Open Source Haskell Design Software

erd is a Haskell-based command-line tool that transforms a plain-text description of a relational database schema into a graphical entity-relationship diagram using common ER conventions. This utility takes a plain text description of entities, their attributes and the relationships between entities and produces a visual diagram modeling the description. The visualization is produced by using Dot with GraphViz. There are limited options for specifying color and font information. Also, erd can output graphs in a variety of formats, including but not limited to: pdf, svg, eps, png, jpg, plain text and dot. In case one wishes to have a statically linked erd as a result, this is possible to have by executing build-static_by-nix.sh: which requires the nix package manager to be installed on the building machine. NixOS itself is not a requirement.

Gitit is a wiki application written in Haskell that uses Happstack for serving and Pandoc for markup conversion. Wiki content and attachments are stored in Git, Darcs, or Mercurial repositories, allowing versioning via VCS or web editing. To run gitit, you'll need git in your system path. (Or darcs or hg, if you're using darcs or mercurial to store the wiki data.) Gitit assumes that the page files (stored in the git repository) are encoded as UTF-8. Even page names may be UTF-8 if the file system supports this. So you should make sure that you are using a UTF-8 locale when running gitit. (To check this, type locale.) The metadata block consists of a list of key-value pairs, each on a separate line. If needed, the value can be continued on one or more additional line, which must begin with a space.

The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies via Homebrew and then downloads and installs the TensorFlow library on your machine under /usr/local. The stack.yaml file describes a NixOS environment containing the necessary dependencies.