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| We support multiple ways of running Fast Downward. The instructions below assume that your PDDL files reside in the directory `$BENCHMARKS`. | Starting with Fast Downward 19.06, we release the planner in four flavours: tarball, Singularity, Docker and Vagrant. We provide below a few instructions to get you started as quickly as possible, but you can find a more detailed description on [[ObtainingAndRunningFastDownward]] and [[PlannerUsage]]. |
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| == Docker image == | == What flavour is for me? == |
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| Install [[https://docs.docker.com/install/|Docker]] and run | * '''running experiments''': We recommend Singularity or the tarball. Docker is an alternative, but be mindful of its significant overhead. * '''teaching:''' We recommend Vagrant. * '''development:''' We recommend working on a clone of the master repository. See WhatFlavourIsForMe for a more detailed discussion. == Running the Singularity image == Let us assume that you have [[https://www.sylabs.io/guides/3.2/user-guide/quick_start.html#quick-installation-steps|Singularity]] installed on your machine (we have tested this with Singularity 2.6 and 3.2), and want to solve a planning problem located in the `$BENCHMARKS` directory. You can run the same LAMA configuration as before: |
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| docker run --rm -v $BENCHMARKS:/benchmarks aibasel/downward:latest --alias lama-first /benchmarks/gripper/prob01.pddl | singularity pull --name downward.simg shub://aibasel/downward singularity run downward.simg --alias lama-first $BENCHMARKS/gripper/prob01.pddl |
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| This makes the local directory `$BENCHMARKS` available in the container under `/benchmarks`. Note that the `$BENCHMARKS` path needs to be absolute. | The first command downloads the Singularity image into the local file `downward.simg`, while the second command is the one running Fast Downward from this image. Unlike Docker, `$BENCHMARKS` does not have to be an absolute path. == Running the Docker image == Let us assume that you have [[https://docs.docker.com/install/|Docker]] installed on your machine and want to solve a planning problem located on the `$BENCHMARKS` directory. You can run the ''satisficing'' LAMA configuration of the planner: {{{#!highlight bash docker run --rm -v $BENCHMARKS:/benchmarks aibasel/downward --alias lama-first /benchmarks/gripper/prob01.pddl }}} Note that this mounts the local directory `$BENCHMARKS` of your host machine under the container directory `/benchmarks`, which is the place where the containerized planner reads the problems from. The path stored in the `$BENCHMARKS` variable needs to be absolute. == Using a Vagrant machine == Assuming that you have [[https://www.vagrantup.com/|Vagrant]] installed on your machine, you can use the Fast Downward Vagrantfile as follows: * Place the Vagrantfile for the desired [[Releases|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 [[ScriptUsage|experiment setups]]. * Go through the full [[../|Documentation]]. |
Back to the HomePage.
Quick start
Starting with Fast Downward 19.06, we release the planner in four flavours: tarball, Singularity, Docker and Vagrant. We provide below a few instructions to get you started as quickly as possible, but 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
Let us assume that you have Singularity installed on your machine (we have tested this with Singularity 2.6 and 3.2), and want to solve a planning problem located in the $BENCHMARKS directory. You can run the same LAMA configuration as before:
The first command downloads the Singularity image into the local file downward.simg, while the second command is the one running Fast Downward from this image. Unlike Docker, $BENCHMARKS does not have to be an absolute path.
Running the Docker image
Let us assume that you have Docker installed on your machine and want to solve a planning problem located on the $BENCHMARKS directory. You can run the satisficing LAMA configuration of the planner:
1 docker run --rm -v $BENCHMARKS:/benchmarks aibasel/downward --alias lama-first /benchmarks/gripper/prob01.pddl
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 reads the problems from. The path stored in the $BENCHMARKS variable needs to be absolute.
Using a Vagrant machine
Assuming that you have Vagrant 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.