aiidateam · ltalirz · Jul 1, 2020 · Jun 30, 2020 · Jun 30, 2020 · Jun 30, 2020
diff --git a/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_shell.rst b/docs/pages/2019_MARVEL_Psik_MaX/sections/verdi_shell.rst
@@ -63,7 +63,7 @@ the command itself:
 
     !some bash command
 
-.. _2020_virtual_loadnode:
+.. _2019_EPFL_loadnode:
 
 Loading a node
 --------------
@@ -298,7 +298,7 @@ In that case two duplicate structures are found for 'Si'.
 Accessing inputs and outputs
 ----------------------------
 
-Load again the calculation node used in Section :ref:`2020_virtual_loadnode`:
+Load again the calculation node used in Section :ref:`2019_EPFL_loadnode`:
 
 .. code:: python
 

diff --git a/docs/pages/2020_Intro_Week/sections/basics.rst b/docs/pages/2020_Intro_Week/sections/basics.rst
@@ -81,6 +81,7 @@ From now on, all ``verdi`` commands will apply to the ``quicksetup`` profile.
     To quickly perform a single command on a profile that is not the default, use the ``-p/--profile`` option:
     For example, ``verdi -p generic code list`` will display the codes for the ``generic`` profile, despite it not being the current default profile.
 
+.. _2020_virtual_importing_data:
 
 Importing data
 --------------
@@ -90,7 +91,7 @@ Let's import one from the web:
 
 .. code:: bash
 
-    verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2019_05_perovskites_v0.3.aiida
+    verdi import https://object.cscs.ch/v1/AUTH_b1d80408b3d340db9f03d373bbde5c1e/marvel-vms/tutorials/aiida_tutorial_2020_07_perovskites_v0.9.aiida
 
 Contrary to most databases, AiiDA databases contain not only *results* of calculations but also their inputs and information on how a particular result was obtained.
 This information, the *data provenance*, is stored in the form of a *directed acyclic graph* (DAG).
@@ -151,7 +152,13 @@ To do so, if you are using Linux/Mac OS X, you can type in your *local* machine:
     scp aiidatutorial:<path_with_the_graph_pdf> <local_folder>
 
 and then open the file.
-Alternatively, you can use graphical software to achieve the same, for instance: on Windows: WinSCP; on a Mac: Cyberduck; on Linux Ubuntu: using the 'Connect to server' option in the main menu after clicking on the desktop.
+
+.. note::
+
+    You can also use the ``jupyter notebook`` setup explained :ref:`here <2020_virtual_intro:setup:jupyter>` to download files.
+    Note that while Firefox will display the PDF directly in the browser `Chrome and Safari block viewing PDFs from jupyter notebook servers <https://stackoverflow.com/a/55264795/1069467>`_ - with these browsers, you will need to tick the checkbox next to the PDF and download the file.
+
+    Alternatively, you can use graphical software to achieve the same, for instance: on Windows: WinSCP; on a Mac: Cyberduck; on Linux Ubuntu: using the 'Connect to server' option in the main menu after clicking on the desktop.
 
 
 The provenance browser
@@ -292,39 +299,39 @@ Producing the output:
 
 .. code:: bash
 
-    Property       Value
-    -------------  ------------------------------------
-    type           CalcJobNode
-    pk             828
-    uuid           ce81c420-7751-48f6-af8e-eb7c6a30cec3
+    Property     Value
+    -----------  ------------------------------------
+    type         PwCalculation
+    state        Finished [0]
+    pk           630
+    uuid         ce81c420-7751-48f6-af8e-eb7c6a30cec3
     label
     description
-    ctime          2014-10-27 17:51:21.781045+00:00
-    mtime          2019-05-09 14:10:09.307986+00:00
-    process state  Finished
-    exit status    0
-    computer       [1] daint
+    ctime        2014-10-27 17:51:21.781045+00:00
+    mtime        2019-05-09 14:10:09.307986+00:00
+    computer     [1] daint
 
     Inputs      PK    Type
     ----------  ----  -------------
     pseudos
-        Ba      611   UpfData
-        O       661   UpfData
-        Ti      989   UpfData
-    code        825   Code
-    kpoints     811   KpointsData
-    parameters  829   Dict
-    settings    813   Dict
-    structure   27    StructureData
+        Ba      1092  UpfData
+        O       1488  UpfData
+        Ti      1855  UpfData
+    code        631   Code
+    kpoints     498   KpointsData
+    parameters  629   Dict
+    settings    500   Dict
+    structure   1133  StructureData
 
     Outputs                    PK  Type
     -----------------------  ----  -------------
-    output_kpoints           1894  KpointsData
-    output_parameters          62  Dict
-    output_structure           61  StructureData
-    output_trajectory_array    63  ArrayData
-    remote_folder             357  RemoteData
-    retrieved                  60  FolderData
+    output_kpoints           1455  KpointsData
+    output_parameters         789  Dict
+    output_structure          788  StructureData
+    output_trajectory_array   790  ArrayData
+    remote_folder            1811  RemoteData
+    retrieved                 787  FolderData
+
 
 You can use the PKs shown for the inputs and outputs to get more information about those nodes.
 
