Update to jupyter-resource-usage

docs/howto/admin/nbresuse.rst

@@ -4,7 +4,7 @@
 Check your memory usage
 =======================
 
-The `nbresuse <https://github.com/yuvipanda/nbresuse>`_ extension is part of
+The `jupyter-resource-usage <https://github.com/jupyter-server/jupyter-resource-usage>`_ extension is part of
 the default installation, and tells you how much memory your user is using
 right now, and what the memory limit for your user is. It is shown in the
 top right corner of the notebook interface. Note that this is memory usage
@@ -12,4 +12,4 @@ for everything your user is running through the Jupyter notebook interface,
 not just the specific notebook it is shown on.
 
 .. image:: ../../images/nbresuse.png
-   :alt: Memory limit / usage shown with nbresuse
+   :alt: Memory limit / usage shown with jupyter-resource-usage

docs/howto/admin/resize.rst

@@ -37,10 +37,10 @@ Verifying a Resize
 #. **To verify changes to memory**, confirm that it worked by starting
    a new server (if you had one previously running, click "Control Panel -> Stop My Server" to
    shut down your active server first), opening a notebook, and checking the value of the
-   `nbresuse <https://github.com/yuvipanda/nbresuse>`_ extension in the upper-right.
+   `jupyter-resource-usage <https://github.com/jupyter-server/jupyter-resource-usage>`_ extension in the upper-right.
 
    .. image:: ../../images/nbresuse.png
-      :alt: nbresuse demonstration
+      :alt: jupyter-resource-usage demonstration
 
 #. **To verify changes to CPU**, use the ``nproc`` from a terminal.
    This command displays the number of available cores, and should be equal to the

docs/howto/admin/resource-estimation.rst

@@ -65,7 +65,7 @@ stop, unlike with RAM.
 The ``20%`` is overhead for TLJH and related services. This is around 20% of a
 single modern CPU. This, of course, is just an estimate. We recommend using
 the same process used to estimate Memory required for estimating CPU required.
-You cannot use nbresuse for this, but you should carry out normal workflow and
+You cannot use jupyter-resource-usage for this, but you should carry out normal workflow and
 investigate the CPU usage on the machine.
 
 Disk space

integration-tests/test_extensions.py

@@ -16,7 +16,7 @@ def test_serverextensions():
         'jupyterlab 3.',
         'nbgitpuller 0.9.',
         'nteract_on_jupyter 2.1.',
-        'nbresuse '
+        'jupyter-resource-usage '
     ]
 
     for e in extensions:
@@ -33,7 +33,7 @@ def test_nbextensions():
     ], stderr=subprocess.PIPE, stdout=subprocess.PIPE)
 
     extensions = [
-        'nbresuse/main',
+        'jupyter-resource-usage/main',
         # This is what ipywidgets nbextension is called
         'jupyter-js-widgets/extension'
     ]

tljh/requirements-base.txt

@@ -10,8 +10,8 @@ nteract-on-jupyter==2.1.*
 # Install jupyterlab extensions from PyPI
 # nbgitpuller for easily pulling in Git repositories
 nbgitpuller==0.9.*
-# nbresuse to show people how much RAM they are using
-nbresuse==0.3.*
+# jupyter-resource-usage to show people how much RAM they are using
+jupyter-resource-usage==0.5.*
 # Most people consider ipywidgets to be part of the core notebook experience
 ipywidgets==7.6.*
 # Pin tornado