
+    xs = torch.sigmoid(output[0]) + grid_x
+    ys = torch.sigmoid(output[1]) + grid_y
+
+    anchor_w = anchors.view(num_anchors, anchor_step).index_select(1, torch.zeros(1, device=output.device).long())
+    anchor_h = anchors.view(num_anchors, anchor_step).index_select(1, torch.ones(1, device=output.device).long())
+    anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w)
+    anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w)
+    ws = torch.exp(output[2]) * anchor_w.float()
+    hs = torch.exp(output[3]) * anchor_h.float()
+
+    det_confs = torch.sigmoid(output[4])
+
+    cls_confs = torch.softmax(output[5: 5+num_classes].transpose(0,1), dim=1)
+    cls_max_confs, cls_max_ids = torch.max(cls_confs, 1)
+    cls_max_confs = cls_max_confs.view(-1)
+    cls_max_ids = cls_max_ids.view(-1)
+
+    sz_hw = h * w
+    boxes = torch.zeros(batch, h * w * num_anchors, 7)
+    # assuming only one image in a batch
+    x1 = xs / w
+    y1 = ys / h
+    x2 = ws / w
+    y2 = hs / h
+    higher_confs = ((det_confs * cls_max_confs) > conf_thresh).nonzero()
+    no_selected_elems = higher_confs.numel()
+    if no_selected_elems > 0:
+        boxes[:, 0:no_selected_elems] = torch.stack(
+            [x1, y1, x2, y2, det_confs, cls_max_confs, cls_max_ids.float()], dim=1)[higher_confs.squeeze()]
+    return boxes
+
+
+def boxes_from_tf(output):
+    boxes = get_region_boxes(output.permute(0, 3, 1, 2).contiguous())
+    boxes = nms(boxes)
+    return boxes
diff --git a/docs/snippets/notebooks/tensorflow_resnet50.output b/docs/snippets/notebooks/tensorflow_resnet50.output
new file mode 100644
index 000000000..53bd2ca71
--- /dev/null
+++ b/docs/snippets/notebooks/tensorflow_resnet50.output
@@ -0,0 +1,5 @@
+b'OK'
+b'OK'
+maximum result 15.48 belongs to image class at position 282 which is tiger cat
+
+Text(0.5, 1.0, 'tiger cat')
\ No newline at end of file
diff --git a/docs/snippets/notebooks/tensorflow_resnet50.py b/docs/snippets/notebooks/tensorflow_resnet50.py
new file mode 100644
index 000000000..7740110bb
--- /dev/null
+++ b/docs/snippets/notebooks/tensorflow_resnet50.py
@@ -0,0 +1,56 @@
+#%%
+!pip install --upgrade "redisai>=0.5.0"
+!pip install --upgrade "ml2rt"
+!pip install --upgrade "scikit-image"
+!pip install --upgrade "numpy"
+!pip install --upgrade "matplotlib"
+!pip install --upgrade "pillow>=7.0.0"
+
+from PIL import Image
+import redisai
+import json
+import redisai as rai
+from ml2rt import load_model
+import numpy as np
+import matplotlib.pyplot as plt
+
+# model, image, and image classes
+tf_model_path = './models/tensorflow/imagenet/resnet50.pb'
+img_jpg = Image.op/('./data/cat.jpg')
+image_classes = json.load(op/("./data/imagenet_classes.json"))
+
+# normalize
+raw_image = np.array(img_jpg).astype(np.float32)
+img = np.expand_dims(raw_image, axis=0)
+img /= 256.0
+
+# load the model from disk
+tf_model = load_model(tf_model_path)
+
+# connect to RedisAI
+con = redisai.client.Client(host="localhost", port=6379)
+
+# set the model ( only once )
+con.modelset(
+    'resnet', rai.Backend.tf, device,
+    inputs=['images'], outputs=['output'], data=tf_model)
+
+# set the tensor 
+tensorset_result = con.tensorset('image', tensor=img)
+print(tensorset_result)
+
+# run the model
+modelrun_result = con.modelrun('resnet', 'image', 'out')
+print(modelrun_result)
+
+# get results
+output_tensor = con.tensorget('out', as_numpy=True)
+
+# Get the indices of maximum element in numpy array
+result = np.where(output_tensor == np.amax(output_tensor))
+
+print("maximum result {:.2f} belongs to image class at position {} which is {}".format(output_tensor.max(),result[1][0],image_classes[str(result[1][0])]))
+# display the image and add image class that it belongs to
+plt.figure()
+plt.imshow(raw_image)
+plt.title(image_classes[str(result[1][0])])
diff --git a/mkdocs.yml b/mkdocs.yml
index 13dd218fa..5c86297f3 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -45,3 +45,7 @@ markdown_extensions:
       guess_lang: False
   - toc:
       permalink: True
+
+plugins:
+   - include:
+       src_path: docs/snippets

From 75b3c07bfff0de67adcd5781c0ed08a658f414f0 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 01:41:57 +0100
Subject: [PATCH 10/45] [wip] kicked off examples page

---
 .../tensorflow_resnet50-checkpoint.ipynb      | 131 ------------------
 1 file changed, 131 deletions(-)
 delete mode 100644 docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb

diff --git a/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb b/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb
deleted file mode 100644
index c82a78074..000000000
--- a/docs/snippets/notebooks/.ipynb_checkpoints/tensorflow_resnet50-checkpoint.ipynb
+++ /dev/null
@@ -1,131 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'PIL.Image' has no attribute 'op'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-2-0a2a397ad722>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# model, image, and image classes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0mtf_model_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'./models/tensorflow/imagenet/resnet50.pb'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mimg_jpg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./data/cat.jpg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0mimage_classes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./data/imagenet_classes.json\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'PIL.Image' has no attribute 'op'"
-     ]
-    }
-   ],
-   "source": [
-    "!pip install --upgrade \"redisai>=0.5.0\"\n",
-    "!pip install --upgrade \"ml2rt\"\n",
-    "!pip install --upgrade \"scikit-image\"\n",
-    "!pip install --upgrade \"numpy\"\n",
-    "!pip install --upgrade \"matplotlib\"\n",
-    "!pip install --upgrade \"pillow>=7.0.0\"\n",
-    "\n",
-    "from PIL import Image\n",
-    "import redisai\n",
-    "import json\n",
-    "import redisai as rai\n",
-    "from ml2rt import load_model\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# model, image, and image classes\n",
-    "tf_model_path = './models/tensorflow/imagenet/resnet50.pb'\n",
-    "img_jpg = Image.op/('./data/cat.jpg')\n",
-    "image_classes = json.load(op/(\"./data/imagenet_classes.json\"))\n",
-    "\n",
-    "# normalize\n",
-    "raw_image = np.array(img_jpg).astype(np.float32)\n",
-    "img = np.expand_dims(raw_image, axis=0)\n",
-    "img /= 256.0\n",
-    "\n",
-    "# load the model from disk\n",
-    "tf_model = load_model(tf_model_path)\n",
-    "\n",
-    "# connect to RedisAI\n",
-    "con = redisai.client.Client(host=\"localhost\", port=6379)\n",
-    "\n",
-    "# set the model ( only once )\n",
-    "con.modelset(\n",
-    "    'resnet', rai.Backend.tf, device,\n",
-    "    inputs=['images'], outputs=['output'], data=tf_model)\n",
-    "\n",
-    "# set the tensor \n",
-    "tensorset_result = con.tensorset('image', tensor=img)\n",
-    "print(tensorset_result)\n",
-    "\n",
-    "# run the model\n",
-    "modelrun_result = con.modelrun('resnet', 'image', 'out')\n",
-    "print(modelrun_result)\n",
-    "\n",
-    "# get results\n",
-    "output_tensor = con.tensorget('out', as_numpy=True)\n",
-    "\n",
-    "# Get the indices of maximum element in numpy array\n",
-    "result = np.where(output_tensor == np.amax(output_tensor))\n",
-    "\n",
-    "print(\"maximum result {:.2f} belongs to image class at position {} which is {}\".format(output_tensor.max(),result[1][0],image_classes[str(result[1][0])]))\n",
-    "# display the image and add image class that it belongs to\n",
-    "plt.figure()\n",
-    "plt.imshow(raw_image)\n",
-    "# plt.title(image_classes[str(result[1][0)])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
-  },
-  "pycharm": {
-   "stem_cell": {
-    "cell_type": "raw",
-    "metadata": {
-     "collapsed": false
-    },
-    "source": []
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}

From 4e8265c0dde20ef7070605462b4fd8f4c3aa2ffe Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 01:45:33 +0100
Subject: [PATCH 11/45] [wip] wip on documentation

---
 docs/requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index b8faa0652..dc54aaae3 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,4 +1,4 @@
-mkdocs>=1.0.4
-mkdocs-material>=4.6.3
+mkdocs==1.0.4
+mkdocs-material==4.6.3
 -e git+https://github.com/RedisLabs/mkdocs-versions-menu.git#egg=mkdocs-versions-menu
 -e git+https://github.com/RedisLabs/mkdocs-include.git#egg=mkdocs-include

From e20aff8e6637ffaa107ef66d619ce90d2bb5e08d Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 01:50:49 +0100
Subject: [PATCH 12/45] [wip] wip on documentation

---
 docs/examples.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples.md b/docs/examples.md
index a104d4c7c..5690ff11b 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -33,7 +33,7 @@ Here is an example of Python code that classifies ImageNet classes with ResNet50
 ```bash
 {{ include('notebooks/tensorflow_resnet50.output') }}
 ```
-![](./snippets/notebooks/data/cat_classified.jpg )
+![](snippets/notebooks/data/cat_classified.jpg)
 
 
 #### YOLO - You only look once

From 23ed2f5a60ccdb9e2408c4989dd117dd5a55e5cd Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 02:00:58 +0100
Subject: [PATCH 13/45] [wip] chaning example image location

---
 docs/examples.md                                            | 2 +-
 docs/{snippets/notebooks/data => images}/cat_classified.jpg | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename docs/{snippets/notebooks/data => images}/cat_classified.jpg (100%)

diff --git a/docs/examples.md b/docs/examples.md
index 5690ff11b..8a1ed294d 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -33,7 +33,7 @@ Here is an example of Python code that classifies ImageNet classes with ResNet50
 ```bash
 {{ include('notebooks/tensorflow_resnet50.output') }}
 ```
-![](snippets/notebooks/data/cat_classified.jpg)
+![](images/cat_classified.jpg)
 
 
 #### YOLO - You only look once
diff --git a/docs/snippets/notebooks/data/cat_classified.jpg b/docs/images/cat_classified.jpg
similarity index 100%
rename from docs/snippets/notebooks/data/cat_classified.jpg
rename to docs/images/cat_classified.jpg

From 80dab804b405b37666886e7d1b025ed9c1c6ef51 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 02:05:36 +0100
Subject: [PATCH 14/45] [wip] experimenting with image positioning on examples

---
 docs/examples.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/examples.md b/docs/examples.md
index 8a1ed294d..ce6d0bf95 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -33,7 +33,8 @@ Here is an example of Python code that classifies ImageNet classes with ResNet50
 ```bash
 {{ include('notebooks/tensorflow_resnet50.output') }}
 ```
-![](images/cat_classified.jpg)
+
+<img src="/images/cat_classified.jpg" alt="cat_classified""/> 
 
 
 #### YOLO - You only look once

From c7977ef0ad6a3ed0bc175abf2e2a3ef8ed0a7075 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 02:11:50 +0100
Subject: [PATCH 15/45] [fix] correcting classified image path

---
 docs/examples.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples.md b/docs/examples.md
index ce6d0bf95..a6a9c2c9d 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -34,7 +34,7 @@ Here is an example of Python code that classifies ImageNet classes with ResNet50
 {{ include('notebooks/tensorflow_resnet50.output') }}
 ```
 
-<img src="/images/cat_classified.jpg" alt="cat_classified""/> 
+<img src="images/cat_classified.jpg" alt="cat classified image"/> 
 
 
 #### YOLO - You only look once

From 274c8f6586ecde960eb47862c441bb762383f43f Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 02:15:44 +0100
Subject: [PATCH 16/45] [fix] fixed example image path

---
 docs/examples.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/examples.md b/docs/examples.md
index a6a9c2c9d..ade435fdf 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -34,7 +34,7 @@ Here is an example of Python code that classifies ImageNet classes with ResNet50
 {{ include('notebooks/tensorflow_resnet50.output') }}
 ```
 
-<img src="images/cat_classified.jpg" alt="cat classified image"/> 
+![](images/cat_classified.jpg)
 
 
 #### YOLO - You only look once

From a3a5916a56665585615ce6a01471cf8c22aa474d Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 22 Apr 2020 10:16:53 +0100
Subject: [PATCH 17/45] [fix] fixed TBD on scriptrun example

---
 docs/intro.md | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/docs/intro.md b/docs/intro.md
index 5b0af6c41..0b56bee35 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -268,7 +268,7 @@ We can create a RedisAI Script that performs the same computation as the 'graph.
 
 ```py
 def multiply(a, b):
-    TBD
+    return a * b
 ```
 
 Assuming that the script is stored in the 'myscript.py' file it can be uploaded via command line and the `AI.SCRIPTSET` command as follows:
@@ -286,6 +286,10 @@ AI.SCRIPTRUN myscript multiply INPUTS tA tB OUTPUTS tScript
 !!! example "Example: running a script"
 
     ```
+    127.0.0.1:6379> AI.TENSORSET tA FLOAT 2 VALUES 2 3
+    OK
+    127.0.0.1:6379> AI.TENSORSET tB FLOAT 2 VALUES 3 5
+    OK
     127.0.0.1:6379> AI.SCRIPTRUN myscript multiply INPUTS tA tB OUTPUTS tScript
     OK
     127.0.0.1:6379> AI.TENSORGET tScript VALUES

From a57e2a85434ff5b83ca8211f4184a84405a9f6f8 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 22 Apr 2020 18:02:40 +0300
Subject: [PATCH 18/45] ASCII-fies diagram

---
 docs/intro.md | 38 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 36 insertions(+), 2 deletions(-)

diff --git a/docs/intro.md b/docs/intro.md
index 0b56bee35..8c7ec184e 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -10,8 +10,42 @@ This introduction is intended to present the core concepts it uses and the funct
 
 In broad strokes, RedisAI's looks as follows:
 
-![RedisAI Data Structures and Backends](images/RedisAI_data_structures_and_backends.png "RedisAI Data Structures and Backends")
-TBD: diagram needs to be simplified and updated to include TensorFlow Lite.
+```+-----------------------------------------------------------------------------+
+| SERVER                                                                      |
+| +-------------------------------------------------------------------------+ |
+| | REDIS                                                                   | |
+| |                                    +----------+                         | |
+| | +----------+   +-----------------+ | Commands | +---------------------+ | |
+| | | KEYSPACE |   |  REDISAI          +----+-----+                       | | |
+| | |          |   |                        ^                             | | |
+| | +----------+   |  Data Structures       |             DL/ML Backends  | | |
+| | |          |   |  +--------+            |             +-------------+ | | |
+| | | mytensor +----->+ Tensor +<--+        |         +-->+ TensorFlow  | | | |
+| | |          |   |  +--------+   |        |         |   +-------------+ | | |
+| | +----------+   |               |        |         |                 | | | |
+| | |          |   |  +--------+   |        v         |   +-------------+ | | |
+| | | mymodel  +----->+ Model  +---+   +----+-----+   +-->+   PyTorch   | | | |
+| | |          |   |  +--------+   |   |          |   |   +-------------+ | | |
+| | +----------+   |               +-->+  Engine  +<--+                   | | |
+| | |          |   |  +--------+   |   |          |   |   +-------------+ | | |
+| | | myscript +----->+ Script +---+   +----+-----+   +-->+ ONNXRuntime | | | |
+| | |          |   |  +--------+   |        ^         |   +-------------+ | | |
+| | +----------+   |               |        |         |                   | | |
+| | |          |   |  +--------+   |        |         |   +-------------+ | | |
+| | | mydag    +----->+  DAG   +---+        |         +-->+     ...     | | | |
+| | |          |   |  +--------+            |             +-------------+ | | |
+| | +----------+   +------------------------|-----------------------------+ | |
+| +--------------+--------------------------|-------------------------------+ |
+|                ^                          |                                 |
+|                v                          v                                 |
+| +--------------+-----------------+   +------------------------------------+ |
+| | RAM                            |   | DEVICES                            | |
+| |                                |   | +-----+           +-----+  +-----+ | |
+| | 00101010 00101010 00101010 ... |   | | GPU |           | CPU |  | ... | | |
+| |                                |   | +-----+           +-----+  +-----+ | |
+| +--------------------------------+   +------------------------------------+ |
++-----------------------------------------------------------------------------+
+```
 
 ### What is Redis?
 **Redis** is an ...

From 760bb5606272a9939b274ccbf29015d2d510135b Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 22 Apr 2020 18:03:58 +0300
Subject: [PATCH 19/45] ASCII-fies diagram

---
 docs/intro.md | 38 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 36 insertions(+), 2 deletions(-)

diff --git a/docs/intro.md b/docs/intro.md
index 0b56bee35..57264d4ec 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -10,8 +10,42 @@ This introduction is intended to present the core concepts it uses and the funct
 
 In broad strokes, RedisAI's looks as follows:
 
-![RedisAI Data Structures and Backends](images/RedisAI_data_structures_and_backends.png "RedisAI Data Structures and Backends")
-TBD: diagram needs to be simplified and updated to include TensorFlow Lite.
+```+-----------------------------------------------------------------------------+
+| SERVER                                                                      |
+| +-------------------------------------------------------------------------+ |
+| | REDIS                                                                   | |
+| |                                    +----------+                         | |
+| | +----------+   +-----------------+ | Commands | +---------------------+ | |
+| | | KEYSPACE |   |  REDISAI          +----+-----+                       | | |
+| | |          |   |                        ^                             | | |
+| | +----------+   |  Data Structures       |             DL/ML Backends  | | |
+| | |          |   |  +--------+            |             +-------------+ | | |
+| | | mytensor +----->+ Tensor +<--+        |         +-->+ TensorFlow  | | | |
+| | |          |   |  +--------+   |        |         |   +-------------+ | | |
+| | +----------+   |               |        |         |                 | | | |
+| | |          |   |  +--------+   |        v         |   +-------------+ | | |
+| | | mymodel  +----->+ Model  +---+   +----+-----+   +-->+   PyTorch   | | | |
+| | |          |   |  +--------+   |   |          |   |   +-------------+ | | |
+| | +----------+   |               +-->+  Engine  +<--+                   | | |
+| | |          |   |  +--------+   |   |          |   |   +-------------+ | | |
+| | | myscript +----->+ Script +---+   +----+-----+   +-->+ ONNXRuntime | | | |
+| | |          |   |  +--------+   |        ^         |   +-------------+ | | |
+| | +----------+   |               |        |         |                   | | |
+| | |          |   |  +--------+   |        |         |   +-------------+ | | |
+| | | mydag    +----->+  DAG   +---+        |         +-->+     ...     | | | |
+| | |          |   |  +--------+            |             +-------------+ | | |
+| | +----------+   +------------------------|-----------------------------+ | |
+| +--------------+--------------------------|-------------------------------+ |
+|                ^                          |                                 |
+|                v                          v                                 |
+| +--------------+-----------------+   +------------------------------------+ |
+| | RAM                            |   | DEVICES                            | |
+| |                                |   | +-----+  +-----+  +-----+  +-----+ | |
+| | 00101010 00101010 00101010 ... |   | | CPU |  | GPU |  | TPU |  | ... | | |
+| |                                |   | +-----+  +-----+  +-----+  +-----+ | |
+| +--------------------------------+   +------------------------------------+ |
++-----------------------------------------------------------------------------+
+```
 
 ### What is Redis?
 **Redis** is an ...

From ad964c5630f2b7a48d0cf080d0682888e20732c7 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 22 Apr 2020 18:08:36 +0300
Subject: [PATCH 20/45] Dang the diagram

---
 docs/intro.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs/intro.md b/docs/intro.md
index 66e70342b..617bbf98a 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -10,7 +10,8 @@ This introduction is intended to present the core concepts it uses and the funct
 
 In broad strokes, RedisAI's looks as follows:
 
