Merge pull request #31 from Az-r-ow/devops

Devops: Docker compose

.devops/python.Dockerfile

@@ -4,7 +4,7 @@ COPY . /app
 
 WORKDIR /app
 
-RUN git submodule init && git submodule update
+RUN git init && git submodule init && git submodule update
 
 # Install cmake (necessary for building shared object)
 RUN apt-get update && \

compose.yml

@@ -0,0 +1,7 @@
+services:
+  python-example:
+    build:
+      context: .
+      dockerfile: .devops/python.Dockerfile
+    volumes:
+      - ./examples/train-predict-MNIST:/app/examples/train-predict-MNIST

docs/index.rst

@@ -10,7 +10,7 @@ Welcome to NeuralNetPy's documentation!
    :maxdepth: 2
    :caption: Contents:
 
-   usage
+   quick-start
    NeuralNetPy
 
 Indices and tables

docs/quick-start.rst

@@ -0,0 +1,108 @@
+Quick Start
+==========
+
+Requirements :
+- `Docker`
+
+Start by building the python image with `docker compose` : 
+
+.. code-block:: bash
+
+  docker-compose up 
+
+.. note::
+  We're assuming that you're running the commands from the root of the project. 
+
+Access the docker service which will act as your working environment :
+
+.. code-block:: bash
+
+  docker-compose run python-example
+
+To know if you're in you should see a change in the cli's prompt :
+
+.. code-block::
+  
+  root@ad245b0ff5c4:/app#
+
+Now all you have to do is navigate to the python example project of your liking and run the scripts just like you would normally.
+The following example is for running the MNIST example which consists of downloading the handwritten digits dataset. Then, creating a Neural Network model and training it on the dataset.
+
+.. code-block:: bash
+
+  cd examples/train-predict-MNIST && mkdir dataset && python main.py
+
+If you look at the `main.py` file, you'll notice : 
+
+- Fetching the MNIST handwritten digits dataset 
+  .. code-block:: python
+
+    if not file_exists(MNIST_DATASET_FILE):
+      print("Mnist dataset not found")
+      get_MNIST_dataset(MNIST_DATASET_FILE)
+
+- Initiating and composing a Neural Network 
+  .. code-block:: python 
+
+    network = NNP.models.Network()
+
+    network.addLayer(NNP.layers.Flatten((28, 28)))
+    network.addLayer(NNP.layers.Dense(128, NNP.ACTIVATION.RELU, NNP.WEIGHT_INIT.HE))
+    network.addLayer(NNP.layers.Dense(10, NNP.ACTIVATION.SOFTMAX, NNP.WEIGHT_INIT.LECUN))
+
+    # Setting up the networks parameters
+    network.setup(optimizer=NNP.optimizers.Adam(0.01), loss=NNP.LOSS.MCE)
+
+- Formatting and normalizing the data and then storing it in a `TrainingData3dI` object. Which simplifies batching. 
+  .. code-block:: python
+
+    # preparing the training data
+    f_x_train = [normalize_img(x) for x in x_train]
+
+    trainingData = NNP.TrainingData3dI(f_x_train[:NUM_TRAININGS], y_train[:NUM_TRAININGS])
+
+    trainingData.batch(128) # Creating batches of 128 inputs
+
+- Training the data
+- Computing the accuracy 
+- Serializing the trained model and saving it in a binary format
+  .. code-block:: python
+
+    # Saving the trained model in a bin file
+    NNP.models.Model.save_to_file('./model.bin', network)
+
+    saved_model = NNP.models.Network()
+
+- Loading the model into a new instance of `Network` from the file again (simply for showcase)
+  .. code-block:: python
+
+    # Saving the trained model in a bin file
+    NNP.models.Model.save_to_file('./model.bin', network)
+
+    saved_model = NNP.models.Network()
+
+    NNP.models.Model.load_from_file('./model.bin', saved_model)
+
+- Testing the model with the `test_data`
+
+Installation
+------------
+
+.. warning::
+  The project must be built with ``cmake`` in order to use the python library.
+
+Then inside your python script, you must include the module path to the system path :
+
+.. code-block:: python
+
+    import sys
+    sys.path.append('/path/to/build/folder')
+
+Then you can import the module just like any other :
+
+.. code-block:: python
+
+  import NeuralNetPy as NNP
+
+.. Attention::
+  The path to the build folder must be set before importing the module.