# 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 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).

The Tensorflow plugin for Fluent Bit has the following limitations:

* Currently, supports single-input models
* Uses Tensorflow 2.3 header files

## Configuration parameters

The plugin supports the following configuration parameters:

| Key                    | Description                                                         | Default |
| ---------------------- | ------------------------------------------------------------------- | ------- |
| `include_input_fields` | Include all input filed in filter's output.                         | `True`  |
| `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*  |
| `normalization_value`  | Divide input values to `normalization_value`.                       | *none*  |

## Creating a Tensorflow Lite shared library

To create a Tensorflow Lite shared library:

1. Clone the [Tensorflow repository](https://github.com/tensorflow/tensorflow).
2. Install the [Bazel](https://bazel.build/) package manager.
3. Run the following command to create the shared library:

   ```shell
   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`.
4. Copy the library to a location such as `/usr/lib` that can be used by Fluent Bit.

## Building Fluent Bit with Tensorflow filter plugin

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):

```shell
cmake -DFLB_FILTER_TENSORFLOW=On -DTensorflow_DIR=<AddressOfTensorflowSourceCode> ...
```

### Command line

If the 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:

```shell
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:

```
'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

{% tabs %}
{% tab title="fluent-bit.yaml" %}

```yaml
service:
  flush: 1
  daemon: off
  log_level: info

pipeline:
  inputs:
    - name: mqtt
      tag: mqtt.data

  filters:
    - name: tensorflow
      match: mqtt.data
      input_field: image
      model_file: /home/m/model.tflite
      include_input_fields: false
      normalization_value: 255

  outputs:
    - name: stdout
      match: '*'
```

{% endtab %}

{% tab title="fluent-bit.conf" %}

```
[SERVICE]
  Flush        1
  Daemon       Off
  Log_Level    info

[INPUT]
  Name mqtt
  Tag  mqtt.data

[FILTER]
  Name                 tensorflow
  Match                mqtt.data
  input_field          image
  model_file           /home/m/model.tflite
  include_input_fields false
  normalization_value  255

[OUTPUT]
  Name  stdout
  Match *
```

{% endtab %}
{% endtabs %}