-```+-----------------------------------------------------------------------------+
+```
++-----------------------------------------------------------------------------+
 | SERVER                                                                      |
 | +-------------------------------------------------------------------------+ |
 | | REDIS                                                                   | |
@@ -44,7 +45,6 @@ In broad strokes, RedisAI's looks as follows:
 | | 00101010 00101010 00101010 ... |   | | CPU |  | GPU |  | TPU |  | ... | | |
 | |                                |   | +-----+  +-----+  +-----+  +-----+ | |
 | +--------------------------------+   +------------------------------------+ |
-
 +-----------------------------------------------------------------------------+
 ```
 

From caa0d1fd5d853da30b2097f0c60f8a4cf7a79e41 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 22 Apr 2020 18:10:43 +0300
Subject: [PATCH 21/45] Diagram again

---
 docs/intro.md | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/docs/intro.md b/docs/intro.md
index 617bbf98a..ca4378349 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -14,8 +14,7 @@ In broad strokes, RedisAI's looks as follows:
 +-----------------------------------------------------------------------------+
 | SERVER                                                                      |
 | +-------------------------------------------------------------------------+ |
-| | REDIS                                                                   | |
-| |                                    +----------+                         | |
+| | REDIS                              +----------+                         | |
 | | +----------+   +-----------------+ | Commands | +---------------------+ | |
 | | | KEYSPACE |   |  REDISAI          +----+-----+                       | | |
 | | |          |   |                        ^                             | | |
@@ -35,9 +34,8 @@ In broad strokes, RedisAI's looks as follows:
 | | |          |   |  +--------+   |        |         |   +-------------+ | | |
 | | | mydag    +----->+  DAG   +---+        |         +-->+     ...     | | | |
 | | |          |   |  +--------+            |             +-------------+ | | |
-| | +----------+   +------------------------|-----------------------------+ | |
-| +--------------+--------------------------|-------------------------------+ |
-|                ^                          |                                 |
+| | +----------+ ^ +------------------------|-----------------------------+ | |
+| +--------------|--------------------------|-------------------------------+ |
 |                v                          v                                 |
 | +--------------+-----------------+   +------------------------------------+ |
 | | RAM                            |   | DEVICES                            | |

From 0fd5c4f2981888d9626155474f960090e5fd64b1 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Thu, 23 Apr 2020 01:04:30 +0100
Subject: [PATCH 22/45] [add] kick-off the developer notes page

---
 docs/developer.md | 92 +++++++++++++++++++++++++++++++++++++++++++++++
 docs/index.md     |  2 ++
 2 files changed, 94 insertions(+)
 create mode 100644 docs/developer.md

diff --git a/docs/developer.md b/docs/developer.md
new file mode 100644
index 000000000..18aa0830e
--- /dev/null
+++ b/docs/developer.md
@@ -0,0 +1,92 @@
+# RedisAI Development
+
+The following sections discusses topics relevant to the development of the RedisAI module itself. We'll start by refering to the general design, followed by the source code layout, and how to prepare your local test and development environment.
+
+## General Design
+
+RedisAI bundles together best-of-breed technologies for delivering stable and performant graph serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain.
+
+### Data Structures
+RedisAI provides the following data structures:
+
+* **Tensor**: represents an n-dimensional array of values
+* **Model**: represents a frozen graph by one of the supported DL/ML framework backends
+* **Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
+
+TBD
+
+## Source code layout
+
+TBD
+
+## Building and Testing
+You can compile and build the module from its source code.
+
+### Prerequisites
+* CUDA needs to be installed for GPU support.
+* CMake 3.0 or higher needs to be installed.
+* Redis v4.0.9 or greater.
+
+### Get the Source Code
+You can obtain the module's source code by cloning the project's repository using git like so:
+
+```sh
+git clone https://github.com/RedisAI/RedisAI
+```
+
+Switch to the project's directory with:
+
+```sh
+cd RedisAI
+```
+
+### Building the Dependencies
+Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
+
+```sh
+bash get_deps.sh
+```
+
+Alternatively, you can run the following to fetch the CPU-only backends.
+
+```sh
+bash get_deps.sh cpu
+```
+
+### Building the Module
+Once the dependencies have been built, you can build the RedisAI module with:
+
+```sh
+make -C opt build
+```
+
+### Running Tests
+
+The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run
+them you'll need:
+
+* lcov (for coverage analysis, on Linux)
+* Python and a few packages (e.g. pytest, redis, etc.)
+* redis-server in your PATH, or in `../redis/src`.
+
+To run tests in a Python virtualenv, follow these steps:
+
+    $ mkdir -p .env
+    $ virtualenv .env
+    $ . .env/bin/active
+    $ pip install -r tests/tests_requirements.txt
+    $ make -C opt tests
+
+Integration tests are based on RLTest, and specific setup parameters can be provided
+to configure tests.
+
+For example, ... ( TBD )
+
+### Unit Test Coverage
+
+TBD
+
+### Integration Tests Coverage
+
+TBD 
+
diff --git a/docs/index.md b/docs/index.md
index 29aefc713..788ea51ab 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -10,6 +10,8 @@ RedisAI is a Redis module for executing Deep Learning/Machine Learning models an
   * The [Commands](commands.md) page is a reference of RedisAI's API
   * The [Clients](clients.md) page lists RedisAI clients by programming language
   * The [Configuration](configuration.md) page explains how to configure RedisAI
+  * The [Developer](developer.md) page has more information about the design and implementation of the RedisAI module and how to contribute.
+
 
 ## Quick Links
   * [Source code repository](https://github.com/RedisAI/RedisAI)

From 83f4fe87488875e3e8025ef5d746cb0edaf198a4 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 00:25:04 +0100
Subject: [PATCH 23/45] [wip] kick-off developer page. reference dlpack on
 documentation

---
 docs/developer.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/docs/developer.md b/docs/developer.md
index 18aa0830e..df583ff81 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -4,7 +4,10 @@ The following sections discusses topics relevant to the development of the Redis
 
 ## General Design
 
-RedisAI bundles together best-of-breed technologies for delivering stable and performant graph serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain.
+RedisAI bundles together best-of-breed technologies for delivering stable and performant model serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain. 
+
+
+As a way of representing tensor data we've embraced [dlpack](https://github.com/dmlc/dlpack) - a community effort to define a common tensor data structure that can be shared by different frameworks, supported by cuPy, cuDF, DGL, TGL, PyTorch, and MxNet.
 
 ### Data Structures
 RedisAI provides the following data structures:

From c9a288ae929b8387b0b31e32b2eeb2fc46ae667d Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 01:00:33 +0100
Subject: [PATCH 24/45] [add] added Developer reference link

---
 mkdocs.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mkdocs.yml b/mkdocs.yml
index 5c86297f3..be8de864b 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -33,6 +33,7 @@ nav:
   - 'Commands': commands.md
   - 'Clients': clients.md
   - 'Configuration': configuration.md
+  - 'Developer': developer.md
   - 'Performance': performance.md
   - 'Examples': examples.md
 

From c34d5177428917c4b1517ec85a4f11f574bb1b06 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 01:28:52 +0100
Subject: [PATCH 25/45] [wip] added main source files to document

---
 docs/developer.md | 60 ++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 59 insertions(+), 1 deletion(-)

diff --git a/docs/developer.md b/docs/developer.md
index df583ff81..13d4aca57 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -20,7 +20,65 @@ TBD
 
 ## Source code layout
 
-TBD
+Inside the root are the following important directories:
+
+* `src`: contains the RedisAI implementation, written in C.
+* `opt`: contains the helper scriptsto setup the development environment.
+* `tests`: contains the unit and flow tests, implemented with python and RLTest.
+* `deps`: contains libraries RedisAI uses.
+
+We'll focus mostly on `src`, where the RedisAI implementation is contained,
+exploring what there is inside each file. The order in which files are
+exposed is the logical one to follow in order to disclose different layers
+of complexity incrementally.
+
+### redisai.h
+---
+TBD...
+
+### redisai.c 
+---
+
+This is the entry point of the RedisAI module, TBD...
+
+### tensor.c
+---
+TBD...
+
+### model.c
+---
+TBD...
+
+### script.c
+---
+TBD...
+
+### dag.c
+---
+TBD...
+
+
+### run_info.c
+---
+TBD...
+
+### model_script_run_session.c
+---
+TBD...
+
+### background_workers.c
+---
+TBD...
+
+### Backends directory
+---
+TBD...
+
+### Other C files
+---
+
+TBD...
+
 
 ## Building and Testing
 You can compile and build the module from its source code.

From d0b5484a4aa9ea0d51b684aa21f83ed4fd21fafe Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 14:49:55 +0100
Subject: [PATCH 26/45] [wip] improved code documentation of dag.h/c err.h/c
 and tensor.h/c

---
 docs/developer.md |   8 ++-
 src/dag.c         |   7 +++
 src/dag.h         |   7 +++
 src/err.c         |   6 +++
 src/err.h         |  20 +++++++
 src/tensor.c      |   9 ++++
 src/tensor.h      | 134 ++++++++++++++++++++++++++++++++++++----------
 7 files changed, 161 insertions(+), 30 deletions(-)

diff --git a/docs/developer.md b/docs/developer.md
index 13d4aca57..50bf8f578 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -43,7 +43,7 @@ This is the entry point of the RedisAI module, TBD...
 
 ### tensor.c
 ---
-TBD...
+Contains the helper methods for both creating, populating, managing and destructing the RedisAI_TensorType, and methods to manage parsing and replying of tensor related commands or operations.
 
 ### model.c
 ---
@@ -55,7 +55,7 @@ TBD...
 
 ### dag.c
 ---
-TBD...
+ Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
 
 
 ### run_info.c
@@ -70,6 +70,10 @@ TBD...
 ---
 TBD...
 
+### err.c
+---
+Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
+
 ### Backends directory
 ---
 TBD...
diff --git a/src/dag.c b/src/dag.c
index 0c242fd2b..ba8794780 100644
--- a/src/dag.c
+++ b/src/dag.c
@@ -1,3 +1,10 @@
+/**
+ * dag.c
+ *
+ * Contains the helper methods for both parsing, running the command in the
+ * background, and replying DAG structured commands.
+ */
+
 #include "dag.h"
 
 #include <pthread.h>
diff --git a/src/dag.h b/src/dag.h
index ee274c158..9c9787a15 100644
--- a/src/dag.h
+++ b/src/dag.h
@@ -1,3 +1,10 @@
+/**
+ * dag.h
+ *
+ * Contains headers for the helper methods for both parsing, running the command in the
+ * background, and replying DAG structured commands.
+ */
+
 #ifndef SRC_DAG_H_
 #define SRC_DAG_H_
 
diff --git a/src/err.c b/src/err.c
index 4fbce946a..9a7224eee 100644
--- a/src/err.c
+++ b/src/err.c
@@ -1,3 +1,9 @@
+/**
+ * err.c
+ *
+ * Contains a formal API to create, initialize, get, reset, and free errors among different backends.
+ */
+
 #include "err.h"
 #include "stdlib.h"
 #include "assert.h"
diff --git a/src/err.h b/src/err.h
index 087b1b6e1..50af9f3ee 100644
--- a/src/err.h
+++ b/src/err.h
@@ -1,3 +1,9 @@
+/**
+ * err.h
+ *
+ * Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
+ */
+
 #ifndef SRC_ERR_H_
 #define SRC_ERR_H_
 
@@ -35,10 +41,24 @@ typedef struct RAI_Error {
  */
 int RAI_InitError(RAI_Error **err);
 
+/**
+ * Populates the RAI_Error data structure with the error details
+ * @param err
+ * @param code
+ * @param detail
+ */
 void RAI_SetError(RAI_Error *err, RAI_ErrorCode code, const char *detail);
 
+/**
+ * Resets an previously used/allocated RAI_Error
+ * @param err
+ */
 void RAI_ClearError(RAI_Error *err);
 
+/**
+ * Frees the memory of the RAI_Error
+ * @param err
+ */
 void RAI_FreeError(RAI_Error *err);
 
 #endif
\ No newline at end of file
diff --git a/src/tensor.c b/src/tensor.c
index 5d5176dbb..dc2e277b8 100644
--- a/src/tensor.c
+++ b/src/tensor.c
@@ -1,3 +1,12 @@
+/**
+ * tensor.c
+ *
+ * Contains the helper methods for both creating, populating,
+ * managing and destructing the RedisAI_TensorType, and methods to manage
+ * parsing and replying of tensor related commands or operations.
+ *
+ */
+
 #include "tensor.h"
 #include "err.h"
 #include "tensor_struct.h"
diff --git a/src/tensor.h b/src/tensor.h
index 2598af9ae..b1afd08df 100644
--- a/src/tensor.h
+++ b/src/tensor.h
@@ -1,18 +1,28 @@
+/**
+ * tensor.h
+ *
+ * Contains headers for the helper methods for both creating, populating,
+ * managing and destructing the RedisAI_TensorType, and methods to manage
+ * parsing and replying of tensor related commands or operations.
+ *
+ */
+
 #ifndef SRC_TENSOR_H_
 #define SRC_TENSOR_H_
 
 #include "config.h"
-#include "tensor_struct.h"
 #include "dlpack/dlpack.h"
+#include "err.h"
 #include "redismodule.h"
+#include "tensor_struct.h"
 #include "util/dict.h"
-#include "err.h"
 
 #define TENSORALLOC_NONE 0
 #define TENSORALLOC_ALLOC 1
 #define TENSORALLOC_CALLOC 2
 
-// Numeric data type of tensor elements, one of FLOAT, DOUBLE, INT8, INT16, INT32, INT64, UINT8, UINT16
+// Numeric data type of tensor elements, one of FLOAT, DOUBLE, INT8, INT16,
+// INT32, INT64, UINT8, UINT16
 static const char* RAI_DATATYPE_STR_FLOAT = "FLOAT";
 static const char* RAI_DATATYPE_STR_DOUBLE = "DOUBLE";
 static const char* RAI_DATATYPE_STR_INT8 = "INT8";
@@ -22,11 +32,13 @@ static const char* RAI_DATATYPE_STR_INT64 = "INT64";
 static const char* RAI_DATATYPE_STR_UINT8 = "UINT8";
 static const char* RAI_DATATYPE_STR_UINT16 = "UINT16";
 
-extern RedisModuleType *RedisAI_TensorType;
+extern RedisModuleType* RedisAI_TensorType;
 
 int RAI_TensorInit(RedisModuleCtx* ctx);
-RAI_Tensor* RAI_TensorCreate(const char* dataType, long long* dims, int ndims, int hasdata);
-RAI_Tensor* RAI_TensorCreateWithDLDataType(DLDataType dtype, long long* dims, int ndims, int tensorAllocMode);
+RAI_Tensor* RAI_TensorCreate(const char* dataType, long long* dims, int ndims,
+                             int hasdata);
+RAI_Tensor* RAI_TensorCreateWithDLDataType(DLDataType dtype, long long* dims,
+                                           int ndims, int tensorAllocMode);
 
 /**
  * Allocate the memory for a new Tensor and copy data fom a tensor to it.
@@ -37,15 +49,17 @@ RAI_Tensor* RAI_TensorCreateWithDLDataType(DLDataType dtype, long long* dims, in
  */
 int RAI_TensorCopyTensor(RAI_Tensor* t, RAI_Tensor** dest);
 RAI_Tensor* RAI_TensorCreateFromDLTensor(DLManagedTensor* dl_tensor);
-RAI_Tensor* RAI_TensorCreateByConcatenatingTensors(RAI_Tensor** ts, long long n);
-RAI_Tensor* RAI_TensorCreateBySlicingTensor(RAI_Tensor* t, long long offset, long long len);
+RAI_Tensor* RAI_TensorCreateByConcatenatingTensors(RAI_Tensor** ts,
+                                                   long long n);
+RAI_Tensor* RAI_TensorCreateBySlicingTensor(RAI_Tensor* t, long long offset,
+                                            long long len);
 size_t RAI_TensorLength(RAI_Tensor* t);
 size_t RAI_TensorDataSize(RAI_Tensor* t);
 size_t RAI_TensorDataSizeFromDLDataType(DLDataType dtype);
 size_t RAI_TensorDataSizeFromString(const char* dataType);
 DLDataType RAI_TensorDataType(RAI_Tensor* t);
 DLDataType RAI_TensorDataTypeFromString(const char* dataType);
-int Tensor_DataTypeStr(DLDataType dtype, char **dtypestr);
+int Tensor_DataTypeStr(DLDataType dtype, char** dtypestr);
 void RAI_TensorFree(RAI_Tensor* t);
 int RAI_TensorSetData(RAI_Tensor* t, const char* data, size_t len);
 int RAI_TensorSetValueFromLongLong(RAI_Tensor* t, long long i, long long val);
@@ -58,32 +72,96 @@ long long RAI_TensorDim(RAI_Tensor* t, int dim);
 size_t RAI_TensorByteSize(RAI_Tensor* t);
 char* RAI_TensorData(RAI_Tensor* t);
 
-/* Return REDISMODULE_ERR if is the key not associated with a tensor type.
- * Return REDISMODULE_OK otherwise. */
-int RAI_OpenKey_Tensor(RedisModuleCtx *ctx, RedisModuleString *keyName,
-                              RedisModuleKey **key,
-                              int mode);
+/**
+ * Helper method to open a key handler for the tensor data type
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param keyName key name
+ * @param key tensor's key handle. On sucess it contains an handle representing
+ * a Redis key with the requested access mode
+ * @param mode key access mode
+ * @return REDISMODULE_OK if it's possible to store at the specified key handle
+ * the tensor type, or REDISMODULE_ERR if is the key not associated with a
+ * tensor type.
+ */
+int RAI_OpenKey_Tensor(RedisModuleCtx* ctx, RedisModuleString* keyName,
+                       RedisModuleKey** key, int mode);
 
-/* Return REDISMODULE_ERR if there was an error getting the Tensor.
- * Return REDISMODULE_OK if the tensor value stored at key was correctly
- * returned and available at *tensor variable. */
-int RAI_GetTensorFromKeyspace(RedisModuleCtx *ctx, RedisModuleString *keyName,
-                               RedisModuleKey **key, RAI_Tensor **tensor,
-                               int mode);
+/**
+ * Helper method to get Tensor from keyspace. In the case of failure the key is
+ * closed and the error is replied ( no cleaning actions required )
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param keyName key name
+ * @param key tensor's key handle. On sucess it contains an handle representing
+ * a Redis key with the requested access mode
+ * @param tensor destination tensor structure
+ * @param mode key access mode
+ * @return REDISMODULE_OK if the tensor value stored at key was correctly
+ * returned and available at *tensor variable, or REDISMODULE_ERR if there was
+ * an error getting the Tensor
+ */
+int RAI_GetTensorFromKeyspace(RedisModuleCtx* ctx, RedisModuleString* keyName,
+                              RedisModuleKey** key, RAI_Tensor** tensor,
+                              int mode);
 
-/* Return REDISMODULE_ERR if there was an error getting the Tensor.
- * Return REDISMODULE_OK if the tensor value is present at the localContextDict. */
-int RAI_getTensorFromLocalContext(RedisModuleCtx *ctx,
-                                  AI_dict *localContextDict,
-                                  const char *localContextKey,
-                                  RAI_Tensor **tensor, RAI_Error *error);
+/**
+ * Helper method to get Tensor from local context ( no keyspace access )
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param localContextDict local non-blocking hash table containing DAG's
+ * tensors
+ * @param localContextKey key name
+ * @param tensor destination tensor
+ * @param error error data structure to store error message in the case of
+ * failure
+ * @return REDISMODULE_OK on success, or REDISMODULE_ERR if failed
+ */
+int RAI_getTensorFromLocalContext(RedisModuleCtx* ctx,
+                                  AI_dict* localContextDict,
+                                  const char* localContextKey,
+                                  RAI_Tensor** tensor, RAI_Error* error);
 
-void RedisAI_ReplicateTensorSet(RedisModuleCtx *ctx, RedisModuleString *key, RAI_Tensor *t);
+/**
+ * Helper method to replicate a tensor via an AI.TENSORSET command to the
+ * replicas. This is used on MODELRUN, SCRIPTRUN, DAGRUN as a way to ensure that
+ * the results present on replicas match the results present on master ( since
+ * multiple modelruns/scripts are not ensured to have the same output values
+ * (non-deterministic) ).
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param key Destination key name
+ * @param t source tensor
+ */
+void RedisAI_ReplicateTensorSet(RedisModuleCtx* ctx, RedisModuleString* key,
+                                RAI_Tensor* t);
 
+/**
+ * Helper method to parse AI.TENSORGET arguments
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param argv Redis command arguments, as an array of strings
+ * @param argc Redis command number of arguments
+ * @param t Destination tensor to store the parsed data
+ * @param enforceArity flag wether to enforce arity checking
+ * @param error error data structure to store error message in the case of
+ * parsing failures
+ * @return processed number of arguments on success, or -1 if the parsing failed
+ */
 int RAI_parseTensorSetArgs(RedisModuleCtx* ctx, RedisModuleString** argv,
                            int argc, RAI_Tensor** t, int enforceArity,
                            RAI_Error* error);
 
-int RAI_parseTensorGetArgs(RedisModuleCtx *ctx, RedisModuleString **argv, int argc, RAI_Tensor *t);
+/**
+ * Helper method to parse AI.TENSORGET arguments
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param argv Redis command arguments, as an array of strings
+ * @param argc Redis command number of arguments
+ * @param t Destination tensor to store the parsed data
+ * @return processed number of arguments on success, or -1 if the parsing failed
+ */
+int RAI_parseTensorGetArgs(RedisModuleCtx* ctx, RedisModuleString** argv,
+                           int argc, RAI_Tensor* t);
 
 #endif /* SRC_TENSOR_H_ */

From 76302375cd5443798dc0ca3c5f05cf3c501a9a44 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 17:46:37 +0100
Subject: [PATCH 27/45] [wip] improved further code documentation on tensor.h

---
 src/dag.c    |   2 +-
 src/err.h    |   4 +
 src/tensor.c |   2 +-
 src/tensor.h | 216 ++++++++++++++++++++++++++++++++++++++++++++++++++-
 4 files changed, 221 insertions(+), 3 deletions(-)

diff --git a/src/dag.c b/src/dag.c
index ba8794780..75dd5b996 100644
--- a/src/dag.c
+++ b/src/dag.c
@@ -56,7 +56,7 @@ void *RedisAI_DagRunSession(RedisAI_RunInfo *rinfo) {
         if (currentOp->result == REDISMODULE_OK) {
           RAI_Tensor *outTensor = NULL;
           // TODO: check tensor copy return value
-          RAI_TensorCopyTensor(t, &outTensor);
+            RAI_TensorDeepCopy(t, &outTensor);
           array_append(currentOp->outTensors, outTensor);
           currentOp->result = REDISMODULE_OK;
         }
diff --git a/src/err.h b/src/err.h
index 50af9f3ee..9cd77a199 100644
--- a/src/err.h
+++ b/src/err.h
@@ -35,6 +35,7 @@ typedef struct RAI_Error {
 
 /**
  * Allocate the memory and initialise the RAI_Error.
+ *
  * @param result Output parameter to capture allocated RAI_Error.
  * @return 0 on success, or 1 if the allocation
  * failed.
@@ -43,6 +44,7 @@ int RAI_InitError(RAI_Error **err);
 
 /**
  * Populates the RAI_Error data structure with the error details
+ *
  * @param err
  * @param code
  * @param detail
@@ -51,12 +53,14 @@ void RAI_SetError(RAI_Error *err, RAI_ErrorCode code, const char *detail);
 
 /**
  * Resets an previously used/allocated RAI_Error
+ *
  * @param err
  */
 void RAI_ClearError(RAI_Error *err);
 
 /**
  * Frees the memory of the RAI_Error
+ *
  * @param err
  */
 void RAI_FreeError(RAI_Error *err);
diff --git a/src/tensor.c b/src/tensor.c
index dc2e277b8..e71ad4dd7 100644
--- a/src/tensor.c
+++ b/src/tensor.c
@@ -408,7 +408,7 @@ RAI_Tensor* RAI_TensorCreateBySlicingTensor(RAI_Tensor* t, long long offset, lon
  * @return 0 on success, or 1 if the copy failed
  * failed.
  */
-int RAI_TensorCopyTensor(RAI_Tensor* t, RAI_Tensor** dest) {
+int RAI_TensorDeepCopy(RAI_Tensor* t, RAI_Tensor** dest) {
   const long long ndims = RAI_TensorNumDims(t);
   long long dims[ndims];
 
diff --git a/src/tensor.h b/src/tensor.h
index b1afd08df..1c5651dcc 100644
--- a/src/tensor.h
+++ b/src/tensor.h
@@ -34,42 +34,256 @@ static const char* RAI_DATATYPE_STR_UINT16 = "UINT16";
 
 extern RedisModuleType* RedisAI_TensorType;
 
+/**
+ * Helper method to register the tensor type exported by the module.
+ *
+ * @param ctx Context in which Redis modules operate
+ * @return
+ */
 int RAI_TensorInit(RedisModuleCtx* ctx);
+
+/**
+ * Allocate the memory and initialise the RAI_Tensor. Creates a tensor based on
+ * the passed 'dataType` string and with the specified number of dimensions
+ * `ndims`, and n-dimension array `dims`.
+ *
+ * @param dataType string containing the numeric data type of tensor elements
+ * @param dims n-dimensional array ( the dimension values are copied )
+ * @param ndims number of dimensions
+ * @param hasdata ( deprecated parameter )
+ * @return allocated RAI_Tensor on success, or NULL if the allocation
+ * failed.
+ */
 RAI_Tensor* RAI_TensorCreate(const char* dataType, long long* dims, int ndims,
                              int hasdata);
+
+/**
+ * Allocate the memory and initialise the RAI_Tensor. Creates a tensor based on
+ * the passed 'DLDataType` and with the specified number of dimensions `ndims`,
+ * and n-dimension array `dims`. Depending on the passed `tensorAllocMode`, the
+ * DLTensor data will be either allocated or no allocation is performed thus
+ * enabling shallow copy of data (no alloc)
+ *
+ * @param dtype DLDataType
+ * @param dims n-dimensional array ( the dimension values are copied )
+ * @param ndims number of dimensions
+ * @param tensorAllocMode
+ * @return allocated RAI_Tensor on success, or NULL if the allocation
+ * failed.
+ */
 RAI_Tensor* RAI_TensorCreateWithDLDataType(DLDataType dtype, long long* dims,
                                            int ndims, int tensorAllocMode);
 
 /**
  * Allocate the memory for a new Tensor and copy data fom a tensor to it.
+ *
  * @param t Source tensor to copy.
  * @param result Destination tensor to copy.
  * @return 0 on success, or 1 if the copy failed
  * failed.
  */
-int RAI_TensorCopyTensor(RAI_Tensor* t, RAI_Tensor** dest);
+int RAI_TensorDeepCopy(RAI_Tensor* t, RAI_Tensor** dest);
+
+/**
+ * Allocate the memory and initialise the RAI_Tensor, performing a shallow copy
+ * of dl_tensor. Beware, this will take ownership of dltensor, and only allocate
+ * the data for the RAI_Tensor, meaning that the freeing the data of the input
+ * dl_tensor, will also free the data of the returned tensor.
+ *
+ * @param dl_tensor source tensor to shallow copy the data
+ * @return allocated RAI_Tensor on success, or NULL if the allocation
+ * failed.
+ */
 RAI_Tensor* RAI_TensorCreateFromDLTensor(DLManagedTensor* dl_tensor);
+
+/**
+ * Allocate the memory and initialise the RAI_Tensor, performing a deep copy of
+ * the passed array of tensors.
+ *
+ * @param ts input array of tensors
+ * @param n number of input tensors
+ * @return allocated RAI_Tensor on success, or NULL if the allocation and deep
+ * copy failed failed.
+ */
 RAI_Tensor* RAI_TensorCreateByConcatenatingTensors(RAI_Tensor** ts,
                                                    long long n);
+
+/**
+ * Allocate the memory and initialise the RAI_Tensor, performing a deep copy of
+ * the passed tensor, at the given data offset and length.
+ *
+ * @param t input tensor
+ * @param offset
+ * @param len
+ * @return allocated RAI_Tensor on success, or NULL if the allocation and deep
+ * copy failed failed.
+ */
 RAI_Tensor* RAI_TensorCreateBySlicingTensor(RAI_Tensor* t, long long offset,
                                             long long len);
+
+/**
+ * Returns the length of the input tensor
+ *
+ * @param t input tensor
+ * @return the length of the input tensor
+ */
 size_t RAI_TensorLength(RAI_Tensor* t);
+
+/**
+ * Returns the size in bytes of each element of the tensor
+ *
+ * @param t input tensor
+ * @return size in bytes of each the underlying tensor data type
+ */
 size_t RAI_TensorDataSize(RAI_Tensor* t);
+
+/**
+ * Returns the size in bytes of the given DLDataType
+ *
+ * @param dtype DLDataType
+ * @return size in bytes of each the given DLDataType
+ */
 size_t RAI_TensorDataSizeFromDLDataType(DLDataType dtype);
+
+/**
+ * Returns the size in bytes of the associated DLDataType represented by the
+ * input string
+ *
+ * @param dataType
+ * @return size in bytes of each the underlying tensor data type
+ */
 size_t RAI_TensorDataSizeFromString(const char* dataType);
+
+/**
+ * Get the associated `DLDataType` for the given input tensor
+ *
+ * @param t input tensor
+ * @return the associated `DLDataType` for the given input tensor
+ */
 DLDataType RAI_TensorDataType(RAI_Tensor* t);
+
+/**
+ * Returns the DLDataType represented by the input string
+ *
+ * @param dataType
+ * @return the DLDataType represented by the input string
+ */
 DLDataType RAI_TensorDataTypeFromString(const char* dataType);
+
+/**
+ * sets in dtypestr the string representing the associated DLDataType
+ *
+ * @param dtype DLDataType
+ * @param dtypestr output string to store the associated string representing the
+ * DLDataType
+ * @return REDISMODULE_OK on success, or REDISMODULE_ERR if failed
+ */
 int Tensor_DataTypeStr(DLDataType dtype, char** dtypestr);
+
+/**
+ * Frees the memory of the RAI_Tensor when the tensor reference count reaches 0.
+ * It is safe to call this function with a NULL input tensor.
+ *
+ * @param t tensor
+ */
 void RAI_TensorFree(RAI_Tensor* t);
+
+/**
+ * Sets the associated data to the deep learning tensor via deep copying the
+ * passed data.
+ *
+ * @param t tensor to set the data
+ * @param data input data
+ * @param len input data length
+ * @return 1 on success
+ */
 int RAI_TensorSetData(RAI_Tensor* t, const char* data, size_t len);
+
+/**
+ * Sets the long value for the given tensor, at the given array data pointer
+ * position
+ *
+ * @param t tensor to set the data
+ * @param i dl_tensor data pointer position
+ * @param val value to set the data from
+ * @return 0 on success, or 1 if the setting failed
+ */
 int RAI_TensorSetValueFromLongLong(RAI_Tensor* t, long long i, long long val);
+
+/**
+ * Sets the double value for the given tensor, at the given array data pointer
+ * position
+ *
+ * @param t tensor to set the data
+ * @param i dl_tensor data pointer position
+ * @param val value to set the data from
+ * @return 0 on success, or 1 if the setting failed
+ */
 int RAI_TensorSetValueFromDouble(RAI_Tensor* t, long long i, double val);
+
+/**
+ * Gets the double value from the given input tensor, at the given array data
+ * pointer position
+ *
+ * @param t tensor to get the data
+ * @param i dl_tensor data pointer position
+ * @param val value to set the data to
+ * @return 0 on success, or 1 if getting the data failed
+ */
 int RAI_TensorGetValueAsDouble(RAI_Tensor* t, long long i, double* val);
+
+/**
+ * Gets the long value from the given input tensor, at the given array data
+ * pointer position
+ *
+ * @param t tensor to get the data
+ * @param i dl_tensor data pointer position
+ * @param val value to set the data to
+ * @return 0 on success, or 1 if getting the data failed
+ */
 int RAI_TensorGetValueAsLongLong(RAI_Tensor* t, long long i, long long* val);
+
+/**
+ * Every call to this function, will make the RAI_Tensor 't' requiring an
+ * additional call to RAI_TensorFree() in order to really free the tensor.
+ * Returns a shallow copy of the tensor.
+ *
+ * @param t input tensor
+ * @return shallow copy of the tensor
+ */
 RAI_Tensor* RAI_TensorGetShallowCopy(RAI_Tensor* t);
+
+/**
+ * Returns the number of dimensions for the given input tensor
+ *
+ * @param t input tensor
+ * @return number of dimensions for the given input tensor
+ */
 int RAI_TensorNumDims(RAI_Tensor* t);
+
+/**
+ * Returns the dimension length for the given input tensor and dimension
+ *
+ * @param t input tensor
+ * @param dim dimension
+ * @return the dimension length
+ */
 long long RAI_TensorDim(RAI_Tensor* t, int dim);
+
+/**
+ * Returns the size in bytes of the underlying deep learning tensor data
+ *
+ * @param t input tensor
+ * @return the size in bytes of the underlying deep learning tensor data
+ */
 size_t RAI_TensorByteSize(RAI_Tensor* t);
+
+/**
+ * Return the pointer the the deep learning tensor data
+ *
+ * @param t input tensor
+ * @return direct access to the array data pointer
+ */
 char* RAI_TensorData(RAI_Tensor* t);
 
 /**

From 7c519d7ea9737813519c3c9f159875361a50b87a Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Sun, 26 Apr 2020 19:06:42 +0100
Subject: [PATCH 28/45] [wip] added backends.h and backends directory developer
 info. pushing forward on developer.md information

---
 docs/developer.md | 81 +++++++++++++++++++++++++++++++++--------------
 1 file changed, 58 insertions(+), 23 deletions(-)

diff --git a/docs/developer.md b/docs/developer.md
index 50bf8f578..516fa33de 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -23,60 +23,90 @@ TBD
 Inside the root are the following important directories:
 
 * `src`: contains the RedisAI implementation, written in C.
-* `opt`: contains the helper scriptsto setup the development environment.
+* `opt`: contains the helper scripts to setup the development environment.
 * `tests`: contains the unit and flow tests, implemented with python and RLTest.
 * `deps`: contains libraries RedisAI uses.
 
 We'll focus mostly on `src`, where the RedisAI implementation is contained,
 exploring what there is inside each file. The order in which files are
 exposed is the logical one to follow in order to disclose different layers
-of complexity incrementally.
-
-### redisai.h
----
-TBD...
+of complexity incrementally. 
 
 ### redisai.c 
 ---
 
-This is the entry point of the RedisAI module, TBD...
+This is the entry point of the RedisAI module, responsible for registering the new commands in the Redis server, and containing all command functions to be called. This file is also responsible for exporting of Tensor, Script and Model APIs to other Modules.
 
-### tensor.c
+### tensor.h
 ---
-Contains the helper methods for both creating, populating, managing and destructing the RedisAI_TensorType, and methods to manage parsing and replying of tensor related commands or operations.
+Contains the helper methods for both creating, populating, managing and destructing the Tensor data structure, and methods to manage parsing and replying of tensor related commands or operations.
 
-### model.c
+### model.h
 ---
-TBD...
+Contains the helper methods for both creating, populating, managing and destructing the Model data structure.
+Contains also methods to manage parsing and replying of model related commands or operations, that take place in the context of the Redis Main thread.
+The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
-### script.c
+### script.h
 ---
-TBD...
+Contains the helper methods for both creating, populating, managing and destructing the Script data structure.
+Contains also methods to manage parsing and replying of script related commands or operations, that take place in the context of the Redis Main thread.
+The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
-### dag.c
+### dag.h
 ---
- Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
-
+Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
 
-### run_info.c
+### run_info.h
 ---
 TBD...
 
-### model_script_run_session.c
+### model_script_run_session.h
 ---
-TBD...
+Contains the methods that are related to async background work that 
+was triggered by either MODELRUN or SCRIPTRUN Command and Called within `RedisAI_Run_ThreadMain`.
+This file also contains the function signatures of the reply callbacks to be called in order to reply to the clients, after the background work on MODELRUN and SCRIPTRUN is done.
 
-### background_workers.c
+### background_workers.h
 ---
 TBD...
 
-### err.c
+### err.h
 ---
 Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
 
-### Backends directory
+### backends.h and backends directory
 ---
-TBD...
+Contains the structure and headers methods required register a new backend to be loaded by the module. 
+ To do so, the backend needs to implement and export the following methods:
+ 
+  * `model_create_with_nodes`:  A callback function pointer that creates a
+  model given the RAI_ModelOpts and input and output nodes.
+ 
+  * `model_create`:  A callback function pointer that creates a model given
+  the RAI_ModelOpts.
+ 
+  * `model_run`:  A callback function pointer that runs a model given the
+  RAI_ModelRunCtx pointer.
+ 
+  * `model_serialize`:  A callback function pointer that serializes a model
+  given the RAI_Model pointer.
+ 
+  * `script_create`:  A callback function pointer that creates a script.
+ 
+  * `script_free`:  A callback function pointer that frees a script given
+  the RAI_Script pointer.
+ 
+  * `script_run`:  A callback function pointer that runs a model given the
+  RAI_ScriptRunCtx pointer.
+
+
+Within the `backends` folder you will find the implementations code required to support the following DL/ML identifiers and respective backend libraries:
+
+* **TF**: `tensorflow.h` and `tensorflow.c` exporting the functions to to register the TensorFlow backend
+* **TFLITE**: `tflite.h` and `tflite.c` exporting the functions to to register the TensorFlow Lite backend
+* **TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
+* **ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
 
 ### Other C files
 ---
@@ -84,6 +114,11 @@ TBD...
 TBD...
 
 
+### backends subfolder
+
+Apart from the files within the `src` folder, the subfolder `src/backends`
+
+
 ## Building and Testing
 You can compile and build the module from its source code.
 

From 9c8a6cd840286ec5fd8a20d3307ef3fdd22f3711 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 00:42:26 +0100
Subject: [PATCH 29/45] [add] updated model.h/c with the documentation of each
 method

---
 docs/developer.md |  69 +++++-----
 src/backends.c    | 329 ++++++++++++++++++++++++++++++----------------
 src/backends.h    |  65 ++++++++-
 src/model.c       |   9 ++
 src/model.h       | 223 +++++++++++++++++++++++++++----
 src/redisai.c     |  12 --
 src/script.h      |  91 +++++++++++++
 7 files changed, 603 insertions(+), 195 deletions(-)

diff --git a/docs/developer.md b/docs/developer.md
index 516fa33de..d2ff250ae 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -9,14 +9,12 @@ RedisAI bundles together best-of-breed technologies for delivering stable and pe
 
 As a way of representing tensor data we've embraced [dlpack](https://github.com/dmlc/dlpack) - a community effort to define a common tensor data structure that can be shared by different frameworks, supported by cuPy, cuDF, DGL, TGL, PyTorch, and MxNet.
 
-### Data Structures
+**Data Structures
 RedisAI provides the following data structures:
 
-* **Tensor**: represents an n-dimensional array of values
-* **Model**: represents a frozen graph by one of the supported DL/ML framework backends
-* **Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
-
-TBD
+***Tensor**: represents an n-dimensional array of values
+***Model**: represents a frozen graph by one of the supported DL/ML framework backends
+***Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
 
 ## Source code layout
 
@@ -32,52 +30,52 @@ exploring what there is inside each file. The order in which files are
 exposed is the logical one to follow in order to disclose different layers
 of complexity incrementally. 
 
-### redisai.c 
+**redisai.c**
 ---
 
 This is the entry point of the RedisAI module, responsible for registering the new commands in the Redis server, and containing all command functions to be called. This file is also responsible for exporting of Tensor, Script and Model APIs to other Modules.
 
-### tensor.h
+**tensor.h**
 ---
 Contains the helper methods for both creating, populating, managing and destructing the Tensor data structure, and methods to manage parsing and replying of tensor related commands or operations.
 
-### model.h
+**model.h**
 ---
 Contains the helper methods for both creating, populating, managing and destructing the Model data structure.
 Contains also methods to manage parsing and replying of model related commands or operations, that take place in the context of the Redis Main thread.
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
-### script.h
+**script.h**
 ---
 Contains the helper methods for both creating, populating, managing and destructing the Script data structure.
 Contains also methods to manage parsing and replying of script related commands or operations, that take place in the context of the Redis Main thread.
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
-### dag.h
+**dag.h**
 ---
 Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
 
-### run_info.h
+**run_info.h**
 ---
-TBD...
+Contains the structure and headers to create, initialize, get, reset, and free the structures that represent the context in which RedisAI blocking commands operate, namely `RedisAI_RunInfo` and the newly added `RAI_DagOp`. 
 
-### model_script_run_session.h
+**model_script_run_session.h**
 ---
 Contains the methods that are related to async background work that 
 was triggered by either MODELRUN or SCRIPTRUN Command and Called within `RedisAI_Run_ThreadMain`.
 This file also contains the function signatures of the reply callbacks to be called in order to reply to the clients, after the background work on MODELRUN and SCRIPTRUN is done.
 
-### background_workers.h
+**background_workers.h**
 ---
-TBD...
+Contains the structure to manage the per-device queues, used for decoupling the work from the main thread to the background worker threads. For each of the incoming ModelRun, ScriptRun, and DagRun commands, The request is queued and evaded asynchronously to one the device queues.
 
-### err.h
+**err.h**
 ---
 Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
 
-### backends.h and backends directory
+**backends.h and backends directory**
 ---
-Contains the structure and headers methods required register a new backend to be loaded by the module. 
+Contains the structure and headers methods required to register a new backend to be loaded by the module. 
  To do so, the backend needs to implement and export the following methods:
  
   * `model_create_with_nodes`:  A callback function pointer that creates a
@@ -103,31 +101,26 @@ Contains the structure and headers methods required register a new backend to be
 
 Within the `backends` folder you will find the implementations code required to support the following DL/ML identifiers and respective backend libraries:
 
-* **TF**: `tensorflow.h` and `tensorflow.c` exporting the functions to to register the TensorFlow backend
-* **TFLITE**: `tflite.h` and `tflite.c` exporting the functions to to register the TensorFlow Lite backend
-* **TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
-* **ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
+***TF**: `tensorflow.h` and `tensorflow.c` exporting the functions to to register the TensorFlow backend
+***TFLITE**: `tflite.h` and `tflite.c` exporting the functions to to register the TensorFlow Lite backend
+***TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
+***ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
 
-### Other C files
+**Other C files
 ---
 
 TBD...
 
 
-### backends subfolder
-
-Apart from the files within the `src` folder, the subfolder `src/backends`
-
-
 ## Building and Testing
 You can compile and build the module from its source code.
 
-### Prerequisites
+**Prerequisites
 * CUDA needs to be installed for GPU support.
 * CMake 3.0 or higher needs to be installed.
 * Redis v4.0.9 or greater.
 
-### Get the Source Code
+**Get the Source Code
 You can obtain the module's source code by cloning the project's repository using git like so:
 
