DEEP UC to DEEP DS template

source/user/howto/develop-model.rst

@@ -1,11 +1,11 @@
 .. highlight:: console
 
 **************************************
-Develop a model using DEEP UC template
+Develop a model using DEEP DS template
 **************************************
 
 
-1. Prepare DEEP UC environment
+1. Prepare DEEP DS environment
 ------------------------------
 
 
@@ -14,12 +14,12 @@ Install cookiecutter (if not yet done)
 
 	$ pip install cookiecutter
 	
-Run the DEEP UC cookiecutter template
+Run the DEEP DS cookiecutter template
 ::
 
 	$ cookiecutter https://github.com/indigo-dc/cookiecutter-data-science
 	
-Answer all questions from DEEP UC cookiecutter template with attentions to 
+Answer all questions from DEEP DS cookiecutter template with attentions to
 ``repo_name`` i.e. the name of your github repositories, etc.
 This creates two project directories:
 ::
@@ -36,8 +36,8 @@ Do ``git push origin master`` in both created directories. This puts your initia
 ----------------------------------------
 
 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 DEEP UC model:
+`DEEP DS template <https://github.com/indigo-dc/cookiecutter-data-science>`_ contains
+the following core items needed to develop a DEEP DS model:
 ::
 
 	requirements.txt
@@ -75,7 +75,7 @@ The output of this step is also raw data, but cleaned and/or pre-formatted.
 
 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 the concrete Use Case, the aim of the application as well as
+The realisation of this step depends on the concrete use case, the aim of the application as well as
 available technological backgrounds (e.g. high-performance supports for data processing).
 ::
 
@@ -87,7 +87,7 @@ available technological backgrounds (e.g. high-performance supports for data pro
 ==================
 
 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``
+The most important thing of DEEP DS 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.

source/user/overview/api.rst

@@ -32,7 +32,7 @@ Here is an `example <https://github.com/indigo-dc/image-classification-tf/blob/m
 definition in the ``setup.cfg`` file.
 
 .. tip::
-    When developing a model with the :doc:`DEEP UC template <cookiecutter-template>`, the entrypoints and the Python file
+    When developing a model with the :doc:`DEEP DS template <cookiecutter-template>`, the entrypoints and the Python file
     for the API methods will automatically be created at ``pkg_name/models/model.py``.
 
 

source/user/overview/cookiecutter-template.rst

@@ -1,7 +1,7 @@
 .. highlight:: console
 
-DEEP Use-Cases template
-========================
+DEEP Data Science template
+==========================
 
 To simplify development and as an easy way integrate your model with the :doc:`DEEPaaS API <api>`,
 a project template, `cookiecutter-data-science <https://github.com/indigo-dc/cookiecutter-data-science>`_ [*]_, is provided in our GitHub.

source/user/quickstart.rst

@@ -151,5 +151,5 @@ Check the full :doc:`API guide <overview/api>` for the detailed info.
 The integration with the API is based on the definition of entrypoints to the model and the creation of standard API methods
 (eg. train, predict, etc).
 An easy way to :ref:`integrate your model with the API <user/overview/api:Integrate your model with the API>` and create
-Dockerfiles for building the Docker image is to use our :doc:`DEEP UC template <overview/cookiecutter-template>` when developing
+Dockerfiles for building the Docker image is to use our :doc:`DEEP DS template <overview/cookiecutter-template>` when developing
 your model.