update docs

source/user/howto/rclone.rst

@@ -60,7 +60,11 @@ Once you machine is launched, you must run the following command in the terminal
 
 .. code-block:: console
 
-    $ export RCLONE_CONFIG_RSHARE_PASS=$(rclone obscure $RCLONE_CONFIG_RSHARE_PASS)
+    $ echo export RCLONE_CONFIG_RSHARE_PASS=$(rclone obscure $RCLONE_CONFIG_RSHARE_PASS) >> /root/.bashrc
+    $ source /root/.bashrc
+
+..
+    Comment: We do this to spare users from having to install rclone in their local machines just to obscure the password.
 
 This is because, to connect with the remote, rclone needs to use an obscured version of the password, not the raw one.
 

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

@@ -150,7 +150,7 @@ In your local machine, run the Template with the ``child-module`` branch.
 .. code-block::
 
     $ pip install cookiecutter
-    $ cookiecutter https://github.com/deephdc/cookiecutter-deep -b child-module
+    $ cookiecutter https://github.com/deephdc/cookiecutter-deep  --checkout child-module
 
 This will create a single repo (``DEEP-OC-**``) with the Docker code.
 
@@ -169,7 +169,9 @@ Check you didn't mess up the JSON formatting by running:
 is the image of the original repo. Modify the appropriate lines to replace
 the original model weights with the new model weights.
 In our case, this could look something like this:
-::
+
+.. code-block:: docker
+
     ENV SWIFT_CONTAINER https://api.cloud.ifca.es:8080/swift/v1/phytoplankton-tf/
     ENV MODEL_TAR phytoplankton.tar.xz
 
@@ -179,7 +181,7 @@ In our case, this could look something like this:
 
 Check your Dockerfile works correctly by building it locally and running it:
 
-  .. code-block:: console
+.. code-block:: console
 
     $ docker build --no-cache -t your_project .
     $ docker run -ti -p 5000:5000 -p 6006:6006 -p 8888:8888 your_project

source/user/howto/train-model-remotely.rst

@@ -95,7 +95,7 @@ See the :doc:`Dashboard guide <../overview/dashboard>` for more details.
 After submitting you will be redirected to the deployment's list.
 In your new deployment go to **Access** and choose **JupyterLab**. You will be redirected to ``http://jupyterlab_endpoint``
 
-Now that you are in JupyterLab, open a **Terminal** window (**[+]** (New launcher) ➜ **Others** ➜ **Terminal**).
+Now that you are in JupyterLab, open a **Terminal** window (:fa:`square-plus` (New launcher) ➜ **Others** ➜ **Terminal**).
 
 First let's check we are seeing our GPU correctly:
 
@@ -172,6 +172,7 @@ For this you have to kill the process running deepaas, and launch it again.
 .. code-block:: console
 
     $ kill -9 $(ps aux | grep '[d]eepaas-run' | awk '{print $2}')
+    $ kill -9 $(ps aux | grep '[t]ensorboard' | awk '{print $2}')  # optionally also kill monitoring process
 
 This is because the user inputs for deepaas are generated at the deepaas launching.
 Thus it is not aware of the newly trained model. Once deepaas is restarted, head to the
@@ -215,7 +216,7 @@ In your **local machine** (not the Dashboard deployment), run the Template with
 .. code-block::
 
     $ pip install cookiecutter
-    $ cookiecutter https://github.com/deephdc/cookiecutter-deep -b child-module
+    $ cookiecutter https://github.com/deephdc/cookiecutter-deep --checkout child-module
 
 Proceed to answer the questions you will be prompted.
 This will create a single repo (``DEEP-OC-**``) with the Docker code.

source/user/overview/cookiecutter-template.rst

@@ -30,7 +30,7 @@ and then run the `cookiecutter <https://cookiecutter.readthedocs.io>`__ tool as
 ::
 
     $ pip install cookiecutter
-    $ cookiecutter https://github.com/deephdc/cookiecutter-deep -b master
+    $ cookiecutter https://github.com/deephdc/cookiecutter-deep --checkout master
 
 You are first provided with ``[Info]`` line about the parameter and in the next line you configure this parameter.
 You will be asked to configure:

source/user/overview/dashboard.rst

@@ -62,7 +62,7 @@ The are two steps in the configuration process:
   reflected in DEEPaaS (which is launched with the initial codebase), thus potentially leading to confusion.
 
   If you want to have access to both services in the same deployment, launch with JupyterLab.
-  In JupyterLab, open a **Terminal** window (**[+]** (New launcher) ➜ **Others** ➜ **Terminal**).
+  In JupyterLab, open a **Terminal** window (:fa:`square-plus` (New launcher) ➜ **Others** ➜ **Terminal**).
   Then run ``deep-start --deepaas`` to launch DEEPaaS.
   If you make subsequent code changes, you will have to kill the old DEEPaaS process and launch a new one.