 ```sh
@@ -140,7 +133,7 @@ Switch to the project's directory with:
 cd RedisAI
 ```
 
-### Building the Dependencies
+**Building the Dependencies
 Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
 
 ```sh
@@ -153,14 +146,14 @@ Alternatively, you can run the following to fetch the CPU-only backends.
 bash get_deps.sh cpu
 ```
 
-### Building the Module
+**Building the Module
 Once the dependencies have been built, you can build the RedisAI module with:
 
 ```sh
 make -C opt build
 ```
 
-### Running Tests
+**Running Tests
 
 The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run
 them you'll need:
@@ -180,13 +173,11 @@ To run tests in a Python virtualenv, follow these steps:
 Integration tests are based on RLTest, and specific setup parameters can be provided
 to configure tests.
 
-For example, ... ( TBD )
-
-### Unit Test Coverage
+For example, to run the tests strictly designed for the TensorFlow backend, follow these steps:
 
-TBD
+    $ make -C opt tests TEST=tests_tensorflow.py
 
-### Integration Tests Coverage
+**Integration Tests Coverage
 
 TBD 
 
diff --git a/src/backends.c b/src/backends.c
index b6bd6306c..138929310 100644
--- a/src/backends.c
+++ b/src/backends.c
@@ -1,41 +1,52 @@
+/**
+ * backends.c
+ *
+ * Contains the methods required to register a new backend to be loaded by the
+ * module.
+ *
+ */
+
 #ifdef __linux__
 #define _GNU_SOURCE
 #endif
 #include "backends.h"
-#include "redismodule.h"
 
-#include <string.h>
 #include <dlfcn.h>
 #include <libgen.h>
+#include <string.h>
+
+#include "redismodule.h"
 
-RedisModuleString* RAI_GetModulePath(RedisModuleCtx *ctx) {
+RedisModuleString *RAI_GetModulePath(RedisModuleCtx *ctx) {
   Dl_info info;
-  RedisModuleString* module_path = NULL;
+  RedisModuleString *module_path = NULL;
   if (dladdr(RAI_GetModulePath, &info)) {
-    char* dli_fname = RedisModule_Strdup(info.dli_fname);
-    const char* dli_dirname = dirname(dli_fname);
-    module_path = RedisModule_CreateString(ctx, dli_dirname, strlen(dli_dirname));
+    char *dli_fname = RedisModule_Strdup(info.dli_fname);
+    const char *dli_dirname = dirname(dli_fname);
+    module_path =
+        RedisModule_CreateString(ctx, dli_dirname, strlen(dli_dirname));
     RedisModule_Free(dli_fname);
   }
 
   return module_path;
 }
 
-RedisModuleString* RAI_GetBackendsPath(RedisModuleCtx *ctx) {
+RedisModuleString *RAI_GetBackendsPath(RedisModuleCtx *ctx) {
   Dl_info info;
-  RedisModuleString* backends_path = NULL;
+  RedisModuleString *backends_path = NULL;
   if (RAI_BackendsPath != NULL) {
-    backends_path = RedisModule_CreateString(ctx, RAI_BackendsPath, strlen(RAI_BackendsPath));
-  }
-  else {
-    RedisModuleString* module_path = RAI_GetModulePath(ctx);
-    backends_path = RedisModule_CreateStringPrintf(ctx, "%s/backends", RedisModule_StringPtrLen(module_path, NULL));
+    backends_path = RedisModule_CreateString(ctx, RAI_BackendsPath,
+                                             strlen(RAI_BackendsPath));
+  } else {
+    RedisModuleString *module_path = RAI_GetModulePath(ctx);
+    backends_path = RedisModule_CreateStringPrintf(
+        ctx, "%s/backends", RedisModule_StringPtrLen(module_path, NULL));
   }
 
   return backends_path;
 }
 
-const char* RAI_BackendName(int backend) {
+const char *RAI_BackendName(int backend) {
   switch (backend) {
     case RAI_BACKEND_TENSORFLOW:
       return "TF";
@@ -51,60 +62,79 @@ const char* RAI_BackendName(int backend) {
 
 int RAI_LoadBackend_TensorFlow(RedisModuleCtx *ctx, const char *path) {
   if (RAI_backends.tf.model_run != NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TF backend: backend already loaded");
+    RedisModule_Log(ctx, "warning",
+                    "Could not load TF backend: backend already loaded");
     return REDISMODULE_ERR;
   }
 
   void *handle = dlopen(path, RTLD_NOW | RTLD_LOCAL);
 
   if (handle == NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TF backend from %s: %s", path, dlerror());
+    RedisModule_Log(ctx, "warning", "Could not load TF backend from %s: %s",
+                    path, dlerror());
     return REDISMODULE_ERR;
-  } 
+  }
 
   RAI_LoadedBackend backend = {0};
 
   int (*init_backend)(int (*)(const char *, void *));
-  init_backend = (int (*)(int (*)(const char *, void *)))
-                  (unsigned long) dlsym(handle, "RAI_InitBackendTF");
+  init_backend = (int (*)(int (*)(const char *, void *)))(unsigned long)dlsym(
+      handle, "RAI_InitBackendTF");
   if (init_backend == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_InitBackendTF. TF backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_InitBackendTF. TF backend not "
+                    "loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
   init_backend(RedisModule_GetApi);
 
-  backend.model_create_with_nodes = (RAI_Model* (*)(RAI_Backend, const char*, RAI_ModelOpts,
-                                     size_t, const char**, size_t, const char**,
-                                     const char*, size_t, RAI_Error*))
-                                     (unsigned long) dlsym(handle, "RAI_ModelCreateTF");
+  backend.model_create_with_nodes =
+      (RAI_Model *
+       (*)(RAI_Backend, const char *, RAI_ModelOpts, size_t, const char **,
+           size_t, const char **, const char *, size_t,
+           RAI_Error *))(unsigned long)dlsym(handle, "RAI_ModelCreateTF");
   if (backend.model_create_with_nodes == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelCreateTF. TF backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelCreateTF. TF backend not "
+                    "loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_free = (void (*)(RAI_Model*, RAI_Error*))
-                        (unsigned long) dlsym(handle, "RAI_ModelFreeTF");
+  backend.model_free = (void (*)(RAI_Model *, RAI_Error *))(unsigned long)dlsym(
+      handle, "RAI_ModelFreeTF");
   if (backend.model_free == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelFreeTF. TF backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelFreeTF. TF backend not "
+                    "loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_run = (int (*)(RAI_ModelRunCtx**, RAI_Error*))
-                      (unsigned long) dlsym(handle, "RAI_ModelRunTF");
+  backend.model_run = (int (*)(RAI_ModelRunCtx **, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ModelRunTF");
   if (backend.model_run == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelRunTF. TF backend not loaded from %s", path);
+    RedisModule_Log(
+        ctx, "warning",
+        "Backend does not export RAI_ModelRunTF. TF backend not loaded from %s",
+        path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_serialize = (int (*)(RAI_Model*, char**, size_t*, RAI_Error*))
-                             (unsigned long) dlsym(handle, "RAI_ModelSerializeTF");
+  backend.model_serialize =
+      (int (*)(RAI_Model *, char **, size_t *, RAI_Error *))(
+          unsigned long)dlsym(handle, "RAI_ModelSerializeTF");
   if (backend.model_serialize == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelSerializeTF. TF backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelSerializeTF. TF backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
@@ -117,59 +147,78 @@ int RAI_LoadBackend_TensorFlow(RedisModuleCtx *ctx, const char *path) {
 
 int RAI_LoadBackend_TFLite(RedisModuleCtx *ctx, const char *path) {
   if (RAI_backends.tflite.model_run != NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TFLITE backend: backend already loaded");
+    RedisModule_Log(ctx, "warning",
+                    "Could not load TFLITE backend: backend already loaded");
     return REDISMODULE_ERR;
   }
 
   void *handle = dlopen(path, RTLD_NOW | RTLD_LOCAL);
 
   if (handle == NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TFLITE backend from %s: %s", path, dlerror());
+    RedisModule_Log(ctx, "warning", "Could not load TFLITE backend from %s: %s",
+                    path, dlerror());
     return REDISMODULE_ERR;
-  } 
+  }
 
   RAI_LoadedBackend backend = {0};
 
   int (*init_backend)(int (*)(const char *, void *));
-  init_backend = (int (*)(int (*)(const char *, void *)))
-                  (unsigned long) dlsym(handle, "RAI_InitBackendTFLite");
+  init_backend = (int (*)(int (*)(const char *, void *)))(unsigned long)dlsym(
+      handle, "RAI_InitBackendTFLite");
   if (init_backend == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_InitBackendTFLite. TFLITE backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_InitBackendTFLite. TFLITE "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
   init_backend(RedisModule_GetApi);
 
-  backend.model_create = (RAI_Model* (*)(RAI_Backend, const char*, RAI_ModelOpts,
-                          const char*, size_t, RAI_Error*))
-                         (unsigned long) dlsym(handle, "RAI_ModelCreateTFLite");
+  backend.model_create =
+      (RAI_Model *
+       (*)(RAI_Backend, const char *, RAI_ModelOpts, const char *, size_t,
+           RAI_Error *))(unsigned long)dlsym(handle, "RAI_ModelCreateTFLite");
   if (backend.model_create == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelCreateTFLite. TFLITE backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelCreateTFLite. TFLITE "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_free = (void (*)(RAI_Model*, RAI_Error*))
-                        (unsigned long) dlsym(handle, "RAI_ModelFreeTFLite");
+  backend.model_free = (void (*)(RAI_Model *, RAI_Error *))(unsigned long)dlsym(
+      handle, "RAI_ModelFreeTFLite");
   if (backend.model_free == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelFreeTFLite. TFLITE backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelFreeTFLite. TFLITE "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_run = (int (*)(RAI_ModelRunCtx**, RAI_Error*))
-                       (unsigned long) dlsym(handle, "RAI_ModelRunTFLite");
+  backend.model_run = (int (*)(RAI_ModelRunCtx **, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ModelRunTFLite");
   if (backend.model_run == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelRunTFLite. TFLITE backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelRunTFLite. TFLITE "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_serialize = (int (*)(RAI_Model*, char**, size_t*, RAI_Error*))
-                             (unsigned long) dlsym(handle, "RAI_ModelSerializeTFLite");
+  backend.model_serialize =
+      (int (*)(RAI_Model *, char **, size_t *, RAI_Error *))(
+          unsigned long)dlsym(handle, "RAI_ModelSerializeTFLite");
   if (backend.model_serialize == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelSerializeTFLite. TFLITE backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelSerializeTFLite. TFLITE "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
@@ -182,83 +231,112 @@ int RAI_LoadBackend_TFLite(RedisModuleCtx *ctx, const char *path) {
 
 int RAI_LoadBackend_Torch(RedisModuleCtx *ctx, const char *path) {
   if (RAI_backends.torch.model_run != NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TORCH backend: backend already loaded");
+    RedisModule_Log(ctx, "warning",
+                    "Could not load TORCH backend: backend already loaded");
     return REDISMODULE_ERR;
   }
 
   void *handle = dlopen(path, RTLD_NOW | RTLD_LOCAL);
 
   if (handle == NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load TORCH backend from %s: %s", path, dlerror());
+    RedisModule_Log(ctx, "warning", "Could not load TORCH backend from %s: %s",
+                    path, dlerror());
     return REDISMODULE_ERR;
-  } 
+  }
 
   RAI_LoadedBackend backend = {0};
 
   int (*init_backend)(int (*)(const char *, void *));
-  init_backend = (int (*)(int (*)(const char *, void *)))
-                  (unsigned long) dlsym(handle, "RAI_InitBackendTorch");
+  init_backend = (int (*)(int (*)(const char *, void *)))(unsigned long)dlsym(
+      handle, "RAI_InitBackendTorch");
   if (init_backend == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_InitBackendTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_InitBackendTorch. TORCH "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
   init_backend(RedisModule_GetApi);
 
-  backend.model_create = (RAI_Model* (*)(RAI_Backend, const char*, RAI_ModelOpts,
-                          const char*, size_t, RAI_Error*))
-                         (unsigned long) dlsym(handle, "RAI_ModelCreateTorch");
+  backend.model_create =
+      (RAI_Model *
+       (*)(RAI_Backend, const char *, RAI_ModelOpts, const char *, size_t,
+           RAI_Error *))(unsigned long)dlsym(handle, "RAI_ModelCreateTorch");
   if (backend.model_create == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelCreateTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelCreateTorch. TORCH "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_free = (void (*)(RAI_Model*, RAI_Error*))
-                        (unsigned long) dlsym(handle, "RAI_ModelFreeTorch");
+  backend.model_free = (void (*)(RAI_Model *, RAI_Error *))(unsigned long)dlsym(
+      handle, "RAI_ModelFreeTorch");
   if (backend.model_free == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelFreeTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelFreeTorch. TORCH backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_run = (int (*)(RAI_ModelRunCtx**, RAI_Error*))
-                       (unsigned long) dlsym(handle, "RAI_ModelRunTorch");
+  backend.model_run = (int (*)(RAI_ModelRunCtx **, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ModelRunTorch");
   if (backend.model_run == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelRunTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelRunTorch. TORCH backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_serialize = (int (*)(RAI_Model*, char**, size_t*, RAI_Error*))
-                             (unsigned long) dlsym(handle, "RAI_ModelSerializeTorch");
+  backend.model_serialize =
+      (int (*)(RAI_Model *, char **, size_t *, RAI_Error *))(
+          unsigned long)dlsym(handle, "RAI_ModelSerializeTorch");
   if (backend.model_serialize == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelSerializeTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelSerializeTorch. TORCH "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.script_create = (RAI_Script* (*)(const char*, const char*,  RAI_Error*))
-                           (unsigned long) dlsym(handle, "RAI_ScriptCreateTorch");
+  backend.script_create =
+      (RAI_Script * (*)(const char *, const char *, RAI_Error *))(
+          unsigned long)dlsym(handle, "RAI_ScriptCreateTorch");
   if (backend.script_create == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ScriptCreateTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ScriptCreateTorch. TORCH "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.script_free = (void (*)(RAI_Script*, RAI_Error*))
-                         (unsigned long) dlsym(handle, "RAI_ScriptFreeTorch");
+  backend.script_free = (void (*)(RAI_Script *, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ScriptFreeTorch");
   if (backend.script_free == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ScriptFreeTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ScriptFreeTorch. TORCH "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.script_run = (int (*)(RAI_ScriptRunCtx*, RAI_Error*))
-                        (unsigned long) dlsym(handle, "RAI_ScriptRunTorch");
+  backend.script_run = (int (*)(RAI_ScriptRunCtx *, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ScriptRunTorch");
   if (backend.script_run == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ScriptRunTorch. TORCH backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ScriptRunTorch. TORCH backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
@@ -271,59 +349,78 @@ int RAI_LoadBackend_Torch(RedisModuleCtx *ctx, const char *path) {
 
 int RAI_LoadBackend_ONNXRuntime(RedisModuleCtx *ctx, const char *path) {
   if (RAI_backends.onnx.model_run != NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load ONNX backend: backend already loaded");
+    RedisModule_Log(ctx, "warning",
+                    "Could not load ONNX backend: backend already loaded");
     return REDISMODULE_ERR;
   }
 
   void *handle = dlopen(path, RTLD_NOW | RTLD_LOCAL);
 
   if (handle == NULL) {
-    RedisModule_Log(ctx, "warning", "Could not load ONNX backend from %s: %s", path, dlerror());
+    RedisModule_Log(ctx, "warning", "Could not load ONNX backend from %s: %s",
+                    path, dlerror());
     return REDISMODULE_ERR;
-  } 
+  }
 
   RAI_LoadedBackend backend = {0};
 
   int (*init_backend)(int (*)(const char *, void *));
-  init_backend = (int (*)(int (*)(const char *, void *)))
-                  (unsigned long) dlsym(handle, "RAI_InitBackendORT");
+  init_backend = (int (*)(int (*)(const char *, void *)))(unsigned long)dlsym(
+      handle, "RAI_InitBackendORT");
   if (init_backend == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_InitBackendORT. ONNX backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_InitBackendORT. ONNX backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
   init_backend(RedisModule_GetApi);
 
-  backend.model_create = (RAI_Model* (*)(RAI_Backend, const char*, RAI_ModelOpts,
-                          const char*, size_t, RAI_Error*))
-                         (unsigned long) dlsym(handle, "RAI_ModelCreateORT");
+  backend.model_create =
+      (RAI_Model *
+       (*)(RAI_Backend, const char *, RAI_ModelOpts, const char *, size_t,
+           RAI_Error *))(unsigned long)dlsym(handle, "RAI_ModelCreateORT");
   if (backend.model_create == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelCreateORT. ONNX backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelCreateORT. ONNX backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_free = (void (*)(RAI_Model*, RAI_Error*))
-                        (unsigned long) dlsym(handle, "RAI_ModelFreeORT");
+  backend.model_free = (void (*)(RAI_Model *, RAI_Error *))(unsigned long)dlsym(
+      handle, "RAI_ModelFreeORT");
   if (backend.model_free == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelFreeORT. ONNX backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelFreeORT. ONNX backend "
+                    "not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_run = (int (*)(RAI_ModelRunCtx**, RAI_Error*))
-                      (unsigned long) dlsym(handle, "RAI_ModelRunORT");
+  backend.model_run = (int (*)(RAI_ModelRunCtx **, RAI_Error *))(
+      unsigned long)dlsym(handle, "RAI_ModelRunORT");
   if (backend.model_run == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelRunORT. ONNX backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelRunORT. ONNX backend not "
+                    "loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
-  backend.model_serialize = (int (*)(RAI_Model*, char**, size_t*, RAI_Error*))
-                            (unsigned long) dlsym(handle, "RAI_ModelSerializeORT");
+  backend.model_serialize =
+      (int (*)(RAI_Model *, char **, size_t *, RAI_Error *))(
+          unsigned long)dlsym(handle, "RAI_ModelSerializeORT");
   if (backend.model_serialize == NULL) {
     dlclose(handle);
-    RedisModule_Log(ctx, "warning", "Backend does not export RAI_ModelSerializeORT. ONNX backend not loaded from %s", path);
+    RedisModule_Log(ctx, "warning",
+                    "Backend does not export RAI_ModelSerializeORT. ONNX "
+                    "backend not loaded from %s",
+                    path);
     return REDISMODULE_ERR;
   }
 
@@ -334,27 +431,31 @@ int RAI_LoadBackend_ONNXRuntime(RedisModuleCtx *ctx, const char *path) {
   return REDISMODULE_OK;
 }
 
-int RAI_LoadBackend(RedisModuleCtx *ctx, int backend, const char* path) {
-  RedisModuleString* fullpath;
+int RAI_LoadBackend(RedisModuleCtx *ctx, int backend, const char *path) {
+  RedisModuleString *fullpath;
 
   if (path[0] == '/') {
     fullpath = RedisModule_CreateString(ctx, path, strlen(path));
-  }
-  else {
-    RedisModuleString* backends_path = RAI_GetBackendsPath(ctx);
-    fullpath = RedisModule_CreateStringPrintf(ctx, "%s/%s", RedisModule_StringPtrLen(backends_path, NULL), path);
+  } else {
+    RedisModuleString *backends_path = RAI_GetBackendsPath(ctx);
+    fullpath = RedisModule_CreateStringPrintf(
+        ctx, "%s/%s", RedisModule_StringPtrLen(backends_path, NULL), path);
   }
 
   int ret;
   switch (backend) {
     case RAI_BACKEND_TENSORFLOW:
-      return RAI_LoadBackend_TensorFlow(ctx, RedisModule_StringPtrLen(fullpath, NULL));
+      return RAI_LoadBackend_TensorFlow(
+          ctx, RedisModule_StringPtrLen(fullpath, NULL));
     case RAI_BACKEND_TFLITE:
-      return RAI_LoadBackend_TFLite(ctx, RedisModule_StringPtrLen(fullpath, NULL));
+      return RAI_LoadBackend_TFLite(ctx,
+                                    RedisModule_StringPtrLen(fullpath, NULL));
     case RAI_BACKEND_TORCH:
-      return RAI_LoadBackend_Torch(ctx, RedisModule_StringPtrLen(fullpath, NULL));
+      return RAI_LoadBackend_Torch(ctx,
+                                   RedisModule_StringPtrLen(fullpath, NULL));
     case RAI_BACKEND_ONNXRUNTIME:
-      return RAI_LoadBackend_ONNXRuntime(ctx, RedisModule_StringPtrLen(fullpath, NULL));
+      return RAI_LoadBackend_ONNXRuntime(
+          ctx, RedisModule_StringPtrLen(fullpath, NULL));
   }
 
   return REDISMODULE_ERR;
@@ -363,13 +464,15 @@ int RAI_LoadBackend(RedisModuleCtx *ctx, int backend, const char* path) {
 int RAI_LoadDefaultBackend(RedisModuleCtx *ctx, int backend) {
   switch (backend) {
     case RAI_BACKEND_TENSORFLOW:
-      return RAI_LoadBackend(ctx, backend, "redisai_tensorflow/redisai_tensorflow.so");
+      return RAI_LoadBackend(ctx, backend,
+                             "redisai_tensorflow/redisai_tensorflow.so");
     case RAI_BACKEND_TFLITE:
       return RAI_LoadBackend(ctx, backend, "redisai_tflite/redisai_tflite.so");
     case RAI_BACKEND_TORCH:
       return RAI_LoadBackend(ctx, backend, "redisai_torch/redisai_torch.so");
     case RAI_BACKEND_ONNXRUNTIME:
-      return RAI_LoadBackend(ctx, backend, "redisai_onnxruntime/redisai_onnxruntime.so");
+      return RAI_LoadBackend(ctx, backend,
+                             "redisai_onnxruntime/redisai_onnxruntime.so");
   }
 
   return REDISMODULE_ERR;
diff --git a/src/backends.h b/src/backends.h
index e94e5e338..f0303d752 100644
--- a/src/backends.h
+++ b/src/backends.h
@@ -1,25 +1,80 @@
+/**
+ * backends.h
+ *
+ * Contains the structure and methods signatures required to register a new
+ * backend to be loaded by the module.
+ *
+ */
+
 #ifndef SRC_BACKENDS_H_
 #define SRC_BACKENDS_H_
 
 #include "config.h"
+#include "err.h"
 #include "model_struct.h"
 #include "script_struct.h"
 #include "tensor.h"
-#include "err.h"
 
+/*
+ * To register a new backend to be loaded by the module, the backend needs to
+ * implement the following:
+ *
+ * * ** model_create_with_nodes **:  A callback function pointer that creates a
+ * model given the RAI_ModelOpts and input and output nodes.
+ *
+ * * ** model_create **:  A callback function pointer that creates a model given
+ * the RAI_ModelOpts.
+ *
+ * * ** model_run **:  A callback function pointer that runs a model given the
+ * RAI_ModelRunCtx pointer.
+ *
+ * * ** model_serialize **:  A callback function pointer that serializes a model
+ * given the RAI_Model pointer.
+ *
+ * * ** script_create **:  A callback function pointer that creates a script.
+ *
+ * * ** script_free **:  A callback function pointer that frees a script given
+ * the RAI_Script pointer.
+ *
+ * * ** script_run **:  A callback function pointer that runs a model given the
+ * RAI_ScriptRunCtx pointer.
+ */
 typedef struct RAI_LoadedBackend {
+  // ** model_create_with_nodes **:  A callback function pointer that creates a
+  // model given the RAI_ModelOpts and input and output nodes
   RAI_Model* (*model_create_with_nodes)(RAI_Backend, const char*, RAI_ModelOpts,
-                                        size_t, const char**, size_t, const char**,
-                                        const char*, size_t, RAI_Error*);
+                                        size_t, const char**, size_t,
+                                        const char**, const char*, size_t,
+                                        RAI_Error*);
+
+  // ** model_create **:  A callback function pointer that creates a model given
+  // the RAI_ModelOpts
   RAI_Model* (*model_create)(RAI_Backend, const char*, RAI_ModelOpts,
                              const char*, size_t, RAI_Error*);
+
+  // ** model_free **:  A callback function pointer that frees a model given the
+  // RAI_Model pointer
   void (*model_free)(RAI_Model*, RAI_Error*);
+
+  // ** model_run **:  A callback function pointer that runs a model given the
+  // RAI_ModelRunCtx pointer
   int (*model_run)(RAI_ModelRunCtx**, RAI_Error*);
+
+  // ** model_serialize **:  A callback function pointer that serializes a model
+  // given the RAI_Model pointer
   int (*model_serialize)(RAI_Model*, char**, size_t*, RAI_Error*);
 
+  // ** script_create **:  A callback function pointer that creates a script
   RAI_Script* (*script_create)(const char*, const char*, RAI_Error*);
+
+  // ** script_free **:  A callback function pointer that frees a script given
+  // the RAI_Script pointer
   void (*script_free)(RAI_Script*, RAI_Error*);
+
+  // ** script_run **:  A callback function pointer that runs a model given the
+  // RAI_ScriptRunCtx pointer
   int (*script_run)(RAI_ScriptRunCtx*, RAI_Error*);
+
 } RAI_LoadedBackend;
 
 typedef struct RAI_LoadedBackends {
@@ -32,8 +87,8 @@ typedef struct RAI_LoadedBackends {
 RAI_LoadedBackends RAI_backends;
 char* RAI_BackendsPath;
 
-int RAI_LoadBackend(RedisModuleCtx *ctx, int backend, const char *path);
-int RAI_LoadDefaultBackend(RedisModuleCtx *ctx, int backend);
+int RAI_LoadBackend(RedisModuleCtx* ctx, int backend, const char* path);
+int RAI_LoadDefaultBackend(RedisModuleCtx* ctx, int backend);
 
 const char* RAI_BackendName(int backend);
 
diff --git a/src/model.c b/src/model.c
index a68248ed3..1d749e213 100644
--- a/src/model.c
+++ b/src/model.c
@@ -1,3 +1,12 @@
+/**
+ * model.c
+ *
+ * Contains the helper methods for both creating, populating,
+ * managing and destructing the RedisModuleType, and methods to manage
+ * parsing and replying of tensor related commands or operations.
+ *
+ */
+
 #include "model.h"
 #include "model_struct.h"
 #include "backends.h"
diff --git a/src/model.h b/src/model.h
index d01b9d510..7e154bf55 100644
--- a/src/model.h
+++ b/src/model.h
@@ -1,52 +1,223 @@
+/**
+ * model.h
+ *
+ * Contains headers for the helper methods for both creating, populating,
+ * managing and destructing the RedisModuleType, and methods to manage
+ * parsing and replying of tensor related commands or operations.
+ *
+ */
+
 #ifndef SRC_MODEL_H_
 #define SRC_MODEL_H_
 
 #include "config.h"
+#include "err.h"
 #include "model_struct.h"
-#include "tensor.h"
+#include "redisai.h"
 #include "redismodule.h"
 #include "run_info.h"
-#include "err.h"
-#include "redisai.h"
+#include "tensor.h"
 #include "util/dict.h"
-#include "run_info.h"
 
-extern RedisModuleType *RedisAI_ModelType;
+extern RedisModuleType* RedisAI_ModelType;
 
+/**
+ * Helper method to register the RedisModuleType type exported by the module.
+ *
+ * @param ctx Context in which Redis modules operate
+ * @return
+ */
 int RAI_ModelInit(RedisModuleCtx* ctx);
-RAI_Model *RAI_ModelCreate(RAI_Backend backend, const char* devicestr, const char* tag, RAI_ModelOpts opts,
-                           size_t ninputs, const char **inputs,
-                           size_t noutputs, const char **outputs,
-                           const char *modeldef, size_t modellen, RAI_Error* err);
+
+/**
+ * Helper method to allocated and initialize a RAI_Model. Depending on the
+ * backend it relies on either `model_create_with_nodes` or `model_create`
+ * callback functions.
+ *
+ * @param backend enum identifying the backend. one of RAI_BACKEND_TENSORFLOW,
+ * RAI_BACKEND_TFLITE,  RAI_BACKEND_TORCH, or RAI_BACKEND_ONNXRUNTIME
+ * @param devicestr device string
+ * @param tag optional model tag
+ * @param opts `RAI_ModelOpts` like batchsize or parallelism settings
+ * @param ninputs optional number of inputs definition
+ * @param inputs optional inputs array
+ * @param noutputs optional number of outputs
+ * @param outputs optional number of outputs array
+ * @param modeldef encoded model definition
+ * @param modellen length of the encoded model definition
+ * @param error error data structure to store error message in the case of
+ * failures
+ * @return RAI_Model model structure on success, or NULL if failed
+ */
+RAI_Model* RAI_ModelCreate(RAI_Backend backend, const char* devicestr,
+                           const char* tag, RAI_ModelOpts opts, size_t ninputs,
+                           const char** inputs, size_t noutputs,
+                           const char** outputs, const char* modeldef,
+                           size_t modellen, RAI_Error* err);
+
+/**
+ *  Frees the memory of the RAI_Tensor when the tensor reference count reaches
+ * 0. It is safe to call this function with a NULL input model.
+ *
+ * @param model input model to be freed
+ * @param error error data structure to store error message in the case of
+ * failures
+ */
 void RAI_ModelFree(RAI_Model* model, RAI_Error* err);
 
+/**
+ * Allocates the RAI_ModelRunCtx data structure required for async background
+ * work within `RedisAI_RunInfo` structure on RedisAI blocking commands
+ *
+ * @param model input model
+ * @return RAI_ModelRunCtx to be used within
+ */
 RAI_ModelRunCtx* RAI_ModelRunCtxCreate(RAI_Model* model);
+
+/**
+ * Frees the RAI_ModelRunCtx data structure used within for async background
+ * work
+ *
+ * @param mctx
+ */
 void RAI_ModelRunCtxFree(RAI_ModelRunCtx* mctx);
 
-int RAI_ModelRunCtxAddInput(RAI_ModelRunCtx* mctx, const char* inputName, RAI_Tensor* inputTensor);
+/**
+ * Allocates a RAI_ModelCtxParam data structure, and enforces a shallow copy of
+ * the provided input tensor, adding it to the input tensors array of the
+ * RAI_ModelRunCtx.
+ *
+ * @param mctx input RAI_ModelRunCtx to add the input tensor
+ * @param inputName input tensor name
+ * @param inputTensor input tensor structure
+ * @return returns 1 on success ( always returns success )
+ */
+int RAI_ModelRunCtxAddInput(RAI_ModelRunCtx* mctx, const char* inputName,
+                            RAI_Tensor* inputTensor);
+
+/**
+ * Allocates a RAI_ModelCtxParam data structure, and sets the tensor reference
+ * to NULL ( will be set after MODELRUN ), adding it to the outputs tensors
+ * array of the RAI_ModelRunCtx.
+ *
+ * @param mctx RAI_ModelRunCtx to add the output tensor
+ * @param outputName output tensor name
+ * @return returns 1 on success ( always returns success )
+ */
 int RAI_ModelRunCtxAddOutput(RAI_ModelRunCtx* mctx, const char* outputName);
+
+/**
+ * Returns the total number of input tensors of the RAI_ModelCtxParam
+ *
+ * @param mctx RAI_ModelRunCtx
+ * @return the total number of input tensors of the RAI_ModelCtxParam
+ */
 size_t RAI_ModelRunCtxNumInputs(RAI_ModelRunCtx* mctx);
+
+/**
+ * Returns the total number of output tensors of the RAI_ModelCtxParam
+ *
+ * @param mctx RAI_ModelRunCtx
+ * @return the total number of output tensors of the RAI_ModelCtxParam
+ */
 size_t RAI_ModelRunCtxNumOutputs(RAI_ModelRunCtx* mctx);
+
+/**
+ * Get the RAI_Tensor at the input array index position
+ *
+ * @param mctx RAI_ModelRunCtx
+ * @param index input array index position
+ * @return RAI_Tensor
+ */
 RAI_Tensor* RAI_ModelRunCtxInputTensor(RAI_ModelRunCtx* mctx, size_t index);
+
+/**
+ * Get the RAI_Tensor at the output array index position
+ *
+ * @param mctx RAI_ModelRunCtx
+ * @param index input array index position
+ * @return RAI_Tensor
+ */
 RAI_Tensor* RAI_ModelRunCtxOutputTensor(RAI_ModelRunCtx* mctx, size_t index);
 
+/**
+ * Given the input array of mctxs, run associated the associated backend
+ * session. On success, the tensors corresponding to outputs[0,noutputs-1] are
+ * placed in each RAI_ModelRunCtx output tensors array
+ *
+ * @param mctxs
+ * @param error error data structure to store error message in the case of
+ * failures
+ * @return REDISMODULE_OK if the underlying backend `model_run` runned
+ * sucessfully, or REDISMODULE_ERR if failed.
+ */
 int RAI_ModelRun(RAI_ModelRunCtx** mctxs, RAI_Error* err);
+
+/**
+ * Every call to this function, will make the RAI_Model 'model' requiring an
+ * additional call to RAI_ModelFree() in order to really free the model.
+ * Returns a shallow copy of the model.
+ *
+ * @param input model
+ * @return model
+ */
 RAI_Model* RAI_ModelGetShallowCopy(RAI_Model* model);
 
-int RAI_ModelSerialize(RAI_Model *model, char **buffer, size_t *len, RAI_Error *err);
-/* Return REDISMODULE_ERR if there was an error getting the Model.
- * Return REDISMODULE_OK if the model value stored at key was correctly
- * returned and available at *model variable. */
-int RAI_GetModelFromKeyspace(RedisModuleCtx *ctx, RedisModuleString *keyName,
-                              RedisModuleKey **key, RAI_Model **model,
-                              int mode);
-
-int RedisAI_Parse_ModelRun_RedisCommand(RedisModuleCtx *ctx,
-                                        RedisModuleString **argv, int argc,
-                                        RAI_ModelRunCtx **mctx,
-                                        RedisModuleString ***outkeys,
-                                        RAI_Model **mto, int useLocalContext,
-                                        AI_dict **localContextDict, 
-                                        int use_chaining_operator,
-                                        const char *chaining_operator, RAI_Error *error);
+/**
+ * Serializes a model given the RAI_Model pointer, saving the serialized data
+ * into `buffer` and proving the saved buffer size
+ *
+ * @param model RAI_Model pointer
+ * @param buffer pointer to the output buffer
+ * @param len pointer to the variable to save the output buffer length
+ * @param error error data structure to store error message in the case of
+ * failures
+ * @return REDISMODULE_OK if the underlying backend `model_serialize` runned
+ * successfully, or REDISMODULE_ERR if failed.
+ * */
+int RAI_ModelSerialize(RAI_Model* model, char** buffer, size_t* len,
+                       RAI_Error* err);
+
+/**
+ * Helper method to get a Model from keyspace. In the case of failure the key is
+ * closed and the error is replied ( no cleaning actions required )
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param keyName key name
+ * @param key models's key handle. On sucess it contains an handle representing
+ * a Redis key with the requested access mode
+ * @param model destination model structure
+ * @param mode key access mode
+ * @return REDISMODULE_OK if the model value stored at key was correctly
+ * returned and available at *model variable, or REDISMODULE_ERR if there was
+ * an error getting the Model
+ */
+int RAI_GetModelFromKeyspace(RedisModuleCtx* ctx, RedisModuleString* keyName,
+                             RedisModuleKey** key, RAI_Model** model, int mode);
+
+/**
+ * Helper method to parse AI.MODELRUN arguments
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param argv Redis command arguments, as an array of strings
+ * @param argc Redis command number of arguments
+ * @param mctx Destination Model context to store the parsed data
+ * @param outkeys array to store the parsed output keys
+ * @param mto model to run the session from
+ * @param useLocalContext flag to enable using local context
+ * @param localContextDict local non-blocking hash table containing DAG's
+ * keynames marked as persistent
+ * @param use_chaining_operator flag to enable enforcing the chaining operator
+ * checking
+ * @param chaining_operator operator used to split operations. Any command
+ * argument after the chaining operator is not considered
+ * @param error error data structure to store error message in the case of
+ * parsing failures
+ * @return processed number of arguments on success, or -1 if the parsing failed
+ */
+int RedisAI_Parse_ModelRun_RedisCommand(
+    RedisModuleCtx* ctx, RedisModuleString** argv, int argc,
+    RAI_ModelRunCtx** mctx, RedisModuleString*** outkeys, RAI_Model** mto,
+    int useLocalContext, AI_dict** localContextDict, int use_chaining_operator,
+    const char* chaining_operator, RAI_Error* error);
 #endif /* SRC_MODEL_H_ */
diff --git a/src/redisai.c b/src/redisai.c
index 842736afc..47e220c03 100644
--- a/src/redisai.c
+++ b/src/redisai.c
@@ -397,18 +397,6 @@ int RedisAI_ModelList_RedisCommand(RedisModuleCtx *ctx, RedisModuleString **argv
  *
  * The request is queued and evaded asynchronously from a separate thread. The
  * client blocks until the computation finishes.
- *
- * 1. clone inputs as needed in the main thread (only the alternative is to
- * lock)
- * 2. spawn the new thread for running the model
- * 3. have reply callback put the data back into the key
- * 
- * This way we avoid any race condition. The only gotcha is making sure no one
- * overwrites the model until it's done computing.
- * This means that setModel will decode on a candidate pointer, and will then
- * be picked up on the next round. We also need to signal when it's time to
- * dispose of the old model. The key is having a single thread looping
- * forexecution
  */
 int RedisAI_ModelRun_RedisCommand(RedisModuleCtx *ctx, RedisModuleString **argv,
                                   int argc) {
diff --git a/src/script.h b/src/script.h
index c9ee90ad6..0a22406f0 100644
--- a/src/script.h
+++ b/src/script.h
@@ -9,22 +9,113 @@
 
 extern RedisModuleType *RedisAI_ScriptType;
 
+/**
+ *
+ *
+ * @param ctx
+ * @return
+ */
 int RAI_ScriptInit(RedisModuleCtx* ctx);
+
+/**
+ *
+ *
+ * @param devicestr
+ * @param tag
+ * @param scriptdef
+ * @param err
+ * @return
+ */
 RAI_Script* RAI_ScriptCreate( const char* devicestr, const char* tag, const char* scriptdef, RAI_Error* err);
+
+/**
+ *
+ *
+ * @param script
+ * @param err
+ */
 void RAI_ScriptFree(RAI_Script* script, RAI_Error* err);
 
+/**
+ *
+ *
+ * @param script
+ * @param fnname
+ * @return
+ */
 RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script, const char *fnname);
+
+/**
+ *
+ *
+ * @param sctx
+ * @param inputTensor
+ * @return
+ */
 int RAI_ScriptRunCtxAddInput(RAI_ScriptRunCtx* sctx, RAI_Tensor* inputTensor);
+
+/**
+ *
+ *
+ * @param sctx
+ * @return
+ */
 int RAI_ScriptRunCtxAddOutput(RAI_ScriptRunCtx* sctx);
+
+/**
+ *
+ *
+ * @param sctx
+ * @return
+ */
 size_t RAI_ScriptRunCtxNumOutputs(RAI_ScriptRunCtx* sctx);
+
+/**
+ *
+ *
+ * @param sctx
+ * @param index
+ * @return
+ */
 RAI_Tensor* RAI_ScriptRunCtxOutputTensor(RAI_ScriptRunCtx* sctx, size_t index);
+
+/**
+ *
+ *
+ * @param sctx
+ */
 void RAI_ScriptRunCtxFree(RAI_ScriptRunCtx* sctx);
 
+/**
+ *
+ *
+ * @param sctx
+ * @param err
+ * @return
+ */
 int RAI_ScriptRun(RAI_ScriptRunCtx* sctx, RAI_Error* err);
+
+/**
+ *
+ *
+ * @param script
+ * @return
+ */
 RAI_Script* RAI_ScriptGetShallowCopy(RAI_Script* script);
+
 /* Return REDISMODULE_ERR if there was an error getting the Script.
  * Return REDISMODULE_OK if the model value stored at key was correctly
  * returned and available at *model variable. */
+/**
+ *
+ *
+ * @param ctx
+ * @param keyName
+ * @param key
+ * @param script
+ * @param mode
+ * @return
+ */
 int RAI_GetScriptFromKeyspace(RedisModuleCtx *ctx, RedisModuleString *keyName,
                               RedisModuleKey **key, RAI_Script **script,
                               int mode);

From 5925598c723834975e03cc6459b8f5600924172f Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 01:41:15 +0100
Subject: [PATCH 30/45] [wip] added further documentation on the
 src/background_workers.h/.c files. wip on coverage fix

---
 .circleci/config.yml     | 18 ++++--------------
 docs/developer.md        | 14 +++++---------
 src/backends.h           |  2 +-
 src/background_workers.c | 10 ++++++++++
 src/background_workers.h | 12 +++++++++++-
 5 files changed, 31 insertions(+), 25 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 0849ff45c..3aae82cfd 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -73,18 +73,8 @@ jobs:
     docker:
       - image: redisfab/rmbuilder:5.0.7-x64-buster
     steps:
-      - checkout
-      - run:
-          name: Install dependencies
-          command: |
-            ./opt/readies/bin/getpy3
-            ./opt/system-setup.py
-            # git clone git://github.com/antirez/redis.git --branch 5.0.7; cd redis; make malloc=libc -j $(nproc); make install; redis-server --version
-      - restore_cache:
-          keys:
-          - build-dependencies-{{ checksum "get_deps.sh" }}
-          # fallback to using the latest cache if no exact match is found
-          - v1-dependencies-
+      - ci_steps:
+          platform: debian
       - run:
           name: Build
           command: |
@@ -189,8 +179,8 @@ workflows:
             tags:
               only: /.*/
       - coverage:
-          requires:
-            - build
+          # requires:
+          #   - build
           filters:
             branches:
               only: /.*/
diff --git a/docs/developer.md b/docs/developer.md
index d2ff250ae..aae1fbf4d 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -9,7 +9,8 @@ RedisAI bundles together best-of-breed technologies for delivering stable and pe
 
 As a way of representing tensor data we've embraced [dlpack](https://github.com/dmlc/dlpack) - a community effort to define a common tensor data structure that can be shared by different frameworks, supported by cuPy, cuDF, DGL, TGL, PyTorch, and MxNet.
 
-**Data Structures
+**Data Structures**
+
 RedisAI provides the following data structures:
 
 ***Tensor**: represents an n-dimensional array of values
@@ -67,7 +68,7 @@ This file also contains the function signatures of the reply callbacks to be cal
 
 **background_workers.h**
 ---
-Contains the structure to manage the per-device queues, used for decoupling the work from the main thread to the background worker threads. For each of the incoming ModelRun, ScriptRun, and DagRun commands, The request is queued and evaded asynchronously to one the device queues.
+Contains the structure to manage the per-device queues, used for decoupling the work from the main thread to the background worker threads. For each of the incoming ModelRun, ScriptRun, and DagRun commands, the request is queued and evaded asynchronously to one the device queues.
 
 **err.h**
 ---
@@ -106,11 +107,6 @@ Within the `backends` folder you will find the implementations code required to
 ***TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
 ***ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
 
-**Other C files
----
-
-TBD...
-
 
 ## Building and Testing
 You can compile and build the module from its source code.
@@ -153,7 +149,7 @@ Once the dependencies have been built, you can build the RedisAI module with:
 make -C opt build
 ```
 
