Setting Up New or Additional Languages

Environment editor provides flexibility to set up your computation environment. After you select a starter environment, you can add additional languages to your capsule.

If you are building a bi or multi-language capsule:

• Use a base Ubuntu (18.04 or 16.04) environment, if you are not using a pre-installed language.

• Start from an environment with the proprietary language if you are using one, for example Matlab.

• Select an environment with GPU access, these will be labeled as such, or will reference CUDA or a deep learning framework, if you are using GPU.

Installing a Language Using Package Managers

The following are all available as apt-get packages:

• build-essential for the C/C++ toolchain (gcc/g++, make, etc.);

• r-base for R (note: r-base-dev will help you install R packages, we recommend adding a for installing archived R packages, see more detail below);

• octave for Octave;

• python-pip for Python 2 and the pip installer;

• python3-pip for Python 3 and the pip3 installer;

• perl for Perl (add cpanm if needed);

• luajit for Lua (add luarocks if needed);

• default-jdk for Java;

• gfortran for Fortran.

Once you add R or python, the commands Rscript or python will become available, respectively.

Adding R Language

Installing R in a non-R environment, or updating to the most recent version

1. In the environment editor, click the gear icon next to apt-get. This will open the apt-get configuration window.

2. In Additional sources, copy and paste deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran35/ (for R 3.6) or deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/ (for R 4.0). This tells apt-get to search for packages on the R project's own repository.

4. Click Save.

5. Navigate back to the environment editor.

6. Add r-base as an apt-get dependency, this step is crucial.

Note: the various R installer options will show up automatically, which you can immediately add packages to.