Pipeline: filters: tensorflow: style

pipeline/filters/tensorflow.md

@@ -1,56 +1,73 @@
 # Tensorflow
 
-## Tensorflow
+The _Tensorflow_ filter plugin allows running machine learning inference tasks on the records of data coming from input plugins or stream processors. This filter uses [Tensorflow Lite](https://www.tensorflow.org/lite/) as the inference engine, and requires Tensorflow Lite shared library to be present during build and at runtime.
 
-_Tensorflow Filter_ allows running Machine Learning inference tasks on the records of data coming from input plugins or stream processor. This filter uses [Tensorflow Lite](https://www.tensorflow.org/lite/) as the inference engine, and **requires Tensorflow Lite shared library to be present during build and at runtime**.
+Tensorflow Lite is a lightweight open source deep learning framework used for mobile and IoT applications. Tensorflow Lite only handles inference, not training. It loads pre-trained models (`.tflite` files) that are converted into Tensorflow Lite format (`FlatBuffer`). You can read more on converting [Tensorflow models](https://www.tensorflow.org/lite/convert).
 
-Tensorflow Lite is a lightweight open-source deep learning framework that is used for mobile and IoT applications. Tensorflow Lite only handles inference \(not training\), therefore, it loads pre-trained models \(`.tflite` files\) that are converted into Tensorflow Lite format \(`FlatBuffer`\). You can read more on converting Tensorflow models [here](https://www.tensorflow.org/lite/convert)
+The Tensorflow plugin for Fluent Bit has the following limitations:
 
-### Configuration Parameters
+- Currently supports single-input models
+- Uses Tensorflow 2.3 header files
+
+## Configuration parameters
 
 The plugin supports the following configuration parameters:
 
 | Key | Description | Default |
 | :--- | :--- | :--- |
-| input\_field | Specify the name of the field in the record to apply inference on. |  |
-| model\_file | Path to the model file \(`.tflite`\) to be loaded by Tensorflow Lite. |  |
-| include\_input\_fields | Include all input filed in filter's output | True |
-| normalization\_value | Divide input values to normalization\_value |  |
+| `input_field` | Specify the name of the field in the record to apply inference on. | _none_ |
+| `model_file` | Path to the model file (`.tflite`) to be loaded by Tensorflow Lite. | _none_ |
+| `include_input_fields` | Include all input filed in filter's output. | `True` |
+| `normalization_value` | Divide input values to `normalization_value`. | _none_ |
 
-### Creating Tensorflow Lite shared library
+## Creating a Tensorflow Lite shared library
 
-Clone [Tensorflow repository](https://github.com/tensorflow/tensorflow), install bazel package manager, and run the following command in order to create the shared library:
+To create a Tensorflow Lite shared library:
 
-```bash
-$ bazel build -c opt //tensorflow/lite/c:tensorflowlite_c  # see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/c
-```
+1. Clone the [Tensorflow repository](https://github.com/tensorflow/tensorflow).
+1. Install the [Bazel](https://bazel.build/) package manager.
+1. Run the following command to create the shared library:
+
+   ```bash
+   bazel build -c opt //tensorflow/lite/c:tensorflowlite_c  # see https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/c
+   ```
 
-The script creates the shared library `bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so`. You need to copy the library to a location \(such as `/usr/lib`\) that can be used by Fluent Bit.
+   The script creates the shared library
+   `bazel-bin/tensorflow/lite/c/libtensorflowlite_c.so`.
+1. Copy the library to a location such as `/usr/lib` that can be used by Fluent Bit.
 
-### Building Fluent Bit with Tensorflow filter plugin
+## Building Fluent Bit with Tensorflow filter plugin
 
-Tensorflow filter plugin is disabled by default. You need to build Fluent Bit with Tensorflow plugin enabled. In addition, it requires access to Tensorflow Lite header files to compile. Therefore, you also need to pass the address of the Tensorflow source code on your machine to the [build script](https://github.com/fluent/fluent-bit#build-from-scratch):
+The Tensorflow filter plugin is disabled by default. You must build Fluent Bit with the Tensorflow plugin enabled. In addition, it requires access to Tensorflow Lite header files to compile. Therefore, you must pass the address of the Tensorflow source code on your machine to the [build script](https://github.com/fluent/fluent-bit#build-from-scratch):
 
 ```bash
 cmake -DFLB_FILTER_TENSORFLOW=On -DTensorflow_DIR=<AddressOfTensorflowSourceCode> ...
 ```
 
-#### Command line
+### Command line
 
-If Tensorflow plugin initializes correctly, it reports successful creation of the interpreter, and prints a summary of model's input/output types and dimensions.
+If Tensorflow plugin initializes correctly, it reports successful creation of the interpreter, and prints a summary of model's input and output types and dimensions.
+
+The command:
 
 ```bash
-$ bin/fluent-bit -i mqtt -p 'tag=mqtt.data' -F tensorflow -m '*' -p 'input_field=image' -p 'model_file=/home/user/model.tflite' -p 'include_input_fields=false' -p 'normalization_value=255' -o stdout
+bin/fluent-bit -i mqtt -p 'tag=mqtt.data' -F tensorflow -m '*' -p 'input_field=image' -p 'model_file=/home/user/model.tflite' -p
+```
+
+produces an output like:
+
+```text
+'include_input_fields=false' -p 'normalization_value=255' -o stdout
 [2020/08/04 20:00:00] [ info] Tensorflow Lite interpreter created!
 [2020/08/04 20:00:00] [ info] [tensorflow] ===== input #1 =====
 [2020/08/04 20:00:00] [ info] [tensorflow] type: FLOAT32  dimensions: {1, 224, 224, 3}
 [2020/08/04 20:00:00] [ info] [tensorflow] ===== output #1 ====
 [2020/08/04 20:00:00] [ info] [tensorflow] type: FLOAT32  dimensions: {1, 2}
 ```
 
-#### Configuration File
+### Configuration file
 
-```text
+```python
 [SERVICE]
     Flush        1
     Daemon       Off
@@ -72,9 +89,3 @@ $ bin/fluent-bit -i mqtt -p 'tag=mqtt.data' -F tensorflow -m '*' -p 'input_field
     Name stdout
     Match *
 ```
-
-## Limitations
-
-1. Currently supports single-input models
-2. Uses Tensorflow 2.3 header files
-

vale-styles/FluentBit/Spelling-exceptions.txt

@@ -10,6 +10,7 @@ autoscaler
 autoscaling
 backoff
 backpressure
+Bazel
 BitBake
 Blackhole
 blocklist
@@ -175,6 +176,7 @@ Tanzu
 Telegraf
 templated
 temporality
+Tensorflow
 Terraform
 Thanos
 Timeshift