-**Running Tests
+**Running Tests**
 
 The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run
 them you'll need:
@@ -177,7 +173,7 @@ For example, to run the tests strictly designed for the TensorFlow backend, foll
 
     $ make -C opt tests TEST=tests_tensorflow.py
 
-**Integration Tests Coverage
+**Integration Tests Coverage**
 
 TBD 
 
diff --git a/src/backends.h b/src/backends.h
index f0303d752..3205a9b9c 100644
--- a/src/backends.h
+++ b/src/backends.h
@@ -1,7 +1,7 @@
 /**
  * backends.h
  *
- * Contains the structure and methods signatures required to register a new
+ * Contains the structure and method signatures required to register a new
  * backend to be loaded by the module.
  *
  */
diff --git a/src/background_workers.c b/src/background_workers.c
index 71e6e3625..fddfccd9d 100644
--- a/src/background_workers.c
+++ b/src/background_workers.c
@@ -1,3 +1,13 @@
+/**
+ * background_workers.c
+ *
+ * Contains the structure to manage the per-device queues, used for decoupling
+ * the work from the main thread to the background worker threads. For each of
+ * the incoming ModelRun, ScriptRun, and DagRun commands, the request is queued
+ * and evaded asynchronously to one the device queues.
+ *
+ */
+
 #include "background_workers.h"
 #include "dag.h"
 #include "model_script_run_session.h"
diff --git a/src/background_workers.h b/src/background_workers.h
index 3f449e0b0..4c850a575 100644
--- a/src/background_workers.h
+++ b/src/background_workers.h
@@ -1,3 +1,14 @@
+/**
+ * background_workers.h
+ *
+ * Contains the structure and method signatures required to manage the
+ * per-device queues, used for decoupling the work from the main thread to the
+ * background worker threads. For each of the incoming ModelRun, ScriptRun, and
+ * DagRun commands, the request is queued and evaded asynchronously to one the
+ * device queues.
+ *
+ */
+
 #ifndef SRC_BACKGROUND_WORKERS_H_
 #define SRC_BACKGROUND_WORKERS_H_
 
@@ -20,7 +31,6 @@
 AI_dict *run_queues;
 long long perqueueThreadPoolSize;
 
-
 typedef struct RunQueueInfo {
   pthread_mutex_t run_queue_mutex;
   pthread_cond_t queue_condition_var;

From 05dea992c4cb09c12197ac2514238d65c2fa330d Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 02:00:59 +0100
Subject: [PATCH 31/45] [wip] wip on coverage fix

---
 .circleci/config.yml | 23 +++++++++++++++++------
 1 file changed, 17 insertions(+), 6 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 3aae82cfd..2e88b048e 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -1,7 +1,7 @@
 version: 2.1
 
 commands:
-  ci_steps:
+  setup_steps:
     parameters:
       platform:
         type: string
@@ -29,6 +29,13 @@ commands:
           command: |
             mkdir -p ~/workspace
             chown `whoami` ~/workspace
+
+  test_steps:
+    parameters:
+      platform:
+        type: string
+    steps:
+      - checkout
       - run:
           name: Build
           command: make -C opt all SHOW=1
@@ -66,24 +73,26 @@ jobs:
     docker:
       - image: redisfab/rmbuilder:5.0.7-x64-buster
     steps:
-      - ci_steps:
+      - setup_steps:
+          platform: debian
+      - test_steps:
           platform: debian
 
   coverage:
     docker:
       - image: redisfab/rmbuilder:5.0.7-x64-buster
     steps:
-      - ci_steps:
+      - setup_steps:
           platform: debian
       - run:
-          name: Build
+          name: Build for coverage
           command: |
             # make fetch SHOW=1
             make -C opt build COV=1 SHOW=1
       - run:
           name: Test with coverage
           command: |
-            make -C opt test SHOW=1 COV=1
+            make -C opt test SHOW=1 COV=1 AOF=0 SLAVES=0
             make -C opt cov-upload
 
   build-macos:
@@ -94,7 +103,9 @@ jobs:
           name: Fix macOS Python installation
           command: |
             brew reinstall -f python2
-      - ci_steps:
+      - setup_steps:
+          platform: macosx
+      - test_steps:
           platform: macosx
 
   build-multiarch-docker:

From 2c85e00387d5dbb2addfb055201dbb7dc6a7eca6 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 11:26:57 +0100
Subject: [PATCH 32/45] [wip] added documentation to the stats.h/c and
 script.h/c files. Ran a spelling on src/* and docs/*

---
 docs/developer.md   |  68 ++++++++++-----------
 docs/intro.md       |   2 +-
 docs/performance.md |   6 +-
 src/model.h         |  24 ++++----
 src/script.c        | 104 ++++++++++++++++++--------------
 src/script.h        | 140 +++++++++++++++++++++++++++-----------------
 src/stats.c         |  23 ++++++--
 src/stats.h         |  50 ++++++++++++++++
 src/tensor.h        |   6 +-
 9 files changed, 266 insertions(+), 157 deletions(-)

diff --git a/docs/developer.md b/docs/developer.md
index aae1fbf4d..4d7144c38 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -1,10 +1,10 @@
 # RedisAI Development
 
-The following sections discusses topics relevant to the development of the RedisAI module itself. We'll start by refering to the general design, followed by the source code layout, and how to prepare your local test and development environment.
+The following sections discusses topics relevant to the development of the RedisAI module itself. We'll start by referring to the general design, followed by the source code layout, and how to prepare your local test and development environment.
 
 ## General Design
 
-RedisAI bundles together best-of-breed technologies for delivering stable and performant model serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain. 
+RedisAI bundles together best-of-breed technologies for delivering stable and fast model serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain. 
 
 
 As a way of representing tensor data we've embraced [dlpack](https://github.com/dmlc/dlpack) - a community effort to define a common tensor data structure that can be shared by different frameworks, supported by cuPy, cuDF, DGL, TGL, PyTorch, and MxNet.
@@ -32,50 +32,55 @@ exposed is the logical one to follow in order to disclose different layers
 of complexity incrementally. 
 
 **redisai.c**
----
+
 
 This is the entry point of the RedisAI module, responsible for registering the new commands in the Redis server, and containing all command functions to be called. This file is also responsible for exporting of Tensor, Script and Model APIs to other Modules.
 
 **tensor.h**
----
+
 Contains the helper methods for both creating, populating, managing and destructing the Tensor data structure, and methods to manage parsing and replying of tensor related commands or operations.
 
 **model.h**
----
+
 Contains the helper methods for both creating, populating, managing and destructing the Model data structure.
 Contains also methods to manage parsing and replying of model related commands or operations, that take place in the context of the Redis Main thread.
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
 **script.h**
----
-Contains the helper methods for both creating, populating, managing and destructing the Script data structure.
+
+Contains the helper methods for both creating, populating, managing and destructing the PyTorch Script data structure.
 Contains also methods to manage parsing and replying of script related commands or operations, that take place in the context of the Redis Main thread.
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
 **dag.h**
----
+
 Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
 
 **run_info.h**
----
+
 Contains the structure and headers to create, initialize, get, reset, and free the structures that represent the context in which RedisAI blocking commands operate, namely `RedisAI_RunInfo` and the newly added `RAI_DagOp`. 
 
 **model_script_run_session.h**
----
+
 Contains the methods that are related to async background work that 
 was triggered by either MODELRUN or SCRIPTRUN Command and Called within `RedisAI_Run_ThreadMain`.
 This file also contains the function signatures of the reply callbacks to be called in order to reply to the clients, after the background work on MODELRUN and SCRIPTRUN is done.
 
 **background_workers.h**
----
+
 Contains the structure to manage the per-device queues, used for decoupling the work from the main thread to the background worker threads. For each of the incoming ModelRun, ScriptRun, and DagRun commands, the request is queued and evaded asynchronously to one the device queues.
 
