planutils 0.10.9

General library for setting up linux-based environments for developing, running, and evaluating planners.

planutils

General library for setting up linux-based environments for developing, running, and evaluating planners.

1. Running the latest Docker release

The released Docker image comes with the latest planutils pre-installed. Note that in order to run a number of the planners (all those that are based on singularity), you will need to run the docker with the --privileged option.

Run the planutils container

docker run -it --privileged aiplanning/planutils:latest bash

Active the planutils environment

planutils activate

This means that in order to run the latest release, it is not necessary to clone this repository.

2. Making your own image with desired solvers

Below is an example for creating your own Dockerfile based on the latest release, with pre-installed solvers

FROM aiplanning/planutils:latest

# Install solvers and tools
RUN planutils install -y val
RUN planutils install -y planning.domains
RUN planutils install -y popf
RUN planutils install -y optic
RUN planutils install -y smtplan

3. Running planutils from source

You can also run the latest unreleased version. For this, clone this repository and run

docker build . -t planutils-dev:latest

4. Usage

Example of currently working functionality

$ lama domain.pddl problem.pddl

Package not installed!
  Download & install? [Y/n] y

About to install the following packages: downward (36M), lama (20K)
  Proceed? [Y/n] y
Installing downward...
INFO:    Downloading shub image
 35.88 MiB / 35.88 MiB [=======================================] 100.00% 3.99 MiB/s 8s
Finished installing downward (size: 36M)
Installing lama...
Finished installing lama (size: 20K)
Successfully installed lama!

Original command: lama
  Re-run command? [Y/n] y

Parsing...
$

Example of upcoming functionality

$ planutils install ipc-2018
Installing planners
This will require 3Gb of storage. Proceed? [Y/n]
Fetching all of the planners from IPC-2018 for use on the command line...

$ planutils install server-environment
Setting up a webserver to call the installed planners...

$ planutils install development-environment
Installing common dependencies for building planners...
Installing common planning libraries...

$ planutils install planning-domains
Installing the command-line utilities...
Installing the python library...
Fetching default benchmarks...

$ planutils setup-evaluation configuration.json
Installing Lab...
Configuring Lab...
Ready!
Run eval.py to evaluate

$

5. Add a new package

Package Configuration

1. Create a folder for new pacakeg, the package name will be the used to call the planner later
2. Set up the install, run, uninstall, and manifest file. You can find the template files under packages/TEMPLATE folder

Write Manifest file

Please create a manifest file named manifest_compact.json if you want to use predefined templates in the packages/TEMPLATE/SERVICE_TEMPLATE folder. The full manifest.json will be generated at the run time. You can overwrite the dafult template by restating the value of json fields.

You can also create a manifest.json file directly if you don't need the template.

Manifest Example

{
    "name": "LAMA-FIRST",
    "description": "http://fast-downward.org/",
    "install-size": "20K",
    "dependencies": [
        "downward"
    ],
    "endpoint": {
        "services": {
            "solve": {
                "args": [
                    {
                        "name": "domain",
                        "type": "file",
                        "description": "domain file"
                    },
                    {
                        "name": "problem",
                        "type": "file",
                        "description": "problem file"
                    }
                ],
                "call": "lama-first {domain} {problem}",
                "return": {
                    "type": "generic",
                    "files": "*plan*"
                }
            }
        }
    }
}

Define Args

There are four types of Args: file, int, string and,categorical. You can add default value for int,string, and categorical arguments

 "args": [
    {
        "name": "domain",
        "type": "file",
        "description": "domain file"
    },
    {
        "name": "number_of_plans",
        "type": "int",
        "description": "Number of Plans",
        "default":3
    },
    {
        "name": "custom_search_algorithm",
        "type": "string",
        "description": "Custom Search Algorithm",
        "default":"kstar(blind(),k=1)"
    },
    {
        "name": "search_algorithm",
        "type": "categorical",
        "description": "Search Algorithm",
        "choices":[
          {
            "display_value":"Kstar Blind k=1",
            "value":"kstar(blind(),k=1)"
          },
          {
            "display_value":"Kstar Blind k=2",
            "value":"kstar(blind(),k=2)"
          }
        ],
        "default":"kstar(blind(),k=1)"
    }
]

Define Return Types

There are three types of return data: generic, json and log. The generic type should be used for all the text based result, the log type should be used for planner like Optic and Tfd which didn't generate a proper plan, and the type json should used for plan in JSON format.

For the value of files, you will have to write a glob pattern. Planning-as-service backend uses glob libary to find and return all the files that matched.

"return": {
                    "type": "generic/log/json",
                    "files": "*plan*"
                }