@@ -408,41 +415,6 @@ Then, you can then copy ``<IDENTIFIER>.xsf`` from the Amazon machine to your loc
 
     xcrysden --xsf <IDENTIFIER>.xsf
 
-Codes and computers
-~~~~~~~~~~~~~~~~~~~
-
-Let us focus now on the nodes of type ``Code``.
-A code represents (in the database) the actual executable used to run the calculation.
-Find the identifier of such a node in the graph (e.g. the input code of the calculation you were inspecting earlier) and type:
-
-.. code:: bash
-
-    verdi code show <IDENTIFIER>
-
-The command prints information on the plugin used to interface the code to AiiDA, the remote machine on which the code is executed, the path of its executable, etc.
-To show a list of all available codes type:
-
-.. code:: bash
-
-    verdi code list
-
-If you want to show all codes, including hidden ones and those created by other users, use ``verdi code list -a -A``.
-Now, among the entries of the output you should also find the code just shown.
-
-Similarly, the list of computers on which AiiDA can submit calculations is accessible by means of the command:
-
-.. code:: bash
-
-    verdi computer list -a
-
-The ``-a`` flag shows all computers, also the one imported in your database but that you did not configure, i.e. to which you don't have access.
-Details about each computer can be obtained by the command:
-
-.. code:: bash
-
-    verdi computer show <COMPUTERNAME>
-
-Now you have the tools to answer the question: what is the scheduler installed on the computer where the calculations of the graph have run?
 
 Calculation results
 ~~~~~~~~~~~~~~~~~~~
@@ -598,20 +570,20 @@ the command itself:
 
     !verdi process list
 
-.. _loadnode:
+.. _2020_virtual_loadnode:
 
 Loading a node
 --------------
 
 Most AiiDA objects are represented by nodes, identified in the database by its
-``PK`` (an integer). You can access a node using the following command
+*PK* (an integer). You can access a node using the following command
 in the shell:
 
 .. code:: python
 
     node = load_node(PK)
 
-Load a node using the ``PK`` of one of the calculations visible in the graph you displayed in the previous section of the tutorial.
+Load a node using the *PK* of one of the calculations visible in the graph you displayed in the previous section of the tutorial.
 Then get the energy of the calculation with the command:
 
 .. code:: python
@@ -632,12 +604,12 @@ Loading specific kinds of nodes
 Pseudopotentials
 ~~~~~~~~~~~~~~~~
 
-From the graph you generated in  section :ref:`2020_virtual_aiidagraph`, find the ``PK`` of the pseudopotential file (LDA).
+From the graph you generated in  section :ref:`2020_virtual_aiidagraph`, find the UUID of the pseudopotential file (LDA).
 Load it and show what elements it corresponds to by typing:
 
 .. code:: python
 
-    upf = load_node(PK)
+    upf = load_node("<UUID>")
     upf.element
 
 All methods of ``UpfData`` are accessible by typing ``upf.`` and then pressing ``TAB``.
@@ -650,6 +622,7 @@ Choose one from the graph of produced in section :ref:`2020_virtual_aiidagraph`,
 
 .. code:: python
 
+    kpoints = load_node("<UUID>")
     kpoints.get_kpoints_mesh()
 
 Then get the full (explicit) list of k-points belonging to this mesh using
@@ -694,12 +667,12 @@ Parameters
 ~~~~~~~~~~
 
 Dictionaries with various parameters are represented in AiiDA by ``Dict`` nodes.
-Get the PK and load the input parameters of a calculation in the graph produced in section :ref:`2020_virtual_aiidagraph`.
+Get the UUID and load the input parameters of a calculation in the graph produced in section :ref:`2020_virtual_aiidagraph`.
 Then display its content by typing
 
 .. code:: python
 
-    params = load_node('<IDENTIFIER>')
+    params = load_node('<UUID>')
     YOUR_DICT = params.get_dict()
     YOUR_DICT
 

diff --git a/docs/pages/2020_Intro_Week/sections/running.rst b/docs/pages/2020_Intro_Week/sections/running.rst
@@ -52,6 +52,11 @@ Your ``localhost`` computer should now show up in
 
    verdi computer list
 
+.. note::
+
+    AiiDA export archives like the one we imported :ref:`in the very beginning <2020_virtual_importing_data>` can also contain computers set up by other AiiDA users (without the private *configuration* information).
+    Use ``verdi computer list -a`` to list both configured and unconfigured computers.
+
 Before proceeding, test that it works:
 
 .. code:: bash
@@ -101,6 +106,9 @@ You can always check the configuration details of an existing code using:
    The ``generic`` profile has already a number of other codes configured.
    See ``verdi -p generic code list``.
 
+   In order to also show codes created by other users (e.g. from imported archives), use ``verdi code list -A``.
+
+
 
 The AiiDA daemon
 ================

diff --git a/docs/pages/2020_Intro_Week/sections/setup.rst b/docs/pages/2020_Intro_Week/sections/setup.rst
@@ -176,11 +176,18 @@ Troubleshooting
    virtual environment with ``workon aiida`` before launching ``python``,
    ``ipython`` or the ``jupyter`` notebook server.
 
+-  If, while connecting to your VM, you get a warning similar to::
+
+       bind [127.0.0.1]:8888: Address already in use
+       channel_setup_fwd_listener_tcpip: cannot listen to port: 8888
+
+   your local port 8888 is already occupied - likely because you are running a ``jupyter notebook`` server locally.
+   We suggest you stop any locally running jupyter notebook servers before connecting to the VM.
+   If necessary, you can start them again *after* you have connected (``jupyter notebook`` will then realize that port 8888 is already taken and simply serve the notebook on a different port).
+
 -  If your browser cannot connect to the jupyter notebook server, check that
-   you have correctly configured SSH tunneling/forwarding as described
-   above.
-   Keep in mind that you need to start the jupyter server from the
-   terminal connected to the VM, while the web browser should be opened locally
-   on your laptop.
+   you have configured SSH tunneling/forwarding as described above.
+   Keep in mind that you need to start the jupyter server from the terminal connected to the VM, while opening the web browser locally on your laptop.
+
 
 -  See the `jupyter notebook documentation <https://jupyter-notebook.readthedocs.io/en/stable/notebook.html#browser-compatibility>`_ for compatibility of jupyter with various web browsers.