+**stats.h**
+
+Contains the structure and headers to create, initialize, get, reset, and free run-time statics, like call count, error count, and aggregate durations of ModelRun and ScriptRun sessions.
+The statistics are ephemeral, meaning that they are not saved to the key space, and on maximum live for the duration of the DB uptime.
+
 **err.h**
----
+
 Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
 
 **backends.h and backends directory**
----
+
 Contains the structure and headers methods required to register a new backend to be loaded by the module. 
  To do so, the backend needs to implement and export the following methods:
  
@@ -102,16 +107,16 @@ Contains the structure and headers methods required to register a new backend to
 
 Within the `backends` folder you will find the implementations code required to support the following DL/ML identifiers and respective backend libraries:
 
-***TF**: `tensorflow.h` and `tensorflow.c` exporting the functions to to register the TensorFlow backend
-***TFLITE**: `tflite.h` and `tflite.c` exporting the functions to to register the TensorFlow Lite backend
-***TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
-***ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
+* **TF**: `tensorflow.h` and `tensorflow.c` exporting the functions to to register the TensorFlow backend
+* **TFLITE**: `tflite.h` and `tflite.c` exporting the functions to to register the TensorFlow Lite backend
+* **TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
+* **ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
 
 
 ## Building and Testing
 You can compile and build the module from its source code.
 
-**Prerequisites
+**Prerequisites**
 * CUDA needs to be installed for GPU support.
 * CMake 3.0 or higher needs to be installed.
 * Redis v4.0.9 or greater.
@@ -119,36 +124,25 @@ You can compile and build the module from its source code.
 **Get the Source Code
 You can obtain the module's source code by cloning the project's repository using git like so:
 
-```sh
-git clone https://github.com/RedisAI/RedisAI
-```
+    $ git clone https://github.com/RedisAI/RedisAI
 
 Switch to the project's directory with:
 
-```sh
-cd RedisAI
-```
+    $ cd RedisAI
 
-**Building the Dependencies
+**Building the Dependencies**
 Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
 
-```sh
-bash get_deps.sh
-```
+    $ bash get_deps.sh
 
 Alternatively, you can run the following to fetch the CPU-only backends.
 
-```sh
-bash get_deps.sh cpu
-```
+    $ bash get_deps.sh cpu
 
-**Building the Module
+**Building the Module**
 Once the dependencies have been built, you can build the RedisAI module with:
-
-```sh
-make -C opt build
-```
-
+    $ make -C opt build
+    
 **Running Tests**
 
 The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run
diff --git a/docs/intro.md b/docs/intro.md
index ca4378349..6ab0788ac 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -336,5 +336,5 @@ AI.SCRIPTRUN myscript multiply INPUTS tA tB OUTPUTS tScript
 This covers the basics of RedisAI's data structures and capabilities. For more information refer to:
 
 * The [Commands page](commands.md) provides a reference of the RedisAI API
-* The [Clients page](clients.md) lists RdisAI clients in several programming languages
+* The [Clients page](clients.md) lists RedisAI clients in several programming languages
 * The [Examples page](examples.md) showcases several examples that can be extended
diff --git a/docs/performance.md b/docs/performance.md
index 89034a90a..800256815 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -20,7 +20,7 @@ Bellow, are the current DL solutions supported:
 
 ### Transaction data dataset characteristics
 
-Currently, aibench supports one use case -- creditcard-fraud from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) with the extension of reference data. This use-case aims to detect a fraudulent transaction based on anonymized credit card transactions and reference data.
+Currently, aibench supports one use case -- creditcard-fraud from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) with the extension of reference data. This use-case aims to detect a fraudulent transaction based on anonymize credit card transactions and reference data.
 
 
 We've decided to extend the initial dataset in the sense that for each Transaction data, we generate random deterministic Reference data, commonly used to enrich financial transactions information. In the financial service industry and regulatory agencies, the reference data that defines and describes such financial transactions, can cover all relevant particulars for highly complex transactions with multiple dependencies, entities, and contingencies, thus resulting in a larger numerical value input tensor of size 1 x 256.
@@ -99,14 +99,14 @@ cd ../aibench_run_inference_flask_tensorflow && go install
 
 As stated previously we encourage the community to to run and request for PR with regards to showcasing the obtained results for any Model Server. With regards to the results methodology we expected that for each tested version/variation:
 
-- A minimum of 3 distinct full repetitions, and reported as a result the median (q50), q95, q99, overall achievable inference throughput, and if possible ( and recomended ) the referal to the full spectrum of latencies.
+- A minimum of 3 distinct full repetitions, and reported as a result the median (q50), q95, q99, overall achievable inference throughput, and if possible ( and recommended ) the referral to the full spectrum of latencies.
 - A full platform description. Ideally you should run the the benchmark and Model Serving instances in separate machines, placed in an optimal networking scenario. 
 - Considering the weight of the network performance in the overall performance of the Model Serving solution we recommend that in addition to the aibench performance runs described above, to also run baseline netperf TCP_RR, in order to understand the underlying network characteristics. 
 
 
 ### What the benchmark suite does not account for
 
-As important as understanding the metrics and takeways that this benchmark provides is to understand what is considered out of scope. This benchmark suite was not designed to test some key aspects of observability, operational and model-lifecycle management, fault tolerance, and high availability of the Model Server. **As important as performance**, these factors should be part of a prudent decision on which solution to adopt, and we hope to provide further guidance on them in other sections of the documentation. 
+As important as understanding the metrics and takeaways that this benchmark provides is to understand what is considered out of scope. This benchmark suite was not designed to test some key aspects of observability, operational and model-lifecycle management, fault tolerance, and high availability of the Model Server. **As important as performance**, these factors should be part of a prudent decision on which solution to adopt, and we hope to provide further guidance on them in other sections of the documentation. 
 
 ### Latest benchmark results
 
diff --git a/src/model.h b/src/model.h
index 7e154bf55..d1b54e2f8 100644
--- a/src/model.h
+++ b/src/model.h
@@ -56,7 +56,7 @@ RAI_Model* RAI_ModelCreate(RAI_Backend backend, const char* devicestr,
                            size_t modellen, RAI_Error* err);
 
 /**
- *  Frees the memory of the RAI_Tensor when the tensor reference count reaches
+ * Frees the memory of the RAI_Model when the model reference count reaches
  * 0. It is safe to call this function with a NULL input model.
  *
  * @param model input model to be freed
@@ -107,10 +107,10 @@ int RAI_ModelRunCtxAddInput(RAI_ModelRunCtx* mctx, const char* inputName,
 int RAI_ModelRunCtxAddOutput(RAI_ModelRunCtx* mctx, const char* outputName);
 
 /**
- * Returns the total number of input tensors of the RAI_ModelCtxParam
+ * Returns the total number of input tensors of the RAI_ModelRunCtx
  *
  * @param mctx RAI_ModelRunCtx
- * @return the total number of input tensors of the RAI_ModelCtxParam
+ * @return the total number of input tensors of the RAI_ModelRunCtx
  */
 size_t RAI_ModelRunCtxNumInputs(RAI_ModelRunCtx* mctx);
 
@@ -141,15 +141,19 @@ RAI_Tensor* RAI_ModelRunCtxInputTensor(RAI_ModelRunCtx* mctx, size_t index);
 RAI_Tensor* RAI_ModelRunCtxOutputTensor(RAI_ModelRunCtx* mctx, size_t index);
 
 /**
- * Given the input array of mctxs, run associated the associated backend
- * session. On success, the tensors corresponding to outputs[0,noutputs-1] are
- * placed in each RAI_ModelRunCtx output tensors array
+ * Given the input array of mctxs, run the associated backend
+ * session. If the input array of model context runs is larger than one, then
+ * each backend's `model_run` is responsible for concatenating tensors, and run
+ * the model in batches with the size of the input array. On success, the
+ * tensors corresponding to outputs[0,noutputs-1] are placed in each
+ * RAI_ModelRunCtx output tensors array. Relies on each backend's `model_run`
+ * definition.
  *
- * @param mctxs
+ * @param mctxs array on input model contexts
  * @param error error data structure to store error message in the case of
  * failures
  * @return REDISMODULE_OK if the underlying backend `model_run` runned
- * sucessfully, or REDISMODULE_ERR if failed.
+ * successfully, or REDISMODULE_ERR if failed.
  */
 int RAI_ModelRun(RAI_ModelRunCtx** mctxs, RAI_Error* err);
 
@@ -172,7 +176,7 @@ RAI_Model* RAI_ModelGetShallowCopy(RAI_Model* model);
  * @param len pointer to the variable to save the output buffer length
  * @param error error data structure to store error message in the case of
  * failures
- * @return REDISMODULE_OK if the underlying backend `model_serialize` runned
+ * @return REDISMODULE_OK if the underlying backend `model_serialize` ran
  * successfully, or REDISMODULE_ERR if failed.
  * */
 int RAI_ModelSerialize(RAI_Model* model, char** buffer, size_t* len,
@@ -184,7 +188,7 @@ int RAI_ModelSerialize(RAI_Model* model, char** buffer, size_t* len,
  *
  * @param ctx Context in which Redis modules operate
  * @param keyName key name
- * @param key models's key handle. On sucess it contains an handle representing
+ * @param key models's key handle. On success it contains an handle representing
  * a Redis key with the requested access mode
  * @param model destination model structure
  * @param mode key access mode
diff --git a/src/script.c b/src/script.c
index c06be1e83..0fcba7958 100644
--- a/src/script.c
+++ b/src/script.c
@@ -1,14 +1,22 @@
+/**
+ * script.c
+ *
+ * Contains the helper methods for both creating, populating,
+ * managing and destructing the PyTorch Script data structure.
+ *
+ */
+
 #include "script.h"
-#include "script_struct.h"
-#include "backends.h"
-#include "stats.h"
 
+#include "backends.h"
 #include "rmutil/alloc.h"
+#include "script_struct.h"
+#include "stats.h"
 #include "util/arr_rm_alloc.h"
 
-RedisModuleType *RedisAI_ScriptType = NULL;
+RedisModuleType* RedisAI_ScriptType = NULL;
 
-static void* RAI_Script_RdbLoad(struct RedisModuleIO *io, int encver) {
+static void* RAI_Script_RdbLoad(struct RedisModuleIO* io, int encver) {
   // if (encver != RAI_ENC_VER) {
   //   /* We should actually log an error here, or try to implement
   //      the ability to load older versions of our data structure. */
@@ -17,13 +25,13 @@ static void* RAI_Script_RdbLoad(struct RedisModuleIO *io, int encver) {
 
   RAI_Error err = {0};
 
-  const char *devicestr = RedisModule_LoadStringBuffer(io, NULL);
-  const char *tag = RedisModule_LoadStringBuffer(io, NULL);
+  const char* devicestr = RedisModule_LoadStringBuffer(io, NULL);
+  const char* tag = RedisModule_LoadStringBuffer(io, NULL);
 
   size_t len;
-  char *scriptdef = RedisModule_LoadStringBuffer(io, &len);
+  char* scriptdef = RedisModule_LoadStringBuffer(io, &len);
 
-  RAI_Script *script = RAI_ScriptCreate(devicestr, tag, scriptdef, &err);
+  RAI_Script* script = RAI_ScriptCreate(devicestr, tag, scriptdef, &err);
 
   if (err.code == RAI_EBACKENDNOTLOADED) {
     RedisModuleCtx* ctx = RedisModule_GetContextFromIO(io);
@@ -36,7 +44,7 @@ static void* RAI_Script_RdbLoad(struct RedisModuleIO *io, int encver) {
     RAI_ClearError(&err);
     script = RAI_ScriptCreate(devicestr, tag, scriptdef, &err);
   }
- 
+
   RedisModule_Free(scriptdef);
 
   if (err.code != RAI_OK) {
@@ -45,35 +53,40 @@ static void* RAI_Script_RdbLoad(struct RedisModuleIO *io, int encver) {
   }
 
   RedisModuleCtx* stats_ctx = RedisModule_GetContextFromIO(io);
-  RedisModuleString* stats_keystr = RedisModule_CreateStringFromString(stats_ctx,
-                                                                       RedisModule_GetKeyNameFromIO(io));
+  RedisModuleString* stats_keystr = RedisModule_CreateStringFromString(
+      stats_ctx, RedisModule_GetKeyNameFromIO(io));
   const char* stats_devicestr = RedisModule_Strdup(devicestr);
   const char* stats_tag = RedisModule_Strdup(tag);
 
-  script->infokey = RAI_AddStatsEntry(stats_ctx, stats_keystr, RAI_SCRIPT, RAI_BACKEND_TORCH, stats_devicestr, stats_tag);
+  script->infokey =
+      RAI_AddStatsEntry(stats_ctx, stats_keystr, RAI_SCRIPT, RAI_BACKEND_TORCH,
+                        stats_devicestr, stats_tag);
 
   RedisModule_Free(stats_keystr);
 
   return script;
 }
 
-static void RAI_Script_RdbSave(RedisModuleIO *io, void *value) {
-  RAI_Script *script = (RAI_Script*)value;
+static void RAI_Script_RdbSave(RedisModuleIO* io, void* value) {
+  RAI_Script* script = (RAI_Script*)value;
 
   size_t len = strlen(script->scriptdef) + 1;
 
-  RedisModule_SaveStringBuffer(io, script->devicestr, strlen(script->devicestr) + 1);
+  RedisModule_SaveStringBuffer(io, script->devicestr,
+                               strlen(script->devicestr) + 1);
   RedisModule_SaveStringBuffer(io, script->tag, strlen(script->tag) + 1);
   RedisModule_SaveStringBuffer(io, script->scriptdef, len);
 }
 
-static void RAI_Script_AofRewrite(RedisModuleIO *aof, RedisModuleString *key, void *value) {
-  RAI_Script *script = (RAI_Script*)value;
+static void RAI_Script_AofRewrite(RedisModuleIO* aof, RedisModuleString* key,
+                                  void* value) {
+  RAI_Script* script = (RAI_Script*)value;
 
-  RedisModule_EmitAOF(aof, "AI.SCRIPTSET", "sccc", key, script->devicestr, script->tag, script->scriptdef);
+  RedisModule_EmitAOF(aof, "AI.SCRIPTSET", "sccc", key, script->devicestr,
+                      script->tag, script->scriptdef);
 }
 
-static void RAI_Script_DTFree(void *value) {
+static void RAI_Script_DTFree(void* value) {
   RAI_Error err = {0};
   RAI_ScriptFree(value, &err);
   if (err.code != RAI_OK) {
@@ -83,26 +96,27 @@ static void RAI_Script_DTFree(void *value) {
 }
 
 int RAI_ScriptInit(RedisModuleCtx* ctx) {
-  RedisModuleTypeMethods tmScript = {
-      .version = REDISMODULE_TYPE_METHOD_VERSION,
-      .rdb_load = RAI_Script_RdbLoad,
-      .rdb_save = RAI_Script_RdbSave,
-      .aof_rewrite = RAI_Script_AofRewrite,
-      .mem_usage = NULL,
-      .free = RAI_Script_DTFree,
-      .digest = NULL
-  };
-
-  RedisAI_ScriptType = RedisModule_CreateDataType(ctx, "AI_SCRIPT", 0, &tmScript);
+  RedisModuleTypeMethods tmScript = {.version = REDISMODULE_TYPE_METHOD_VERSION,
+                                     .rdb_load = RAI_Script_RdbLoad,
+                                     .rdb_save = RAI_Script_RdbSave,
+                                     .aof_rewrite = RAI_Script_AofRewrite,
+                                     .mem_usage = NULL,
+                                     .free = RAI_Script_DTFree,
+                                     .digest = NULL};
+
+  RedisAI_ScriptType =
+      RedisModule_CreateDataType(ctx, "AI_SCRIPT", 0, &tmScript);
   return RedisAI_ScriptType != NULL;
 }
 
-RAI_Script *RAI_ScriptCreate( const char* devicestr, const char* tag, const char *scriptdef, RAI_Error* err) {
+RAI_Script* RAI_ScriptCreate(const char* devicestr, const char* tag,
+                             const char* scriptdef, RAI_Error* err) {
   if (!RAI_backends.torch.script_create) {
     RAI_SetError(err, RAI_EBACKENDNOTLOADED, "ERR Backend not loaded: TORCH");
     return NULL;
   }
-  RAI_Script* script = RAI_backends.torch.script_create(devicestr, scriptdef, err);
+  RAI_Script* script =
+      RAI_backends.torch.script_create(devicestr, scriptdef, err);
 
   if (script) {
     script->tag = RedisModule_Strdup(tag);
@@ -112,7 +126,7 @@ RAI_Script *RAI_ScriptCreate( const char* devicestr, const char* tag, const char
 }
 
 void RAI_ScriptFree(RAI_Script* script, RAI_Error* err) {
-  if (--script->refCount > 0){
+  if (--script->refCount > 0) {
     return;
   }
 
@@ -124,11 +138,12 @@ void RAI_ScriptFree(RAI_Script* script, RAI_Error* err) {
   RedisModule_Free(script->tag);
 
   RAI_RemoveStatsEntry(script->infokey);
- 
+
   RAI_backends.torch.script_free(script, err);
 }
 
-RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script, const char *fnname) {
+RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script,
+                                         const char* fnname) {
 #define PARAM_INITIAL_SIZE 10
   RAI_ScriptRunCtx* sctx = RedisModule_Calloc(1, sizeof(*sctx));
   sctx->script = RAI_ScriptGetShallowCopy(script);
@@ -140,11 +155,11 @@ RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script, const char *fnname)
   return sctx;
 }
 
-static int Script_RunCtxAddParam(RAI_ScriptRunCtx* sctx, RAI_ScriptCtxParam* paramArr,
+static int Script_RunCtxAddParam(RAI_ScriptRunCtx* sctx,
+                                 RAI_ScriptCtxParam* paramArr,
                                  RAI_Tensor* tensor) {
-
   RAI_ScriptCtxParam param = {
-      .tensor = tensor ? RAI_TensorGetShallowCopy(tensor): NULL,
+      .tensor = tensor ? RAI_TensorGetShallowCopy(tensor) : NULL,
   };
   paramArr = array_append(paramArr, param);
   return 1;
@@ -168,12 +183,12 @@ RAI_Tensor* RAI_ScriptRunCtxOutputTensor(RAI_ScriptRunCtx* sctx, size_t index) {
 }
 
 void RAI_ScriptRunCtxFree(RAI_ScriptRunCtx* sctx) {
-  for (size_t i = 0 ; i < array_len(sctx->inputs) ; ++i) {
+  for (size_t i = 0; i < array_len(sctx->inputs); ++i) {
     RAI_TensorFree(sctx->inputs[i].tensor);
   }
   array_free(sctx->inputs);
 
-  for (size_t i = 0 ; i < array_len(sctx->outputs) ; ++i) {
+  for (size_t i = 0; i < array_len(sctx->outputs); ++i) {
     if (sctx->outputs[i].tensor) {
       RAI_TensorFree(sctx->outputs[i].tensor);
     }
@@ -199,7 +214,7 @@ int RAI_ScriptRun(RAI_ScriptRunCtx* sctx, RAI_Error* err) {
     RAI_SetError(err, RAI_EBACKENDNOTLOADED, "ERR Backend not loaded: TORCH");
     return REDISMODULE_ERR;
   }
- 
+
   return RAI_backends.torch.script_run(sctx, err);
 }
 
@@ -208,12 +223,11 @@ RAI_Script* RAI_ScriptGetShallowCopy(RAI_Script* script) {
   return script;
 }
 
-
 /* Return REDISMODULE_ERR if there was an error getting the Script.
  * Return REDISMODULE_OK if the model value stored at key was correctly
  * returned and available at *model variable. */
-int RAI_GetScriptFromKeyspace(RedisModuleCtx *ctx, RedisModuleString *keyName,
-                              RedisModuleKey **key, RAI_Script **script,
+int RAI_GetScriptFromKeyspace(RedisModuleCtx* ctx, RedisModuleString* keyName,
+                              RedisModuleKey** key, RAI_Script** script,
                               int mode) {
   *key = RedisModule_OpenKey(ctx, keyName, mode);
   if (RedisModule_KeyType(*key) == REDISMODULE_KEYTYPE_EMPTY) {
diff --git a/src/script.h b/src/script.h
index 0a22406f0..17337ca30 100644
--- a/src/script.h
+++ b/src/script.h
@@ -1,123 +1,155 @@
+/**
+ * script.h
+ *
+ * Contains headers for the helper methods for both creating, populating,
+ * managing and destructing the PyTorch Script data structure.
+ *
+ */
+
 #ifndef SRC_SCRIPT_H_
 #define SRC_SCRIPT_H_
 
 #include "config.h"
+#include "err.h"
+#include "redismodule.h"
 #include "script_struct.h"
 #include "tensor.h"
-#include "redismodule.h"
-#include "err.h"
 
-extern RedisModuleType *RedisAI_ScriptType;
+extern RedisModuleType* RedisAI_ScriptType;
 
 /**
+ * Helper method to register the script type exported by the module.
  *
- *
- * @param ctx
+ * @param ctx Context in which Redis modules operate
  * @return
  */
 int RAI_ScriptInit(RedisModuleCtx* ctx);
 
 /**
- *
- *
- * @param devicestr
- * @param tag
- * @param scriptdef
- * @param err
- * @return
+ * Helper method to allocated and initialize a RAI_Script. Relies on Pytorch
+ * backend `script_create` callback function.
+ *
+ * @param devicestr device string
+ * @param tag script model tag
+ * @param scriptdef encoded script definition
+ * @param error error data structure to store error message in the case of
+ * failures
+ * @return RAI_Script script structure on success, or NULL if failed
  */
-RAI_Script* RAI_ScriptCreate( const char* devicestr, const char* tag, const char* scriptdef, RAI_Error* err);
+RAI_Script* RAI_ScriptCreate(const char* devicestr, const char* tag,
+                             const char* scriptdef, RAI_Error* err);
 
 /**
+ * Frees the memory of the RAI_Script when the script reference count reaches
+ * 0. It is safe to call this function with a NULL input script.
  *
- *
- * @param script
- * @param err
+ * @param script input script to be freed
+ * @param error error data structure to store error message in the case of
+ * failures
  */
 void RAI_ScriptFree(RAI_Script* script, RAI_Error* err);
 
 /**
+ * Allocates the RAI_ScriptRunCtx data structure required for async background
+ * work within `RedisAI_RunInfo` structure on RedisAI blocking commands
  *
- *
- * @param script
- * @param fnname
- * @return
+ * @param script input script
+ * @param fnname function name to used from the script
+ * @return RAI_ScriptRunCtx to be used within
  */
-RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script, const char *fnname);
+RAI_ScriptRunCtx* RAI_ScriptRunCtxCreate(RAI_Script* script,
+                                         const char* fnname);
 
 /**
+ * Allocates a RAI_ScriptCtxParam data structure, and enforces a shallow copy of
+ * the provided input tensor, adding it to the input tensors array of the
+ * RAI_ScriptRunCtx.
  *
- *
- * @param sctx
- * @param inputTensor
- * @return
+ * @param sctx input RAI_ScriptRunCtx to add the input tensor
+ * @param inputTensor input tensor structure
+ * @return returns 1 on success ( always returns success )
  */
 int RAI_ScriptRunCtxAddInput(RAI_ScriptRunCtx* sctx, RAI_Tensor* inputTensor);
 
 /**
+ * Allocates a RAI_ScriptCtxParam data structure, and sets the tensor reference
+ * to NULL ( will be set after SCRIPTRUN ), adding it to the outputs tensors
+ * array of the RAI_ScriptRunCtx.
  *
- *
- * @param sctx
- * @return
+ * @param sctx input RAI_ScriptRunCtx to add the output tensor
+ * @return returns 1 on success ( always returns success )
  */
 int RAI_ScriptRunCtxAddOutput(RAI_ScriptRunCtx* sctx);
 
 /**
+ * Returns the total number of output tensors of the RAI_ScriptRunCtx
  *
- *
- * @param sctx
- * @return
+ * @param sctx RAI_ScriptRunCtx
+ * @return the total number of output tensors of the RAI_ScriptRunCtx
  */
 size_t RAI_ScriptRunCtxNumOutputs(RAI_ScriptRunCtx* sctx);
 
 /**
+ * Get the RAI_Tensor at the output array index position
  *
- *
- * @param sctx
- * @param index
- * @return
+ * @param sctx RAI_ScriptRunCtx
+ * @param index input array index position
+ * @return RAI_Tensor
  */
 RAI_Tensor* RAI_ScriptRunCtxOutputTensor(RAI_ScriptRunCtx* sctx, size_t index);
 
 /**
- *
+ * Frees the RAI_ScriptRunCtx data structure used within for async background
+ * work
  *
  * @param sctx
  */
 void RAI_ScriptRunCtxFree(RAI_ScriptRunCtx* sctx);
 
 /**
- *
- *
- * @param sctx
- * @param err
- * @return
+ * Given the input script context, run associated script
+ * session. On success, the tensors corresponding to outputs[0,noutputs-1] are
+ * placed in the RAI_ScriptRunCtx output tensors array. Relies on PyTorch's
+ * `script_run` definition.
+ *
+ * @param sctx input script context
+ * @param error error data structure to store error message in the case of
+ * failures
+ * @return REDISMODULE_OK if the underlying backend `script_run` ran
+ * successfully, or REDISMODULE_ERR if failed.
  */
 int RAI_ScriptRun(RAI_ScriptRunCtx* sctx, RAI_Error* err);
 
 /**
+ * Every call to this function, will make the RAI_Script 'script' requiring an
+ * additional call to RAI_ScriptFree() in order to really free the script.
+ * Returns a shallow copy of the script.
  *
- *
- * @param script
- * @return
+ * @param script input script
+ * @return script
  */
 RAI_Script* RAI_ScriptGetShallowCopy(RAI_Script* script);
 
 /* Return REDISMODULE_ERR if there was an error getting the Script.
  * Return REDISMODULE_OK if the model value stored at key was correctly
  * returned and available at *model variable. */
+
 /**
- *
- *
- * @param ctx
- * @param keyName
- * @param key
- * @param script
- * @param mode
- * @return
+ * Helper method to get a Script from keyspace. In the case of failure the key
+ * is closed and the error is replied ( no cleaning actions required )
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param keyName key name
+ * @param key script's key handle. On success it contains an handle representing
+ * a Redis key with the requested access mode
+ * @param script destination script structure
+ * @param mode key access mode
+ * @return REDISMODULE_OK if the script value stored at key was correctly
+ * returned and available at *script variable, or REDISMODULE_ERR if there was
+ * an error getting the Script
  */
-int RAI_GetScriptFromKeyspace(RedisModuleCtx *ctx, RedisModuleString *keyName,
-                              RedisModuleKey **key, RAI_Script **script,
+int RAI_GetScriptFromKeyspace(RedisModuleCtx* ctx, RedisModuleString* keyName,
+                              RedisModuleKey** key, RAI_Script** script,
                               int mode);
 
 #endif /* SRC_SCRIPT_H_ */
diff --git a/src/stats.c b/src/stats.c
index a2f942650..5ab47a8fd 100644
--- a/src/stats.c
+++ b/src/stats.c
@@ -1,3 +1,12 @@
+/**
+ * stats.c
+ *
+ * Contains the helper methods to create,
+ * initialize, get, reset, and free run-time statics, like call count, error
+ * count, and aggregate durations of ModelRun and ScriptRun sessions.
+ *
+ */
+
 #include "stats.h"
 
 #include <sys/time.h>
@@ -69,17 +78,23 @@ void RAI_RemoveStatsEntry(void* infokey) {
     struct RedisAI_RunStats* rstats = AI_dictGetVal(stats_entry);
     AI_dictDelete(run_stats, infokey);
     RAI_FreeRunStats(rstats);
-    RedisModule_Free(rstats);
   }
 }
 
 void RAI_FreeRunStats(struct RedisAI_RunStats* rstats) {
-  RedisModule_Free(rstats->devicestr);
-  RedisModule_Free(rstats->tag);
+  if (rstats) {
+    if (rstats->devicestr) {
+      RedisModule_Free(rstats->devicestr);
+    }
+    if (rstats->tag) {
+      RedisModule_Free(rstats->tag);
+    }
+    RedisModule_Free(rstats);
+  }
 }
 
 void RedisAI_FreeRunStats(RedisModuleCtx* ctx,
                           struct RedisAI_RunStats* rstats) {
   RedisModule_FreeString(ctx, rstats->key);
-  RedisModule_Free(rstats->devicestr);
+  RAI_FreeRunStats(rstats);
 }
\ No newline at end of file
diff --git a/src/stats.h b/src/stats.h
index 76ee7f122..f5592b77d 100644
--- a/src/stats.h
+++ b/src/stats.h
@@ -1,3 +1,12 @@
+/**
+ * stats.h
+ *
+ * Contains the structure and headers for the helper methods to create,
+ * initialize, get, reset, and free run-time statics, like call count, error
+ * count, and aggregate durations of ModelRun and ScriptRun sessions.
+ *
+ */
+
 #ifndef SRC_STATS_H_
 #define SRC_STATS_H_
 
@@ -24,17 +33,58 @@ AI_dict* run_stats;
 long long ustime(void);
 mstime_t mstime(void);
 
