try model locally

source/user/develop-model.rst

@@ -1,8 +1,9 @@
+**************************************
 Develop a model using DEEP UC template
-======================================
+**************************************
 
-Prepare DEEP UC environment
----------------------------
+1. Prepare DEEP UC environment
+------------------------------
 
 Install cookiecutter (if not yet done)
 ::
@@ -14,62 +15,69 @@ Run the DEEP UC cookiecutter template
 	
 Answer all questions from DEEP UC cookiecutter template with attentions to 
 ``repo_name`` i.e. the name of your github repositories, etc.
-
 This creates two project directories:
 ::
 	~/DEEP-OC-your_project
 	~/your_project
 	
 Go to ``github.com/your_account`` and 
 create corresponding repositories: ``DEEP-OC-your_project`` and ``your_project``
-
 Do ``git push origin master`` in both created directories. This puts your initial code to ``github``.
 
-
-Develop a model according to DEEP UC template
----------------------------------------------
+	
+2. Improve the initial code of the model
+----------------------------------------
 
 The structure of ``your_project`` created using 
 `DEEP UC template <https://github.com/indigo-dc/cookiecutter-data-science>`__ contains 
-the following core items needed to develop a model
+the following core items needed to develop a DEEP UC model:
 ::
 	requirements.txt
 	data/
 	models/
-	{{cookiecutter.repo_name}}/dataset/make_dataset.py
-	{{cookiecutter.repo_name}}/features/build_features.py
-	{{cookiecutter.repo_name}}/models/model.py
+	{{repo_name}}/dataset/make_dataset.py
+	{{repo_name}}/features/build_features.py
+	{{repo_name}}/models/model.py
 	
-**Installing development requirements**
+2.1 Installing development requirements
+=======================================
 
 Modify ``requirements.txt`` according to your needs (e.g. add more libraries) then run
 ::
 	$ pip install -r requirements.txt
 	
+You can modify and add more ``source files`` and put them 
+accordingly into the directory structure.
 
-**Improve the initial code**
+2.2 Make datasets 
+==================
 
-You can modify as well as add more source files and put them accordingly into the directory structure.
-
-**1. Make datasets:** source files in this directory aim to manipulate with raw dataset(s). 
-The output of this step is raw data, which can be cleaned and/or pre-formatted.
+Source files in this directory aim to manipulate with raw dataset(s). 
+The output of this step is also raw data, but cleaned and/or pre-formatted.
 ::
-	{{cookiecutter.repo_name}}/dataset/make_dataset.py
-	{{cookiecutter.repo_name}}/dataset/
+	{{repo_name}}/dataset/
+	{{repo_name}}/dataset/make_dataset.py
+
+2.3 Build features
+===================
 
-**2. Build features** takes the output from the previous step (Make datasets) and 
-creates ML train, test as well as validation data from raw data.
-The concrete realisation is depend on concrete UC, the aim of the application as well as 
-technological background (e.g. high-performance supports).
+This step takes the output from the previous step ``Make datasets`` and 
+creates train, test as well as validation ML data from raw but cleaned and pre-formatted data.
+The realisation of this step depends on concrete UC, the aim of the application as well as 
+available technological backgrounds (e.g. high-performance supports for data processing).
 ::
-	{{cookiecutter.repo_name}}/features/build_features.py
-	{{cookiecutter.repo_name}}/features/
+	{{repo_name}}/features/
+	{{repo_name}}/features/build_features.py
+
+2.4 Develop models
+==================
 
-**3. Develop models** dealing with the most interesting ML core i.e. modelling. 
-The most important thing in the ``model.py`` are implementations for DEEP entry points, 
-which are defined according to :ref:`API methods <user/overview/api:Methods>`. 
-You don't need to implement all the methods, just the ones you need.
+This step deals with the most interesting phase in ML i.e. modelling. 
+The most important thing of DEEP UC models is located in ``model.py``
+containing DEEP entry point implementations. 
+DEEP entry points are defined using :ref:`API methods <user/overview/api:Methods>`. 
+You don't need to implement all of them, just the ones you need.
 ::
-	{{cookiecutter.repo_name}}/models/model.py
-	{{cookiecutter.repo_name}}/models/
+	{{repo_name}}/models/
+	{{repo_name}}/models/model.py
 

source/user/try-model-locally.rst

@@ -1,25 +1,68 @@
+*******************
 Try a model locally
-===================
+*******************
 
-1. **Get Docker**
+1. Get Docker
+-------------
 
+The first step is having `Docker <https://www.docker.com>`_ installed. To have an up-to-date installation please follow
+the `official Docker installation guide <https://docs.docker.com/install>`_.
 
-2. **Search for a model in the marketplace**
-
 
-3. **Get the model**
+2. Search for a model in the marketplace
+----------------------------------------
 
-clone the repo
+The next step is to look for a model 
+in the `DEEP Open Catalog marketplace <https://marketplace.deep-hybrid-datacloud.eu/>`_
+you want to try locally.  
+The marketplace contains an extensible list of existing models e.g. 
+	`DEEP OC Image Classification <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-image-classification-tensorflow.html>`_,
+	`DEEP OC Retinopathy <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-retinopathy.html>`_,
+	`DEEP OC Massive Online Data Streams <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-massive-online-data-streams.html>`_,
+	`DEEP OC Seed Classification <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-seed-classification-theano.html>`_,
+	`DEEP OC Phytopankton (Theano) <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-phytopankton-theano.html>`_,
+	`DEEP OC Conus Classification <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-conus-classification-theano.html>`_, 
+	`DEEP OC dogs breed determination <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-dogs-breed-determination.html>`_, 
+	and many more.
+
 
-build the container or get from DockerHub
+3. Get the model
+----------------
 
+You will find that each model has an associate Docker container in DockerHub
+	`DEEP OC Image Classification <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-image-classification-tensorflow.html>`_
+	is associated with `deephdc/deep-oc-image-classification-tf <https://hub.docker.com/r/deephdc/deep-oc-image-classification-tf>`_,
+	`DEEP OC Massive Online Data Streams <https://marketplace.deep-hybrid-datacloud.eu/models/deep-oc-massive-online-data-streams.html>`_
+	is associated with `deephdc/deep-oc-mods <https://hub.docker.com/r/deephdc/deep-oc-mods>`_, etc.
 
-4. **Run the model**
+Let call the model you selected ``deep-oc-model_of_interest``. 
+Please, download the container with:
+::
+    $ docker pull deephdc/deep-oc-model_of_interest
+    
 
+4. Run the model
+----------------
 
-5. **Go to the API, get the results**
+Run the container with:
+::
+	$ docker run -ti -p 5000:5000 deephdc/deep-oc-model_of_interest
+	
 
-MODS - probs as outpus
+5. Go to the API, get the results
+---------------------------------
 
-Need reviewing/improvements for other UCs
+Once running, point your browser to `http://127.0.0.1:5000/ <http://127.0.0.1:5000/>`_ 
+and you will see the API documentation, 
+where you can test the model functionality, as well as perform other actions.
 
+All models in the `DEEP Open Catalog marketplace <https://marketplace.deep-hybrid-datacloud.eu/>`_
+utilize `DEEPaaS API <https://github.com/indigo-dc/DEEPaaS>`_.
+The API enables a user friendly interaction with the underlying Deep Learning models and 
+can be used both for training and inference with the models.
+
+.. image:: ../_static/deepaas.png
+
+The concrete results can vary from model to model e.g. 
+the results of ``deephdc/deep-oc-image-classification-tf`` are image type(s) and picture(s),
+the results of ``deephdc/deep-oc-mods`` are predicted values (float numbers).