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Quick start

Fast Downward is released in four flavours: tarball, Singularity, Docker and Vagrant. We provide below a few instructions to get you started as quickly as possible. You can find a more detailed description on ObtainingAndRunningFastDownward and PlannerUsage.

What flavour is for me?

running experiments: We recommend Singularity or the tarball. Docker is an alternative, but be mindful of its significant overhead.
teaching: We recommend Vagrant.
development: We recommend working on a clone of the master repository.

See WhatFlavourIsForMe for a more detailed discussion.

Running the Singularity image

We assume that Singularity is installed on your machine. We have tested Singularity 2.6, 3.2, and 3.5. Here is how you can download the planner image and run the LAMA-first configuration to solve a planning task located in the $BENCHMARKS directory. LAMA-first is designed to find solutions quickly without much regard for plan cost:

   1 # Download the planner image and store it in the file "downward.sif". Only do this once.
   2 singularity pull --name downward.sif shub://aibasel/downward
   3 # Run the planner.
   4 singularity run downward.sif --alias lama-first $BENCHMARKS/gripper/prob01.pddl

Unlike Docker (see below), $BENCHMARKS does not have to be an absolute path.

Alternative way without "singularity run"

With reasonably recent versions of Singularity, the image file can be executed directly like a script, leading to a simpler usage than via singularity run. For example, you can download and run the planner like this:

   1 # Download the planner and store it in the file "downward". Only do this once.
   2 singularity pull --name downward shub://aibasel/downward
   3 # Run the planner.
   4 ./downward --alias lama-first $BENCHMARKS/gripper/prob01.pddl

Troubleshooting

Depending on your configuration, the singularity pull step might produce an error message like the following:

ERROR  : Called singularity_config_get_value on uninitialized config subsystem
ABORT  : Retval = 255

In our experience, this error can be ignored.

Running the Docker image

We assume that Docker is installed on your machine. You want to solve a planning problem located on the $BENCHMARKS directory. You can run the same LAMA-first configuration as before:

   1 sudo docker run --rm -v $BENCHMARKS:/benchmarks aibasel/downward --alias lama-first /benchmarks/gripper/prob01.pddl

Note the use of sudo (Docker usually requires root privileges).

Note that this mounts the local directory $BENCHMARKS of your host machine under the container directory /benchmarks, which is the place where the containerized planner looks for the problem. The path stored in the $BENCHMARKS variable must be absolute.

The Docker image for Fast Downward is installed on your machine as a side-effect of the command.

Using a Vagrant machine

We assume that Vagrant is installed on your machine. You can use the Fast Downward Vagrantfile as follows:

Place the Vagrantfile for the desired release into some empty directory.
Put your PDDL files into the same directory as the Vagrantfile, say, under names domain.pddl and problem.pddl.
Navigate to this directory and type vagrant up.
Log into the Vagrant virtual machine by typing vagrant ssh.
Run downward/fast-downward.py --alias lama-first /vagrant/domain.pddl /vagrant/problem.pddl.
Type exit to leave the virtual machine, then vagrant halt to stop it.

Source code

See ObtainingAndRunningFastDownward for a complete description on how to build the planner from source.

Next steps

See other ways of invoking the planner on PlannerUsage.
Read about recommended experiment setups.
Go through the full Documentation.

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