+/**
+ * Adds an entry to the ephemeral run-time statistic. The statistics are not
+ * saved to the keyspace, and on maximum live for the duration of the DB uptime.
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param keyName key name to use as unique stats identifier
+ * @param type type of stats identifier ( one of RAI_MODEL or RAI_SCRIPT )
+ * @param backend backend identifier (one of RAI_BACKEND_TENSORFLOW,
+ * RAI_BACKEND_TFLITE, RAI_BACKEND_TORCH, RAI_BACKEND_ONNXRUNTIME,)
+ * @param devicestr
+ * @param tag optional tag of Model/Script
+ * @return
+ */
 void* RAI_AddStatsEntry(RedisModuleCtx* ctx, RedisModuleString* key,
                         RAI_RunType type, RAI_Backend backend,
                         const char* devicestr, const char* tag);
 
+/**
+ * Removes the statistical entry with the provided unique stats identifier
+ *
+ * @param infokey
+ */
 void RAI_RemoveStatsEntry(void* infokey);
 
+/**
+ * Returns a list of all statistical entries that match a specific RAI_RunType (
+ * model or script )
+ *
+ * @param type type of stats identifier to provide the list for ( one of
+ * RAI_MODEL or RAI_SCRIPT )
+ * @param nkeys output variable containing the number of returned stats
+ * @param keys output variable containing the list of returned keys
+ * @param tags output variable containing the list of returned tags
+ */
 void RAI_ListStatsEntries(RAI_RunType type, long long* nkeys,
                           RedisModuleString*** keys, const char*** tags);
 
+/**
+ * Frees the memory of the RedisAI_RunStats, excluding the one managed by the
+ * Context in which Redis modules operate
+ *
+ * @param rstats
+ */
 void RAI_FreeRunStats(struct RedisAI_RunStats* rstats);
 
+/**
+ * Frees the memory of the RedisAI_RunStats, including the one managed by the
+ * Context in which Redis modules operate
+ *
+ * @param ctx Context in which Redis modules operate
+ * @param rstats
+ */
 void RedisAI_FreeRunStats(RedisModuleCtx* ctx, struct RedisAI_RunStats* rstats);
 
 #endif /* SRC_SATTS_H_ */
diff --git a/src/tensor.h b/src/tensor.h
index 1c5651dcc..2fe89e124 100644
--- a/src/tensor.h
+++ b/src/tensor.h
@@ -291,7 +291,7 @@ char* RAI_TensorData(RAI_Tensor* t);
  *
  * @param ctx Context in which Redis modules operate
  * @param keyName key name
- * @param key tensor's key handle. On sucess it contains an handle representing
+ * @param key tensor's key handle. On success it contains an handle representing
  * a Redis key with the requested access mode
  * @param mode key access mode
  * @return REDISMODULE_OK if it's possible to store at the specified key handle
@@ -307,7 +307,7 @@ int RAI_OpenKey_Tensor(RedisModuleCtx* ctx, RedisModuleString* keyName,
  *
  * @param ctx Context in which Redis modules operate
  * @param keyName key name
- * @param key tensor's key handle. On sucess it contains an handle representing
+ * @param key tensor's key handle. On success it contains an handle representing
  * a Redis key with the requested access mode
  * @param tensor destination tensor structure
  * @param mode key access mode
@@ -357,7 +357,7 @@ void RedisAI_ReplicateTensorSet(RedisModuleCtx* ctx, RedisModuleString* key,
  * @param argv Redis command arguments, as an array of strings
  * @param argc Redis command number of arguments
  * @param t Destination tensor to store the parsed data
- * @param enforceArity flag wether to enforce arity checking
+ * @param enforceArity flag whether to enforce arity checking
  * @param error error data structure to store error message in the case of
  * parsing failures
  * @return processed number of arguments on success, or -1 if the parsing failed

From e24479dc60789014563bd2b8acdee127e6084ac3 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 11:28:23 +0100
Subject: [PATCH 33/45] [fix] removed repeating ai.tensorget from dag's
 documentation

---
 docs/commands.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/commands.md b/docs/commands.md
index ab3833494..430431c3d 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -487,7 +487,7 @@ _Arguments_
 
 * **LOAD**: an optional argument, that denotes the beginning of the input tensors keys' list, followed by the number of keys, and one or more key names
 * **PERSIST**: an optional argument, that denotes the beginning of the output tensors keys' list, followed by the number of keys, and one or more key names
-* **|> command**: the chaining operator, that denotes the beginning of a RedisAI command, followed by one of the supported RedisAI commands ( for now `AI.TENSORGET`, `AI.TENSORGET`, and `AI.MODELRUN` ). Command spliting is done by the presence of another `|>`.
+* **|> command**: the chaining operator, that denotes the beginning of a RedisAI command, followed by one of the supported RedisAI commands ( for now `AI.TENSORSET`, `AI.TENSORGET`, and `AI.MODELRUN` ). Command spliting is done by the presence of another `|>`.
 
 _Return_
 

From 91cfbdf6ec0737af7b6c367f10a1f1f53513dc6b Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 11:58:00 +0100
Subject: [PATCH 34/45] [wip] fixed formatting issues on developer.md

---
 docs/developer.md | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/docs/developer.md b/docs/developer.md
index 4d7144c38..85ea5c253 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -13,9 +13,9 @@ As a way of representing tensor data we've embraced [dlpack](https://github.com/
 
 RedisAI provides the following data structures:
 
-***Tensor**: represents an n-dimensional array of values
-***Model**: represents a frozen graph by one of the supported DL/ML framework backends
-***Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
+* **Tensor**: represents an n-dimensional array of values
+* **Model**: represents a frozen graph by one of the supported DL/ML framework backends
+* **Script**: represents a [TorchScript](https://pytorch.org/docs/stable/jit.html)
 
 ## Source code layout
 
@@ -117,11 +117,13 @@ Within the `backends` folder you will find the implementations code required to
 You can compile and build the module from its source code.
 
 **Prerequisites**
+
 * CUDA needs to be installed for GPU support.
 * CMake 3.0 or higher needs to be installed.
 * Redis v4.0.9 or greater.
 
-**Get the Source Code
+**Get the Source Code**
+
 You can obtain the module's source code by cloning the project's repository using git like so:
 
     $ git clone https://github.com/RedisAI/RedisAI
@@ -131,6 +133,7 @@ Switch to the project's directory with:
     $ cd RedisAI
 
 **Building the Dependencies**
+
 Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
 
     $ bash get_deps.sh
@@ -140,7 +143,9 @@ Alternatively, you can run the following to fetch the CPU-only backends.
     $ bash get_deps.sh cpu
 
 **Building the Module**
+
 Once the dependencies have been built, you can build the RedisAI module with:
+
     $ make -C opt build
     
 **Running Tests**
@@ -158,16 +163,20 @@ To run tests in a Python virtualenv, follow these steps:
     $ virtualenv .env
     $ . .env/bin/active
     $ pip install -r tests/tests_requirements.txt
-    $ make -C opt tests
+    $ make -C opt test
 
 Integration tests are based on RLTest, and specific setup parameters can be provided
 to configure tests.
 
 For example, to run the tests strictly designed for the TensorFlow backend, follow these steps:
 
-    $ make -C opt tests TEST=tests_tensorflow.py
+    $ make -C opt test TEST=tests_tensorflow.py
 
 **Integration Tests Coverage**
 
-TBD 
+For coverage analysis we rely on lcov, and you can easily run the tests on your local machine following these steps:
+
+    $ make -C opt build COV=1 SHOW=1
+    $ make -C opt test COV=1 SHOW=1 AOF=0 SLAVES=0
 
+Setting `AOF=0 SLAVES=0` is not mandatory but will not run AOF and replication tests, thus getting us results faster.

From bfd4444dd355f7d6dc9b86a71c02e62d2daaff24 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 14:34:14 +0100
Subject: [PATCH 35/45] [fix] fixed documentation on ai.tensorget

---
 docs/commands.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/commands.md b/docs/commands.md
index 4ea2ed9a7..88bc633c7 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -74,6 +74,7 @@ _Arguments_
 _Return_
 
 Depending on the specified reply format:
+
  * **META**: Array containing the tensor's meta data exclusively. The returned array consists of the following elements:
     1. The tensor's data type as a String
     1. The tensor's shape as an Array consisting of an item per dimension
@@ -178,7 +179,7 @@ _Arguments_
 * **MINBATCHSIZE**: when provided with an `m` that is greater than 0, the engine will postpone calls to `AI.MODELRUN` until the batch's size had reached `m`. This is primarily used to force batching during testing, but it can also be used under normal operation. In this case, note that requests for which `m` is not reached will hang indefinitely (defai;t value: 0).
 * **INPUTS**: one or more names of the model's input nodes (applicable only for TensorFlow models)
 * **OUTPUTS**: one or more names of the model's output nodes (applicable only for TensorFlow models)
-* **model**: the Protobuf-serialized model.Since Redis supports strings up to 512MB, blobs for very large models need to be chunked, e.g. `BLOB chunk1 chunk2 ...`.
+* **model**: the Protobuf-serialized model. Since Redis supports strings up to 512MB, blobs for very large models need to be chunked, e.g. `BLOB chunk1 chunk2 ...`.
 
 _Return_
 

From f9f349eb29390f09a433c1c53c5b2292eed20a42 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Mon, 27 Apr 2020 14:51:49 +0100
Subject: [PATCH 36/45] [wip] finished code comment all methods on the major
 building blocks of RedisAI

---
 src/err.c                      | 25 +++++++++++++------------
 src/err.h                      |  7 ++++---
 src/model_script_run_session.c | 18 ++++++++++++++----
 src/model_script_run_session.h | 20 +++++++++++++++-----
 src/run_info.c                 |  9 +++++++++
 src/run_info.h                 |  9 ++++++++-
 6 files changed, 63 insertions(+), 25 deletions(-)

diff --git a/src/err.c b/src/err.c
index 9a7224eee..a2fe390d7 100644
--- a/src/err.c
+++ b/src/err.c
@@ -1,20 +1,21 @@
 /**
  * err.c
  *
- * Contains a formal API to create, initialize, get, reset, and free errors among different backends.
+ * Contains a formal API to create, initialize, get, reset, and free errors
+ * among different backends.
  */
 
 #include "err.h"
-#include "stdlib.h"
-#include "assert.h"
-#include "string.h"
 
+#include "assert.h"
 #include "redismodule.h"
+#include "stdlib.h"
+#include "string.h"
 
 char *RAI_Chomp(const char *src) {
-  char* str = RedisModule_Strdup(src);
+  char *str = RedisModule_Strdup(src);
   size_t len = strlen(src);
-  for (size_t i=0; i<len; i++) {
+  for (size_t i = 0; i < len; i++) {
     if (str[i] == '\n' || str[i] == '\r') {
       str[i] = ' ';
     }
@@ -43,15 +44,15 @@ void RAI_SetError(RAI_Error *err, RAI_ErrorCode code, const char *detail) {
  * @return 0 on success, or 1 if the allocation
  * failed.
  */
-int RAI_InitError(RAI_Error** result) {
-  RAI_Error* err;
-  err = (RAI_Error*)RedisModule_Calloc(1, sizeof(RAI_Error));
+int RAI_InitError(RAI_Error **result) {
+  RAI_Error *err;
+  err = (RAI_Error *)RedisModule_Calloc(1, sizeof(RAI_Error));
   if (!err) {
     return 1;
   }
-  err->code=0;
-  err->detail=NULL;
-  err->detail_oneline=NULL;
+  err->code = 0;
+  err->detail = NULL;
+  err->detail_oneline = NULL;
   *result = err;
   return 0;
 }
diff --git a/src/err.h b/src/err.h
index 9cd77a199..adf549005 100644
--- a/src/err.h
+++ b/src/err.h
@@ -1,7 +1,8 @@
 /**
  * err.h
  *
- * Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
+ * Contains the structure and headers for a formal API to create, initialize,
+ * get, reset, and free errors among different backends.
  */
 
 #ifndef SRC_ERR_H_
@@ -29,8 +30,8 @@ typedef enum {
 
 typedef struct RAI_Error {
   RAI_ErrorCode code;
-  char* detail;
-  char* detail_oneline;
+  char *detail;
+  char *detail_oneline;
 } RAI_Error;
 
 /**
diff --git a/src/model_script_run_session.c b/src/model_script_run_session.c
index a974849f2..38442b769 100644
--- a/src/model_script_run_session.c
+++ b/src/model_script_run_session.c
@@ -1,3 +1,14 @@
+/**
+ * model_scrip_run_session.c
+ * 
+ * Contains the methods that are related to async background work that was
+ * triggered by either MODELRUN or SCRIPTRUN Command and Called within
+ * `RedisAI_Run_ThreadMain`. This file also contains the function signatures of
+ * the reply callbacks to be called in order to reply to the clients, after the
+ * background work on MODELRUN and SCRIPTRUN is done.
+ *
+ */
+
 #include "model_script_run_session.h"
 
 #include "model.h"
@@ -12,7 +23,6 @@
 #include "util/dict.h"
 #include "util/queue.h"
 
-
 /**
  * Actual method running the MODELRUN and SCRIPTRUN Commands in the background
  * thread Called within `RedisAI_Run_ThreadMain`
@@ -91,7 +101,7 @@ int RAI_ModelRunScriptRunReply(RedisModuleCtx *ctx, RedisModuleString **argv,
 
   if (rinfo->result == REDISMODULE_ERR) {
     RedisModule_Log(ctx, "warning", "ERR %s", rinfo->err->detail);
-    RAI_SafeAddDataPoint(rstats,0,1,1,0);
+    RAI_SafeAddDataPoint(rstats, 0, 1, 1, 0);
     int ret = RedisModule_ReplyWithError(ctx, rinfo->err->detail_oneline);
     RAI_FreeRunInfo(ctx, rinfo);
     return ret;
@@ -112,7 +122,7 @@ int RAI_ModelRunScriptRunReply(RedisModuleCtx *ctx, RedisModuleString **argv,
                                           REDISMODULE_READ | REDISMODULE_WRITE);
     if (status == REDISMODULE_ERR) {
       RAI_FreeRunInfo(ctx, rinfo);
-      RAI_SafeAddDataPoint(rstats,0,1,1,0);
+      RAI_SafeAddDataPoint(rstats, 0, 1, 1, 0);
       return REDISMODULE_ERR;
     }
     RAI_Tensor *t = NULL;
@@ -134,7 +144,7 @@ int RAI_ModelRunScriptRunReply(RedisModuleCtx *ctx, RedisModuleString **argv,
       RedisAI_ReplicateTensorSet(ctx, rinfo->outkeys[i], t);
     }
   }
-  RAI_SafeAddDataPoint(rstats,rinfo->duration_us,1,0,batch_size);
+  RAI_SafeAddDataPoint(rstats, rinfo->duration_us, 1, 0, batch_size);
   RAI_FreeRunInfo(ctx, rinfo);
   return RedisModule_ReplyWithSimpleString(ctx, "OK");
 }
diff --git a/src/model_script_run_session.h b/src/model_script_run_session.h
index 6c4639133..3dc645239 100644
--- a/src/model_script_run_session.h
+++ b/src/model_script_run_session.h
@@ -1,3 +1,14 @@
+/**
+ * model_scrip_run_session.h
+ * 
+ * Contains the headers of the methods that are related to async background work
+ * that was triggered by either MODELRUN or SCRIPTRUN Command and Called within
+ * `RedisAI_Run_ThreadMain`. This file also contains the function signatures of
+ * the reply callbacks to be called in order to reply to the clients, after the
+ * background work on MODELRUN and SCRIPTRUN is done.
+ *
+ */
+
 #ifndef SRC_MODEL_SCRIPT_RUN_SESSION_H_
 #define SRC_MODEL_SCRIPT_RUN_SESSION_H_
 
@@ -13,8 +24,6 @@
 #include "util/dict.h"
 #include "util/queue.h"
 
-
-
 /**
  * Actual method running the MODELRUN and SCRIPTRUN Commands in the background
  * thread Called within `RedisAI_Run_ThreadMain`
@@ -23,7 +32,8 @@
  * The 'rinfo' argument will be accessible by the reply callback, for each of
  * the runinfo present in batch_rinfo
  *
- * @param batch_rinfo array of `RedisAI_RunInfo *rinfo` contexts in which RedisAI blocking commands operate.
+ * @param batch_rinfo array of `RedisAI_RunInfo *rinfo` contexts in which
+ * RedisAI blocking commands operate.
  * @return
  */
 void *RAI_ModelRunScriptRunSession(RedisAI_RunInfo **batch_rinfo);
@@ -35,7 +45,8 @@ void *RAI_ModelRunScriptRunSession(RedisAI_RunInfo **batch_rinfo);
  * @param ctx Context in which Redis modules operate
  * @param argv Redis command arguments, as an array of strings
  * @param argc Redis command number of arguments
- * @return REDISMODULE_OK on success, or REDISMODULE_ERR  if the MODELRUN/SCRIPTRUN failed
+ * @return REDISMODULE_OK on success, or REDISMODULE_ERR  if the
+ * MODELRUN/SCRIPTRUN failed
  */
 int RAI_ModelRunScriptRunReply(RedisModuleCtx *ctx, RedisModuleString **argv,
                                int argc);
@@ -57,5 +68,4 @@ void RedisAI_FreeData(RedisModuleCtx *ctx, void *rinfo);
  */
 void RedisAI_Disconnected(RedisModuleCtx *ctx, RedisModuleBlockedClient *bc);
 
-
 #endif /* SRC_MODEL_SCRIPT_RUN_SESSION_H_ */
diff --git a/src/run_info.c b/src/run_info.c
index d35634e76..dad991a5f 100644
--- a/src/run_info.c
+++ b/src/run_info.c
@@ -1,3 +1,12 @@
+/**
+ * run_info.c
+ *
+ * Contains the methods to create, initialize, get, reset, and
+ * free the structures that represent the context in which RedisAI blocking
+ * commands operate, namely RedisAI_RunInfo and the newly added RAI_DagOp.
+ *
+ */
+
 #include "err.h"
 #include "model.h"
 #include "model_struct.h"
diff --git a/src/run_info.h b/src/run_info.h
index 0b4f15958..4f6963d24 100644
--- a/src/run_info.h
+++ b/src/run_info.h
@@ -1,4 +1,11 @@
-
+/**
+ * run_info.h
+ *
+ * Contains the structure and headers to create, initialize, get, reset, and
+ * free the structures that represent the context in which RedisAI blocking
+ * commands operate, namely RedisAI_RunInfo and the newly added RAI_DagOp.
+ *
+ */
 
 #ifndef SRC_RUN_INFO_H_
 #define SRC_RUN_INFO_H_

From d3ff862050d0b4dc678a196239951a69c5b311d6 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Tue, 28 Apr 2020 19:15:09 +0300
Subject: [PATCH 37/45] Adds contrib.md

---
 docs/contrib.md | 2 ++
 mkdocs.yml      | 1 +
 2 files changed, 3 insertions(+)
 create mode 100644 docs/contrib.md

diff --git a/docs/contrib.md b/docs/contrib.md
new file mode 100644
index 000000000..253b16383
--- /dev/null
+++ b/docs/contrib.md
@@ -0,0 +1,2 @@
+# Contributor Agreement
+Please refer to the following page for the agreement: [Redis Labs Software Grant and Contributor License Agreement](https://cla-assistant.io/RedisAI/RedisAI)
diff --git a/mkdocs.yml b/mkdocs.yml
index be8de864b..12c622e75 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -36,6 +36,7 @@ nav:
   - 'Developer': developer.md
   - 'Performance': performance.md
   - 'Examples': examples.md
+  - 'Contributing': contrib.md
 
 markdown_extensions:
   - admonition

From 67f248a579f1d6f362c548be36b14d7b2242cc73 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Tue, 28 Apr 2020 18:47:32 +0100
Subject: [PATCH 38/45] [add] documentation revision for RC - iteration 1

---
 .gitignore                                    |  2 +
 docs/commands.md                              |  1 -
 docs/developer.md                             | 19 ++--
 docs/examples.md                              | 59 ------------
 .../notebooks/data/imagenet_classes.json      |  1 -
 .../tensorflow/tinyyolo/yolo_boxes_script.py  | 89 -------------------
 .../notebooks/tensorflow_resnet50.output      |  5 --
 .../snippets/notebooks/tensorflow_resnet50.py | 56 ------------
 test/test_requirements.txt                    |  5 +-
 9 files changed, 17 insertions(+), 220 deletions(-)
 delete mode 100644 docs/snippets/notebooks/data/imagenet_classes.json
 delete mode 100644 docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
 delete mode 100644 docs/snippets/notebooks/tensorflow_resnet50.output
 delete mode 100644 docs/snippets/notebooks/tensorflow_resnet50.py

diff --git a/.gitignore b/.gitignore
index 389589f9b..c3d1bf604 100644
--- a/.gitignore
+++ b/.gitignore
@@ -5,6 +5,8 @@
 /test/venv/
 logs/
 /test/logs/
+.env/
+env*/
 .venv/
 venv*/
 /install
diff --git a/docs/commands.md b/docs/commands.md
index 88bc633c7..62b12e066 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -49,7 +49,6 @@ OK
 
 !!! note "Uninitialized Tensor Values"
     As both `BLOB` and `VALUES` are optional arguments, it is possible to use the `AI.TENSORSET` to create an uninitialized tensor.
-    TBD: why is that useful?
 
 !!! important "Using `BLOB` is preferable to `VALUES`"
     While it is possible to set the tensor using binary data or numerical values, it is recommended that you use the `BLOB` option. It requires less resources and performs better compared to specifying the values discretely.
diff --git a/docs/developer.md b/docs/developer.md
index 85ea5c253..996065057 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -150,33 +150,36 @@ Once the dependencies have been built, you can build the RedisAI module with:
     
 **Running Tests**
 
-The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run
-them you'll need:
+The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run them you'll need:
 
 * lcov (for coverage analysis, on Linux)
 * Python and a few packages (e.g. pytest, redis, etc.)
 * redis-server in your PATH, or in `../redis/src`.
 
-To run tests in a Python virtualenv, follow these steps:
+To run all tests in a Python virtualenv, follow these steps:
 
     $ mkdir -p .env
     $ virtualenv .env
-    $ . .env/bin/active
-    $ pip install -r tests/tests_requirements.txt
+    $ source .env/bin/activate
+    $ pip install -r test/test_requirements.txt
     $ make -C opt test
 
+**Integration tests**
+
 Integration tests are based on RLTest, and specific setup parameters can be provided
-to configure tests.
+to configure tests. By default the tests will be ran for all backends and common commands, and with variation of persistency and replication. 
+To fully understand what test options are available simply run:
+
+    $ make -C opt help
 
 For example, to run the tests strictly designed for the TensorFlow backend, follow these steps:
 
     $ make -C opt test TEST=tests_tensorflow.py
 
-**Integration Tests Coverage**
+**Coverage**
 
 For coverage analysis we rely on lcov, and you can easily run the tests on your local machine following these steps:
 
     $ make -C opt build COV=1 SHOW=1
     $ make -C opt test COV=1 SHOW=1 AOF=0 SLAVES=0
 
-Setting `AOF=0 SLAVES=0` is not mandatory but will not run AOF and replication tests, thus getting us results faster.
diff --git a/docs/examples.md b/docs/examples.md
index ade435fdf..ab257101e 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -12,62 +12,3 @@ This is a list of RedisAI sample projects that can be used as-is or as an inspir
 | AnimalRecognitionDemo | An example of using Redis Streams, RedisGears and RedisAI for Realtime Video Analytics (i.e. filtering cats) | [RedisLabs](https://redislabs.com/) | BSD-3-Clause | [git](https://github.com/RedisGears/AnimalRecognitionDemo) |
 | EdgeRealtimeVideoAnalytics | An example of using Redis Streams, RedisGears, RedisAI and RedisTimeSeries for Realtime Video Analytics (i.e. counting people) | [RedisLabs](https://redislabs.com/) | Apache-2.0 | [git](https://github.com/RedisGears/EdgeRealtimeVideoAnalytics) |
 
-## Python notebook examples
-
-### Visual Recognition Models
-
-
-#### Resnet50
-
-Here is an example of Python code that classifies ImageNet classes with ResNet50 using a trained tensorflow model.
-
-**Author: [RedisLabs](https://redislabs.com/)**
-
-**Python notebook example**
-
-```python
-{{ include('notebooks/tensorflow_resnet50.py') }}
-```
-**Sample output**
-
-```bash
-{{ include('notebooks/tensorflow_resnet50.output') }}
-```
-
-![](images/cat_classified.jpg)
-
-
-#### YOLO - You only look once
-
-Here is an example of Python code that classifies images with object detection by YOLO algorithm with bounding boxes using a trained tensorflow model.
-
-**Author: [RedisLabs](https://redislabs.com/)**
-
-**Python notebook example**
-
-```python
-TBD
-```
-**Sample output**
-
-```bash
-TBD
-```
-
-### Natural Language Classification
-
-
-Here is an example of Python code that classifies text using a trained Natural Language Classifier model.
-
-**Author: [RedisLabs](https://redislabs.com/)**
-
-**Python notebook example**
-
-```python
-TBD
-```
-**Sample output**
-
-```bash
-TBD
-```
diff --git a/docs/snippets/notebooks/data/imagenet_classes.json b/docs/snippets/notebooks/data/imagenet_classes.json
deleted file mode 100644
index c4ea65927..000000000
--- a/docs/snippets/notebooks/data/imagenet_classes.json
+++ /dev/null
@@ -1 +0,0 @@
-{"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby catamount", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "\"dragonfly, darning needle, devils darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk\"", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "\"academic gown, academic robe, judges robe\"", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "\"carpenters kit, tool kit\"", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "\"jack-o-lantern\"", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "\"loupe, jewelers loupe\"", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "\"pajama, pyjama, pjs, jammies\"", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "\"plane, carpenters plane, woodworking plane\"", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "\"plunger, plumbers helper\"", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "\"potters wheel\"", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "\"spider web, spiders web\"", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow ladys slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue", "1000": "None of the above"}
\ No newline at end of file
diff --git a/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py b/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
deleted file mode 100644
index 423c28bde..000000000
--- a/docs/snippets/notebooks/models/tensorflow/tinyyolo/yolo_boxes_script.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# This will be loaded as script hence no imports
-# import torch
-
-
-def nms(boxes):
-    # assuming only one image in one batch
-    boxes = boxes.squeeze()
-    nms_thresh = 0.45
-    conf_thresh = 0.2
-    no_of_valid_elems = (boxes[:, 4] > conf_thresh).nonzero().numel()
-    boxes_confs_inv = 1 - boxes[:, 4]
-    _, sort_ids = torch.sort(boxes_confs_inv)
-    x1 = boxes[:, 0]
-    y1 = boxes[:, 1]
-    x2 = boxes[:, 2]
-    y2 = boxes[:, 3]
-    area = (x2 - x1 + 1) * (y2 - y1 + 1)
-    for index in range(no_of_valid_elems):
-        i = sort_ids[index]
-        new_ind = index + 1
-        if float(boxes[i, 4]) > conf_thresh:
-            xx1 = torch.max(x1[i], x1[sort_ids[new_ind:]])
-            yy1 = torch.max(y1[i], y1[sort_ids[new_ind:]])
-            xx2 = torch.min(x2[i], x2[sort_ids[new_ind:]])
-            yy2 = torch.min(y2[i], y2[sort_ids[new_ind:]])
-            w = torch.max(torch.zeros(1, device=boxes.device), xx2 - xx1 + 1)
-            h = torch.max(torch.zeros(1, device=boxes.device), yy2 - yy1 + 1)
-            overlap = (w * h) / area[sort_ids[new_ind:]]
-            higher_nms_ind = (overlap > nms_thresh).nonzero()
-            boxes[sort_ids[new_ind:][higher_nms_ind]] = torch.zeros(7, device=boxes.device)
-    return boxes.unsqueeze(0)
-
-
-def get_region_boxes(output):
-    conf_thresh = 0.2
-    num_classes = 20
-    num_anchors = 5
-    anchor_step = 2
-    anchors_ = [1.08, 1.19, 3.42, 4.41, 6.63, 11.38, 9.42, 5.11, 16.62, 10.52]
-
-    anchors = torch.empty(num_anchors * 2, device=output.device)
-    for i in range(num_anchors * 2):
-        anchors[i] = anchors_[i]
-
-    batch = output.size(0)
-    # assert(output.size(1) == (5+num_classes)*num_anchors)
-    h = output.size(2)
-    w = output.size(3)
-
-    output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w)
-
-    grid_x = torch.linspace(0, w-1, w, device=output.device).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
-    grid_y = torch.linspace(0, h-1, h, device=output.device).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w)
-    xs = torch.sigmoid(output[0]) + grid_x
-    ys = torch.sigmoid(output[1]) + grid_y
-
-    anchor_w = anchors.view(num_anchors, anchor_step).index_select(1, torch.zeros(1, device=output.device).long())
-    anchor_h = anchors.view(num_anchors, anchor_step).index_select(1, torch.ones(1, device=output.device).long())
-    anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w)
-    anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w)
-    ws = torch.exp(output[2]) * anchor_w.float()
-    hs = torch.exp(output[3]) * anchor_h.float()
-
-    det_confs = torch.sigmoid(output[4])
-
-    cls_confs = torch.softmax(output[5: 5+num_classes].transpose(0,1), dim=1)
-    cls_max_confs, cls_max_ids = torch.max(cls_confs, 1)
-    cls_max_confs = cls_max_confs.view(-1)
-    cls_max_ids = cls_max_ids.view(-1)
-
-    sz_hw = h * w
-    boxes = torch.zeros(batch, h * w * num_anchors, 7)
-    # assuming only one image in a batch
-    x1 = xs / w
-    y1 = ys / h
-    x2 = ws / w
-    y2 = hs / h
-    higher_confs = ((det_confs * cls_max_confs) > conf_thresh).nonzero()
-    no_selected_elems = higher_confs.numel()
-    if no_selected_elems > 0:
-        boxes[:, 0:no_selected_elems] = torch.stack(
-            [x1, y1, x2, y2, det_confs, cls_max_confs, cls_max_ids.float()], dim=1)[higher_confs.squeeze()]
-    return boxes
-
-
-def boxes_from_tf(output):
-    boxes = get_region_boxes(output.permute(0, 3, 1, 2).contiguous())
-    boxes = nms(boxes)
-    return boxes
diff --git a/docs/snippets/notebooks/tensorflow_resnet50.output b/docs/snippets/notebooks/tensorflow_resnet50.output
deleted file mode 100644
index 53bd2ca71..000000000
--- a/docs/snippets/notebooks/tensorflow_resnet50.output
+++ /dev/null
@@ -1,5 +0,0 @@
-b'OK'
-b'OK'
-maximum result 15.48 belongs to image class at position 282 which is tiger cat
-
-Text(0.5, 1.0, 'tiger cat')
\ No newline at end of file
diff --git a/docs/snippets/notebooks/tensorflow_resnet50.py b/docs/snippets/notebooks/tensorflow_resnet50.py
deleted file mode 100644
index 7740110bb..000000000
--- a/docs/snippets/notebooks/tensorflow_resnet50.py
+++ /dev/null
@@ -1,56 +0,0 @@
-#%%
-!pip install --upgrade "redisai>=0.5.0"
-!pip install --upgrade "ml2rt"
-!pip install --upgrade "scikit-image"
-!pip install --upgrade "numpy"
-!pip install --upgrade "matplotlib"
-!pip install --upgrade "pillow>=7.0.0"
-
-from PIL import Image
-import redisai
-import json
-import redisai as rai
-from ml2rt import load_model
-import numpy as np
-import matplotlib.pyplot as plt
-
-# model, image, and image classes
-tf_model_path = './models/tensorflow/imagenet/resnet50.pb'
-img_jpg = Image.op/('./data/cat.jpg')
-image_classes = json.load(op/("./data/imagenet_classes.json"))
-
-# normalize
-raw_image = np.array(img_jpg).astype(np.float32)
-img = np.expand_dims(raw_image, axis=0)
-img /= 256.0
-
-# load the model from disk
-tf_model = load_model(tf_model_path)
-
-# connect to RedisAI
-con = redisai.client.Client(host="localhost", port=6379)
-
-# set the model ( only once )
-con.modelset(
-    'resnet', rai.Backend.tf, device,
-    inputs=['images'], outputs=['output'], data=tf_model)
-
-# set the tensor 
-tensorset_result = con.tensorset('image', tensor=img)
-print(tensorset_result)
-
-# run the model
-modelrun_result = con.modelrun('resnet', 'image', 'out')
-print(modelrun_result)
-
-# get results
-output_tensor = con.tensorget('out', as_numpy=True)
-
-# Get the indices of maximum element in numpy array
-result = np.where(output_tensor == np.amax(output_tensor))
-
-print("maximum result {:.2f} belongs to image class at position {} which is {}".format(output_tensor.max(),result[1][0],image_classes[str(result[1][0])]))
-# display the image and add image class that it belongs to
-plt.figure()
-plt.imshow(raw_image)
-plt.title(image_classes[str(result[1][0])])
diff --git a/test/test_requirements.txt b/test/test_requirements.txt
index 674878911..d3aea07c3 100644
--- a/test/test_requirements.txt
+++ b/test/test_requirements.txt
@@ -1,4 +1,7 @@
 numpy>=1.17.1
 scikit-image
 redis-py-cluster
-redisai
\ No newline at end of file
+redisai
+redis>=3.0.0
+git+https://github.com/Grokzen/redis-py-cluster.git@master
+git+https://github.com/RedisLabsModules/RLTest.git@master
\ No newline at end of file

