fixes

source/user/develop-model.rst

@@ -1,7 +1,9 @@
+.. highlight:: console
+
+**************************************
 Develop a model using DEEP UC template
-======================================
+**************************************
 
-.. highlight:: console
 
 Prepare DEEP UC environment
 ---------------------------
@@ -18,7 +20,6 @@ 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:
 ::
 
@@ -27,59 +28,70 @@ This creates two project directories:
 	
 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
-	
-**Installing development requirements**
+	{{repo_name}}/dataset/make_dataset.py
+	{{repo_name}}/features/build_features.py
+	{{repo_name}}/models/model.py
+
+
+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**
 
-You can modify as well as add more source files and put them accordingly into the directory structure.
+2.2 Make datasets 
+==================
 
-**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/
 
-**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).
+
+2.3 Build features
+===================
+
+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/
 
-**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.
+
+2.4 Develop models
+==================
+
+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/
-

source/user/overview/api.rst

@@ -1,10 +1,10 @@
 DEEPaaS API
 ===========
 
-
 The `DEEPaaS API <https://github.com/indigo-dc/DEEPaaS>`_ enables a user friendly interaction with the underlying Deep
 Learning models and can be used both for training and inference with the models.
 
+
 Integrate your model with the API
 ---------------------------------
 

source/user/overview/architecture.rst

@@ -1,10 +1,10 @@
 Architecture overview
 ---------------------
 
-
 There are several different components in the DEEP-HDC project that are relevant for the users. Later on you will see
 how each :doc:`different type of user <user-roles>` can take advantage of the different components.
 
+
 The marketplace
 ===============
 

source/user/overview/train-model-locally.rst

@@ -4,12 +4,14 @@ Train a model locally
 This is a step by step guide on how to train a model from the Marketplace with your own dataset.
 You can also look at `video demo <https://www.youtube.com/watch?v=Mh6rdlqX-7I&feature=youtu.be>`_ on how to do this.
 
+
 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
 ========================================
 
@@ -18,6 +20,7 @@ the `official Docker installation guide <https://docs.docker.com/install>`_.
 The next step is to look for a model in the marketplace you want to retrain on your own data.
 Possible option include image classifiers, etc.
 
+
 3. Get the model
 ================
 
@@ -40,11 +43,13 @@ For example if you wanted to download the image classifier model you would have
 We are using the port ``5000`` to deploy the API and the port ``6006`` to monitor the training (for example using
 `Tensorboard <https://www.tensorflow.org/guide/summaries_and_tensorboard>`_).
 
+
 4. Upload your data to storage resources
 ========================================
 
 .. todo:: Lara and Valentin: Fill this section. Look at the possibility of using rclone with Google Drive, Dropbox, etc.
 
+
 5. Train the model
 ==================
 

source/user/overview/user-roles.rst

@@ -4,6 +4,7 @@ Our different user roles
 DEEPaaS is focused with three different types of users in mind, whcih vary in their machine learning knowledge and their
 usage of DEEPaaS.
 
+
 The basic user
 --------------
 

source/user/try-model-locally.rst

@@ -1,25 +1,74 @@
+.. highlight:: console
+
+*******************
 Try a model locally
-===================
+*******************
+
+
+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
+----------------------------------------
+
+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.
+
 
-1. **Get Docker**
+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.
 
-2. **Search for a model in the marketplace**
- 
+Let call the model you selected ``deep-oc-model_of_interest``. 
+Please, download the container with:
 
-3. **Get the model**
+::
 
-clone the repo
+    $ docker pull deephdc/deep-oc-model_of_interest
+    
 
-build the container or get from DockerHub
+4. Run the model
+----------------
 
+Run the container with:
+::
 
-4. **Run the model**
+	$ docker run -ti -p 5000:5000 deephdc/deep-oc-model_of_interest
+	
 
+5. Go to the API, get the results
+---------------------------------
 
-5. **Go to the API, get the results**
+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.
 
-MODS - probs as outpus
+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.
 
-Need reviewing/improvements for other UCs
+.. 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).