The Cadabra build scripts can now build a Jupyter kernel, so that you can use the Jupyter notebook to write Cadabra code (using all of the Cadabra notation, i.e. without having to resort to the much more ugly Python interface). At the moment this is only supported by compiling against a Conda python, simply because that enables us to build on the 'xeus' library more easily.
After installation, first activate your miniconda distribution:
source ~/miniconda3/bin/activate
All dependencies to build a Conda package of Cadabra can then be installed from Conda directly, with:
conda install conda-build
Then build with:
cd conda conda-build .
To install:
conda install --use-local cadabra2
The following instructions have been tested on a clean Ubuntu 18.04 installation.
The Cadabra Jupyter kernel uses the Xeus library, which is most easily obtained by getting it from Conda. If you do not have Conda yet, get a minimal installation (MiniConda) from
https://docs.conda.io/en/latest/miniconda.html
(install a Python3.x version).
When building against Conda, Cadabra will build only the Python module and the cadabra-jupyter-kernel binary. It is not possible to build many of the other parts of Cadabra using Conda, for various reasons: Conda's glibmm is not built with C++11 enabled, there is no gtkmm library, and probably others. For a discussion on this, see
https://groups.google.com/a/continuum.io/d/msg/anaconda/oHtExJU9oiM/oMZLGpn1CAAJ
and if you don't think this is a problem, see e.g.
https://unix.stackexchange.com/questions/414904/anaconda-qt-vs-system-qt
After installation, first activate your miniconda distribution:
source ~/miniconda3/bin/activate
All dependencies for Cadabra's Jupyter kernel can then be installed from Conda directly, with:
conda install cmake pkg-config glibmm zeromq cppzmq xtl cryptopp \
sqlite util-linux xeus nlohmann_json sympy \
jupyter -c conda-forge
Now it is time to do the Cadabra build. Configure with options which ensure that CMake picks up the Conda libraries first, and make it install the Cadabra things in a place which does not interfere with any 'normal' build you may have sitting around:
cd cadabra2
mkdir build
cd build
cmake -DENABLE_JUPYTER=ON -DENABLE_FRONTEND=OFF \
-DCMAKE_INCLUDE_PATH=${HOME}/miniconda3/include \
-DCMAKE_LIBRARY_PATH=${HOME}/miniconda3/lib \
-DCMAKE_INSTALL_PREFIX=${HOME}/miniconda3 \
..
You should see that it has configured using the Conda Python; look for the Configuring Python block, which should be something like:
------------------------------------------- Configuring Python ------------------------------------------- -- Building for use with Python 3 (good!) -- Found PythonInterp: /home/kasper/miniconda3/bin/python3.7 (found version "3.7.1") -- Found PythonLibs: /home/kasper/miniconda3/lib/libpython3.7m.so -- pybind11 v2.3.dev0 -- Found python /home/kasper/miniconda3/lib/libpython3.7m.so
Note the reference to the Conda installation path. Further down you should then also see a block for the Jupyter kernel:
------------------------------------------- Configuring Jupyter kernel build -------------------------------------------
If that's all ok, you can build with the standard:
make sudo make install
This will install the kernel in:
${HOME}/miniconda3/bin/
and the JSON configuration files in:
${HOME}/miniconda3/share/jupyter/kernels/cadabra/
If you now start Jupyter, you should be able to choose a Cadabra kernel:
${HOME}/miniconda3/bin/jupyter notebook
There is a sample schwarzschild.ipynb in the examples directory.
The following instructions setup a JupyterHub installation using 'The Littlest JupyterHub' (TLJH). These instructions have been tested on a clean Ubuntu 18.04 installation.
First install TLJH as per the instructions at:
https://the-littlest-jupyterhub.readthedocs.io/en/latest/install/custom-server.html
(note that you first need to do a sudo command, otherwise the installer will ask for the password but you won't see that prompt, making it look like the installation process hangs).
New: it should now also be possible to simply install the conda package for the Jupyter kernel. New
Become root (you cannot write into /opt/tljh otherwise) and set the conda path using:
sudo su
export PATH=/opt/tljh/user/bin:${PATH}
Install the prerequisites with:
conda install cmake pkg-config glibmm zeromq cppzmq xtl cryptopp \
sqlite util-linux xeus nlohmann_json sympy \
-c conda-forge
Build the Cadabra Jupyter kernel with:
cd cadabra2
mkdir build
cd build
cmake -DENABLE_JUPYTER=ON -DENABLE_FRONTEND=OFF \
-DCMAKE_INCLUDE_PATH=/opt/tljh/user/include \
-DCMAKE_LIBRARY_PATH=/opt/tljh/user/lib \
-DCMAKE_INSTALL_PREFIX=/opt/tljh/user/ \
..
make install
The 'new' button in the Jupyterhub file browser should now offer you the option of creating a new Cadabra notebook.
To build a Conda package of the Cadabra Jupyter kernel from scratch, first install miniconda as above, and activate:
source ~/miniconda3/bin/activate
Now the fun starts. Conda is an absolutely horrendous packaging system, which absolutely does not get dependencies right, but we will have to live with it. First, update the base conda distribution:
conda update -n base -c defaults conda conda update --all
Then activate the conda-forge channel, and update to the latest of everything:
conda config --add channels conda-forge conda update --all
Do not use conda config --set channel_priority strict as that will break the build with an endless list of package conflicts. There are other ways to add the conda-forge channel, all subtly different; avoid adding -c conda-forge as that is just broken beyond belief too. Now install the prerequisites for building conda packages:
conda install conda-build anaconda-client \
xeus pkg-config glibmm
That last line should not have been necessary, as build requirements in meta.yaml should have taken care of it, but alas, it does not work that way. It spits out various messages about packages being downgraded; don't ask, I told you the system was broken.
Now change to the conda directory and build the package:
cd cadabra2/conda
export PKG_CONFIG_PATH=${HOME}/miniconda3/lib/pkgconfig
conda build .
Again, that path setting should have been handled automatically... To upload:
anaconda login anaconda upload /path/to/conda-package.tar.bz2