From d7c13a84d1f8277237ac94f4f9f4c87fbed39d37 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Tue, 28 Apr 2020 18:52:24 +0100
Subject: [PATCH 39/45] [fix] updated developer notes

---
 .circleci/config.yml | 2 +-
 docs/developer.md    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index f42fcaaef..012ee0a45 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -104,7 +104,7 @@ jobs:
       - run:
           name: Test with coverage
           command: |
-            make -C opt test SHOW=1 COV=1 AOF=0 SLAVES=0
+            make -C opt test SHOW=1 COV=1
             make -C opt cov-upload
           no_output_timeout: 20m
 
diff --git a/docs/developer.md b/docs/developer.md
index 996065057..bdfe43078 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -181,5 +181,5 @@ For example, to run the tests strictly designed for the TensorFlow backend, foll
 For coverage analysis we rely on lcov, and you can easily run the tests on your local machine following these steps:
 
     $ make -C opt build COV=1 SHOW=1
-    $ make -C opt test COV=1 SHOW=1 AOF=0 SLAVES=0
+    $ make -C opt test COV=1 SHOW=1
 

From 7213a485314924d88be539ac36bd9dec388cdcf7 Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Tue, 28 Apr 2020 19:08:06 +0100
Subject: [PATCH 40/45] [fix] fixed the quickstart commands and made reference
 to #developer

---
 docs/quickstart.md | 19 +++++++++++--------
 1 file changed, 11 insertions(+), 8 deletions(-)

diff --git a/docs/quickstart.md b/docs/quickstart.md
index b754662fa..50de38ee1 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -4,14 +4,21 @@ RedisAI is a Redis module. To run it you'll need a Redis server (v4.0.9 or great
 The following sections describe how to get started with RedisAI.
 
 ## Docker
-The quickest way to try RedisAI is by launching its official Docker container image:
+The quickest way to try RedisAI is by launching its official Docker container images:
 
+### On a CPU only machine
 ```
 docker run -p 6379:6379 redisai/redisai:latest
 ```
 
+### On a GPU machine
+
+```
+docker run -p 6379:6379 --gpus all -it --rm redisai/redisai:latest-gpu
+```
+
 ## Building and Running
-You can compile and build the module from its source code.
+You can compile and build the module from its source code. The [Developer](developer.md) page has more information about the design and implementation of the RedisAI module and how to contribute.
 
 ### Prerequisites
 * CUDA needs to be installed for GPU support.
@@ -48,11 +55,7 @@ bash get_deps.sh cpu
 Once the dependencies have been built, you can build the RedisAI module with:
 
 ```sh
-mkdir build
-cd build
-cmake -DDEPS_PATH=../deps/install ..
-make && make install
-cd ..
+make -C opt build
 ```
 
 ### Loading the Module
@@ -61,5 +64,5 @@ To load the module on the same server is was compiled on simply use the `--loadm
 For example, to load the module from the project's path with a server command line switch use the following:
 
 ```sh
-redis-server --loadmodule ./install/redisai.so
+redis-server --loadmodule ./install-cpu/redisai.so
 ```

From 15f27c92151a39c740a817ea73809fcfba1593d8 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 29 Apr 2020 16:45:16 +0300
Subject: [PATCH 41/45] Tweeks pt. 1

---
 docs/commands.md   | 62 +++++++++++++++++++++++++---------------------
 docs/index.md      |  4 +--
 docs/intro.md      | 20 +++++++--------
 docs/quickstart.md |  2 +-
 4 files changed, 47 insertions(+), 41 deletions(-)

diff --git a/docs/commands.md b/docs/commands.md
index 62b12e066..1b6db8ccc 100644
--- a/docs/commands.md
+++ b/docs/commands.md
@@ -51,7 +51,7 @@ OK
     As both `BLOB` and `VALUES` are optional arguments, it is possible to use the `AI.TENSORSET` to create an uninitialized tensor.
 
 !!! important "Using `BLOB` is preferable to `VALUES`"
-    While it is possible to set the tensor using binary data or numerical values, it is recommended that you use the `BLOB` option. It requires less resources and performs better compared to specifying the values discretely.
+    While it is possible to set the tensor using binary data or numerical values, it is recommended that you use the `BLOB` option. It requires fewer resources and performs better compared to specifying the values discretely.
 
 ## AI.TENSORGET
 The **`AI.TENSORGET`** command returns a tensor stored as key's value.
@@ -65,7 +65,7 @@ AI.TENSORGET <key> [META] [format]
 _Arguments_
 
 * **key**: the tensor's key name
-* **META**: returns the tensor's meta data
+* **META**: returns the tensor's metadata
 * **format**: the tensor's reply format can be one of the following:
     * **BLOB**: returns the binary representation of the tensor's data
     * **VALUES**: returns the numerical representation of the tensor's data
@@ -74,11 +74,11 @@ _Return_
 
 Depending on the specified reply format:
 
- * **META**: Array containing the tensor's meta data exclusively. The returned array consists of the following elements:
+ * **META**: Array containing the tensor's metadata exclusively. The returned array consists of the following elements:
     1. The tensor's data type as a String
     1. The tensor's shape as an Array consisting of an item per dimension
- * **BLOB**: the tensor's binary data as a String. If used together with the **META** option, the binary data string will put after the meta data in the array reply. 
- * **VALUES**: Array containing the numerical representation of the tensor's data. If used together with the **META** option, the binary data string will put after the meta data in the array reply. 
+ * **BLOB**: the tensor's binary data as a String. If used together with the **META** option, the binary data string will put after the metadata in the array reply.
+ * **VALUES**: Array containing the numerical representation of the tensor's data. If used together with the **META** option, the binary data string will put after the metadata in the array reply.
 
 
 
@@ -120,7 +120,7 @@ redis> AI.TENSORGET mytensor BLOB
 ```
 
 
-The following shows how the combine the retrieval of the tensor's meta data, and the tensor's values as an Array:
+The following shows how the combine the retrieval of the tensor's metadata, and the tensor's values as an Array:
 
 ```
 redis> AI.TENSORGET mytensor META VALUES
@@ -136,7 +136,7 @@ redis> AI.TENSORGET mytensor META VALUES
    4) "4"
 ```
 
-The following shows how the combine the retrieval of the tensor's meta data, and binary data as a String:
+The following shows how the combine the retrieval of the tensor's metadata, and binary data as a String:
 
 ```
 redis> AI.TENSORGET mytensor META BLOB
@@ -150,7 +150,7 @@ redis> AI.TENSORGET mytensor META BLOB
 ```
 
 !!! important "Using `BLOB` is preferable to `VALUES`"
-    While it is possible to get the tensor as binary data or numerical values, it is recommended that you use the `BLOB` option. It requires less resources and performs better compared to returning the values discretely.
+    While it is possible to get the tensor as binary data or numerical values, it is recommended that you use the `BLOB` option. It requires fewer resources and performs better compared to returning the values discretely.
 
 ## AI.MODELSET
 The **`AI.MODELSET`** commands stores a model as the value of a key.
@@ -175,7 +175,7 @@ _Arguments_
     * **GPU**: a GPU device
 * **TAG**: an optional string for tagging the model such as a version number or any arbitrary identifier
 * **BATCHSIZE**: when provided with an `n` that is greater than 0, the engine will batch incoming requests from multiple clients that use the model with input tensors of the same shape. When `AI.MODELRUN` is called the requests queue is visited and input tensors from compatible requests are concatenated along the 0th (batch) dimension until `n` is exceeded. The model is then run for the entire batch and the results are unpacked back to the individual requests unblocking their respective clients. If the batch size of the inputs to of first request in the queue exceeds `BATCHSIZE`, the request is served immediately (default value: 0).
-* **MINBATCHSIZE**: when provided with an `m` that is greater than 0, the engine will postpone calls to `AI.MODELRUN` until the batch's size had reached `m`. This is primarily used to force batching during testing, but it can also be used under normal operation. In this case, note that requests for which `m` is not reached will hang indefinitely (defai;t value: 0).
+* **MINBATCHSIZE**: when provided with an `m` that is greater than 0, the engine will postpone calls to `AI.MODELRUN` until the batch's size had reached `m`. This is primarily used to force batching during testing, but it can also be used under normal operation. In this case, note that requests for which `m` is not reached will hang indefinitely (default value: 0).
 * **INPUTS**: one or more names of the model's input nodes (applicable only for TensorFlow models)
 * **OUTPUTS**: one or more names of the model's output nodes (applicable only for TensorFlow models)
 * **model**: the Protobuf-serialized model. Since Redis supports strings up to 512MB, blobs for very large models need to be chunked, e.g. `BLOB chunk1 chunk2 ...`.
@@ -186,7 +186,7 @@ A simple 'OK' string or an error.
 
 **Examples**
 
-This example shows to set a model 'mymodel' key using the contents of a local file with [`redis-cli`](https://redis.io/topics/cli). Refer to the [Clients Page](clients.md) for additional client choice that are native to your programming language:
+This example shows to set a model 'mymodel' key using the contents of a local file with [`redis-cli`](https://redis.io/topics/cli). Refer to the [Clients Page](clients.md) for additional client choices that are native to your programming language:
 
 ```
 $ cat resnet50.pb | redis-cli -x AI.MODELSET mymodel TF CPU TAG imagenet:5.0 INPUTS images OUTPUTS output BLOB
@@ -194,7 +194,7 @@ OK
 ```
 
 ## AI.MODELGET
-The **`AI.MODELGET`** command returns a model's meta data and blob stored as a key's value.
+The **`AI.MODELGET`** command returns a model's metadata and blob stored as a key's value.
 
 **Redis API**
 
@@ -317,7 +317,7 @@ None.
 
 _Return_
 
-An array with an entry per model. Each entry is a an array with two entries:
+An array with an entry per model. Each entry is an array with two entries:
 
 1. The model's key name as a String
 1. The model's tag as a String
@@ -496,7 +496,7 @@ None.
 
 _Return_
 
-An array with an entry per script. Each entry is a an array with two entries:
+An array with an entry per script. Each entry is an array with two entries:
 
 1. The script's key name as a String
 1. The script's tag as a String
@@ -510,41 +510,37 @@ redis> > AI._SCRIPTSCAN
 ```
 
 ## AI.DAGRUN
-The **`AI.DAGRUN`** command specifies a direct acyclic graph of operations to run within RedisAI. 
-It accepts one or more operations, split by the pipe-forward operator (`|>`). 
+The **`AI.DAGRUN`** command specifies a direct acyclic graph of operations to run within RedisAI.
 
+It accepts one or more operations, split by the pipe-forward operator (`|>`).
 
 By default, the DAG execution context is local, meaning that loading and persisting tensors should be done explicitly. The user should specify which key tensors to load from keyspace using the `LOAD` keyword, and which command outputs to persist to the keyspace using the `PERSIST` keyspace.
 
 When `PERSIST` is not present, object savings are done locally and kept only during the context of the DAG meaning that no output keys are open.
 
-As an example, if `command 1` sets a tensor, it can be referenced by any further command on the chaining. 
+As an example, if `command 1` sets a tensor, it can be referenced by any further command on the chaining.
 
 
 **Redis API**
 
 ```
-AI.DAGRUN [LOAD <number keys> <key 1> ... <key n>]
-          [PERSIST <number keys> <key 1> ... <key n>] |>
-          [command 1] |>  [command 2] |> [command n]
+AI.DAGRUN [LOAD <n> <key-1> <key-2> ... <key-n>]
+          [PERSIST <n> <key-1> <key-2> ... <key-n>]
+          |> <command> [|>  command ...]
 ```
 
 _Arguments_
 
 * **LOAD**: an optional argument, that denotes the beginning of the input tensors keys' list, followed by the number of keys, and one or more key names
 * **PERSIST**: an optional argument, that denotes the beginning of the output tensors keys' list, followed by the number of keys, and one or more key names
-* **|> command**: the chaining operator, that denotes the beginning of a RedisAI command, followed by one of the supported RedisAI commands ( for now `AI.TENSORSET`, `AI.TENSORGET`, and `AI.MODELRUN` ). Command spliting is done by the presence of another `|>`.
+* **|> command**: the chaining operator, that denotes the beginning of a RedisAI command, followed by one of RedisAI's commands. Command splitting is done by the presence of another `|>`. The supported commands are:
+    * `AI.TENSORSET`
+    * `AI.TENSORGET`
+    * `AI.MODELRUN`
 
 _Return_
 
-An array with an entry reply per command. Each entry format respects the specified command reply.
-
-_Read Only Variant_
-
-Since **AI.DAGRUN** have a **PERSIST** option it is technically flagged as writing commands in the Redis command table. For this reason read-only replicas will flag them, and Redis Cluster replicas will redirect them to the master instance even if the connection is in read only mode (See the READONLY command of Redis Cluster).
-
-**AI.DAGRUN_RO** is exactly like the original commands but refuse the **PERSIST** option, and can safely be used in replicas.
-
+An array with an entry per command's reply. Each entry format respects the specified command reply.
 
 **Examples**
 
@@ -567,6 +563,16 @@ redis> AI.DAGRUN PERSIST 1 predictions |>
 !!! warning "Intermediate memory overhead"
     The execution of models and scripts within the DAG may generate intermediate tensors that are not allocated by the Redis allocator, but by whatever allocator is used in the backends (which may act on main memory or GPU memory, depending on the device), thus not being limited by `maxmemory` configuration settings of Redis.
 
+## AI.DAGRUN_RO
+
+The **`AI.DAGRUN_RO`** command is a read-only variant of `AI.DAGRUN`.
+
+Because `AI.DAGRUN` provides the `PERSIST` option it is flagged as a 'write' command in the Redis command table. However, even when `PERSIST` isn't used, read-only cluster replicas will refuse tp run the command and it will be redirected to the master even if the connection is using read-only mode.
+
+`AI.DAGRUN_RO` behaves exactly like the original command, excluding the `PERSIST` option. It is a read-only command that can safely be with read-only replicas.
+
+!!! info "Further reference"
+    Refer to the Redis [`READONLY` command](https://redis.io/commands/readonly) for further information about read-only cluster replicas.
 
 ## AI.INFO
 The **`AI.INFO`** command returns information about the execution a model or a script.
diff --git a/docs/index.md b/docs/index.md
index 101aa7ecc..2999c9ffc 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -10,7 +10,7 @@ RedisAI is a Redis module for executing Deep Learning/Machine Learning models an
   * The [Commands](commands.md) page is a reference of RedisAI's API
   * The [Clients](clients.md) page lists RedisAI clients by programming language
   * The [Configuration](configuration.md) page explains how to configure RedisAI
-  * The [Developer](developer.md) page has more information about the design and implementation of the RedisAI module and how to contribute.
+  * The [Developer](developer.md) page has more information about the design and implementation of the RedisAI module
 
 
 ## Quick Links
@@ -21,7 +21,7 @@ RedisAI is a Redis module for executing Deep Learning/Machine Learning models an
 ## Contact Us
 If you have questions, want to provide feedback or perhaps report an issue or [contribute some code](contrib.md), here's where we're listening to you:
 
-  * [![Forum](https://img.shields.io/badge/Forum-RedisAI-blue)](https://forum.redislabs.com/c/modules/redisai)
+  * [Forum](https://forum.redislabs.com/c/modules/redisai)
   * [Gitter chatroom](https://gitter.im/RedisLabs/RedisAI)
   * [Repository](https://github.com/RedisAI/RedisAI/issues)
 
diff --git a/docs/intro.md b/docs/intro.md
index 6ab0788ac..5b3302d54 100644
--- a/docs/intro.md
+++ b/docs/intro.md
@@ -74,7 +74,7 @@ redis> GET mykey
 ```
 
 ### What is a Redis Module?
-A **Redis module** is a shared library that can be loaded by the Redis server during runtime. Modules are useful for extending Redis with new data structures and logic. A Redis module leverages on the server's infrastructure. RedisAI is implemented as module, which allows it direct access to the data managed by the server.
+A **Redis module** is a shared library that can be loaded by the Redis server during runtime. Modules are useful for extending Redis with new data structures and logic. A Redis module leverages the server's infrastructure. RedisAI is implemented as a module, which allows it direct access to the data managed by the server.
 
 ??? info "Further reference"
     You can learn more about Redis modules at:
@@ -83,13 +83,13 @@ A **Redis module** is a shared library that can be loaded by the Redis server du
       * [Modules published at redis.io](https://redis.io/modules)
 
 ### Why RedisAI?
-RedisAI bundles together best-of-breed technologies for delivering stable and performant graph serving. Every DL/ML framework offers its own backend for executing the graphs developed by it, and the common practice for serving these is building a simple server.
+RedisAI bundles together best-of-breed technologies for delivering stable and performant graph serving. Every DL/ML framework ships with a backend for executing the graphs developed by it, and the common practice for serving these is building a simple server.
 
-RedisAI aims to be that server, saving you from the need of installing the backend you're using and developing a server for it. By itself that does not justify RedisAI's existence so there's more to it. Because RedisAI is implemented as a Redis module it automatically benefits from the server's capabilities: be it Redis' native data types, its robust eco-system of clients, high-availability, persistence, clustering and Enterprise support.
+RedisAI aims to be that server, saving you from the need of installing the backend you're using and developing a server for it. By itself that does not justify RedisAI's existence so there's more to it. Because RedisAI is implemented as a Redis module it automatically benefits from the server's capabilities: be it Redis' native data types, its robust eco-system of clients, high-availability, persistence, clustering, and Enterprise support.
 
 Because Redis is an in-memory data structure server RedisAI uses it for storing all of its data. The main data type supported by RedisAI is the Tensor that is the standard representation of data in the DL/ML domain. Because tensors are stored memory space of the Redis server they are readily accessible to any of RedisAI's backend libraries at minimal latency.
 
-The locality of data, that is tensor data in adjacency to DL/ML models backends, allows RedisAI to provide optimal performance when serving models. It also makes it a perfect choice for deploying DL/ML models in production and allowing them to be used by any application.
+The locality of data, which is tensor data in adjacency to DL/ML models backends, allows RedisAI to provide optimal performance when serving models. It also makes it a perfect choice for deploying DL/ML models in production and allowing them to be used by any application.
 
 Furthermore, RedisAI is also an optimal testbed for models as it allows the parallel execution of multiple graphs and, in future versions, assessing their respective performance in real-time.
 
@@ -145,7 +145,7 @@ We can create the RedisAI Tensor with the key name 'tA' with the following comma
 AI.TENSORSET tA FLOAT 2 VALUES 2 3
 ```
 
-Copy the command to your cli and hit the `<ENTER>` on your keyboard to execute it. It should looks as follows:
+Copy the command to your cli and hit the `<ENTER>` on your keyboard to execute it. It should look as follows:
 
 !!! example "Example: setting a tensor"
     ```
@@ -154,9 +154,9 @@ Copy the command to your cli and hit the `<ENTER>` on your keyboard to execute i
     OK
     ```
 
-The reply 'OK' means that the operation was successful. We've called the `AI.TENSORSET` command to set the key named 'tA' with the tensor's data, but the name could have been any string value. The `FLOAT` argument specifies the type of values that the tensor stores, and in this case a double-precision floating point. After the type argument comes the tensor's shape as a list of its dimensions, or just a single dimension of 2.
+The reply 'OK' means that the operation was successful. We've called the `AI.TENSORSET` command to set the key named 'tA' with the tensor's data, but the name could have been any string value. The `FLOAT` argument specifies the type of values that the tensor stores, and in this case a double-precision floating-point. After the type argument comes the tensor's shape as a list of its dimensions, or just a single dimension of 2.
 
-The `VALUES` argument tells RedisAI that the tensor's data will be given as a sequence of numeric values, and in this case the numbers 2 and 3. This is useful for development purposes and creating small tensors, however for practical purposes the `AI.TENSORSET` command also supports importing data in binary format.
+The `VALUES` argument tells RedisAI that the tensor's data will be given as a sequence of numeric values and in this case the numbers 2 and 3. This is useful for development purposes and creating small tensors, however for practical purposes the `AI.TENSORSET` command also supports importing data in binary format.
 
 The Redis key 'tA' now stores a RedisAI Tensor. We can verify that using standard Redis commands such as [`EXISTS`](https://redis.io/commands/exists) and [`TYPE`](https://redis.io/commands/type):
 
@@ -188,7 +188,7 @@ 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.MODELSET` command](commands.md#aimodelset) 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/test/test_data/graph.pb).
+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/test/test_data/graph.pb).
 
 ??? info "Downloading 'graph.pb'"
     Use a web browser or the command line to download 'graph.pb':
@@ -212,12 +212,12 @@ cat graph.pb | docker exec -i redisai redis-cli -x \
     ```
 
 !!! important "Use a client in your language of choice"
-    For practical purposes you are encouraged to use a programmatic Redis or RedisAI client in the language of your choice for interacting with RedisAI. Refer to the following pages for further information:
+    For practical purposes, you are encouraged to use a programmatic Redis or RedisAI client in the language of your choice for interacting with RedisAI. Refer to the following pages for further information:
 
     * [Redis clients page](https://redis.io/clients)
     * [RedisAI clients page](clients.md)
 
-Like most commands, `AI.MODELSET`'s first argument is a key's name, which is 'mymodel' in the example. The next two arguments are the model's DL/ML backend and device it will be executed on. 'graph.pb' in the example is a TensorFlow graph and is denoted by `TF` argument. The model will be executed on the CPU as instructed by the `CPU` argument.
+Like most commands, `AI.MODELSET`'s first argument is a key's name, which is 'mymodel' in the example. The next two arguments are the model's DL/ML backend and the device it will be executed on. 'graph.pb' in the example is a TensorFlow graph and is denoted by `TF` argument. The model will be executed on the CPU as instructed by the `CPU` argument.
 
 TensorFlow models also require declaring the names of their inputs and outputs. The inputs for 'graph.pb' are called 'a' and 'b', whereas its single output is called 'c'. These names are provided as additional arguments after the 'INPUTS' and 'OUTPUTS' arguments, respectively.
 
diff --git a/docs/quickstart.md b/docs/quickstart.md
index 50de38ee1..ae6782988 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -1,5 +1,5 @@
 # RedisAI Quickstart
-RedisAI is a Redis module. To run it you'll need a Redis server (v4.0.9 or greater), the module's shared library and its dependencies.
+RedisAI is a Redis module. To run it you'll need a Redis server (v4.0.9 or greater), the module's shared library, and its dependencies.
 
 The following sections describe how to get started with RedisAI.
 

From 8bd031589e2bb41d9bf98602733e06d95a1b2c5b Mon Sep 17 00:00:00 2001
From: filipecosta90 <filipecosta.90@gmail.com>
Date: Wed, 29 Apr 2020 16:25:16 +0100
Subject: [PATCH 42/45] [fix] fix performance diagram links

---
 docs/performance.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/performance.md b/docs/performance.md
index 800256815..0bf6e51b4 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -42,7 +42,7 @@ Benchmarking inference performance has four phases:
 
 ### Inference query execution diagrams per Model Serving Solution
 
-The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md#aibench-supplemental-guide-dl-rest-api-and-redis).
+The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/tfserving_and_redis.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md).
   
 Bellow you can find a simplified version of the diagrams, including both tests cases ( with and without reference data).
 

From c63ca2a106a270c586f5442a369e51098e43a43d Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Wed, 29 Apr 2020 18:36:54 +0300
Subject: [PATCH 43/45] Tweaks pt.2

---
 docs/configuration.md |   8 +--
 docs/developer.md     | 118 ++++++++++++++----------------------------
 docs/examples.md      |   1 +
 docs/performance.md   |  68 ++++++++----------------
 4 files changed, 67 insertions(+), 128 deletions(-)

diff --git a/docs/configuration.md b/docs/configuration.md
index 27c379fae..ca0fed07f 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -58,7 +58,7 @@ Supported.
 
