Created page for test model remotely

source/user/index.rst

@@ -16,6 +16,7 @@ If you want a quickstart guide, please check the following link.
 
    quickstart
    try-model-locally
+   try-model-remotely
    develop-model
 
 .. raw:: html

source/user/models/image_classification_tf.rst

@@ -59,8 +59,8 @@ By running the `saliency maps notebook <https://github.com/indigo-dc/image-class
 Finally you can `launch a simple webpage <https://github.com/indigo-dc/image-classification-tf/tree/master/imgclas/webpage/README.md>`_ to use the trained classifier to predict images (both local and urls) on your favorite brownser.
 
 
-Launching the full API
-----------------------------
+Launching the full DEEPaas API
+---------------------------------------
 
 **Preliminaries for prediction**
 
@@ -69,8 +69,5 @@ If you want to use the API for prediction,  you have to do some preliminary step
 * Copy your desired `.models/[timestamp]` to `.models/api`. If there is no `.models/api` folder, the default is to use the last available timestamp.
 * In the `.models/api/ckpts` leave only the desired checkpoint to use for prediction. If there are more than one chekpoints, the default is to use the last available checkpoint.
 
-Running the API
---------------------
-
-To access this package's complete functionality (both for training and predicting) through an API you have to follow the instructions here: :ref:`api-integration`
+To access this package's complete functionality (both for training and predicting) through the DEEPaaS API you have to follow the instructions here: :ref:`api-integration`
 

source/user/try-model-remotelly.rst

@@ -0,0 +1,75 @@
+.. highlight:: console
+
+*******************
+Try a model remotelly
+*******************
+
+
+
+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.
+
+
+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.
+
+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
+----------------
+
+Run the container with:
+::
+
+	$ docker run -ti -p 5000:5000 deephdc/deep-oc-model_of_interest
+	
+
+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.
+
+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).