All the instructions below work on Linux, macOS and Windows.
The recommended way to install LFortran is using Conda.
Install Conda for example by installing the
Miniconda installation by following instructions there for your platform.
Then create a new environment (you can choose any name, here we chose lf
) and
activate it:
conda create -n lf
conda activate lf
Then install LFortran by:
conda install lfortran -c conda-forge
Now the lf
environment has the lfortran
compiler available, you can start the
interactive prompt by executing lfortran
, or see the command line options using
lfortran -h
.
The Jupyter kernel is automatically installed by the above command, so after installing Jupyter itself:
conda install jupyter -c conda-forge
You can create a Fortran based Jupyter notebook by executing:
jupyter notebook
and selecting New->Fortran
.
This method is the recommended method if you just want to install LFortran, either yourself or in a package manager (Spack, Conda, Debian, etc.). The source tarball has all the generated files included and has minimal dependencies.
First we have to install dependencies, for example using Conda:
conda create -n lf python cmake llvmdev
conda activate lf
Then download a tarball from https://lfortran.org/download/, e.g.:
wget https://lfortran.github.io/tarballs/dev/lfortran-0.9.0.tar.gz
tar xzf lfortran-0.9.0.tar.gz
cd lfortran-0.9.0
And build:
cmake -DWITH_LLVM=yes -DCMAKE_INSTALL_PREFIX=`pwd`/inst .
make -j8
make install
This will install the lfortran
into the inst/bin
.
We assume you have C++ compilers installed, as well as git
and wget
.
In Ubuntu, you can also install binutils-dev
for stacktraces.
If you do not have Conda installed, you can do so on Linux (and similarly on other platforms):
wget --no-check-certificate https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p $HOME/conda_root
export PATH="$HOME/conda_root/bin:$PATH"
Then prepare the environment:
conda create -n lf -c conda-forge llvmdev=11.0.1 bison=3.4 re2c python cmake make toml
conda activate lf
Clone the LFortran git repository:
git clone https://gitlab.com/lfortran/lfortran.git
cd lfortran
Generate files that are needed for the build (this step depends on re2c
, bison
and python
):
./build0.sh
Now the process is the same as installing from the source tarball. For example to build in Debug mode:
cmake -DCMAKE_BUILD_TYPE=Debug -DWITH_LLVM=yes -DCMAKE_INSTALL_PREFIX=`pwd`/inst .
make -j8
Run tests:
ctest
./run_tests.py
Run an interactive prompt:
./src/bin/lfortran
To install the Jupyter kernel, install the following Conda packages also:
conda install xeus xtl nlohmann_json cppzmq
and enable the kernel by -DWITH_XEUS=yes
and install into $CONDA_PREFIX
. For
example:
cmake \
-DCMAKE_BUILD_TYPE=Debug \
-DWITH_LLVM=yes \
-DWITH_XEUS=yes \
-DCMAKE_PREFIX_PATH="$CONDA_PREFIX" \
-DCMAKE_INSTALL_PREFIX="$CONDA_PREFIX" \
.
cmake --build . -j4 --target install
To use it, install Jupyter (conda install jupyter
) and test that the LFortran
kernel was found:
jupyter kernelspec list --json
Then launch a Jupyter notebook as follows:
jupyter notebook
Click New->Fortran
. To launch a terminal jupyter LFortran console:
jupyter console --kernel=fortran
One of the ways to ensure exact environment and dependencies is with nix
. This will ensure that system dependencies do not interfere with the development environment. If you want, you can report bugs in a nix-shell
environment to make it easier for others to reproduce.
We start by getting nix
. The following multi-user installation will work on any machine with a Linux distribution, MacOS or Windows (via WSL):
sh <(curl -L https://nixos.org/nix/install) --daemon
If you would like to not provide nix
with root access to your machine, on Linux distributions we can use nix-portable.
wget https://github.com/DavHau/nix-portable/releases/download/v003/nix-portable
Now just prepend all nix-shell
commands with NP_RUNTIME=bwrap ./nix-portable
. So:
# Do not
nix-shell --run "bash"
# Do
NP_RUNTIME=bwrap ./nix-portable nix-shell --run "bash"
Now we can enter the development environment:
nix-shell --run "bash" --cores 4 -j4 --pure ci/shell.nix
The --pure
flag ensures no system dependencies are used in the environment.
The build steps are the same as with the ci
:
./build0.sh
./build1.sh
To change the compilation environment from gcc
(default) to clang
we can use --argstr
:
nix-shell --run "bash" --cores 4 -j4 --pure ci/shell.nix --argstr clangOnly "yes"
End users (and distributions) are encouraged to use the tarball from https://lfortran.org/download/, which only depends on LLVM, CMake and a C++ compiler.
The tarball is generated automatically by our CI (continuous integration) and contains some autogenerated files: the parser, the AST and ASR nodes, which is generated by an ASDL translator (requires Python).
The instructions from git are to be used when developing LFortran itself.
Following are the dependencies necessary for installing this repository in development mode,
- Bison - 3.5.1
- LLVM - 11.0.1
- re2c - 2.0.3
- binutils - 2.31.90 - Make sure that you should enable the required options related to this dependency to build the dynamic libraries (the ones ending with
.so
).
LFortran can print stacktraces when there is an unhandled exception, as well as
on any compiler error with the --show-stacktrace
option. This is very helpful
for developing the compiler itself to see where in LFortran the problem is. The
stacktrace support is turned off by default, to enable it, install binutils
and compile LFortran with the -DWITH_STACKTRACE=yes
cmake option.
In Ubuntu, apt install binutils-dev
.
On macOS, you can install Spack, then:
spack install binutils
spack find -p binutils
The last command will show a full path to the installed binutils
package. Add
this path to your shell config file, e.g.:
export CMAKE_PREFIX_PATH_LFORTRAN=/Users/ondrej/repos/spack/opt/spack/darwin-catalina-broadwell/apple-clang-11.0.0/binutils-2.36.1-wy6osfm6bp2323g3jpv2sjuttthwx3gd
and compile LFortran with the
-DCMAKE_PREFIX_PATH="$CMAKE_PREFIX_PATH_LFORTRAN;$CONDA_PREFIX"
cmake option.
The $CONDA_PREFIX
is there if you install some other dependencies (such as
llvm
) using Conda, otherwise you can remove it.