 **Examples**
 
-To set the default backends path to '/usr/lib/backends' when loading the module from command line use the following:
+To set the default backends path to '/usr/lib/backends' when loading the module from the command line use the following:
 
 ```sh
 redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
@@ -71,7 +71,7 @@ The **INTER_OP_PARALLELISM** configuration option sets the number of threads use
 
 _Expected Value_
 
-An Integer greater or equal than zero.
+An Integer equal or greater than zero.
 
 _Default Value_
 
@@ -83,7 +83,7 @@ Not supported.
 
 **Examples**
 
-To set the number of threads used for parallelism between independent operations to 1, when loading the module from command line use the following:
+To set the number of threads used for parallelism between independent operations to 1, when loading the module from the command line use the following:
 
 ```sh
 redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
@@ -167,7 +167,7 @@ Not supported.
 
 **Examples**
 
-To set the number of threads per queue to 4 when loading the module from command line use the following:
+To set the number of threads per queue to 4 when loading the module from the command line use the following:
 
 ```
 redis-server --loadmodule /usr/lib/redis/modules/redisai.so \
diff --git a/docs/developer.md b/docs/developer.md
index bdfe43078..087439b09 100644
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -1,10 +1,10 @@
 # RedisAI Development
 
-The following sections discusses topics relevant to the development of the RedisAI module itself. We'll start by referring to the general design, followed by the source code layout, and how to prepare your local test and development environment.
+The following sections discuss topics relevant to the development of the RedisAI module itself. We'll start by referring to the general design, followed by the source code layout, and how to prepare your local test and development environment.
 
 ## General Design
 
-RedisAI bundles together best-of-breed technologies for delivering stable and fast model serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain. 
+RedisAI bundles together best-of-breed technologies for delivering stable and fast model serving. To do so, we need to abstract from what each specific DL/ML framework offers and provide common data structures and APIs to the DL/ML domain.
 
 
 As a way of representing tensor data we've embraced [dlpack](https://github.com/dmlc/dlpack) - a community effort to define a common tensor data structure that can be shared by different frameworks, supported by cuPy, cuDF, DGL, TGL, PyTorch, and MxNet.
@@ -22,14 +22,14 @@ RedisAI provides the following data structures:
 Inside the root are the following important directories:
 
 * `src`: contains the RedisAI implementation, written in C.
-* `opt`: contains the helper scripts to setup the development environment.
+* `opt`: contains the helper scripts to set up the development environment.
 * `tests`: contains the unit and flow tests, implemented with python and RLTest.
 * `deps`: contains libraries RedisAI uses.
 
 We'll focus mostly on `src`, where the RedisAI implementation is contained,
 exploring what there is inside each file. The order in which files are
 exposed is the logical one to follow in order to disclose different layers
-of complexity incrementally. 
+of complexity incrementally.
 
 **redisai.c**
 
@@ -38,72 +38,67 @@ This is the entry point of the RedisAI module, responsible for registering the n
 
 **tensor.h**
 
-Contains the helper methods for both creating, populating, managing and destructing the Tensor data structure, and methods to manage parsing and replying of tensor related commands or operations.
+Contains the helper methods for both creating, populating, managing and freeing the Tensor data structure, as well as the methods for managing, parsing and replying to tensor related commands or operations.
 
 **model.h**
 
-Contains the helper methods for both creating, populating, managing and destructing the Model data structure.
-Contains also methods to manage parsing and replying of model related commands or operations, that take place in the context of the Redis Main thread.
+Contains the helper methods for creating, populating, managing and freeing the Model data structure. Also contains methods for managing, parsing and replying to model related commands or operations, that take place in the context of the Redis main thread.
+
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
 **script.h**
 
-Contains the helper methods for both creating, populating, managing and destructing the PyTorch Script data structure.
-Contains also methods to manage parsing and replying of script related commands or operations, that take place in the context of the Redis Main thread.
+Contains the helper methods for creating, populating, managing and freeing the PyTorch Script data structure. Also contains methods for managing, parsing and replying to script related commands or operations, that take place in the context of the Redis main thread.
+
 The helper methods that are related to async background work are available at `model_script_run_session.h` header file.
 
 **dag.h**
 
-Contains the helper methods for both parsing, running the command in the background, and replying DAG structured commands.
+Contains the helper methods for parsing, running the command in the background, and replying to DAG structured commands.
 
 **run_info.h**
 
-Contains the structure and headers to create, initialize, get, reset, and free the structures that represent the context in which RedisAI blocking commands operate, namely `RedisAI_RunInfo` and the newly added `RAI_DagOp`. 
+Contains the structure and headers that create, initialize, get, reset, and free the structures that represent the context in which RedisAI blocking commands operate, namely `RedisAI_RunInfo` and the `RAI_DagOp`.
 
 **model_script_run_session.h**
 
-Contains the methods that are related to async background work that 
-was triggered by either MODELRUN or SCRIPTRUN Command and Called within `RedisAI_Run_ThreadMain`.
-This file also contains the function signatures of the reply callbacks to be called in order to reply to the clients, after the background work on MODELRUN and SCRIPTRUN is done.
+Contains the methods that are related to async background work that was triggered by either `MODELRUN` or `AI.SCRIPTRUN` commands, and called from `RedisAI_Run_ThreadMain`.
+
+This file also contains the function signatures of the reply callbacks to be called in order to reply to the clients, after the background work on `AI.MODELRUN` and `AI.SCRIPTRUN` is done.
 
 **background_workers.h**
 
-Contains the structure to manage the per-device queues, used for decoupling the work from the main thread to the background worker threads. For each of the incoming ModelRun, ScriptRun, and DagRun commands, the request is queued and evaded asynchronously to one the device queues.
+Contains the structure for managing per-device queues that are used for decoupling the work from the main thread to the background worker threads. For each of the incoming `AI.MODELRUN`, `AI.SCRIPTRUN`, and `AI.DAGRUN` commands, the request is queued and then executed asynchronously to one the device queues.
 
 **stats.h**
 
-Contains the structure and headers to create, initialize, get, reset, and free run-time statics, like call count, error count, and aggregate durations of ModelRun and ScriptRun sessions.
-The statistics are ephemeral, meaning that they are not saved to the key space, and on maximum live for the duration of the DB uptime.
+Contains the structure and headers that create, initialize, get, reset, and free run-time statics, like call count, error count, and aggregate durations of `AI.MODELRUN` and `AI.SCRIPTRUN` sessions.
+
+The statistics are ephemeral, meaning that they are not persisted to the Redis key space, and are reset when the server is restarted.
 
 **err.h**
 
-Contains the structure and headers for a formal API to create, initialize, get, reset, and free errors among different backends.
+Contains the structure and headers of an API for creating, initializing, getting, resetting, and freeing errors of the different backends.
 
 **backends.h and backends directory**
 
-Contains the structure and headers methods required to register a new backend to be loaded by the module. 
- To do so, the backend needs to implement and export the following methods:
- 
-  * `model_create_with_nodes`:  A callback function pointer that creates a
-  model given the RAI_ModelOpts and input and output nodes.
- 
-  * `model_create`:  A callback function pointer that creates a model given
-  the RAI_ModelOpts.
- 
-  * `model_run`:  A callback function pointer that runs a model given the
-  RAI_ModelRunCtx pointer.
- 
-  * `model_serialize`:  A callback function pointer that serializes a model
-  given the RAI_Model pointer.
- 
-  * `script_create`:  A callback function pointer that creates a script.
- 
-  * `script_free`:  A callback function pointer that frees a script given
-  the RAI_Script pointer.
- 
-  * `script_run`:  A callback function pointer that runs a model given the
-  RAI_ScriptRunCtx pointer.
+Contains the structure and headers methods required to register a new backend that can be loaded by the module.
+
+To do so, the backend needs to implement and export the following methods:
 
+* `model_create_with_nodes`: A callback function pointer that creates a
+  model given the `RAI_ModelOpts` and input and output nodes.
+* `model_create`: A callback function pointer that creates a model given
+the `RAI_ModelOpts`.
+* `model_run`: A callback function pointer that runs a model given the
+`RAI_ModelRunCtx` pointer.
+* `model_serialize`: A callback function pointer that serializes a model
+given the `RAI_Model` pointer.
+* `script_create`: A callback function pointer that creates a script.
+* `script_free`: A callback function pointer that frees a script given
+the `RAI_Script` pointer.
+* `script_run`: A callback function pointer that runs a model given the
+`RAI_ScriptRunCtx` pointer.
 
 Within the `backends` folder you will find the implementations code required to support the following DL/ML identifiers and respective backend libraries:
 
@@ -112,42 +107,9 @@ Within the `backends` folder you will find the implementations code required to
 * **TORCH**: `torch.h` and `torch.c` exporting the functions to to register the PyTorch backend
 * **ONNX**: `onnxruntime.h` and `onnxruntime.c` exporting the functions to to register the ONNXRuntime backend
 
-
 ## Building and Testing
-You can compile and build the module from its source code.
-
-**Prerequisites**
-
-* CUDA needs to be installed for GPU support.
-* CMake 3.0 or higher needs to be installed.
-* Redis v4.0.9 or greater.
-
-**Get the Source Code**
-
-You can obtain the module's source code by cloning the project's repository using git like so:
-
-    $ git clone https://github.com/RedisAI/RedisAI
+You can compile and build the module from its source code - refer to the [Building and Running section](quickstart.md#building-and-running) of the Quickstart page for instructions on how to do that.
 
-Switch to the project's directory with:
-
-    $ cd RedisAI
-
-**Building the Dependencies**
-
-Use the following script to download and build the libraries of the various RedisAI backends (TensorFlow, PyTorch, ONNXRuntime) for your platform with GPU support:
-
-    $ bash get_deps.sh
-
-Alternatively, you can run the following to fetch the CPU-only backends.
-
-    $ bash get_deps.sh cpu
-
-**Building the Module**
-
-Once the dependencies have been built, you can build the RedisAI module with:
-
-    $ make -C opt build
-    
 **Running Tests**
 
 The module includes a basic set of unit tests and integration tests, split across common and backend specific files. To run them you'll need:
@@ -167,8 +129,9 @@ To run all tests in a Python virtualenv, follow these steps:
 **Integration tests**
 
 Integration tests are based on RLTest, and specific setup parameters can be provided
-to configure tests. By default the tests will be ran for all backends and common commands, and with variation of persistency and replication. 
-To fully understand what test options are available simply run:
+to configure tests. By default the tests will be ran for all backends and common commands, and with variation of persistency and replication.
+
+To understand what test options are available simply run:
 
     $ make -C opt help
 
@@ -178,8 +141,7 @@ For example, to run the tests strictly designed for the TensorFlow backend, foll
 
 **Coverage**
 
-For coverage analysis we rely on lcov, and you can easily run the tests on your local machine following these steps:
+For coverage analysis we rely on `lcov` that can be run by following these steps:
 
     $ make -C opt build COV=1 SHOW=1
     $ make -C opt test COV=1 SHOW=1
-
diff --git a/docs/examples.md b/docs/examples.md
index ab257101e..8740b3d2a 100644
--- a/docs/examples.md
+++ b/docs/examples.md
@@ -8,6 +8,7 @@ This is a list of RedisAI sample projects that can be used as-is or as an inspir
 
 | Example | Description | Author | License | URL |
 | --- | --- | --- | --- | --- |
+| RedisAI showcase | Demonstrates using RedisAI functionality, backends and clients | [RedisLabs](https://redislabs.com/) | MIT | [git](https://github.com/RedisAI/redisai-examples) |
 | ChatBotDemo | An example of using RedisAI and a Web App for Conversational AI (i.e. chatbot) | [RedisLabs](https://redislabs.com/) | Apache-2.0 | [git](https://github.com/RedisAI/ChatBotDemo) |
 | AnimalRecognitionDemo | An example of using Redis Streams, RedisGears and RedisAI for Realtime Video Analytics (i.e. filtering cats) | [RedisLabs](https://redislabs.com/) | BSD-3-Clause | [git](https://github.com/RedisGears/AnimalRecognitionDemo) |
 | EdgeRealtimeVideoAnalytics | An example of using Redis Streams, RedisGears, RedisAI and RedisTimeSeries for Realtime Video Analytics (i.e. counting people) | [RedisLabs](https://redislabs.com/) | Apache-2.0 | [git](https://github.com/RedisGears/EdgeRealtimeVideoAnalytics) |
diff --git a/docs/performance.md b/docs/performance.md
index 800256815..73abca1c6 100644
--- a/docs/performance.md
+++ b/docs/performance.md
@@ -1,82 +1,62 @@
 # Performance
+To get an early sense of what RedisAI is capable of, you can test it with [`redis-benchmark`](https://redis.io/topics/benchmarks) or [`memtier_benchmark`](https://github.com/RedisLabs/memtier_benchmark) just like any other Redis command. However, in order to have more control over the tests, we'll use a tool written in Go called [_AIBench_](https://github.com/RedisAI/aibench).
 
-To get an early sense of what RedisAI is capable of, you can test it with [`redis-benchmark`](https://redis.io/topics/benchmarks) or [`memtier_benchmark`](https://github.com/RedisLabs/memtier_benchmark) just like
-any other Redis command. However, in order to have more control over the tests, we'll use a 
-a tool written in Go called [_AIBench_](https://github.com/RedisAI/aibench). 
-
-This page is intended to provide clarity on how to obtain the benchmark numbers, and links to the most recent results. We encourage developers, data scientists, and architects to run these benchmarks for themselves on their particular hardware,datasets, and Model Servers and pull request this documentation with links for the actual numbers.
-
-
+This page is intended to provide clarity on how to obtain the benchmark numbers and links to the most recent results. We encourage developers, data scientists, and architects to run these benchmarks for themselves on their particular hardware, datasets, and Model Servers and pull request this documentation with links for the actual numbers.
 
 ## Inside the benchmark suite
+_AIBench_ is a collection of Go programs that are used to generate datasets and then benchmark the inference performance of various Model Servers. The intent is to make the AIBench extensible so that a variety of use cases and Model Servers can be included and benchmarked.
 
-_AIBench_ is a collection of Go programs that are used to generate datasets and then benchmark inference performance of various Model Servers. The intent is to make the AIBench extensible so that a variety of use cases, and Model Servers can be included and benchmarked. 
-Bellow, are the current DL solutions supported:
+Bellow, are the current DL solutions supported by the suite:
 
 - RedisAI
 - TFServing + Redis
 - Rest API + Redis
 
-
 ### Transaction data dataset characteristics
+Presently, aibench supports a single use case -- [creditcard-fraud from Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) -- with the extension of reference data. This use-case aims to detect a fraudulent transaction based on anonymized credit card transactions and reference data.
 
-Currently, aibench supports one use case -- creditcard-fraud from [Kaggle](https://www.kaggle.com/mlg-ulb/creditcardfraud) with the extension of reference data. This use-case aims to detect a fraudulent transaction based on anonymize credit card transactions and reference data.
-
-
-We've decided to extend the initial dataset in the sense that for each Transaction data, we generate random deterministic Reference data, commonly used to enrich financial transactions information. In the financial service industry and regulatory agencies, the reference data that defines and describes such financial transactions, can cover all relevant particulars for highly complex transactions with multiple dependencies, entities, and contingencies, thus resulting in a larger numerical value input tensor of size 1 x 256.
+We've decided to extend the initial dataset in the sense that for each Transaction data, we generate random deterministic Reference data, commonly used to enrich financial transaction information. In the financial service industry and regulatory agencies, the reference data that defines and describes such financial transaction, can cover all relevant particulars for highly complex transactions with multiple dependencies, entities, and contingencies, thus resulting in a larger numerical value input tensor of size 1 x 256.
 
-Following the previously described, the predictive model to be developed is a neural network implemented in tensorflow with input tensors containing both transaction (1 x 30 tensor) and reference data (1 x 256 tensor) and with a single output tensor (1 x 2 tensor), presenting the fraudulent and genuine probabilities of each financial transaction.
+Following the previously described, the predictive model to be developed is a neural network implemented in TensorFlow with input tensors containing both transaction (1 x 30 tensor) and reference data (1 x 256 tensor) and with a single output tensor (1 x 2 tensor), presenting the fraudulent and genuine probabilities of each financial transaction.
 
 ### Understanding the benchmark phases
+Benchmarking inference performance is made of four phases:
 
-Benchmarking inference performance has four phases: 
-
- - **Model setup**: This step is specific for each DL solution being tested (see the benchmark GitHub repo for detailed deployment info for each supported DL solution).
-
- - **Transaction data parsing and reference data generation**: To keep benchmarking results from being affected by generating data on the fly, the proposed benchmark suite can generate the data required for the inference benchmarks first, and we can then use it as input to the benchmarking and reference data-loading phases. All inference benchmarks use the same dataset, together with random deterministic reference data if using the same random seed.
-
- - **Reference data loading**: The reference data that defines and describes the financial transactions already resides on a datastore common to all benchmarks. We use Redis as the primary datastore for the inference benchmarks.
-
- - **Inference query execution**: This phase looks at the achievable inference throughput of each Model Serving solution and performs a full percentile inference latency analysis. Model serving with and without reference data is supported across all solution to enable a deeper insight on the overall weight of the reference data and the extra network hop to fetch it.
- 
+1. **Model setup**: This step is specific for each DL solution being tested (see the benchmark GitHub repo for detailed deployment info for each supported DL solution).
+2. **Transaction data parsing and reference data generation**: To keep benchmarking results from being affected by generating data on the fly, the proposed benchmark suite can generate the data required for the inference benchmarks first, and we can then use it as input to the benchmarking and reference data-loading phases. All inference benchmarks use the same dataset, together with random deterministic reference data if using the same random seed.
+3. **Reference data loading**: The reference data that defines and describes the financial transactions already resides on a datastore common to all benchmarks. We use Redis as the primary data store for the inference benchmarks.
+4. **Inference query execution**: This phase looks at the achievable inference throughput of each Model Serving solution and performs a full percentile inference latency analysis. Model serving with and without reference data is supported across all solutions to enable a deeper insight into the overall weight of the reference data and the extra network hop to fetch it.
 
 ### Inference query execution diagrams per Model Serving Solution
+The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/tfserving_and_redis.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md#aibench-supplemental-guide-dl-rest-api-and-redis).
 
-The sequence of requests that each solution executes is described thoughtfully in the benchmark client GitHub diagrams, [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), [here](https://github.com/RedisAI/aibench/blob/master/docs/redisai.md), and [here](https://github.com/RedisAI/aibench/blob/master/docs/restapi_and_redis.md#aibench-supplemental-guide-dl-rest-api-and-redis).
-  
-Bellow you can find a simplified version of the diagrams, including both tests cases ( with and without reference data).
-
+Below you can find simplified versions of the diagrams, including both test cases (with and without reference data).
 
 #### RedisAI
-
 ![RedisAI Model Serving Solution Diagram](images/redisai_modelserver.png "RedisAI Model Serving Solution Diagram")
 
 
 #### TFServing + Redis
-
 ![TFServing + Redis Model Serving Solution Diagram](images/tfs_modelserver.png "TFServing + Redis Model Serving Solution Diagram")
 
 #### Rest API + Redis
-
 ![Rest API + Redis Model Serving Solution Diagram](images/flask_modelserver.png "Rest API + Redis Model Serving Solution Diagram")
 
 
 ### Model Serving optimizations
-
-We follow best practices for high-performance Model Serving for each Model Server’s configuration, its protocol layer, and the most efficient serialization method. For protocols to serve models for inference, we leveraged gRPC for Google’s TensorFlow serving, HTTP for standard web APIs, and RESP2 for RedisAI. 
+We follow best practices for high-performance Model Serving for each Model Server’s configuration, its protocol layer, and the most efficient serialization method. For protocols to serve models for inference, we leveraged gRPC for Google’s TensorFlow serving, HTTP for standard web APIs, and RESP2 for RedisAI.
 
 Across all Model Servers, we recommend retaining batching to its default value of false, meaning that each Model Server processes one inference at a time per available executor, so we can track the raw performance of the servers.
 
 Some Model Servers have components that are lazily initialized, which can increase latency for the first requests sent to a model after it is loaded. To reduce this “cold-start” effect on overall performance, you should add burn-in full-inference cycles that are not taken into account by the benchmark, .
 
-Specifically for TensorFlow serving we’ve achieved a better configuration that enabled for higher inference throughput by setting the inter_op_parallelism_threads option to 4 (enabling us to parallelize operations that have sub-operations that are inherently independent up to a degree of 4 concurrent sub-operations). We improved TensorFlow serving intra_op_parallelism_threads results by allowing the tool to adjust with the default behaviour. 
+Specifically for TensorFlow serving, we’ve achieved a better configuration that enabled for higher inference throughput by setting the [`inter_op_parallelism_threads` configuration option](configuration.md#inter_op_parallelism_threads) to 4 (enabling us to parallelize operations that have sub-operations that are inherently independent up to a degree of 4 concurrent sub-operations). We improved TensorFlow serving intra_op_parallelism_threads results by allowing the tool to adjust with the default behavior.
 
-For RedisAI, we maximized inference throughput by limiting the inter-op and intra-op parallelism to 1 per shard, meaning that each added redis standalone instance/shard has an associated backend thread, in a shared-nothing architecture - a strategy that allows to maximize the hardware usage, and maintain a linear scalability both on vertical and horizontal flavours.
+For RedisAI, we maximized inference throughput by limiting the inter-op and intra-op parallelism to 1 per shard, meaning that each added redis standalone instance/shard has an associated backend thread, in a shared-nothing architecture - a strategy that allows to maximize the hardware usage, and maintain linear scalability both on vertical and horizontal flavors.
 
 For the standard web API Model Server, setting Gunicorn worker processes number to 24 for handling concurrent requests resulted in the highest inference throughput.
 
 ### How to install aibench
-
 The easiest way to get and install the aibench Go programs is to use
 `go get` and then `go install`:
 
@@ -94,20 +74,16 @@ cd ../aibench_run_inference_tensorflow_serving && go install
 cd ../aibench_run_inference_flask_tensorflow && go install
 ```
 
-
 ### Methodology and Infrastructure checklist
-
-As stated previously we encourage the community to to run and request for PR with regards to showcasing the obtained results for any Model Server. With regards to the results methodology we expected that for each tested version/variation:
+As stated previously we encourage the community to run and request for PR with regards to showcasing the obtained results for any Model Server. Regarding the results methodology we expected that for each tested version/variation:
 
 - A minimum of 3 distinct full repetitions, and reported as a result the median (q50), q95, q99, overall achievable inference throughput, and if possible ( and recommended ) the referral to the full spectrum of latencies.
-- A full platform description. Ideally you should run the the benchmark and Model Serving instances in separate machines, placed in an optimal networking scenario. 
-- Considering the weight of the network performance in the overall performance of the Model Serving solution we recommend that in addition to the aibench performance runs described above, to also run baseline netperf TCP_RR, in order to understand the underlying network characteristics. 
-
+- A full platform description. Ideally, you should run the benchmark and Model Serving instances in separate machines, placed in an optimal networking scenario.
+- Considering the weight of the network performance in the overall performance of the Model Serving solution we recommend that in addition to the aibench performance runs described above, to also run baseline netperf TCP_RR, in order to understand the underlying network characteristics.
 
 ### What the benchmark suite does not account for
+As important as understanding the metrics and takeaways that this benchmark provides is to understand what is considered out of scope. This benchmark suite was not designed to test some key aspects of observability, operational and model-lifecycle management, fault tolerance, and high availability of the Model Server.
 
-As important as understanding the metrics and takeaways that this benchmark provides is to understand what is considered out of scope. This benchmark suite was not designed to test some key aspects of observability, operational and model-lifecycle management, fault tolerance, and high availability of the Model Server. **As important as performance**, these factors should be part of a prudent decision on which solution to adopt, and we hope to provide further guidance on them in other sections of the documentation. 
+**As important as performance**, these factors should be part of a prudent decision on which solution to adopt, and we hope to provide further guidance on them in other sections of the documentation.
 
 ### Latest benchmark results
-
-

From e6e108239419f44dd50bf3d8041e591cfc5c76c9 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Thu, 30 Apr 2020 12:16:49 +0300
Subject: [PATCH 44/45] Corrects to `MODULE LOAD`

ht @DvirDukhan
---
 .gitignore            | 7 +++++--
 docs/configuration.md | 2 +-
 2 files changed, 6 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index c3d1bf604..7035c412b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -81,7 +81,7 @@ __pycache__
 
 # Debug/Profile files
 # ignore perf html reports
-*.html 
+*.html
 
 # Kernel Module Compile Results
 *.mod*
@@ -131,4 +131,7 @@ dkms.conf
 /out/
 
 # mpeltonen/sbt-idea plugin
-.idea_modules/
\ No newline at end of file
+.idea_modules/
+
+# docs site
+site/
\ No newline at end of file
diff --git a/docs/configuration.md b/docs/configuration.md
index ca0fed07f..441c8fc3a 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -25,7 +25,7 @@ Configuration options can be set when the module is loaded. The options are pass
     For setting the module's configuration with the [`MODULE LOAD`](https://redis.io/commands/module-load) command use:
 
     ```
-    redis> loadmodule /usr/lib/redis/modules/redisai.so <opt1> <val1> ...
+    redis> MODULE LOAD /usr/lib/redis/modules/redisai.so <opt1> <val1> ...
     ```
 
 **Runtime Configuration**

From 11b01f33fa5caec209753ae0fdc929562a3e9580 Mon Sep 17 00:00:00 2001
From: Itamar Haber <itamar@redislabs.com>
Date: Thu, 30 Apr 2020 16:50:48 +0300
Subject: [PATCH 45/45] Adds versioned docs and deploys docs from a GitHub
 Action

---
 .circleci/config.yml               | 23 +++--------------------
 .github/workflows/deploy-docs.yaml | 10 +++++++---
 mkdocs.yml                         |  5 +++--
 3 files changed, 13 insertions(+), 25 deletions(-)

diff --git a/.circleci/config.yml b/.circleci/config.yml
index 012ee0a45..0e0b22d3d 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -55,7 +55,7 @@ commands:
             - 'packages/branch/*.tgz'
       - store_artifacts:
           path: test/logs
-          
+
   deploy:
     parameters:
       from:
@@ -181,19 +181,6 @@ jobs:
       - store_artifacts:
           path: ~/workspace/packages/<<parameters.package>>
 
-  deploy_docs:
-    docker:
-      - image: redisfab/rmbuilder:5.0.7-x64-buster
-    steps:
-      - checkout
-      - run:
-          name: Build Docs
-          command: python3 -m mkdocs build
-      - run:
-          name: Deploy Docs to S3
-          command: |
-            aws s3 cp site s3://oss.redislabs.com/$WEBSITE_FOLDER/ --acl public-read --recursive
-
 workflows:
   version: 2
   build_and_package:
@@ -207,7 +194,7 @@ workflows:
             branches:
               only: /.*/
             tags:
-              only: /.*/ 
+              only: /.*/
       # - build-macos:
       #     filters:
       #       branches:
@@ -217,7 +204,7 @@ workflows:
       #- build-multiarch-docker:
       #    filters:
       #      tags:
-      #        only: /^v[0-9].*/ 
+      #        only: /^v[0-9].*/
       #      branches:
       #        ignore: /.*/
       - build-gpu:
@@ -242,10 +229,6 @@ workflows:
               ignore: /.*/
             tags:
               only: '/^v[0-9].*/'
-      - deploy_docs:
-          filters:
-            branches:
-              only: master
 
   #nightly:
   #  triggers:
diff --git a/.github/workflows/deploy-docs.yaml b/.github/workflows/deploy-docs.yaml
index 6ccc80a18..75ac868ec 100644
--- a/.github/workflows/deploy-docs.yaml
+++ b/.github/workflows/deploy-docs.yaml
@@ -1,9 +1,13 @@
-name: Upload draft website to S3
+name: Deploy docs to website
 
 on:
   push:
     branches:
-    - docs-ih
+      - master
+      - '[0-9]+.[0-9]+'
+    paths:
+      - 'mkdocs.yml'
+      - 'docs/**'
 
 jobs:
   deploy:
@@ -25,8 +29,8 @@ jobs:
       with:
         args: --acl public-read --follow-symlinks --delete
       env:
+        AWS_S3_ENDPOINT: ${{ secrets.DOCS_AWS_S3_ENDPOINT }}
         AWS_S3_BUCKET: ${{ secrets.DOCS_AWS_S3_BUCKET }}
         AWS_ACCESS_KEY_ID: ${{ secrets.DOCS_AWS_ACCESS_KEY_ID }}
         AWS_SECRET_ACCESS_KEY: ${{ secrets.DOCS_AWS_SECRET_ACCESS_KEY }}
-        AWS_REGION: 'us-east-2'
         SOURCE_DIR: 'site'
\ No newline at end of file
diff --git a/mkdocs.yml b/mkdocs.yml
index 12c622e75..8edc1b836 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -49,5 +49,6 @@ markdown_extensions:
       permalink: True
 
 plugins:
-   - include:
-       src_path: docs/snippets
+  - include:
+      src_path: docs/snippets
+  - versions-menu: